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Sample records for binding holo-structure prediction

  1. CDOCKER and lambda λ -dynamics for prospective prediction in D3R Grand Challenge 2

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    Ding, Xinqiang; Hayes, Ryan L.; Vilseck, Jonah Z.; Charles, Murchtricia K.; Brooks, Charles L.

    2018-01-01

    The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and λ-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 Å. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 Å for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For λ-dynamics techniques, including multisite λ-dynamics (MSλD), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their

  2. Knowledge-based Fragment Binding Prediction

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    Tang, Grace W.; Altman, Russ B.

    2014-01-01

    Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

  3. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

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    Huiying Zhao

    Full Text Available As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions. A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC of 0.77 with high precision (94% and high sensitivity (65%. We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA] is available as an on-line server at http://sparks-lab.org.

  4. CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

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    Abbasi, Wajid Arshad; Asif, Amina; Andleeb, Saiqa; Minhas, Fayyaz Ul Amir Afsar

    2017-09-01

    Due to Ca 2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels. © 2017 Wiley Periodicals, Inc.

  5. Predicting protein-binding RNA nucleotides with consideration of binding partners.

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    Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

    2015-06-01

    In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in

  6. Transcription factor binding sites prediction based on modified nucleosomes.

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    Mohammad Talebzadeh

    Full Text Available In computational methods, position weight matrices (PWMs are commonly applied for transcription factor binding site (TFBS prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method

  7. Predicting nucleic acid binding interfaces from structural models of proteins.

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    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  8. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

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    Waqasuddin Khan

    Full Text Available Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58.Next, we trained a bidirectional recurrent neural network (BRNN using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72 showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

  9. Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms

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    Sangmin Seo

    2018-01-01

    Full Text Available We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944, because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.

  10. A systems biology approach to transcription factor binding site prediction.

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    Xiang Zhou

    2010-03-01

    Full Text Available The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs, identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy

  11. Automated benchmarking of peptide-MHC class I binding predictions

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    Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason A.; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

    2015-01-01

    Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. Results: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. Contact: mniel@cbs.dtu.dk or bpeters@liai.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25717196

  12. Peptide binding predictions for HLA DR, DP and DQ molecules

    DEFF Research Database (Denmark)

    Wang, P.; Sidney, J.; Kim, Y.

    2010-01-01

    a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally. RESULTS: In this study, we aimed to narrow this gap by providing a large scale dataset of over 17,000 HLA-peptide binding...... affinities for a set of 11 HLA DP and DQ alleles. We also expanded our dataset for HLA DR alleles resulting in a total of 40,000 MHC class II binding affinities covering 26 allelic variants. Utilizing this dataset, we generated prediction tools utilizing several machine learning algorithms and evaluated...... include all training data for maximum performance. 4) The recently developed NN-align prediction method significantly outperformed all other algorithms, including a naïve consensus based on all prediction methods. A new consensus method dropping the comparably weak ARB prediction method could outperform...

  13. Predicting accurate absolute binding energies in aqueous solution

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    Jensen, Jan Halborg

    2015-01-01

    Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal......-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent....

  14. Prediction of chloride ingress and binding in cement paste

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    Geiker, Mette Rica; Nielsen, Erik Pram; Herforth, Duncan

    2007-01-01

    This paper summarizes recent work on an analytical model for predicting the ingress rate of chlorides in cement-based materials. An integral part of this is a thermodynamic model for predicting the phase equilibria in hydrated Portland cement. The model’s ability to predict chloride binding...... in Portland cement pastes at any content of chloride, alkalis, sulfates and carbonate was verified experimentally and found to be equally valid when applied to other data in the literature. The thermodynamic model for predicting the phase equilibria in hydrated Portland cement was introduced into an existing...... Finite Difference Model for the ingress of chlorides into concrete which takes into account its multi-component nature. The “composite theory” was then used to predict the diffusivity of each ion based on the phase assemblage present in the hydrated Portland cement paste. Agreement was found between...

  15. DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

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    Ma, Xin; Guo, Jing; Sun, Xiao

    2016-01-01

    DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

  16. Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

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    Liu, Yong; Munteanu, Cristian R; Fernández Blanco, Enrique; Tan, Zhiliang; Santos Del Riego, Antonino; Pazos, Alejandro

    2015-11-01

    The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Predicting "Hot" and "Warm" Spots for Fragment Binding.

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    Rathi, Prakash Chandra; Ludlow, R Frederick; Hall, Richard J; Murray, Christopher W; Mortenson, Paul N; Verdonk, Marcel L

    2017-05-11

    Computational fragment mapping methods aim to predict hotspots on protein surfaces where small fragments will bind. Such methods are popular for druggability assessment as well as structure-based design. However, to date researchers developing or using such tools have had no clear way of assessing the performance of these methods. Here, we introduce the first diverse, high quality validation set for computational fragment mapping. The set contains 52 diverse examples of fragment binding "hot" and "warm" spots from the Protein Data Bank (PDB). Additionally, we describe PLImap, a novel protocol for fragment mapping based on the Protein-Ligand Informatics force field (PLIff). We evaluate PLImap against the new fragment mapping test set, and compare its performance to that of simple shape-based algorithms and fragment docking using GOLD. PLImap is made publicly available from https://bitbucket.org/AstexUK/pli .

  18. Memory Binding Test Predicts Incident Amnestic Mild Cognitive Impairment.

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    Mowrey, Wenzhu B; Lipton, Richard B; Katz, Mindy J; Ramratan, Wendy S; Loewenstein, David A; Zimmerman, Molly E; Buschke, Herman

    2016-07-14

    The Memory Binding Test (MBT), previously known as Memory Capacity Test, has demonstrated discriminative validity for distinguishing persons with amnestic mild cognitive impairment (aMCI) and dementia from cognitively normal elderly. We aimed to assess the predictive validity of the MBT for incident aMCI. In a longitudinal, community-based study of adults aged 70+, we administered the MBT to 246 cognitively normal elderly adults at baseline and followed them annually. Based on previous work, a subtle reduction in memory binding at baseline was defined by a Total Items in the Paired (TIP) condition score of ≤22 on the MBT. Cox proportional hazards models were used to assess the predictive validity of the MBT for incident aMCI accounting for the effects of covariates. The hazard ratio of incident aMCI was also assessed for different prediction time windows ranging from 4 to 7 years of follow-up, separately. Among 246 controls who were cognitively normal at baseline, 48 developed incident aMCI during follow-up. A baseline MBT reduction was associated with an increased risk for developing incident aMCI (hazard ratio (HR) = 2.44, 95% confidence interval: 1.30-4.56, p = 0.005). When varying the prediction window from 4-7 years, the MBT reduction remained significant for predicting incident aMCI (HR range: 2.33-3.12, p: 0.0007-0.04). Persons with poor performance on the MBT are at significantly greater risk for developing incident aMCI. High hazard ratios up to seven years of follow-up suggest that the MBT is sensitive to early disease.

  19. Convolutional neural network architectures for predicting DNA–protein binding

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    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  20. Integrating water exclusion theory into βcontacts to predict binding free energy changes and binding hot spots

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    2014-01-01

    Background Binding free energy and binding hot spots at protein-protein interfaces are two important research areas for understanding protein interactions. Computational methods have been developed previously for accurate prediction of binding free energy change upon mutation for interfacial residues. However, a large number of interrupted and unimportant atomic contacts are used in the training phase which caused accuracy loss. Results This work proposes a new method, βACV ASA , to predict the change of binding free energy after alanine mutations. βACV ASA integrates accessible surface area (ASA) and our newly defined β contacts together into an atomic contact vector (ACV). A β contact between two atoms is a direct contact without being interrupted by any other atom between them. A β contact’s potential contribution to protein binding is also supposed to be inversely proportional to its ASA to follow the water exclusion hypothesis of binding hot spots. Tested on a dataset of 396 alanine mutations, our method is found to be superior in classification performance to many other methods, including Robetta, FoldX, HotPOINT, an ACV method of β contacts without ASA integration, and ACV ASA methods (similar to βACV ASA but based on distance-cutoff contacts). Based on our data analysis and results, we can draw conclusions that: (i) our method is powerful in the prediction of binding free energy change after alanine mutation; (ii) β contacts are better than distance-cutoff contacts for modeling the well-organized protein-binding interfaces; (iii) β contacts usually are only a small fraction number of the distance-based contacts; and (iv) water exclusion is a necessary condition for a residue to become a binding hot spot. Conclusions βACV ASA is designed using the advantages of both β contacts and water exclusion. It is an excellent tool to predict binding free energy changes and binding hot spots after alanine mutation. PMID:24568581

  1. Prediction of RNA-Binding Proteins by Voting Systems

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    C. R. Peng

    2011-01-01

    Full Text Available It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy value of 79.72% and MCC (Matthew’s correlation coefficient value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier’s vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

  2. Mechanisms of Intentional Binding and Sensory Attenuation: The Role of Temporal Prediction, Temporal Control, Identity Prediction, and Motor Prediction

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    Hughes, Gethin; Desantis, Andrea; Waszak, Florian

    2013-01-01

    Sensory processing of action effects has been shown to differ from that of externally triggered stimuli, with respect both to the perceived timing of their occurrence (intentional binding) and to their intensity (sensory attenuation). These phenomena are normally attributed to forward action models, such that when action prediction is consistent…

  3. Development of estrogen receptor beta binding prediction model using large sets of chemicals.

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    Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

    2017-11-03

    We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

  4. Prediction of nucleosome positioning based on transcription factor binding sites.

    Directory of Open Access Journals (Sweden)

    Xianfu Yi

    Full Text Available BACKGROUND: The DNA of all eukaryotic organisms is packaged into nucleosomes, the basic repeating units of chromatin. The nucleosome consists of a histone octamer around which a DNA core is wrapped and the linker histone H1, which is associated with linker DNA. By altering the accessibility of DNA sequences, the nucleosome has profound effects on all DNA-dependent processes. Understanding the factors that influence nucleosome positioning is of great importance for the study of genomic control mechanisms. Transcription factors (TFs have been suggested to play a role in nucleosome positioning in vivo. PRINCIPAL FINDINGS: Here, the minimum redundancy maximum relevance (mRMR feature selection algorithm, the nearest neighbor algorithm (NNA, and the incremental feature selection (IFS method were used to identify the most important TFs that either favor or inhibit nucleosome positioning by analyzing the numbers of transcription factor binding sites (TFBSs in 53,021 nucleosomal DNA sequences and 50,299 linker DNA sequences. A total of nine important families of TFs were extracted from 35 families, and the overall prediction accuracy was 87.4% as evaluated by the jackknife cross-validation test. CONCLUSIONS: Our results are consistent with the notion that TFs are more likely to bind linker DNA sequences than the sequences in the nucleosomes. In addition, our results imply that there may be some TFs that are important for nucleosome positioning but that play an insignificant role in discriminating nucleosome-forming DNA sequences from nucleosome-inhibiting DNA sequences. The hypothesis that TFs play a role in nucleosome positioning is, thus, confirmed by the results of this study.

  5. HemeBIND: a novel method for heme binding residue prediction by combining structural and sequence information

    Directory of Open Access Journals (Sweden)

    Hu Jianjun

    2011-05-01

    Full Text Available Abstract Background Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues. Results Here we introduced an efficient algorithm HemeBIND for predicting heme binding residues by integrating structural and sequence information. We systematically investigated the characteristics of binding interfaces based on a non-redundant dataset of heme-protein complexes. It was found that several sequence and structural attributes such as evolutionary conservation, solvent accessibility, depth and protrusion clearly illustrate the differences between heme binding and non-binding residues. These features can then be separately used or combined to build the structure-based classifiers using support vector machine (SVM. The results showed that the information contained in these features is largely complementary and their combination achieved the best performance. To further improve the performance, an attempt has been made to develop a post-processing procedure to reduce the number of false positives. In addition, we built a sequence-based classifier based on SVM and sequence profile as an alternative when only sequence information can be used. Finally, we employed a voting method to combine the outputs of structure-based and sequence-based classifiers, which demonstrated remarkably better performance than the individual classifier alone

  6. Machine learning competition in immunology – Prediction of HLA class I binding peptides

    DEFF Research Database (Denmark)

    Zhang, Guang Lan; Ansari, Hifzur Rahman; Bradley, Phil

    2011-01-01

    of peptide binding, therefore, determines the accuracy of the overall method. Computational predictions of peptide binding to HLA, both class I and class II, use a variety of algorithms ranging from binding motifs to advanced machine learning techniques ( [Brusic et al., 2004] and [Lafuente and Reche, 2009...

  7. Prediction of DNA-binding specificity in zinc finger proteins

    Indian Academy of Sciences (India)

    2012-06-25

    Jun 25, 2012 ... Support Vector Machine (SVM) is a state-of-the-art classifica- tion technique. Using canonical binding model, the C2H2 zinc finger protein–DNA interaction interface is modelled by the pairwise amino acid–base interactions. Using a classification framework, known examples of non-binding ZF–DNA pairs.

  8. NetMHCpan, a method for MHC class I binding prediction beyond humans

    DEFF Research Database (Denmark)

    Hoof, Ilka; Peters, B; Sidney, J

    2009-01-01

    molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide...

  9. A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

    KAUST Repository

    Chen, Peng

    2015-12-03

    Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures

  10. A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

    KAUST Repository

    Chen, Peng; Hu, ShanShan; Zhang, Jun; Gao, Xin; Li, Jinyan; Xia, Junfeng; Wang, Bing

    2015-01-01

    Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures

  11. Asymmetry of 3H- imipramine binding may predict psychiatric illness

    International Nuclear Information System (INIS)

    Demeter, E.; Tekes, K.; Majorossy, K.; Palkovits, M.; Soos, M.; Magyar, K.; Somogyl, E.

    1989-01-01

    The B/sub max/ and Kd values for 3 H-imipramine binding were measured in post-mortem human brains from drug-free selected psychiatric subject homicide victims and normal controls. The two groups were comparable in age and gender. The number of imipramine binding sites in the frontal cortices of psychiatric subjects had significantly higher B/sub max/ values in the left hemisphere than in the right hemisphere. Inversely, the number of imipramine binding sites in the frontal cortices of normal controls were significantly higher in the right brain than in the left brain. It was postulated that the inhibiting effect of central serotonin has weakened in psychiatric cases, therefore the changes of presynaptic serotonergic activity might be associated with psychiatric illness in the left hemisphere of human brain

  12. Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

    Directory of Open Access Journals (Sweden)

    Mittelmann Hans D

    2010-01-01

    Full Text Available Abstract Background The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC. Results We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides. Conclusions The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

  13. An Overview of the Prediction of Protein DNA-Binding Sites

    Directory of Open Access Journals (Sweden)

    Jingna Si

    2015-03-01

    Full Text Available Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

  14. An overview of the prediction of protein DNA-binding sites.

    Science.gov (United States)

    Si, Jingna; Zhao, Rui; Wu, Rongling

    2015-03-06

    Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

  15. Predicting Flavin and Nicotinamide Adenine Dinucleotide-Binding Sites in Proteins Using the Fragment Transformation Method

    Directory of Open Access Journals (Sweden)

    Chih-Hao Lu

    2015-01-01

    Full Text Available We developed a computational method to identify NAD- and FAD-binding sites in proteins. First, we extracted from the Protein Data Bank structures of proteins that bind to at least one of these ligands. NAD-/FAD-binding residue templates were then constructed by identifying binding residues through the ligand-binding database BioLiP. The fragment transformation method was used to identify structures within query proteins that resembled the ligand-binding templates. By comparing residue types and their relative spatial positions, potential binding sites were identified and a ligand-binding potential for each residue was calculated. Setting the false positive rate at 5%, our method predicted NAD- and FAD-binding sites at true positive rates of 67.1% and 68.4%, respectively. Our method provides excellent results for identifying FAD- and NAD-binding sites in proteins, and the most important is that the requirement of conservation of residue types and local structures in the FAD- and NAD-binding sites can be verified.

  16. Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.

    Science.gov (United States)

    Liu, Sheng; Zibetti, Cristina; Wan, Jun; Wang, Guohua; Blackshaw, Seth; Qian, Jiang

    2017-07-27

    Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events. We assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types. Integrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal "rules". A computational tool was developed to predict TF binding sites based on the universal "rules".

  17. Using TESS to predict transcription factor binding sites in DNA sequence.

    Science.gov (United States)

    Schug, Jonathan

    2008-03-01

    This unit describes how to use the Transcription Element Search System (TESS). This Web site predicts transcription factor binding sites (TFBS) in DNA sequence using two different kinds of models of sites, strings and positional weight matrices. The binding of transcription factors to DNA is a major part of the control of gene expression. Transcription factors exhibit sequence-specific binding; they form stronger bonds to some DNA sequences than to others. Identification of a good binding site in the promoter for a gene suggests the possibility that the corresponding factor may play a role in the regulation of that gene. However, the sequences transcription factors recognize are typically short and allow for some amount of mismatch. Because of this, binding sites for a factor can typically be found at random every few hundred to a thousand base pairs. TESS has features to help sort through and evaluate the significance of predicted sites.

  18. Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

    Directory of Open Access Journals (Sweden)

    Lee Sael

    2010-12-01

    Full Text Available Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

  19. Binding ligand prediction for proteins using partial matching of local surface patches.

    Science.gov (United States)

    Sael, Lee; Kihara, Daisuke

    2010-01-01

    Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

  20. Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

    Directory of Open Access Journals (Sweden)

    C. Ruben Vosmeer

    2014-01-01

    Full Text Available Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.

  1. Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains.

    Science.gov (United States)

    Bulashevska, Alla; Stein, Martin; Jackson, David; Eils, Roland

    2009-12-01

    Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.

  2. SABinder: A Web Service for Predicting Streptavidin-Binding Peptides.

    Science.gov (United States)

    He, Bifang; Kang, Juanjuan; Ru, Beibei; Ding, Hui; Zhou, Peng; Huang, Jian

    2016-01-01

    Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p binders. In either case, it will be helpful and can benefit related scientific community.

  3. Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

    Directory of Open Access Journals (Sweden)

    Feng Lin

    2007-11-01

    Full Text Available Abstract Background Peptides binding to Major Histocompatibility Complex (MHC class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs. Results The proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1 an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2 quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules. Conclusion We present two MOEA-based algorithms for finding motifs

  4. Using sequence-specific chemical and structural properties of DNA to predict transcription factor binding sites.

    Directory of Open Access Journals (Sweden)

    Amy L Bauer

    2010-11-01

    Full Text Available An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF. Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similar to SiteSleuth in that it involves a learning algorithm. The main advantage of SiteSleuth is a lower false positive rate.

  5. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    Science.gov (United States)

    Tsai, Zing Tsung-Yeh; Shiu, Shin-Han; Tsai, Huai-Kuang

    2015-08-01

    Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  6. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    Directory of Open Access Journals (Sweden)

    Zing Tsung-Yeh Tsai

    2015-08-01

    Full Text Available Transcription factor (TF binding is determined by the presence of specific sequence motifs (SM and chromatin accessibility, where the latter is influenced by both chromatin state (CS and DNA structure (DS properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  7. Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

    Science.gov (United States)

    Han, Youngmahn; Kim, Dongsup

    2017-12-28

    Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions. Nonapeptide-HLA-A and -B binding data were encoded into ILA data. A DCNN, as a pan-specific prediction model, was trained on the ILA data. The DCNN showed higher performance than other prediction tools for the latest benchmark datasets, which consist of 43 datasets for 15 HLA-A alleles and 25 datasets for 10 HLA-B alleles. In particular, the DCNN outperformed other tools for alleles belonging to the HLA-A3 supertype. The F1 scores of the DCNN were 0.86, 0.94, and 0.67 for HLA-A*31:01, HLA-A*03:01, and HLA-A*68:01 alleles, respectively, which were significantly higher than those of other tools. We found that the DCNN was able to recognize locally-clustered interactions that could synergistically stabilize peptide binding. We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN. ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/convmhc

  8. PatchSurfers: Two methods for local molecular property-based binding ligand prediction.

    Science.gov (United States)

    Shin, Woong-Hee; Bures, Mark Gregory; Kihara, Daisuke

    2016-01-15

    Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. A community resource benchmarking predictions of peptide binding to MHC-I molecules.

    Science.gov (United States)

    Peters, Bjoern; Bui, Huynh-Hoa; Frankild, Sune; Nielson, Morten; Lundegaard, Claus; Kostem, Emrah; Basch, Derek; Lamberth, Kasper; Harndahl, Mikkel; Fleri, Ward; Wilson, Stephen S; Sidney, John; Lund, Ole; Buus, Soren; Sette, Alessandro

    2006-06-09

    Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

  10. Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain.

    Science.gov (United States)

    Cerisier, Natacha; Regad, Leslie; Triki, Dhoha; Petitjean, Michel; Flatters, Delphine; Camproux, Anne-Claude

    2017-10-01

    While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations.

    Science.gov (United States)

    Petukh, Marharyta; Dai, Luogeng; Alexov, Emil

    2016-04-12

    Predicting the effect of amino acid substitutions on protein-protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.

  12. Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions

    DEFF Research Database (Denmark)

    Kim, Yohan; Sidney, John; Buus, Søren

    2014-01-01

    Background: It is important to accurately determine the performance of peptide: MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance...... are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last...

  13. A web server for analysis, comparison and prediction of protein ligand binding sites.

    Science.gov (United States)

    Singh, Harinder; Srivastava, Hemant Kumar; Raghava, Gajendra P S

    2016-03-25

    One of the major challenges in the field of system biology is to understand the interaction between a wide range of proteins and ligands. In the past, methods have been developed for predicting binding sites in a protein for a limited number of ligands. In order to address this problem, we developed a web server named 'LPIcom' to facilitate users in understanding protein-ligand interaction. Analysis, comparison and prediction modules are available in the "LPIcom' server to predict protein-ligand interacting residues for 824 ligands. Each ligand must have at least 30 protein binding sites in PDB. Analysis module of the server can identify residues preferred in interaction and binding motif for a given ligand; for example residues glycine, lysine and arginine are preferred in ATP binding sites. Comparison module of the server allows comparing protein-binding sites of multiple ligands to understand the similarity between ligands based on their binding site. This module indicates that ATP, ADP and GTP ligands are in the same cluster and thus their binding sites or interacting residues exhibit a high level of similarity. Propensity-based prediction module has been developed for predicting ligand-interacting residues in a protein for more than 800 ligands. In addition, a number of web-based tools have been integrated to facilitate users in creating web logo and two-sample between ligand interacting and non-interacting residues. In summary, this manuscript presents a web-server for analysis of ligand interacting residue. This server is available for public use from URL http://crdd.osdd.net/raghava/lpicom .

  14. Cell-type specificity of ChIP-predicted transcription factor binding sites

    Directory of Open Access Journals (Sweden)

    Håndstad Tony

    2012-08-01

    Full Text Available Abstract Background Context-dependent transcription factor (TF binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? Results We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Conclusions Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

  15. MHC2NNZ: A novel peptide binding prediction approach for HLA DQ molecules

    Science.gov (United States)

    Xie, Jiang; Zeng, Xu; Lu, Dongfang; Liu, Zhixiang; Wang, Jiao

    2017-07-01

    The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods.

  16. Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals

    Science.gov (United States)

    Tian, Zhen; Liu, Jiyuan; Zhang, Yalin

    2016-03-01

    Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses.

  17. HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

    Science.gov (United States)

    Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

    2017-01-01

    DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

  18. HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

    Directory of Open Access Journals (Sweden)

    Rianon Zaman

    2017-01-01

    Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

  19. PRODIGY : a web server for predicting the binding affinity of protein-protein complexes

    NARCIS (Netherlands)

    Xue, Li; Garcia Lopes Maia Rodrigues, João; Kastritis, Panagiotis L; Bonvin, Alexandre Mjj; Vangone, Anna

    2016-01-01

    Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given

  20. Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

    Science.gov (United States)

    Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

    2016-01-01

    While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

  1. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

    Science.gov (United States)

    Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

    2016-01-01

    Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

  2. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

    2018-01-01

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  3. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.

    2018-03-20

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  4. Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

    Science.gov (United States)

    Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

    2016-03-15

    Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  5. Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction

    Directory of Open Access Journals (Sweden)

    Sarai Akinori

    2011-02-01

    Full Text Available Abstract Background Protein-RNA interactions play important role in many biological processes such as gene regulation, replication, protein synthesis and virus assembly. Although many structures of various types of protein-RNA complexes have been determined, the mechanism of protein-RNA recognition remains elusive. We have earlier shown that the simplest electrostatic properties viz. charge, dipole and quadrupole moments, calculated from backbone atomic coordinates of proteins are biased relative to other proteins, and these quantities can be used to identify DNA-binding proteins. Closely related, RNA-binding proteins are investigated in this study. In particular, discrimination between various types of RNA-binding proteins, evolutionary conservation of these bulk electrostatic features and effect of conformational changes by complex formation are investigated. Basic binding mechanism of a putative RNA-binding protein (HI1333 from Haemophilus influenza is suggested as a potential application of this study. Results We found that similar to DNA-binding proteins (DBPs, RNA-binding proteins (RBPs also show significantly higher values of electric moments. However, higher moments in RBPs are found to strongly depend on their functional class: proteins binding to ribosomal RNA (rRNA constitute the only class with all three of the properties (charge, dipole and quadrupole moments being higher than control proteins. Neural networks were trained using leave-one-out cross-validation to predict RBPs from control data as well as pair-wise classification capacity between proteins binding to various RNA types. RBPs and control proteins reached up to 78% accuracy measured by the area under the ROC curve. Proteins binding to rRNA are found to be best distinguished (AUC = 79%. Changes in dipole and quadrupole moments between unbound and bound structures were small and these properties are found to be robust under complex formation. Conclusions Bulk electric

  6. Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Seizi Someya

    2010-01-01

    Full Text Available Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs. We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

  7. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict bind...... of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa....

  8. SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

    Science.gov (United States)

    Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

    2015-01-01

    Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

  9. Computational prediction of cAMP receptor protein (CRP binding sites in cyanobacterial genomes

    Directory of Open Access Journals (Sweden)

    Su Zhengchang

    2009-01-01

    Full Text Available Abstract Background Cyclic AMP receptor protein (CRP, also known as catabolite gene activator protein (CAP, is an important transcriptional regulator widely distributed in many bacteria. The biological processes under the regulation of CRP are highly diverse among different groups of bacterial species. Elucidation of CRP regulons in cyanobacteria will further our understanding of the physiology and ecology of this important group of microorganisms. Previously, CRP has been experimentally studied in only two cyanobacterial strains: Synechocystis sp. PCC 6803 and Anabaena sp. PCC 7120; therefore, a systematic genome-scale study of the potential CRP target genes and binding sites in cyanobacterial genomes is urgently needed. Results We have predicted and analyzed the CRP binding sites and regulons in 12 sequenced cyanobacterial genomes using a highly effective cis-regulatory binding site scanning algorithm. Our results show that cyanobacterial CRP binding sites are very similar to those in E. coli; however, the regulons are very different from that of E. coli. Furthermore, CRP regulons in different cyanobacterial species/ecotypes are also highly diversified, ranging from photosynthesis, carbon fixation and nitrogen assimilation, to chemotaxis and signal transduction. In addition, our prediction indicates that crp genes in modern cyanobacteria are likely inherited from a common ancestral gene in their last common ancestor, and have adapted various cellular functions in different environments, while some cyanobacteria lost their crp genes as well as CRP binding sites during the course of evolution. Conclusion The CRP regulons in cyanobacteria are highly diversified, probably as a result of divergent evolution to adapt to various ecological niches. Cyanobacterial CRPs may function as lineage-specific regulators participating in various cellular processes, and are important in some lineages. However, they are dispensable in some other lineages. The

  10. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

    Science.gov (United States)

    Mey, Antonia S. J. S.; Jiménez, Jordi Juárez; Michel, Julien

    2018-01-01

    The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

  11. Structure-based prediction of free energy changes of binding of PTP1B inhibitors

    Science.gov (United States)

    Wang, Jing; Ling Chan, Shek; Ramnarayan, Kal

    2003-08-01

    The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.

  12. Cloud computing approaches for prediction of ligand binding poses and pathways.

    Science.gov (United States)

    Lawrenz, Morgan; Shukla, Diwakar; Pande, Vijay S

    2015-01-22

    We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.

  13. The calcium binding properties and structure prediction of the Hax-1 protein.

    Science.gov (United States)

    Balcerak, Anna; Rowinski, Sebastian; Szafron, Lukasz M; Grzybowska, Ewa A

    2017-01-01

    Hax-1 is a protein involved in regulation of different cellular processes, but its properties and exact mechanisms of action remain unknown. In this work, using purified, recombinant Hax-1 and by applying an in vitro autoradiography assay we have shown that this protein binds Ca 2+ . Additionally, we performed structure prediction analysis which shows that Hax-1 displays definitive structural features, such as two α-helices, short β-strands and four disordered segments.

  14. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    Directory of Open Access Journals (Sweden)

    Gregory A Ross

    Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  15. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    Science.gov (United States)

    Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

    2015-01-01

    Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

  16. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Carson M Andorf

    Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  17. Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores

    Science.gov (United States)

    Benson, Mark L.; Faver, John C.; Ucisik, Melek N.; Dashti, Danial S.; Zheng, Zheng; Merz, Kenneth M.

    2012-05-01

    Two families of binding affinity estimation methodologies are described which were utilized in the SAMPL3 trypsin/fragment binding affinity challenge. The first is a free energy decomposition scheme based on a thermodynamic cycle, which included separate contributions from enthalpy and entropy of binding as well as a solvent contribution. Enthalpic contributions were estimated with PM6-DH2 semiempirical quantum mechanical interaction energies, which were modified with a statistical error correction procedure. Entropic contributions were estimated with the rigid-rotor harmonic approximation, and solvent contributions to the free energy were estimated with several different methods. The second general methodology is the empirical score LISA, which contains several physics-based terms trained with the large PDBBind database of protein/ligand complexes. Here we also introduce LISA+, an updated version of LISA which, prior to scoring, classifies systems into one of four classes based on a ligand's hydrophobicity and molecular weight. Each version of the two methodologies (a total of 11 methods) was trained against a compiled set of known trypsin binders available in the Protein Data Bank to yield scaling parameters for linear regression models. Both raw and scaled scores were submitted to SAMPL3. Variants of LISA showed relatively low absolute errors but also low correlation with experiment, while the free energy decomposition methods had modest success when scaling factors were included. Nonetheless, re-scaled LISA yielded the best predictions in the challenge in terms of RMS error, and six of these models placed in the top ten best predictions by RMS error. This work highlights some of the difficulties of predicting binding affinities of small molecular fragments to protein receptors as well as the benefit of using training data.

  18. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

    Science.gov (United States)

    Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

    2011-07-01

    The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

  19. Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins

    DEFF Research Database (Denmark)

    Rognan, D; Lauemoller, S L; Holm, A

    1999-01-01

    A simple and fast free energy scoring function (Fresno) has been developed to predict the binding free energy of peptides to class I major histocompatibility (MHC) proteins. It differs from existing scoring functions mainly by the explicit treatment of ligand desolvation and of unfavorable protein...... coordinates of the MHC-bound peptide have first been determined with an accuracy of about 1-1.5 A. Furthermore, it may be easily recalibrated for any protein-ligand complex.......) and of a series of 16 peptides to H-2K(k). Predictions were more accurate for HLA-A2-binding peptides as the training set had been built from experimentally determined structures. The average error in predicting the binding free energy of the test peptides was 3.1 kJ/mol. For the homology model-derived equation...

  20. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lundegaard, Claus; Lund, Ole

    2007-01-01

    the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance...... of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. RESULTS: The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation...... between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance...

  1. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-06-12

    Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

  2. G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures.

    Science.gov (United States)

    Lee, Hui Sun; Im, Wonpil

    2017-01-01

    Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

  3. Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

    Science.gov (United States)

    Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

    2018-01-01

    Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of

  4. Improved methods for predicting peptide binding affinity to MHC class II molecules.

    Science.gov (United States)

    Jensen, Kamilla Kjaergaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A; Yan, Zhen; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

    2018-01-06

    Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2. © 2018 John Wiley & Sons Ltd.

  5. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    Science.gov (United States)

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  6. SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

    Directory of Open Access Journals (Sweden)

    Xiaoxia Yang

    Full Text Available Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

  7. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael

    2015-01-01

    with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped...

  8. Prediction of consensus binding mode geometries for related chemical series of positive allosteric modulators of adenosine and muscarinic acetylcholine receptors.

    Science.gov (United States)

    Sakkal, Leon A; Rajkowski, Kyle Z; Armen, Roger S

    2017-06-05

    Following insights from recent crystal structures of the muscarinic acetylcholine receptor, binding modes of Positive Allosteric Modulators (PAMs) were predicted under the assumption that PAMs should bind to the extracellular surface of the active state. A series of well-characterized PAMs for adenosine (A 1 R, A 2A R, A 3 R) and muscarinic acetylcholine (M 1 R, M 5 R) receptors were modeled using both rigid and flexible receptor CHARMM-based molecular docking. Studies of adenosine receptors investigated the molecular basis of the probe-dependence of PAM activity by modeling in complex with specific agonist radioligands. Consensus binding modes map common pharmacophore features of several chemical series to specific binding interactions. These models provide a rationalization of how PAM binding slows agonist radioligand dissociation kinetics. M 1 R PAMs were predicted to bind in the analogous M 2 R PAM LY2119620 binding site. The M 5 R NAM (ML-375) was predicted to bind in the PAM (ML-380) binding site with a unique induced-fit receptor conformation. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  9. LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation

    Directory of Open Access Journals (Sweden)

    Schroeder Michael

    2006-09-01

    Full Text Available Abstract Background Identifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed. Results We present LIGSITEcsc, an extension and implementation of the LIGSITE algorithm. LIGSITEcsc is based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcsc performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Conclusion The use of the Connolly surface leads to slight improvements, the prediction re-ranking by conservation to significant improvements of the binding site predictions. A web server for LIGSITEcsc and its source code is available at scoppi.biotec.tu-dresden.de/pocket.

  10. CaFE: a tool for binding affinity prediction using end-point free energy methods.

    Science.gov (United States)

    Liu, Hui; Hou, Tingjun

    2016-07-15

    Accurate prediction of binding free energy is of particular importance to computational biology and structure-based drug design. Among those methods for binding affinity predictions, the end-point approaches, such as MM/PBSA and LIE, have been widely used because they can achieve a good balance between prediction accuracy and computational cost. Here we present an easy-to-use pipeline tool named Calculation of Free Energy (CaFE) to conduct MM/PBSA and LIE calculations. Powered by the VMD and NAMD programs, CaFE is able to handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages and supports various force field parameters. CaFE source code and documentation are freely available under the GNU General Public License via GitHub at https://github.com/huiliucode/cafe_plugin It is a VMD plugin written in Tcl and the usage is platform-independent. tingjunhou@zju.edu.cn. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Principal component analysis for predicting transcription-factor binding motifs from array-derived data

    Directory of Open Access Journals (Sweden)

    Vincenti Matthew P

    2005-11-01

    Full Text Available Abstract Background The responses to interleukin 1 (IL-1 in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs. In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD is a powerful method to derive primary components of a given matrix. Applying SVD to a promoter matrix defined from regulatory DNA sequences, we derived a novel method to predict the critical set of TFBMs. Results The promoter matrix was defined to establish a quantitative relationship between the IL-1-driven mRNA alteration and genomic DNA sequences of the IL-1 responsive genes. The matrix was decomposed with SVD, and the effects of 8 potential TFBMs (5'-CAGGC-3', 5'-CGCCC-3', 5'-CCGCC-3', 5'-ATGGG-3', 5'-GGGAA-3', 5'-CGTCC-3', 5'-AAAGG-3', and 5'-ACCCA-3' were predicted from a pool of 512 random DNA sequences. The prediction included matches to the core binding motifs of biologically known TFBMs such as AP2, SP1, EGR1, KROX, GC-BOX, ABI4, ETF, E2F, SRF, STAT, IK-1, PPARγ, STAF, ROAZ, and NFκB, and their significance was evaluated numerically using Monte Carlo simulation and genetic algorithm. Conclusion The described SVD-based prediction is an analytical method to provide a set of potential TFBMs involved in transcriptional regulation. The results would be useful to evaluate analytically a contribution of individual DNA sequences.

  12. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  13. Binding mode and free energy prediction of fisetin/β-cyclodextrin inclusion complexes

    Directory of Open Access Journals (Sweden)

    Bodee Nutho

    2014-11-01

    Full Text Available In the present study, our aim is to investigate the preferential binding mode and encapsulation of the flavonoid fisetin in the nano-pore of β-cyclodextrin (β-CD at the molecular level using various theoretical approaches: molecular docking, molecular dynamics (MD simulations and binding free energy calculations. The molecular docking suggested four possible fisetin orientations in the cavity through its chromone or phenyl ring with two different geometries of fisetin due to the rotatable bond between the two rings. From the multiple MD results, the phenyl ring of fisetin favours its inclusion into the β-CD cavity, whilst less binding or even unbinding preference was observed in the complexes where the larger chromone ring is located in the cavity. All MM- and QM-PBSA/GBSA free energy predictions supported the more stable fisetin/β-CD complex of the bound phenyl ring. Van der Waals interaction is the key force in forming the complexes. In addition, the quantum mechanics calculations with M06-2X/6-31G(d,p clearly showed that both solvation effect and BSSE correction cannot be neglected for the energy determination of the chosen system.

  14. Predicting DNA binding proteins using support vector machine with hybrid fractal features.

    Science.gov (United States)

    Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo

    2014-02-21

    DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances. © 2013 The Authors. Published by Elsevier Ltd All rights reserved.

  15. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  16. Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

    Science.gov (United States)

    Grebner, Christoph; Iegre, Jessica; Ulander, Johan; Edman, Karl; Hogner, Anders; Tyrchan, Christian

    2016-04-25

    Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.

  17. Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model.

    Science.gov (United States)

    Setny, Piotr

    2015-12-08

    Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

  18. Proteochemometric model for predicting the inhibition of penicillin-binding proteins

    Science.gov (United States)

    Nabu, Sunanta; Nantasenamat, Chanin; Owasirikul, Wiwat; Lawung, Ratana; Isarankura-Na-Ayudhya, Chartchalerm; Lapins, Maris; Wikberg, Jarl E. S.; Prachayasittikul, Virapong

    2015-02-01

    Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing β-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic β-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability ( R 2 = 0.91, Q 2 = 0.77, Q Ext 2 = 0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.

  19. In silico predictive studies of mAHR congener binding using homology modelling and molecular docking.

    Science.gov (United States)

    Panda, Roshni; Cleave, A Suneetha Susan; Suresh, P K

    2014-09-01

    The aryl hydrocarbon receptor (AHR) is one of the principal xenobiotic, nuclear receptor that is responsible for the early events involved in the transcription of a complex set of genes comprising the CYP450 gene family. In the present computational study, homology modelling and molecular docking were carried out with the objective of predicting the relationship between the binding efficiency and the lipophilicity of different polychlorinated biphenyl (PCB) congeners and the AHR in silico. Homology model of the murine AHR was constructed by several automated servers and assessed by PROCHECK, ERRAT, VERIFY3D and WHAT IF. The resulting model of the AHR by MODWEB was used to carry out molecular docking of 36 PCB congeners using PatchDock server. The lipophilicity of the congeners was predicted using the XLOGP3 tool. The results suggest that the lipophilicity influences binding energy scores and is positively correlated with the same. Score and Log P were correlated with r = +0.506 at p = 0.01 level. In addition, the number of chlorine (Cl) atoms and Log P were highly correlated with r = +0.900 at p = 0.01 level. The number of Cl atoms and scores also showed a moderate positive correlation of r = +0.481 at p = 0.01 level. To the best of our knowledge, this is the first study employing PatchDock in the docking of AHR to the environmentally deleterious congeners and attempting to correlate structural features of the AHR with its biochemical properties with regards to PCBs. The result of this study are consistent with those of other computational studies reported in the previous literature that suggests that a combination of docking, scoring and ranking organic pollutants could be a possible predictive tool for investigating ligand-mediated toxicity, for their subsequent validation using wet lab-based studies. © The Author(s) 2012.

  20. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...

  1. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.

    Science.gov (United States)

    Nielsen, Morten; Lundegaard, Claus; Lund, Ole

    2007-07-04

    Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. The SMM-align method was shown to outperform other

  2. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2007-07-01

    Full Text Available Abstract Background Antigen presenting cells (APCs sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC and three mouse H2-IA alleles. Results The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR, we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. Conclusion

  3. Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.

    Science.gov (United States)

    Panwar, Bharat; Gupta, Sudheer; Raghava, Gajendra P S

    2013-02-07

    The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0

  4. Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane

    Science.gov (United States)

    Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S.; Gray, C. G.

    2008-03-01

    Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05±0.39kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

  5. Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

    Science.gov (United States)

    Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

    2014-11-01

    As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of

  6. Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

    Science.gov (United States)

    Xu, Xianjin; Yan, Chengfei; Zou, Xiaoqin

    2017-08-01

    The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

  7. Structure prediction and binding sites analysis of curcin protein of Jatropha curcas using computational approaches.

    Science.gov (United States)

    Srivastava, Mugdha; Gupta, Shishir K; Abhilash, P C; Singh, Nandita

    2012-07-01

    Ribosome inactivating proteins (RIPs) are defense proteins in a number of higher-plant species that are directly targeted toward herbivores. Jatropha curcas is one of the biodiesel plants having RIPs. The Jatropha seed meal, after extraction of oil, is rich in curcin, a highly toxic RIP similar to ricin, which makes it unsuitable for animal feed. Although the toxicity of curcin is well documented in the literature, the detailed toxic properties and the 3D structure of curcin has not been determined by X-ray crystallography, NMR spectroscopy or any in silico techniques to date. In this pursuit, the structure of curcin was modeled by a composite approach of 3D structure prediction using threading and ab initio modeling. Assessment of model quality was assessed by methods which include Ramachandran plot analysis and Qmean score estimation. Further, we applied the protein-ligand docking approach to identify the r-RNA binding residue of curcin. The present work provides the first structural insight into the binding mode of r-RNA adenine to the curcin protein and forms the basis for designing future inhibitors of curcin. Cloning of a future peptide inhibitor within J. curcas can produce non-toxic varieties of J. curcas, which would make the seed-cake suitable as animal feed without curcin detoxification.

  8. Predicted MHC peptide binding promiscuity explains MHC class I 'hotspots' of antigen presentation defined by mass spectrometry eluted ligand data.

    Science.gov (United States)

    Jappe, Emma Christine; Kringelum, Jens; Trolle, Thomas; Nielsen, Morten

    2018-02-15

    Peptides that bind to and are presented by MHC class I and class II molecules collectively make up the immunopeptidome. In the context of vaccine development, an understanding of the immunopeptidome is essential, and much effort has been dedicated to its accurate and cost-effective identification. Current state-of-the-art methods mainly comprise in silico tools for predicting MHC binding, which is strongly correlated with peptide immunogenicity. However, only a small proportion of the peptides that bind to MHC molecules are, in fact, immunogenic, and substantial work has been dedicated to uncovering additional determinants of peptide immunogenicity. In this context, and in light of recent advancements in mass spectrometry (MS), the existence of immunological hotspots has been given new life, inciting the hypothesis that hotspots are associated with MHC class I peptide immunogenicity. We here introduce a precise terminology for defining these hotspots and carry out a systematic analysis of MS and in silico predicted hotspots. We find that hotspots defined from MS data are largely captured by peptide binding predictions, enabling their replication in silico. This leads us to conclude that hotspots, to a great degree, are simply a result of promiscuous HLA binding, which disproves the hypothesis that the identification of hotspots provides novel information in the context of immunogenic peptide prediction. Furthermore, our analyses demonstrate that the signal of ligand processing, although present in the MS data, has very low predictive power to discriminate between MS and in silico defined hotspots. © 2018 John Wiley & Sons Ltd.

  9. ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites.

    Science.gov (United States)

    Konc, Janez; Janežič, Dušanka

    2014-07-01

    The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Improving density functional tight binding predictions of free energy surfaces for peptide condensation reactions in solution

    Science.gov (United States)

    Kroonblawd, Matthew; Goldman, Nir

    First principles molecular dynamics using highly accurate density functional theory (DFT) is a common tool for predicting chemistry, but the accessible time and space scales are often orders of magnitude beyond the resolution of experiments. Semi-empirical methods such as density functional tight binding (DFTB) offer up to a thousand-fold reduction in required CPU hours and can approach experimental scales. However, standard DFTB parameter sets lack good transferability and calibration for a particular system is usually necessary. Force matching the pairwise repulsive energy term in DFTB to short DFT trajectories can improve the former's accuracy for chemistry that is fast relative to DFT simulation times (Contract DE-AC52-07NA27344.

  11. Improving Density Functional Tight Binding Predictions of Free Energy Surfaces for Slow Chemical Reactions in Solution

    Science.gov (United States)

    Kroonblawd, Matthew; Goldman, Nir

    2017-06-01

    First principles molecular dynamics using highly accurate density functional theory (DFT) is a common tool for predicting chemistry, but the accessible time and space scales are often orders of magnitude beyond the resolution of experiments. Semi-empirical methods such as density functional tight binding (DFTB) offer up to a thousand-fold reduction in required CPU hours and can approach experimental scales. However, standard DFTB parameter sets lack good transferability and calibration for a particular system is usually necessary. Force matching the pairwise repulsive energy term in DFTB to short DFT trajectories can improve the former's accuracy for reactions that are fast relative to DFT simulation times (Contract DE-AC52-07NA27344.

  12. Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

    Science.gov (United States)

    Zhu, Xiaolei; Xiong, Yi; Kihara, Daisuke

    2015-03-01

    Ligand binding is a key aspect of the function of many proteins. Thus, binding ligand prediction provides important insight in understanding the biological function of proteins. Binding ligand prediction is also useful for drug design and examining potential drug side effects. We present a computational method named Patch-Surfer2.0, which predicts binding ligands for a protein pocket. By representing and comparing pockets at the level of small local surface patches that characterize physicochemical properties of the local regions, the method can identify binding pockets of the same ligand even if they do not share globally similar shapes. Properties of local patches are represented by an efficient mathematical representation, 3D Zernike Descriptor. Patch-Surfer2.0 has significant technical improvements over our previous prototype, which includes a new feature that captures approximate patch position with a geodesic distance histogram. Moreover, we constructed a large comprehensive database of ligand binding pockets that will be searched against by a query. The benchmark shows better performance of Patch-Surfer2.0 over existing methods. http://kiharalab.org/patchsurfer2.0/ CONTACT: dkihara@purdue.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  13. Ensemble Architecture for Prediction of Enzyme-ligand Binding Residues Using Evolutionary Information.

    Science.gov (United States)

    Pai, Priyadarshini P; Dattatreya, Rohit Kadam; Mondal, Sukanta

    2017-11-01

    Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning. Here, we propose a support vector machines-based ensemble for prediction of protein-ligand interacting residues using one of the most important discriminative contributing properties in the interacting residue neighbourhood, i. e., evolutionary information in the form of position-specific- scoring matrix (PSSM). The study has been performed on a non-redundant dataset comprising of 9269 interacting and 91773 non-interacting residues for prediction model generation and further evaluation. Of the various PSSM-based models explored, the proposed method named ROBBY (pRediction Of Biologically relevant small molecule Binding residues on enzYmes) shows an accuracy of 84.0 %, Matthews Correlation Coefficient of 0.343 and F-measure of 39.0 % on 78 test enzymes. Further, scope of adding domain knowledge such as pocket information has also been investigated; results showed significant enhancement in method precision. Findings are hoped to boost the reliability of small-molecule ligand interaction prediction for enzyme applications and drug design. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.

    Science.gov (United States)

    Du, Hanwen; Cai, Yingchun; Yang, Hongbin; Zhang, Hongxiao; Xue, Yuhan; Liu, Guixia; Tang, Yun; Li, Weihua

    2017-05-15

    Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

  15. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Usefulness of intestinal fatty acid-binding protein in predicting strangulated small bowel obstruction.

    Directory of Open Access Journals (Sweden)

    Hirotada Kittaka

    Full Text Available BACKGROUND: The level of intestinal fatty acid-binding protein (I-FABP is considered to be useful diagnostic markers of small bowel ischemia. The purpose of this retrospective study was to investigate whether the serum I-FABP level is a predictive marker of strangulation in patients with small bowel obstruction (SBO. METHODS: A total of 37 patients diagnosed with SBO were included in this study. The serum I-FABP levels were retrospectively compared between the patients with strangulation and those with simple obstruction, and cut-off values for the diagnosis of strangulation were calculated using a receiver operating characteristic curve. In addition, the sensitivity, specificity, positive predictive value (PPV and negative predictive value (NPV were calculated. RESULTS: Twenty-one patients were diagnosed with strangulated SBO. The serum I-FABP levels were significantly higher in the patients with strangulation compared with those observed in the patients with simple obstruction (18.5 vs. 1.6 ng/ml p<0.001. Using a cut-off value of 6.5 ng/ml, the sensitivity, specificity, PPV and NPV were 71.4%, 93.8%, 93.8% and 71.4%, respectively. An I-FABP level greater than 6.5 ng/ml was found to be the only independent significant factor for a higher likelihood of strangulated SBO (P =  0.02; odds ratio: 19.826; 95% confidence interval: 2.1560 - 488.300. CONCLUSIONS: The I-FABP level is a useful marker for discriminating between strangulated SBO and simple SBO in patients with SBO.

  17. Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

    Directory of Open Access Journals (Sweden)

    Xin Ma

    2015-01-01

    Full Text Available The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR method, followed by incremental feature selection (IFS. We incorporated features of conjoint triad features and three novel features: binding propensity (BP, nonbinding propensity (NBP, and evolutionary information combined with physicochemical properties (EIPP. The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient. High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

  18. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals.

    Science.gov (United States)

    Hong, Huixiao; Shen, Jie; Ng, Hui Wen; Sakkiah, Sugunadevi; Ye, Hao; Ge, Weigong; Gong, Ping; Xiao, Wenming; Tong, Weida

    2016-03-25

    Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

  19. Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

    Directory of Open Access Journals (Sweden)

    Panwar Bharat

    2013-02-01

    Full Text Available Abstract Background The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. Results In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL. It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i vitamin interacting residues (VIRs, (ii vitamin-A interacting residues (VAIRs, (iii vitamin-B interacting residues (VBIRs and (iv pyridoxal-5-phosphate (vitamin B6 interacting residues (PLPIRs have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM features of protein sequences. Finally, we selected best performing SVM modules and

  20. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

    Science.gov (United States)

    de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

    2017-12-09

    Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

    Science.gov (United States)

    Deng, Nanjie; Flynn, William F; Xia, Junchao; Vijayan, R S K; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M

    2016-09-01

    We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate

  2. Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

    International Nuclear Information System (INIS)

    Sun Zhong-Hua; Jiang Fan

    2010-01-01

    In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

  3. Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations

    DEFF Research Database (Denmark)

    Poongavanam, Vasanthanathan; Olsen, Lars; Jørgensen, Flemming Steen

    2010-01-01

    , and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both...... substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent......Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structure-based ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method...

  4. Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features

    Directory of Open Access Journals (Sweden)

    Zhang Ya-Nan

    2012-05-01

    Full Text Available Abstract Background Adenosine-5′-triphosphate (ATP is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. Results In this paper, we propose a novel framework for predicting the proteins’ functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target’s knowledge and multiple sources of descriptors can be complementary. Conclusions Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.

  5. Computational analysis and prediction of the binding motif and protein interacting partners of the Abl SH3 domain.

    Directory of Open Access Journals (Sweden)

    Tingjun Hou

    2006-01-01

    Full Text Available Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains, such as Src Homology 2 and 3 (SH2 and SH3 domains, which bind to specific sequence and structural motifs. It is important but challenging to determine the binding specificity of these domains accurately and to predict their physiological interacting partners. In this study, the interactions between 35 peptide ligands (15 binders and 20 non-binders and the Abl SH3 domain were analyzed using molecular dynamics simulation and the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. The calculated binding free energies correlated well with the rank order of the binding peptides and clearly distinguished binders from non-binders. Free energy component analysis revealed that the van der Waals interactions dictate the binding strength of peptides, whereas the binding specificity is determined by the electrostatic interaction and the polar contribution of desolvation. The binding motif of the Abl SH3 domain was then determined by a virtual mutagenesis method, which mutates the residue at each position of the template peptide relative to all other 19 amino acids and calculates the binding free energy difference between the template and the mutated peptides using the Molecular Mechanics/Poisson-Boltzmann Solvent Area method. A single position mutation free energy profile was thus established and used as a scoring matrix to search peptides recognized by the Abl SH3 domain in the human genome. Our approach successfully picked ten out of 13 experimentally determined binding partners of the Abl SH3 domain among the top 600 candidates from the 218,540 decapeptides with the PXXP motif in the SWISS-PROT database. We expect that this physical-principle based method can be applied to other protein domains as well.

  6. Predicting transcription factor binding sites using local over-representation and comparative genomics

    Directory of Open Access Journals (Sweden)

    Touzet Hélène

    2006-08-01

    Full Text Available Abstract Background Identifying cis-regulatory elements is crucial to understanding gene expression, which highlights the importance of the computational detection of overrepresented transcription factor binding sites (TFBSs in coexpressed or coregulated genes. However, this is a challenging problem, especially when considering higher eukaryotic organisms. Results We have developed a method, named TFM-Explorer, that searches for locally overrepresented TFBSs in a set of coregulated genes, which are modeled by profiles provided by a database of position weight matrices. The novelty of the method is that it takes advantage of spatial conservation in the sequence and supports multiple species. The efficiency of the underlying algorithm and its robustness to noise allow weak regulatory signals to be detected in large heterogeneous data sets. Conclusion TFM-Explorer provides an efficient way to predict TFBS overrepresentation in related sequences. Promising results were obtained in a variety of examples in human, mouse, and rat genomes. The software is publicly available at http://bioinfo.lifl.fr/TFM-Explorer.

  7. Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

    Science.gov (United States)

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2018-01-01

    Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  8. COMPARATIVE MODELLING AND LIGAND BINDING SITE PREDICTION OF A FAMILY 43 GLYCOSIDE HYDROLASE FROM Clostridium thermocellum

    Directory of Open Access Journals (Sweden)

    Shadab Ahmed

    2012-06-01

    Full Text Available The phylogenetic analysis of Clostridium thermocellum family 43 glycoside hydrolase (CtGH43 showed close evolutionary relation with carbohydrate binding family 6 proteins from C. cellulolyticum, C. papyrosolvens, C. cellulyticum, and A. cellulyticum. Comparative modeling of CtGH43 was performed based on crystal structures with PDB IDs 3C7F, 1YIF, 1YRZ, 2EXH and 1WL7. The structure having lowest MODELLER objective function was selected. The three-dimensional structure revealed typical 5-fold beta–propeller architecture. Energy minimization and validation of predicted model with VERIFY 3D indicated acceptability of the proposed atomic structure. The Ramachandran plot analysis by RAMPAGE confirmed that family 43 glycoside hydrolase (CtGH43 contains little or negligible segments of helices. It also showed that out of 301 residues, 267 (89.3% were in most favoured region, 23 (7.7% were in allowed region and 9 (3.0% were in outlier region. IUPred analysis of CtGH43 showed no disordered region. Active site analysis showed presence of two Asp and one Glu, assumed to form a catalytic triad. This study gives us information about three-dimensional structure and reaffirms the fact that it has the similar core 5-fold beta–propeller architecture and so probably has the same inverting mechanism of action with the formation of above mentioned catalytic triad for catalysis of polysaccharides.

  9. Seasonal difference in brain serotonin transporter binding predicts symptom severity in patients with seasonal affective disorder

    DEFF Research Database (Denmark)

    Mc Mahon, Brenda; Andersen, Sofie B.; Madsen, Martin K.

    2016-01-01

    controls with low seasonality scores and 17 patients diagnosed with seasonal affective disorder were scanned in both summer and winter to investigate differences in cerebral serotonin transporter binding across groups and across seasons. The two groups had similar cerebral serotonin transporter binding...... between summer and winter (Psex-(P = 0.02) and genotype-(P = 0.04) dependent. In the patients with seasonal affective disorder, the seasonal change in serotonin transporter binding was positively associated with change in depressive symptom...

  10. Conservation of transcription factor binding events predicts gene expression across species

    OpenAIRE

    Hemberg, Martin; Kreiman, Gabriel

    2011-01-01

    Recent technological advances have made it possible to determine the genome-wide binding sites of transcription factors (TFs). Comparisons across species have suggested a relatively low degree of evolutionary conservation of experimentally defined TF binding events (TFBEs). Using binding data for six different TFs in hepatocytes and embryonic stem cells from human and mouse, we demonstrate that evolutionary conservation of TFBEs within orthologous proximal promoters is closely linked to funct...

  11. Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.

    Directory of Open Access Journals (Sweden)

    Marharyta Petukh

    2015-07-01

    Full Text Available A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624 while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation.

  12. In silico engineering and optimization of Transcription Activator-Like Effectors and their derivatives for improved DNA binding predictions.

    KAUST Repository

    Piatek, Marek J.

    2015-12-01

    Transcription Activator-Like Effectors (TALEs) can be used as adaptable DNAbinding modules to create site-specific chimeric nucleases or synthetic transcriptional regulators. The central repeat domain mediates specific DNA binding via hypervariable repeat di-residues (RVDs). This DNA-Binding Domain can be engineered to bind preferentially to any user-selected DNA sequence if engineered appropriately. Therefore, TALEs and their derivatives have become indispensable molecular tools in site-specific manipulation of genes and genomes. This thesis revolves around two problems: in silico design and improved binding site prediction of TALEs. In the first part, a study is shown where TALEs are successfully designed in silico and validated in laboratory to yield the anticipated effects on selected genes. Software is developed to accompany the process of designing and prediction of binding sites. I expanded the functionality of the software to be used as a more generic set of tools for the design, target and offtarget searching. Part two contributes a method and associated toolkit developed to allow users to design in silico optimized synthetic TALEs with user-defined specificities for various experimental purposes. This method is based on a mutual relationship of three consecutive tandem repeats in the DNA-binding domain. This approach revealed positional and compositional bias behind the binding of TALEs to DNA. In conclusion, I developed methods, approaches, and software to enhance the functionality of synthetic TALEs, which should improve understanding of TALEs biology and will further advance genome-engineering applications in various organisms and cell types.

  13. New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

    Science.gov (United States)

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2004-11-21

    Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).

  14. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2014-08-27

    State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.

  15. Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation.

    Directory of Open Access Journals (Sweden)

    Luigi Capoferri

    Full Text Available Prediction of human Cytochrome P450 (CYP binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD simulations and Linear Interaction Energy (LIE theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE of 4.1 kJ mol-1 and a standard error in prediction (SDEP in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units.

  16. MHC class I epitope binding prediction trained on small data sets

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Nielsen, Morten; Lamberth, K.

    2004-01-01

    The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding pre...... in situations where only very limited data are available for training....

  17. PROCARB: A Database of Known and Modelled Carbohydrate-Binding Protein Structures with Sequence-Based Prediction Tools

    Directory of Open Access Journals (Sweden)

    Adeel Malik

    2010-01-01

    Full Text Available Understanding of the three-dimensional structures of proteins that interact with carbohydrates covalently (glycoproteins as well as noncovalently (protein-carbohydrate complexes is essential to many biological processes and plays a significant role in normal and disease-associated functions. It is important to have a central repository of knowledge available about these protein-carbohydrate complexes as well as preprocessed data of predicted structures. This can be significantly enhanced by tools de novo which can predict carbohydrate-binding sites for proteins in the absence of structure of experimentally known binding site. PROCARB is an open-access database comprising three independently working components, namely, (i Core PROCARB module, consisting of three-dimensional structures of protein-carbohydrate complexes taken from Protein Data Bank (PDB, (ii Homology Models module, consisting of manually developed three-dimensional models of N-linked and O-linked glycoproteins of unknown three-dimensional structure, and (iii CBS-Pred prediction module, consisting of web servers to predict carbohydrate-binding sites using single sequence or server-generated PSSM. Several precomputed structural and functional properties of complexes are also included in the database for quick analysis. In particular, information about function, secondary structure, solvent accessibility, hydrogen bonds and literature reference, and so forth, is included. In addition, each protein in the database is mapped to Uniprot, Pfam, PDB, and so forth.

  18. Mapping the heparin-binding site of the BMP antagonist gremlin by site-directed mutagenesis based on predictive modelling.

    Science.gov (United States)

    Tatsinkam, Arnold Junior; Mulloy, Barbara; Rider, Christopher C

    2015-08-15

    Gremlin is a member of the CAN (cerberus and DAN) family of secreted BMP (bone morphogenetic protein) antagonists and also an agonist of VEGF (vascular endothelial growth factor) receptor-2. It is critical in limb skeleton and kidney development and is re-expressed during tissue fibrosis. Gremlin binds strongly to heparin and heparan sulfate and, in the present study, we sought to investigate its heparin-binding site. In order to explore a putative non-contiguous binding site predicted by computational molecular modelling, we substituted a total of 11 key arginines and lysines located in three basic residue sequence clusters with homologous sequences from cerberus and DAN (differential screening selected gene abberative in neuroblastoma), CAN proteins which lack basic residues in these positions. A panel of six Myc-tagged gremlin mutants, MGR-1-MGR-6 (MGR, mutant gremlin), each containing different combinations of targeted substitutions, all showed markedly reduced affinity for heparin as demonstrated by their NaCl elution on heparin affinity chromatography, thus verifying our predictions. Both MGR-5 and MGR-6 retained BMP-4-binding activity comparable to that of wild-type gremlin. Low-molecular-mass heparin neither promoted nor inhibited BMP-4 binding. Finally, glutaraldehyde cross-linking demonstrated that gremlin forms non-covalent dimers, similar behaviour to that of DAN and also PRDC (protein related to cerberus and DAN), another CAN protein. The resulting dimer would possess two heparin-binding sites, each running along an exposed surface on the second β-strand finger loop of one of the monomers. © 2015 Authors; published by Portland Press Limited.

  19. A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain.

    Science.gov (United States)

    Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J; Simonson, Thomas

    2017-01-01

    PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or "PB/LIE" free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α 2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo . The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

  20. Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.

    Science.gov (United States)

    Schwessinger, Ron; Suciu, Maria C; McGowan, Simon J; Telenius, Jelena; Taylor, Stephen; Higgs, Doug R; Hughes, Jim R

    2017-10-01

    In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive k -mer-based analysis of DNase footprints to determine any k -mer's potential for protein binding in a specific cell type and how this may be changed by sequence variants. Therefore, Sasquatch uses an unbiased approach, independent of known transcription factor binding sites and motifs. Sasquatch only requires a single DNase-seq data set per cell type, from any genotype, and produces consistent predictions from data generated by different experimental procedures and at different sequence depths. Here we demonstrate the effectiveness of Sasquatch using previously validated functional SNPs and benchmark its performance against existing approaches. Sasquatch is available as a versatile webtool incorporating publicly available data, including the human ENCODE collection. Thus, Sasquatch provides a powerful tool and repository for prioritizing likely regulatory SNPs in the noncoding genome. © 2017 Schwessinger et al.; Published by Cold Spring Harbor Laboratory Press.

  1. A strategy for interaction site prediction between phospho-binding modules and their partners identified from proteomic data.

    Science.gov (United States)

    Aucher, Willy; Becker, Emmanuelle; Ma, Emilie; Miron, Simona; Martel, Arnaud; Ochsenbein, Françoise; Marsolier-Kergoat, Marie-Claude; Guerois, Raphaël

    2010-12-01

    Small and large scale proteomic technologies are providing a wealth of potential interactions between proteins bearing phospho-recognition modules and their substrates. Resulting interaction maps reveal such a dense network of interactions that the functional dissection and understanding of these networks often require to break specific interactions while keeping the rest intact. Here, we developed a computational strategy, called STRIP, to predict the precise interaction site involved in an interaction with a phospho-recognition module. The method was validated by a two-hybrid screen carried out using the ForkHead Associated (FHA)1 domain of Rad53, a key protein of Saccharomyces cerevisiae DNA checkpoint, as a bait. In this screen we detected 11 partners, including Cdc7 and Cdc45, essential components of the DNA replication machinery. FHA domains are phospho-threonine binding modules and the threonines involved in both interactions could be predicted using the STRIP strategy. The threonines T484 and T189 in Cdc7 and Cdc45, respectively, were mutated and loss of binding could be monitored experimentally with the full-length proteins. The method was further tested for the analysis of 63 known Rad53 binding partners and provided several key insights regarding the threonines likely involved in these interactions. The STRIP method relies on a combination of conservation, phosphorylation likelihood, and binding specificity criteria and can be accessed via a web interface at http://biodev.extra.cea.fr/strip/.

  2. A Strategy for Interaction Site Prediction between Phospho-binding Modules and their Partners Identified from Proteomic Data*

    Science.gov (United States)

    Aucher, Willy; Becker, Emmanuelle; Ma, Emilie; Miron, Simona; Martel, Arnaud; Ochsenbein, Françoise; Marsolier-Kergoat, Marie-Claude; Guerois, Raphaël

    2010-01-01

    Small and large scale proteomic technologies are providing a wealth of potential interactions between proteins bearing phospho-recognition modules and their substrates. Resulting interaction maps reveal such a dense network of interactions that the functional dissection and understanding of these networks often require to break specific interactions while keeping the rest intact. Here, we developed a computational strategy, called STRIP, to predict the precise interaction site involved in an interaction with a phospho-recognition module. The method was validated by a two-hybrid screen carried out using the ForkHead Associated (FHA)1 domain of Rad53, a key protein of Saccharomyces cerevisiae DNA checkpoint, as a bait. In this screen we detected 11 partners, including Cdc7 and Cdc45, essential components of the DNA replication machinery. FHA domains are phospho-threonine binding modules and the threonines involved in both interactions could be predicted using the STRIP strategy. The threonines T484 and T189 in Cdc7 and Cdc45, respectively, were mutated and loss of binding could be monitored experimentally with the full-length proteins. The method was further tested for the analysis of 63 known Rad53 binding partners and provided several key insights regarding the threonines likely involved in these interactions. The STRIP method relies on a combination of conservation, phosphorylation likelihood, and binding specificity criteria and can be accessed via a web interface at http://biodev.extra.cea.fr/strip/. PMID:20733106

  3. A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain

    Directory of Open Access Journals (Sweden)

    Nicolas Panel

    2017-09-01

    Full Text Available PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or “PB/LIE” free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo. The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

  4. SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors.

    Science.gov (United States)

    Kadam, Kiran; Prabhakar, Prashant; Jayaraman, V K

    2012-11-01

    Bacterial lipoproteins play critical roles in various physiological processes including the maintenance of pathogenicity and numbers of them are being considered as potential candidates for generating novel vaccines. In this work, we put forth an algorithm to identify and predict ligand-binding sites in bacterial lipoproteins. The method uses three types of pocket descriptors, namely fpocket descriptors, 3D Zernike descriptors and shell descriptors, and combines them with Support Vector Machine (SVM) method for the classification. The three types of descriptors represent shape-based properties of the pocket as well as its local physio-chemical features. All three types of descriptors, along with their hybrid combinations are evaluated with SVM and to improve classification performance, WEKA-InfoGain feature selection is applied. Results obtained in the study show that the classifier successfully differentiates between ligand-binding and non-binding pockets. For the combination of three types of descriptors, 10 fold cross-validation accuracy of 86.83% is obtained for training while the selected model achieved test Matthews Correlation Coefficient (MCC) of 0.534. Individually or in combination with new and existing methods, our model can be a very useful tool for the prediction of potential ligand-binding sites in bacterial lipoproteins.

  5. An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

    DEFF Research Database (Denmark)

    Larsen, Mette Voldby; Lundegaard, Claus; Lamberth, K

    2005-01-01

    Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have al.......The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php....

  6. Predictions of RNA-binding ability and aggregation propensity of proteins

    OpenAIRE

    Agostini, Federico, 1985-

    2014-01-01

    RNA-binding proteins (RBPs) control the fate of a multitude of coding and non-coding transcripts. Formation of ribonucleoprotein (RNP) complexes fine-tunes regulation of post-transcriptional events and influences gene expression. Recently, it has been observed that non-canonical proteins with RNA-binding ability are enriched in structurally disordered and low-complexity regions that are generally involved in functional and dysfunctional associations. Therefore, it is possible that interaction...

  7. Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints

    OpenAIRE

    Schwessinger, R; Suciu, MC; McGowan, SJ; Telenius, J; Taylor, S; Higgs, DR; Hughes, JR

    2017-01-01

    In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor bin...

  8. Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.

    Directory of Open Access Journals (Sweden)

    Niklas Berliner

    Full Text Available Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

  9. Binding mode prediction and MD/MMPBSA-based free energy ranking for agonists of REV-ERBα/NCoR.

    Science.gov (United States)

    Westermaier, Yvonne; Ruiz-Carmona, Sergio; Theret, Isabelle; Perron-Sierra, Françoise; Poissonnet, Guillaume; Dacquet, Catherine; Boutin, Jean A; Ducrot, Pierre; Barril, Xavier

    2017-08-01

    The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

  10. Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy

    DEFF Research Database (Denmark)

    Mattsson, Andreas Holm; Kringelum, Jens Vindahl; Garde, C.

    2016-01-01

    Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical....... Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor-ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding...

  11. A community resource benchmarking predictions of peptide binding to MHC-I molecules

    DEFF Research Database (Denmark)

    Peters, B; Bui, HH; Pletscher-Frankild, Sune

    2006-01-01

    on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison...... of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current...

  12. Confirmation of a predicted lack of IgE binding to Cry3Bb1 from genetically modified (GM) crops.

    Science.gov (United States)

    Nakajima, Osamu; Koyano, Satoru; Akiyama, Hiroshi; Sawada, Jun-Ichi; Teshima, Reiko

    2010-04-01

    Some GM crops including MON863 corn and stack varieties contain Cry3Bb1 protein. Cry3Bb1 is very important from the standpoint of assessing the safety of GM crops. In this study Cry3Bb1 was assessed from the standpoint of possible binding to IgE from allergy patients. First, an ELISA that was improved in our laboratory was used to test serum samples from 13 corn allergy patients in the United States with recombinant Cry3Bb1 expressed in Escherichia coli, and serum samples from 55 patients in Japan with various food allergies were also assayed. Two samples from the Japanese allergy patients were suspected of being positive, but Western blotting analysis with purified Cry3Bb1 indicated that the binding between IgE and Cry3Bb1 was nonspecific. Ultimately, no specific binding between IgE and recombinant Cry3Bb1 was detected. Next, all proteins extracted from MON863 corn and non-GM corn were probed with IgE antibodies in serum samples from the corn allergy patients by Western blotting, but the staining patterns of MON863 and non-GM corn were similar, meaning that unintended allergic reactions to MON863 are unlikely to occur. Our study provides additional information that confirms the predicted lack of IgE binding to Cry3Bb1 in people with existing food allergies. Copyright 2009 Elsevier Inc. All rights reserved.

  13. Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+

    Science.gov (United States)

    Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

    2018-01-01

    Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

  14. Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

    Science.gov (United States)

    Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

    2018-01-01

    Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

  15. Sex hormone-binding globulin levels predict insulin sensitivity, disposition index, and cardiovascular risk during puberty

    DEFF Research Database (Denmark)

    Sørensen, Kaspar; Aksglaede, Lise; Munch-Andersen, Thor

    2009-01-01

    Early puberty is associated with increased risk of subsequent cardiovascular disease. Low sex hormone-binding globulin (SHBG) levels are a feature of early puberty and of conditions associated with increased cardiovascular risk. The aim of the present study was to evaluate SHBG as a predictor...... of glucose metabolism and metabolic risk during puberty....

  16. Conservation of transcription factor binding events predicts gene expression across species

    Science.gov (United States)

    Hemberg, Martin; Kreiman, Gabriel

    2011-01-01

    Recent technological advances have made it possible to determine the genome-wide binding sites of transcription factors (TFs). Comparisons across species have suggested a relatively low degree of evolutionary conservation of experimentally defined TF binding events (TFBEs). Using binding data for six different TFs in hepatocytes and embryonic stem cells from human and mouse, we demonstrate that evolutionary conservation of TFBEs within orthologous proximal promoters is closely linked to function, defined as expression of the target genes. We show that (i) there is a significantly higher degree of conservation of TFBEs when the target gene is expressed in both species; (ii) there is increased conservation of binding events for groups of TFs compared to individual TFs; and (iii) conserved TFBEs have a greater impact on the expression of their target genes than non-conserved ones. These results link conservation of structural elements (TFBEs) to conservation of function (gene expression) and suggest a higher degree of functional conservation than implied by previous studies. PMID:21622661

  17. NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

    OpenAIRE

    Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

    2017-01-01

    Cytotoxic T cells are of central importance in the immune systems response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention. In the past, predictors of peptide-...

  18. Prediction of DtxR regulon: Identification of binding sites and operons controlled by Diphtheria toxin repressor in Corynebacterium diphtheriae

    Directory of Open Access Journals (Sweden)

    Hasnain Seyed

    2004-09-01

    Full Text Available Abstract Background The diphtheria toxin repressor, DtxR, of Corynebacterium diphtheriae has been shown to be an iron-activated transcription regulator that controls not only the expression of diphtheria toxin but also of iron uptake genes. This study aims to identify putative binding sites and operons controlled by DtxR to understand the role of DtxR in patho-physiology of Corynebacterium diphtheriae. Result Positional Shannon relative entropy method was used to build the DtxR-binding site recognition profile and the later was used to identify putative regulatory sites of DtxR within C. diphtheriae genome. In addition, DtxR-regulated operons were also identified taking into account the predicted DtxR regulatory sites and genome annotation. Few of the predicted motifs were experimentally validated by electrophoretic mobility shift assay. The analysis identifies motifs upstream to the novel iron-regulated genes that code for Formamidopyrimidine-DNA glycosylase (FpG, an enzyme involved in DNA-repair and starvation inducible DNA-binding protein (Dps which is involved in iron storage and oxidative stress defense. In addition, we have found the DtxR motifs upstream to the genes that code for sortase which catalyzes anchoring of host-interacting proteins to the cell wall of pathogenic bacteria and the proteins of secretory system which could be involved in translocation of various iron-regulated virulence factors including diphtheria toxin. Conclusions We have used an in silico approach to identify the putative binding sites and genes controlled by DtxR in Corynebacterium diphtheriae. Our analysis shows that DtxR could provide a molecular link between Fe+2-induced Fenton's reaction and protection of DNA from oxidative damage. DtxR-regulated Dps prevents lethal combination of Fe+2 and H2O2 and also protects DNA by nonspecific DNA-binding. In addition DtxR could play an important role in host interaction and virulence by regulating the levels of sortase

  19. Evaluation of B3LYP, X3LYP, and M06-class density functionals for predicting the binding energies of neutral, protonated, and deprotonated water clusters

    OpenAIRE

    Bryantsev, Vyacheslav S.; Diallo, Mamadou S.; van Duin, Adri C. T.; Goddard, William A., III

    2009-01-01

    In this paper we assess the accuracy of the B3LYP, X3LYP, and newly developed M06-L, M06-2X, and M06 functionals to predict the binding energies of neutral and charged water clusters including (H_2O)_n, n = 2−8, 20), H_3O+(H_2O_)n, n = 1−6, and OH−(H_2O)_n, n = 1−6. We also compare the predicted energies of two ion hydration and neutralization reactions on the basis of the calculated binding energies. In all cases, we use as benchmarks calculated binding energies of water clusters extrapolate...

  20. DBAC: A simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts

    Science.gov (United States)

    2011-01-01

    Background A protein binding hot spot is a cluster of residues in the interface that are energetically important for the binding of the protein with its interaction partner. Identifying protein binding hot spots can give useful information to protein engineering and drug design, and can also deepen our understanding of protein-protein interaction. These residues are usually buried inside the interface with very low solvent accessible surface area (SASA). Thus SASA is widely used as an outstanding feature in hot spot prediction by many computational methods. However, SASA is not capable of distinguishing slightly buried residues, of which most are non hot spots, and deeply buried ones that are usually inside a hot spot. Results We propose a new descriptor called “burial level” for characterizing residues, atoms and atomic contacts. Specifically, burial level captures the depth the residues are buried. We identify different kinds of deeply buried atomic contacts (DBAC) at different burial levels that are directly broken in alanine substitution. We use their numbers as input for SVM to classify between hot spot or non hot spot residues. We achieve F measure of 0.6237 under the leave-one-out cross-validation on a data set containing 258 mutations. This performance is better than other computational methods. Conclusions Our results show that hot spot residues tend to be deeply buried in the interface, not just having a low SASA value. This indicates that a high burial level is not only a necessary but also a more sufficient condition than a low SASA for a residue to be a hot spot residue. We find that those deeply buried atoms become increasingly more important when their burial levels rise up. This work also confirms the contribution of deeply buried interfacial atomic contacts to the energy of protein binding hot spot. PMID:21689480

  1. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system.

    Directory of Open Access Journals (Sweden)

    Nicolas Rapin

    Full Text Available We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.

  2. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

    Science.gov (United States)

    Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

    2017-12-04

    Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

  3. Serum Mac-2 binding protein glycosylation isomer predicts grade F4 liver fibrosis in patients with biliary atresia.

    Science.gov (United States)

    Yamada, Naoya; Sanada, Yukihiro; Tashiro, Masahisa; Hirata, Yuta; Okada, Noriki; Ihara, Yoshiyuki; Urahashi, Taizen; Mizuta, Koichi

    2017-02-01

    Mac-2 Binding Protein Glycosylation Isomer (M2BPGi) is a novel fibrosis marker. We examined the ability of M2BPGi to predict liver fibrosis in patients with biliary atresia. Sixty-four patients who underwent living donor liver transplantation (LDLT) were included [median age, 1.1 years (range 0.4-16.0), male 16 patients (25.0 %)]. We examined M2BPGi levels in serum obtained the day before LDLT, and we compared the value of the preoperative M2BPGi levels with the histological evaluation of fibrosis using the METAVIR fibrosis score. Subsequently, we assessed the ability of M2BPGi levels to predict fibrosis. The median M2BPGi level in patients with BA was 6.02 (range, 0.36-20.0), and 0, 1, 1, 11, and 51 patients had METAVIR fibrosis scores of F0, F1, F2, F3, and F4, respectively. In patients with F4 fibrosis, the median M2BPGi level was 6.88 (quartile; 5.235, 12.10), significantly higher than that in patients with F3 fibrosis who had a median level of 2.42 (quartile; 1.93, 2.895, p F4 fibrosis. M2BPGi is a novel fibrosis marker for evaluating the status of the liver in patients with BA, especially when predicting grade F4 fibrosis.

  4. Mannose binding lectin (MBL levels predict lung function decline in severe asthma

    Directory of Open Access Journals (Sweden)

    Ilonka. H. van Veen

    2006-12-01

    Full Text Available There is increasing evidence that activation of the complement system in asthma contributes to ongoing inflammation, tissue damage and airway remodeling. Mannose binding lectin (MBL is a pattern recognition molecule that serves as the key mediator of the lectin pathway of complement activation. MBL levels are genetically determined and vary widely amongst individuals. In the present study we hypothesized that high MBL levels in asthma are associated with increased loss of lung function over time, as a consequence of inflammatory tissue damage. We measured serum MBL levels by ELISA in 68 patients with severe asthma and prospectively determined the change in post-bronchodilator (pb FEV1 over a mean period of 5.7 years. The relationship between MBL and change in pbFEV1 (FEV1 was analysed using (multiple regression analysis and corrected for possible confounders (age, sex, age of onset, asthma duration, and pbFEV1. The median (range MBL level was 332 (10.8-3587 ng·ml–1. MBL was significantly associated with FEV1 (p<0.04. Patients with a high MBL level (332 ng·ml–1 had an increased risk of lung function decline compared to those with low MBL levels (OR (CI: 3.16 (1.14-8.79, p = 0.027; the excess decline being 42 ml·yr–1 (p = 0.01. We conclude that a high MBL level is associated with an increased decline in lung function in patients with severe asthma. MBL might provide a clue towards better understanding of the pathophysiology of ongoing inflammation and subsequent decline in lung function of patients with severe asthma.

  5. RBscore&NBench: a high-level web server for nucleic acid binding residues prediction with a large-scale benchmarking database.

    Science.gov (United States)

    Miao, Zhichao; Westhof, Eric

    2016-07-08

    RBscore&NBench combines a web server, RBscore and a database, NBench. RBscore predicts RNA-/DNA-binding residues in proteins and visualizes the prediction scores and features on protein structures. The scoring scheme of RBscore directly links feature values to nucleic acid binding probabilities and illustrates the nucleic acid binding energy funnel on the protein surface. To avoid dataset, binding site definition and assessment metric biases, we compared RBscore with 18 web servers and 3 stand-alone programs on 41 datasets, which demonstrated the high and stable accuracy of RBscore. A comprehensive comparison led us to develop a benchmark database named NBench. The web server is available on: http://ahsoka.u-strasbg.fr/rbscorenbench/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  6. Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System

    DEFF Research Database (Denmark)

    Rapin, N.; Lund, Ole; Bernaschi, M.

    2010-01-01

    potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype...... proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives......We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-IMMSIM, such that it represents pathogens, as well...

  7. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

    DEFF Research Database (Denmark)

    Jurtz, Vanessa Isabell; Paul, Sinu; Andreatta, Massimo

    2017-01-01

    by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging......Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway....... Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified...

  8. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

    DEFF Research Database (Denmark)

    Nielsen, Morten; Andreatta, Massimo

    2016-01-01

    Background: Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells.Results: Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining informat...... specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0....

  9. Penicillin-Binding Protein Transpeptidase Signatures for Tracking and Predicting β-Lactam Resistance Levels in Streptococcus pneumoniae

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2016-06-01

    Full Text Available β-Lactam antibiotics are the drugs of choice to treat pneumococcal infections. The spread of β-lactam-resistant pneumococci is a major concern in choosing an effective therapy for patients. Systematically tracking β-lactam resistance could benefit disease surveillance. Here we developed a classification system in which a pneumococcal isolate is assigned to a “PBP type” based on sequence signatures in the transpeptidase domains (TPDs of the three critical penicillin-binding proteins (PBPs, PBP1a, PBP2b, and PBP2x. We identified 307 unique PBP types from 2,528 invasive pneumococcal isolates, which had known MICs to six β-lactams based on broth microdilution. We found that increased β-lactam MICs strongly correlated with PBP types containing divergent TPD sequences. The PBP type explained 94 to 99% of variation in MICs both before and after accounting for genomic backgrounds defined by multilocus sequence typing, indicating that genomic backgrounds made little independent contribution to β-lactam MICs at the population level. We further developed and evaluated predictive models of MICs based on PBP type. Compared to microdilution MICs, MICs predicted by PBP type showed essential agreement (MICs agree within 1 dilution of >98%, category agreement (interpretive results agree of >94%, a major discrepancy (sensitive isolate predicted as resistant rate of <3%, and a very major discrepancy (resistant isolate predicted as sensitive rate of <2% for all six β-lactams. Thus, the PBP transpeptidase signatures are robust indicators of MICs to different β-lactam antibiotics in clinical pneumococcal isolates and serve as an accurate alternative to phenotypic susceptibility testing.

  10. Structural and binding studies of a C-type galactose-binding lectin from Bothrops jararacussu snake venom.

    Science.gov (United States)

    Sartim, Marco A; Pinheiro, Matheus P; de Pádua, Ricardo A P; Sampaio, Suely V; Nonato, M Cristina

    2017-02-01

    BJcuL is a snake venom galactoside-binding lectin (SVgalL) isolated from Bothrops jararacussu and is involved in a wide variety of biological activities including triggering of pro-inflammatory response, disruption of microbial biofilm structure and induction of apoptosis. In the present work, we determined the crystallographic structure of BJcuL, the first holo structure of a SVgalL, and introduced the fluorescence-based thermal stability assay (Thermofluor) as a tool for screening and characterization of the binding mechanism of SVgalL ligands. BJcuL structure revealed the existence of a porous and flexible decameric arrangement composed of disulfide-linked dimers related by a five-fold symmetry. Each monomer contains the canonical carbohydrate recognition domain, a calcium ion required for BJcuL lectinic activity and a sodium ion required for protein stabilization. BJcuL thermostability was found to be induced by calcium ion and galactoside sugars which exhibit hyperbolic saturation profiles dependent on ligand concentration. Serendipitously, the gentamicin group of aminoglycoside antibiotics (gAGAs) was also identified as BJcuL ligands. On contrast, gAGAs exhibited a sigmoidal saturation profile compatible with a cooperative mechanism of binding. Thermofluor, hemagglutination inhibition assay and molecular docking strategies were used to identify a distinct binding site in BJcuL localized at the dimeric interface near the fully conserved intermolecular Cys86-Cys86 disulfide bond. The hybrid approach used in the present work provided novel insights into structural behavior and functional diversification of SVgaLs. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

    Science.gov (United States)

    Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

    2013-10-01

    Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

  12. Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment

    DEFF Research Database (Denmark)

    Carrasco Pro, S.; Zimic, M.; Nielsen, Morten

    2014-01-01

    of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available...... of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train...

  13. Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

    Science.gov (United States)

    2011-01-01

    Background Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van Der

  14. Sex hormone binding globulin - an important biomarker for predicting PCOS risk: A systematic review and meta-analysis.

    Science.gov (United States)

    Deswal, Ritu; Yadav, Arun; Dang, Amita Suneja

    2018-02-01

    Sex hormone-binding globulin (SHBG) is a glycoprotein which regulates bioavailability of sex steroid hormones. Interest in SHBG has escalated in recent years because of its inverse association with polycystic ovary syndrome (PCOS), obesity, insulin resistance, metabolic syndrome, and diabetes type II. This meta-analysis was performed to examine the associations of SHBG with PCOS and to correlate serum SHBG levels with various PCOS associated endocrine and metabolic dysregulation as well as to determine the effects of various therapeutic agents on serum SHBG levels in PCOS patients in order to assess the true accuracy of SHBG in the prediction of PCOS. A literature search was performed using Pub-Med, Science direct, google scholar, EMBASE, and Cochrane library. A total of 675 relevant records were identified, of which 62 articles were included. Meta-analysis using a random-effects model was performed using STATA version 13 to calculate standardized mean difference (SMD) with 95% confidence intervals (95 % CIs). SHBG levels in controls were significantly higher than that of PCOS patients (SMD= -0.83, 95%CI = -1.01, -0.64), with significant heterogeneity across studies (I 2 = 93.9% and p=0.000). Our results suggest that the lower serum SHBG levels are associated with the risk of PCOS. SHBG may also play an important role in various metabolic disturbances in PCOS patients. Therapeutic interventions improved SHBG levels in PCOS women which further reduced PCOS associated complications. Therefore, SHBG levels may prove to be a useful biomarker for the diagnosis and treatment of PCOS. Systematic review registration: PROSPERO CRD42017057972 Abbreviations: PCOS: polycystic ovary syndrome; SHBG: sex hormone-binding globulin.

  15. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  16. AutoSite: an automated approach for pseudo-ligands prediction—from ligand-binding sites identification to predicting key ligand atoms

    Science.gov (United States)

    Ravindranath, Pradeep Anand; Sanner, Michel F.

    2016-01-01

    Motivation: The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms. Results: We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand. In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects. Availability and Implementation: http://adfr.scripps.edu/AutoDockFR/autosite.html Contact: sanner@scripps.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27354702

  17. Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

    Science.gov (United States)

    Grudinin, Sergei; Kadukova, Maria; Eisenbarth, Andreas; Marillet, Simon; Cazals, Frédéric

    2016-09-01

    The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.

  18. The timing of associative memory formation: frontal lobe and anterior medial temporal lobe activity at associative binding predicts memory

    Science.gov (United States)

    Hales, J. B.

    2011-01-01

    The process of associating items encountered over time and across variable time delays is fundamental for creating memories in daily life, such as for stories and episodes. Forming associative memory for temporally discontiguous items involves medial temporal lobe structures and additional neocortical processing regions, including prefrontal cortex, parietal lobe, and lateral occipital regions. However, most prior memory studies, using concurrently presented stimuli, have failed to examine the temporal aspect of successful associative memory formation to identify when activity in these brain regions is predictive of associative memory formation. In the current study, functional MRI data were acquired while subjects were shown pairs of sequentially presented visual images with a fixed interitem delay within pairs. This design allowed the entire time course of the trial to be analyzed, starting from onset of the first item, across the 5.5-s delay period, and through offset of the second item. Subjects then completed a postscan recognition test for the items and associations they encoded during the scan and their confidence for each. After controlling for item-memory strength, we isolated brain regions selectively involved in associative encoding. Consistent with prior findings, increased regional activity predicting subsequent associative memory success was found in anterior medial temporal lobe regions of left perirhinal and entorhinal cortices and in left prefrontal cortex and lateral occipital regions. The temporal separation within each pair, however, allowed extension of these findings by isolating the timing of regional involvement, showing that increased response in these regions occurs during binding but not during maintenance. PMID:21248058

  19. Binding Mode Prediction of 5-Hydroxytryptamine 2C Receptor Ligands by Homology Modeling and Molecular Docking Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed, Asif; Nagarajan, Shanthi; Doddareddy, Munikumar Reddy; Cho, Yong Seo; Pae, Ae Nim [Korea Institute of Science and Technology, Seoul (Korea, Republic of)

    2011-06-15

    Serotonin or 5-hydroxytryptamine subtype 2C (5-HT{sub 2C}) receptor belongs to class A amine subfamily of Gprotein- coupled receptor (GPCR) super family and its ligands has therapeutic promise as anti-depressant and -obesity agents. So far, bovine rhodopsin from class A opsin subfamily was the mostly used X-ray crystal template to model this receptor. Here, we explained homology model using beta 2 adrenergic receptor (β2AR), the model was energetically minimized and validated by flexible ligand docking with known agonists and antagonists. In the active site Asp134, Ser138 of transmembrane 3 (TM3), Arg195 of extracellular loop 2 (ECL2) and Tyr358 of TM7 were found as important residues to interact with agonists. In addition to these, V208 of ECL2 and N351 of TM7 was found to interact with antagonists. Several conserved residues including Trp324, Phe327 and Phe328 were also found to contribute hydrophobic interaction. The predicted ligand binding mode is in good agreement with published mutagenesis and homology model data. This new template derived homology model can be useful for further virtual screening based lead identification.

  20. An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins.

    Directory of Open Access Journals (Sweden)

    Cheng Zheng

    Full Text Available Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function but also important for the prediction of 3D structure. Here, we present a new integrative framework that combines multiple sequence and structural properties and graph-theoretic network features, followed by an efficient feature selection to improve prediction of zinc-binding sites. We investigate what information can be retrieved from the sequence, structure and network levels that is relevant to zinc-binding site prediction. We perform a two-step feature selection using random forest to remove redundant features and quantify the relative importance of the retrieved features. Benchmarking on a high-quality structural dataset containing 1,103 protein chains and 484 zinc-binding residues, our method achieved >80% recall at a precision of 75% for the zinc-binding residues Cys, His, Glu and Asp on 5-fold cross-validation tests, which is a 10%-28% higher recall at the 75% equal precision compared to SitePredict and zincfinder at residue level using the same dataset. The independent test also indicates that our method has achieved recall of 0.790 and 0.759 at residue and protein levels, respectively, which is a performance better than the other two methods. Moreover, AUC (the Area Under the Curve and AURPC (the Area Under the Recall-Precision Curve by our method are also respectively better than those of the other two methods. Our method can not only be applied to large-scale identification of zinc-binding sites when structural information of the target is available, but also give valuable insights into important features arising from different levels that collectively characterize the zinc-binding sites. The scripts and datasets are available at http://protein.cau.edu.cn/zincidentifier/.

  1. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

    Science.gov (United States)

    Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

    2017-11-01

    Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes. Copyright © 2017 by The American Association of Immunologists, Inc.

  2. Fatty acid-binding protein 4 predicts gestational hypertension and preeclampsia in women with gestational diabetes mellitus.

    Directory of Open Access Journals (Sweden)

    Boya Li

    Full Text Available Fatty acid-binding protein 4 (FABP4 has been proposed to be a potential predictive factor of gestational hypertension or preeclampsia (GH/PE because of its integrating metabolic and inflammatory responses. Women with gestational diabetes mellitus (GDM are more likely to develop both GH/PE, than the normal population. The aim of our study was to examine the relationship between plasma FABP4 in the second trimester of pregnancy and the risk of GH/PE in women with GDM.This was a nested case-control study conducted within a large on-going prospective cohort study conducted at Peking University First Hospital. A total of 1344 women, who were diagnosed with GDM, according to a 75 g oral glucose tolerance test, participated in the GDM One-Day Clinic at Peking University First Hospital from February 24, 2016 to February 9, 2017. Of the 748 GDM women who agreed to the blood sample collection, 637 were followed until their delivery. The cases included GDM patients who developed gestational hypertension or preeclampsia (GDM-GH/PE group, n = 41. Another 41 matched GDM women without major complications were selected as the control group (GDM group.The incidence of GH/PE was 6.44% and 3.30% for preeclampsia. The level of the second trimester plasma FABP4 in the GDM-GH/PE group was significantly higher than the GDM group (17.53±11.35 vs. 12.79±6.04 ng/ml, P = 0.020. The AUC ROC for the second trimester plasma FABP4 predicted GH/PE in the GDM patients alone was 0.647 (95%CI 0.529-0.766. Multivariate analysis showed that the elevated second trimester FABP4 level was independently associated with GH/PE in the GDM patients (OR 1.136 [95% CI 1.003-1.286], P = 0.045.Increased second trimester plasma FABP4 independently predicted GH/PE in GDM patients.

  3. A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2015-01-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

  4. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2016-04-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  5. Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.

    Directory of Open Access Journals (Sweden)

    Wangchao Lou

    Full Text Available Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that

  6. Evaluation of B3LYP, X3LYP, and M06-Class Density Functionals for Predicting the Binding Energies of Neutral, Protonated, and Deprotonated Water Clusters.

    Science.gov (United States)

    Bryantsev, Vyacheslav S; Diallo, Mamadou S; van Duin, Adri C T; Goddard, William A

    2009-04-14

    In this paper we assess the accuracy of the B3LYP, X3LYP, and newly developed M06-L, M06-2X, and M06 functionals to predict the binding energies of neutral and charged water clusters including (H2O)n, n = 2-8, 20), H3O(+)(H2O)n, n = 1-6, and OH(-)(H2O)n, n = 1-6. We also compare the predicted energies of two ion hydration and neutralization reactions on the basis of the calculated binding energies. In all cases, we use as benchmarks calculated binding energies of water clusters extrapolated to the complete basis set limit of the second-order Møller-Plesset perturbation theory with the effects of higher order correlation estimated at the coupled-cluster theory with single, double, and perturbative triple excitations in the aug-cc-pVDZ basis set. We rank the accuracy of the functionals on the basis of the mean unsigned error (MUE) between calculated benchmark and density functional theory energies. The corresponding MUE (kcal/mol) for each functional is listed in parentheses. We find that M06-L (0.73) and M06 (0.84) give the most accurate binding energies using very extended basis sets such as aug-cc-pV5Z. For more affordable basis sets, the best methods for predicting the binding energies of water clusters are M06-L/aug-cc-pVTZ (1.24), B3LYP/6-311++G(2d,2p) (1.29), and M06/aug-cc-PVTZ (1.33). M06-L/aug-cc-pVTZ also gives more accurate energies for the neutralization reactions (1.38), whereas B3LYP/6-311++G(2d,2p) gives more accurate energies for the ion hydration reactions (1.69).

  7. Three-dimensional (3D) structure prediction and function analysis of the chitin-binding domain 3 protein HD73_3189 from Bacillus thuringiensis HD73.

    Science.gov (United States)

    Zhan, Yiling; Guo, Shuyuan

    2015-01-01

    Bacillus thuringiensis (Bt) is capable of producing a chitin-binding protein believed to be functionally important to bacteria during the stationary phase of its growth cycle. In this paper, the chitin-binding domain 3 protein HD73_3189 from B. thuringiensis has been analyzed by computer technology. Primary and secondary structural analyses demonstrated that HD73_3189 is negatively charged and contains several α-helices, aperiodical coils and β-strands. Domain and motif analyses revealed that HD73_3189 contains a signal peptide, an N-terminal chitin binding 3 domains, two copies of a fibronectin-like domain 3 and a C-terminal carbohydrate binding domain classified as CBM_5_12. Moreover, analysis predicted the protein's associated localization site to be the cell wall. Ligand site prediction determined that amino acid residues GLU-312, TRP-334, ILE-341 and VAL-382 exposed on the surface of the target protein exhibit polar interactions with the substrate.

  8. A method for predicting individual residue contributions to enzyme specificity and binding-site energies, and its application to MTH1.

    Science.gov (United States)

    Stewart, James J P

    2016-11-01

    A new method for predicting the energy contributions to substrate binding and to specificity has been developed. Conventional global optimization methods do not permit the subtle effects responsible for these properties to be modeled with sufficient precision to allow confidence to be placed in the results, but by making simple alterations to the model, the precisions of the various energies involved can be improved from about ±2 kcal mol -1 to ±0.1 kcal mol -1 . This technique was applied to the oxidized nucleotide pyrophosphohydrolase enzyme MTH1. MTH1 is unusual in that the binding and reaction sites are well separated-an advantage from a computational chemistry perspective, as it allows the energetics involved in docking to be modeled without the need to consider any issues relating to reaction mechanisms. In this study, two types of energy terms were investigated: the noncovalent interactions between the binding site and the substrate, and those responsible for discriminating between the oxidized nucleotide 8-oxo-dGTP and the normal dGTP. Both of these were investigated using the semiempirical method PM7 in the program MOPAC. The contributions of the individual residues to both the binding energy and the specificity of MTH1 were calculated by simulating the effect of mutations. Where comparisons were possible, all calculated results were in agreement with experimental observations. This technique provides fresh insight into the binding mechanism that enzymes use for discriminating between possible substrates.

  9. Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme.

    Science.gov (United States)

    Leong, Max K; Syu, Ren-Guei; Ding, Yi-Lung; Weng, Ching-Feng

    2017-01-06

    The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r 2  = 0.928-0.988,  = 0.894-0.954, RMSE = 0.002-0.412, s = 0.001-0.214), and the predicted pK i values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r 2  = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q 2  = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

  10. Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

    Science.gov (United States)

    Fukunishi, Yoshifumi

    2010-01-01

    For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

  11. Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ou, Yu-Yen

    2016-07-30

    Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists. We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron

  12. Coarse-grained/molecular mechanics of the TAS2R38 bitter taste receptor: experimentally-validated detailed structural prediction of agonist binding.

    Directory of Open Access Journals (Sweden)

    Alessandro Marchiori

    Full Text Available Bitter molecules in humans are detected by ∼25 G protein-coupled receptors (GPCRs. The lack of atomic resolution structure for any of them is complicating an in depth understanding of the molecular mechanisms underlying bitter taste perception. Here, we investigate the molecular determinants of the interaction of the TAS2R38 bitter taste receptor with its agonists phenylthiocarbamide (PTC and propylthiouracil (PROP. We use the recently developed hybrid Molecular Mechanics/Coarse Grained (MM/CG method tailored specifically for GPCRs. The method, through an extensive exploration of the conformational space in the binding pocket, allows the identification of several residues important for agonist binding that would have been very difficult to capture from the standard bioinformatics/docking approach. Our calculations suggest that both agonists bind to Asn103, Phe197, Phe264 and Trp201, whilst they do not interact with the so-called extra cellular loop 2, involved in cis-retinal binding in the GPCR rhodopsin. These predictions are consistent with data sets based on more than 20 site-directed mutagenesis and functional calcium imaging experiments of TAS2R38. The method could be readily used for other GPCRs for which experimental information is currently lacking.

  13. Predicted RNA Binding Proteins Pes4 and Mip6 Regulate mRNA Levels, Translation, and Localization during Sporulation in Budding Yeast.

    Science.gov (United States)

    Jin, Liang; Zhang, Kai; Sternglanz, Rolf; Neiman, Aaron M

    2017-05-01

    In response to starvation, diploid cells of Saccharomyces cerevisiae undergo meiosis and form haploid spores, a process collectively referred to as sporulation. The differentiation into spores requires extensive changes in gene expression. The transcriptional activator Ndt80 is a central regulator of this process, which controls many genes essential for sporulation. Ndt80 induces ∼300 genes coordinately during meiotic prophase, but different mRNAs within the NDT80 regulon are translated at different times during sporulation. The protein kinase Ime2 and RNA binding protein Rim4 are general regulators of meiotic translational delay, but how differential timing of individual transcripts is achieved was not known. This report describes the characterization of two related NDT80 -induced genes, PES4 and MIP6 , encoding predicted RNA binding proteins. These genes are necessary to regulate the steady-state expression, translational timing, and localization of a set of mRNAs that are transcribed by NDT80 but not translated until the end of meiosis II. Mutations in the predicted RNA binding domains within PES4 alter the stability of target mRNAs. PES4 and MIP6 affect only a small portion of the NDT80 regulon, indicating that they act as modulators of the general Ime2/Rim4 pathway for specific transcripts. Copyright © 2017 American Society for Microbiology.

  14. Use of thermodynamic coupling between antibody-antigen binding and phospholipid acyl chain phase transition energetics to predict immunoliposome targeting affinity.

    Science.gov (United States)

    Klegerman, Melvin E; Zou, Yuejiao; Golunski, Eva; Peng, Tao; Huang, Shao-Ling; McPherson, David D

    2014-09-01

    Thermodynamic analysis of ligand-target binding has been a useful tool for dissecting the nature of the binding mechanism and, therefore, potentially can provide valuable information regarding the utility of targeted formulations. Based on a consistent coupling of antibody-antigen binding and gel-liquid crystal transition energetics observed for antibody-phosphatidylethanolamine (Ab-PE) conjugates, we hypothesized that the thermodynamic parameters and the affinity for antigen of the Ab-PE conjugates could be effectively predicted once the corresponding information for the unconjugated antibody is determined. This hypothesis has now been tested in nine different antibody-targeted echogenic liposome (ELIP) preparations, where antibody is conjugated to dipalmitoylphosphatidylethanolamine (DPPE) head groups through a thioether linkage. Predictions were satisfactory (affinity not significantly different from the population of values found) in five cases (55.6%), but the affinity of the unconjugated antibody was not significantly different from the population of values found in six cases (66.7%), indicating that the affinities of the conjugated antibody tended not to deviate appreciably from those of the free antibody. While knowledge of the affinities of free antibodies may be sufficient to judge their suitability as targeting agents, thermodynamic analysis may still provide valuable information regarding their usefulness for specific applications.

  15. Bedside Heart Type Fatty Acid Binding Protein (H-FABP): Is an Early Predictive Marker of Cardiac Syncope

    International Nuclear Information System (INIS)

    Sonmez, B. M.; Yilmaz, F.; Durdu, T.; Hakbilir, O.; Ongar, M.; Ozturk, D.; Altinbilek, E.; Kavalci, C.; Turhan, T.

    2015-01-01

    Objective: To determine the value of bedside heart-type fatty acid binding protein in diagnosis of cardiac syncope in patients presenting with syncope or presyncope. Methods: The prospective study was conducted at Ankara Numune Training and Research Hospital, Ankara, Turkey, between September 1, 2010, and January 1, 2011, and comprised patients aged over 18 years who presented with syncope or presyncope. Patients presenting to emergency department within 4 hours of syncope or presyncope underwent a bedside heart-type fatty acid binding protein test measurement. SPSS 16 was used for statistical analysis, Results: Of the 100 patients evaluated, 22(22 percent) were diagnosed with cardiac syncope. Of them, 13(59.1 percent) patients had a positive and 9(40.9 percent) had a negative heart-type fatty acid binding protein result. Consequently, the test result was 12.64 times more positive in patients with cardiac syncope compared to those without. Conclusions: Bedside heart-type fatty acid binding protein, particularly at early phase of myocardial injury, reduces diagnostic and therapeutic uncertainity of cardiac origin in syncope patients. (author)

  16. Urinary liver-type fatty acid-binding protein predicts progression to nephropathy in type 1 diabetic patients

    DEFF Research Database (Denmark)

    Nielsen, Stine Elkjaer; Sugaya, Takeshi; Hovind, Peter

    2010-01-01

    Urinary liver-type fatty acid-binding protein (u-LFABP) is a marker of tubulointerstitial inflammation and has been shown to be increased in patients with type 1 diabetes and is further increased in patients who progress to micro- and macroalbuminuria. Our aim was to evaluate u-LFABP as a predictor...... of progression to micro- and macroalbuminuria in type 1 diabetes....

  17. Interplay between magnetism and energetics in Fe-Cr alloys from a predictive noncollinear magnetic tight-binding model

    DEFF Research Database (Denmark)

    Soulairol, R.; Barreteau, Cyrille; Fu, Chu-Chun

    2016-01-01

    Magnetism is a key driving force controlling several thermodynamic and kinetic properties of Fe-Cr systems. We present a tight-binding model for Fe-Cr, where magnetism is treated beyond the usual collinear approximation. A major advantage of this model consists in a rather simple fitting procedur...

  18. Urinary liver-type fatty acid-binding protein predicts progression to nephropathy in type 1 diabetic patients

    DEFF Research Database (Denmark)

    Nielsen, Stine Elkjaer; Sugaya, Takeshi; Hovind, Peter

    2010-01-01

    Urinary liver-type fatty acid-binding protein (u-LFABP) is a marker of tubulointerstitial inflammation and has been shown to be increased in patients with type 1 diabetes and is further increased in patients who progress to micro- and macroalbuminuria. Our aim was to evaluate u-LFABP as a predictor...

  19. In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function.

    Science.gov (United States)

    Follin, Elna; Karlsson, Maria; Lundegaard, Claus; Nielsen, Morten; Wallin, Stefan; Paulsson, Kajsa; Westerdahl, Helena

    2013-04-01

    The major histocompatibility complex (MHC) genes are the most polymorphic genes found in the vertebrate genome, and they encode proteins that play an essential role in the adaptive immune response. Many songbirds (passerines) have been shown to have a large number of transcribed MHC class I genes compared to most mammals. To elucidate the reason for this large number of genes, we compared 14 MHC class I alleles (α1-α3 domains), from great reed warbler, house sparrow and tree sparrow, via phylogenetic analysis, homology modelling and in silico peptide-binding predictions to investigate their functional and genetic relationships. We found more pronounced clustering of the MHC class I allomorphs (allele specific proteins) in regards to their function (peptide-binding specificities) compared to their genetic relationships (amino acid sequences), indicating that the high number of alleles is of functional significance. The MHC class I allomorphs from house sparrow and tree sparrow, species that diverged 10 million years ago (MYA), had overlapping peptide-binding specificities, and these similarities across species were also confirmed in phylogenetic analyses based on amino acid sequences. Notably, there were also overlapping peptide-binding specificities in the allomorphs from house sparrow and great reed warbler, although these species diverged 30 MYA. This overlap was not found in a tree based on amino acid sequences. Our interpretation is that convergent evolution on the level of the protein function, possibly driven by selection from shared pathogens, has resulted in allomorphs with similar peptide-binding repertoires, although trans-species evolution in combination with gene conversion cannot be ruled out.

  20. Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H.

    Science.gov (United States)

    Yang, Fengyuan; Zheng, Guoxun; Fu, Tingting; Li, Xiaofeng; Tu, Gao; Li, Ying Hong; Yao, Xiaojun; Xue, Weiwei; Zhu, Feng

    2018-06-27

    The rapid emergence of drug-resistant variants is one of the most common causes of highly active antiretroviral therapeutic (HAART) failure in patients infected with HIV-1. Compared with the existing HAART, the recently developed pyrrolyl diketo acid scaffold targeting both HIV-1 integrase (IN) and reverse transcriptase-associated ribonuclease H (RNase H) is an efficient approach to counteract the failure of anti-HIV treatment due to drug resistance. However, the binding mode and potential resistance profile of these inhibitors with important mechanistic principles remain poorly understood. To address this issue, an integrated computational method was employed to investigate the binding mode of inhibitor JMC6F with HIV-1 IN and RNase H. By using per-residue binding free energy decomposition analysis, the following residues: Asp64, Thr66, Leu68, Asp116, Tyr143, Gln148 and Glu152 in IN, Asp443, Glu478, Trp536, Lys541 and Asp549 in RNase H were identified as key residues for JMC6F binding. And then computational alanine scanning was carried to further verify the key residues. Moreover, the resistance profile of the currently known major mutations in HIV-1 IN and 2 mutations in RNase H against JMC6F was predicted by in silico mutagenesis studies. The results demonstrated that only three mutations in HIV-1 IN (Y143C, Q148R and N155H) and two mutations in HIV-1 RNase H (Y501R and Y501W) resulted in a reduction of JMC6F potency, thus indicating their potential role in providing resistance to JMC6F. These data provided important insights into the binding mode and resistance profile of the inhibitors with a pyrrolyl diketo acid scaffold in HIV-1 IN and RNase H, which would be helpful for the development of more effective dual HIV-1 IN and RNase H inhibitors.

  1. Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration.

    Science.gov (United States)

    Lee, Tai-Sung; Hu, Yuan; Sherborne, Brad; Guo, Zhuyan; York, Darrin M

    2017-07-11

    We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

  2. MULTIPRED2: A computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

    DEFF Research Database (Denmark)

    Zhang, Guang Lan; DeLuca, David S.; Keskin, Derin B.

    2011-01-01

    binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major...... groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/....

  3. The PRC2-binding long non-coding RNAs in human and mouse genomes are associated with predictive sequence features

    Science.gov (United States)

    Tu, Shiqi; Yuan, Guo-Cheng; Shao, Zhen

    2017-01-01

    Recently, long non-coding RNAs (lncRNAs) have emerged as an important class of molecules involved in many cellular processes. One of their primary functions is to shape epigenetic landscape through interactions with chromatin modifying proteins. However, mechanisms contributing to the specificity of such interactions remain poorly understood. Here we took the human and mouse lncRNAs that were experimentally determined to have physical interactions with Polycomb repressive complex 2 (PRC2), and systematically investigated the sequence features of these lncRNAs by developing a new computational pipeline for sequences composition analysis, in which each sequence is considered as a series of transitions between adjacent nucleotides. Through that, PRC2-binding lncRNAs were found to be associated with a set of distinctive and evolutionarily conserved sequence features, which can be utilized to distinguish them from the others with considerable accuracy. We further identified fragments of PRC2-binding lncRNAs that are enriched with these sequence features, and found they show strong PRC2-binding signals and are more highly conserved across species than the other parts, implying their functional importance.

  4. Experimental validation of plant peroxisomal targeting prediction algorithms by systematic comparison of in vivo import efficiency and in vitro PTS1 binding affinity.

    Science.gov (United States)

    Skoulding, Nicola S; Chowdhary, Gopal; Deus, Mara J; Baker, Alison; Reumann, Sigrun; Warriner, Stuart L

    2015-03-13

    Most peroxisomal matrix proteins possess a C-terminal targeting signal type 1 (PTS1). Accurate prediction of functional PTS1 sequences and their relative strength by computational methods is essential for determination of peroxisomal proteomes in silico but has proved challenging due to high levels of sequence variability of non-canonical targeting signals, particularly in higher plants, and low levels of availability of experimentally validated non-canonical examples. In this study, in silico predictions were compared with in vivo targeting analyses and in vitro thermodynamic binding of mutated variants within the context of one model targeting sequence. There was broad agreement between the methods for entire PTS1 domains and position-specific single amino acid residues, including residues upstream of the PTS1 tripeptide. The hierarchy Leu>Met>Ile>Val at the C-terminal position was determined for all methods but both experimental approaches suggest that Tyr is underweighted in the prediction algorithm due to the absence of this residue in the positive training dataset. A combination of methods better defines the score range that discriminates a functional PTS1. In vitro binding to the PEX5 receptor could discriminate among strong targeting signals while in vivo targeting assays were more sensitive, allowing detection of weak functional import signals that were below the limit of detection in the binding assay. Together, the data provide a comprehensive assessment of the factors driving PTS1 efficacy and provide a framework for the more quantitative assessment of the protein import pathway in higher plants. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. In silico simulations of STAT1 and STAT3 inhibitors predict SH2 domain cross-binding specificity.

    Science.gov (United States)

    Szelag, Malgorzata; Sikorski, Krzysztof; Czerwoniec, Anna; Szatkowska, Katarzyna; Wesoly, Joanna; Bluyssen, Hans A R

    2013-11-15

    Signal transducers and activators of transcription (STATs) comprise a family of transcription factors that are structurally related and which participate in signaling pathways activated by cytokines, growth factors and pathogens. Activation of STAT proteins is mediated by the highly conserved Src homology 2 (SH2) domain, which interacts with phosphotyrosine motifs for specific contacts between STATs and receptors and for STAT dimerization. By generating new models for human (h)STAT1, hSTAT2 and hSTAT3 we applied comparative in silico docking to determine SH2-binding specificity of the STAT3 inhibitor stattic, and of fludarabine (STAT1 inhibitor). Thus, we provide evidence that by primarily targeting the highly conserved phosphotyrosine (pY+0) SH2 binding pocket stattic is not a specific hSTAT3 inhibitor, but is equally effective towards hSTAT1 and hSTAT2. This was confirmed in Human Micro-vascular Endothelial Cells (HMECs) in vitro, in which stattic inhibited interferon-α-induced phosphorylation of all three STATs. Likewise, fludarabine inhibits both hSTAT1 and hSTAT3 phosphorylation, but not hSTAT2, by competing with the highly conserved pY+0 and pY-X binding sites, which are less well-preserved in hSTAT2. Moreover we observed that in HMECs in vitro fludarabine inhibits cytokine and lipopolysaccharide-induced phosphorylation of hSTAT1 and hSTAT3 but does not affect hSTAT2. Finally, multiple sequence alignment of STAT-SH2 domain sequences confirmed high conservation between hSTAT1 and hSTAT3, but not hSTAT2, with respect to stattic and fludarabine binding sites. Together our data offer a molecular basis that explains STAT cross-binding specificity of stattic and fludarabine, thereby questioning the present selection strategies of SH2 domain-based competitive small inhibitors. © 2013 Elsevier B.V. All rights reserved.

  6. Crystal complexes of a predicted S-adenosylmethionine-dependent methyltransferase reveal a typical AdoMet binding domain and a substrate recognition domain

    Energy Technology Data Exchange (ETDEWEB)

    Miller, D.J.; Ouellette, N.; Evodokimova, E.; Savchenko, A.; Edwards, A.; Anderson, W.F. (Toronto); (NWU)

    2010-03-08

    S-adenosyl-L-methionine-dependent methyltransferases (MTs) are abundant, and highly conserved across phylogeny. These enzymes use the cofactor AdoMet to methylate a wide variety of molecular targets, thereby modulating important cellular and metabolic activities. Thermotoga maritima protein 0872 (TM0872) belongs to a large sequence family of predicted MTs, ranging phylogenetically from relatively simple bacteria to humans. The genes for many of the bacterial homologs are located within operons involved in cell wall synthesis and cell division. Despite preliminary biochemical studies in E. coli and B. subtilis, the substrate specificity of this group of more than 150 proteins is unknown. As part of the Midwest Center for Structural Genomics initiative (www.mcsg.anl.gov), we have determined the structure of TM0872 in complexes with AdoMet and with S-adenosyl-L-homocysteine (AdoHcy). As predicted, TM0872 has a typical MT domain, and binds endogenous AdoMet, or co-crystallized AdoHcy, in a manner consistent with other known MT structures. In addition, TM0872 has a second domain that is novel among MTs in both its location in the sequence and its structure. The second domain likely acts in substrate recognition and binding, and there is a potential substrate-binding cleft spanning the two domains. This long and narrow cleft is lined with positively charged residues which are located opposite the S{sup +}-CH{sub 3} bond, suggesting that a negatively charged molecule might be targeted for catalysis. However, AdoMet and AdoHcy are both buried, and access to the methyl group would presumably require structural rearrangement. These TM0872 crystal structures offer the first structural glimpses at this phylogenetically conserved sequence family.

  7. Predicting treatment response in Schizophrenia: the role of stratal and frontal dopamine D2/D3 receptor binding potential

    DEFF Research Database (Denmark)

    Wulff, Sanne; Nørbak-Emig, Henrik; Nielsen, Mette Ødegaard

    2014-01-01

    Background One of the best validated findings in schizophrenia is an association between increased presynaptic striatal dopaminergic activity and psychotic symptoms. We have previously reported an association between positive symptoms and dopamine D2 receptor binding potentials (BPs) in frontal...... cortex in antipsychotic-naïve first-episode male schizophrenia patients(1). Preclinical studies suggest an inverse relationship between frontal and striatal dopamine activity. This activity can indirectly be expressed by the BP of dopamine receptors using Single Photon Emission Computed Tomography (SPECT......) where low striatal BP is believed to reflect high dopamine availability. We aim to assess the association between D2 receptor BPs in antipsychotic-naïve first-episode schizophrenia patients and their response to the first treatment with an antipsychotic compound. We hypothesise that patients with low...

  8. Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization.

    Science.gov (United States)

    Beuming, Thijs; Che, Ye; Abel, Robert; Kim, Byungchan; Shanmugasundaram, Veerabahu; Sherman, Woody

    2012-03-01

    Water plays an essential role in determining the structure and function of all biological systems. Recent methodological advances allow for an accurate and efficient estimation of the thermodynamic properties of water molecules at the surface of proteins. In this work, we characterize these thermodynamic properties and relate them to various structural and functional characteristics of the protein. We find that high-energy hydration sites often exist near protein motifs typically characterized as hydrophilic, such as backbone amide groups. We also find that waters around alpha helices and beta sheets tend to be less stable than waters around loops. Furthermore, we find no significant correlation between the hydration site-free energy and the solvent accessible surface area of the site. In addition, we find that the distribution of high-energy hydration sites on the protein surface can be used to identify the location of binding sites and that binding sites of druggable targets tend to have a greater density of thermodynamically unstable hydration sites. Using this information, we characterize the FKBP12 protein and show good agreement between fragment screening hit rates from NMR spectroscopy and hydration site energetics. Finally, we show that water molecules observed in crystal structures are less stable on average than bulk water as a consequence of the high degree of spatial localization, thereby resulting in a significant loss in entropy. These findings should help to better understand the characteristics of waters at the surface of proteins and are expected to lead to insights that can guide structure-based drug design efforts. Copyright © 2011 Wiley Periodicals, Inc.

  9. Identification and Validation of Novel Hedgehog-Responsive Enhancers Predicted by Computational Analysis of Ci/Gli Binding Site Density

    Science.gov (United States)

    Richards, Neil; Parker, David S.; Johnson, Lisa A.; Allen, Benjamin L.; Barolo, Scott; Gumucio, Deborah L.

    2015-01-01

    The Hedgehog (Hh) signaling pathway directs a multitude of cellular responses during embryogenesis and adult tissue homeostasis. Stimulation of the pathway results in activation of Hh target genes by the transcription factor Ci/Gli, which binds to specific motifs in genomic enhancers. In Drosophila, only a few enhancers (patched, decapentaplegic, wingless, stripe, knot, hairy, orthodenticle) have been shown by in vivo functional assays to depend on direct Ci/Gli regulation. All but one (orthodenticle) contain more than one Ci/Gli site, prompting us to directly test whether homotypic clustering of Ci/Gli binding sites is sufficient to define a Hh-regulated enhancer. We therefore developed a computational algorithm to identify Ci/Gli clusters that are enriched over random expectation, within a given region of the genome. Candidate genomic regions containing Ci/Gli clusters were functionally tested in chicken neural tube electroporation assays and in transgenic flies. Of the 22 Ci/Gli clusters tested, seven novel enhancers (and the previously known patched enhancer) were identified as Hh-responsive and Ci/Gli-dependent in one or both of these assays, including: Cuticular protein 100A (Cpr100A); invected (inv), which encodes an engrailed-related transcription factor expressed at the anterior/posterior wing disc boundary; roadkill (rdx), the fly homolog of vertebrate Spop; the segment polarity gene gooseberry (gsb); and two previously untested regions of the Hh receptor-encoding patched (ptc) gene. We conclude that homotypic Ci/Gli clustering is not sufficient information to ensure Hh-responsiveness; however, it can provide a clue for enhancer recognition within putative Hedgehog target gene loci. PMID:26710299

  10. The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants.

    Science.gov (United States)

    Archie, Elizabeth A; Moss, Cynthia J; Alberts, Susan C

    2006-03-07

    Many social animals live in stable groups. In contrast, African savannah elephants (Loxodonta africana) live in unusually fluid, fission-fusion societies. That is, 'core' social groups are composed of predictable sets of individuals; however, over the course of hours or days, these groups may temporarily divide and reunite, or they may fuse with other social groups to form much larger social units. Here, we test the hypothesis that genetic relatedness predicts patterns of group fission and fusion among wild, female African elephants. Our study of a single Kenyan population spans 236 individuals in 45 core social groups, genotyped at 11 microsatellite and one mitochondrial DNA (mtDNA) locus. We found that genetic relatedness predicted group fission; adult females remained with their first order maternal relatives when core groups fissioned temporarily. Relatedness also predicted temporary fusion between social groups; core groups were more likely to fuse with each other when the oldest females in each group were genetic relatives. Groups that shared mtDNA haplotypes were also significantly more likely to fuse than groups that did not share mtDNA. Our results suggest that associations between core social groups persist for decades after the original maternal kin have died. We discuss these results in the context of kin selection and its possible role in the evolution of elephant sociality.

  11. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

    Science.gov (United States)

    Gaieb, Zied; Liu, Shuai; Gathiaka, Symon; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Feher, Victoria A.; Walters, W. Patrick; Kuhn, Bernd; Rudolph, Markus G.; Burley, Stephen K.; Gilson, Michael K.; Amaro, Rommie E.

    2018-01-01

    The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

  12. An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude

    Energy Technology Data Exchange (ETDEWEB)

    Orts, Julien [EMBL, Structure and Computational Biology Unit (Germany); Bartoschek, Stefan [Industriepark Hoechst, Sanofi-Aventis Deutschland GmbH, R and D LGCR/Parallel Synthesis and Natural Products (Germany); Griesinger, Christian [Max Planck Institute for Biophysical Chemistry (Germany); Monecke, Peter [Industriepark Hoechst, Sanofi-Aventis Deutschland GmbH, R and D LGCR/Structure, Design and Informatics (Germany); Carlomagno, Teresa, E-mail: teresa.carlomagno@embl.de [EMBL, Structure and Computational Biology Unit (Germany)

    2012-01-15

    Low-affinity ligands can be efficiently optimized into high-affinity drug leads by structure based drug design when atomic-resolution structural information on the protein/ligand complexes is available. In this work we show that the use of a few, easily obtainable, experimental restraints improves the accuracy of the docking experiments by two orders of magnitude. The experimental data are measured in nuclear magnetic resonance spectra and consist of protein-mediated NOEs between two competitively binding ligands. The methodology can be widely applied as the data are readily obtained for low-affinity ligands in the presence of non-labelled receptor at low concentration. The experimental inter-ligand NOEs are efficiently used to filter and rank complex model structures that have been pre-selected by docking protocols. This approach dramatically reduces the degeneracy and inaccuracy of the chosen model in docking experiments, is robust with respect to inaccuracy of the structural model used to represent the free receptor and is suitable for high-throughput docking campaigns.

  13. Novel N-allyl/propargyl tetrahydroquinolines: Synthesis via Three-component Cationic Imino Diels-Alder Reaction, Binding Prediction, and Evaluation as Cholinesterase Inhibitors.

    Science.gov (United States)

    Rodríguez, Yeray A; Gutiérrez, Margarita; Ramírez, David; Alzate-Morales, Jans; Bernal, Cristian C; Güiza, Fausto M; Romero Bohórquez, Arnold R

    2016-10-01

    New N-allyl/propargyl 4-substituted 1,2,3,4-tetrahydroquinolines derivatives were efficiently synthesized using acid-catalyzed three components cationic imino Diels-Alder reaction (70-95%). All compounds were tested in vitro as dual acetylcholinesterase and butyryl-cholinesterase inhibitors and their potential binding modes, and affinity, were predicted by molecular docking and binding free energy calculations (∆G) respectively. The compound 4af (IC50 = 72 μm) presented the most effective inhibition against acetylcholinesterase despite its poor selectivity (SI = 2), while the best inhibitory activity on butyryl-cholinesterase was exhibited by compound 4ae (IC50 = 25.58 μm) with considerable selectivity (SI = 0.15). Molecular docking studies indicated that the most active compounds fit in the reported acetylcholinesterase and butyryl-cholinesterase active sites. Moreover, our computational data indicated a high correlation between the calculated ∆G and the experimental activity values in both targets. © 2016 The Authors Chemical Biology & Drug Design Published by John Wiley & Sons Ltd.

  14. A novel RUNX2 missense mutation predicted to disrupt DNA binding causes cleidocranial dysplasia in a large Chinese family with hyperplastic nails

    Directory of Open Access Journals (Sweden)

    Wang Xiaoqin

    2007-12-01

    Full Text Available Abstract Background Cleidocranial dysplasia (CCD is a dominantly inherited disease characterized by hypoplastic or absent clavicles, large fontanels, dental dysplasia, and delayed skeletal development. The purpose of this study is to investigate the genetic basis of Chinese family with CCD. Methods Here, a large Chinese family with CCD and hyperplastic nails was recruited. The clinical features displayed a significant intrafamilial variation. We sequenced the coding region of the RUNX2 gene for the mutation and phenotype analysis. Results The family carries a c.T407C (p.L136P mutation in the DNA- and CBFβ-binding Runt domain of RUNX2. Based on the crystal structure, we predict this novel missense mutation is likely to disrupt DNA binding by RUNX2, and at least locally affect the Runt domain structure. Conclusion A novel missense mutation was identified in a large Chinese family with CCD with hyperplastic nails. This report further extends the mutation spectrum and clinical features of CCD. The identification of this mutation will facilitate prenatal diagnosis and preimplantation genetic diagnosis.

  15. IGF-1 receptor and IGF binding protein-3 might predict prognosis of patients with resectable pancreatic cancer

    International Nuclear Information System (INIS)

    Hirakawa, Toshiki; Yashiro, Masakazu; Murata, Akihiro; Hirata, Keiichiro; Kimura, Kenjiro; Amano, Ryosuke; Yamada, Nobuya; Nakata, Bunzo; Hirakawa, Kosei

    2013-01-01

    The present study aimed to elucidate the clinicopathologic role of insulin-like growth factor-1 receptor (IGF1R) and IGF binding protein-3 (IGFBP3) in patients with pancreatic cancer. The function of IGFBP3 is controversial, because both inhibition and facilitation of the action of IGF as well as IGF-independent effects have been reported. In this study, IGF1R and IGFBP3 expression was examined, and their potential roles as prognostic markers in patients with pancreatic cancer were evaluated. Clinicopathological features of 122 patients with curatively resected pancreatic cancer were retrospectively reviewed, and expression of IGF1R and IGFBP3 was immunohistochemically analyzed. Expression of IGF1R and IGFBP3 was observed in 50 (41.0%) and 37 (30.3%) patients, respectively. IGF1R expression was significantly associated with histological grade (p = 0.037). IGFBP3 expression had a significant association with tumor location (p = 0.023), and a significant inverse association with venous invasion (p = 0.037). Tumors with IGF1R-positive and IGFBP3-negative expression (n = 32) were significantly frequently Stage II and III (p = 0.011). The prognosis for IGF1R positive patients was significantly poorer than that for IGF1R negative patients (p = 0.0181). IGFBP3 protein expression did not correlate significantly with patient survival. The subset of patients with both positive IGF1R and negative IGFBP3 had worse overall survival (8.8 months versus 12.6 months, respectively, p < 0.001). IGF1R signaling might be associated with tumor aggressiveness, and IGFBP3 might show antiproliferative effects in pancreatic cancer. Both high IGF1R expression and low IGFBP3 expression represent useful prognostic markers for patients with curatively resected pancreatic cancer

  16. In situ study of binding of copper by fulvic acid: comparison of differential absorbance data and model predictions.

    Science.gov (United States)

    Yan, Mingquan; Dryer, Deborah; Korshin, Gregory V; Benedetti, Marc F

    2013-02-01

    This study examined the binding of copper(II) by Suwannee River fulvic acid (SRFA) using the method of differential absorbance that was used at environmentally-relevant concentrations of copper and SRFA. The pH- and metal-differential spectra were processed via numeric deconvolution to establish commonalities seen in the changes of absorbance caused by deprotonation of SRFA and its interactions with copper(II) ions. Six Gaussian bands were determined to be present in both the pH- and Cu-differential spectra. Their maxima were located, in the order of increasing wavelengths at 208 nm, 242 nm, 276 nm, 314 nm, 378 nm and 551 nm. The bands with these maxima were denoted as A0, A1, A2, A3, A4 and A5, respectively. Properties of these bands were compared with those existing in the spectra of model compounds such as sulfosalicylic acid (SSA), tannic acid (TA), and polystyrenesulfonic acid-co-maleic acid (PSMA). While none of the features observed in differential spectra of the model compound were identical to those present in the case of SRFA, Gaussian bands A1, A3 and possibly A2 were concluded to be largely attributable to a combination of responses of salicylic- and polyhydroxyphenolic groups. In contrast, bands A4 and A5 were detected in the differential spectra of SRFA only. Their nature remains to be elucidated. To examine correlations between the amount of copper(II) bound by SRFA and changes of its absorbance, differential absorbances measured at indicative wavelengths 250 nm and 400 nm were compared with the total amount of SRFA-bound copper estimated based on Visual MINTEQ calculations. This examination showed that the differential absorbances of SRFA in a wide range of pH values and copper concentrations were strongly correlated with the concentration of SRFA-bound copper. The approach presented in this study can be used to generate in situ information concerning the nature of functional groups in humic substances engaged in interactions with metals ions. This

  17. Serum Wisteria floribunda agglutinin-positive Mac-2-binding protein expression predicts disease severity in chronic hepatitis C patients

    Directory of Open Access Journals (Sweden)

    Ching-I Huang

    2017-08-01

    Full Text Available Wisteria floribunda agglutinin-positive Mac-2 binding protein (WFA+-M2BP has recently been developed as a promising liver fibrosis glyco biomarker. We assessed its efficacy in evaluating liver disease severity in chronic hepatitis C (CHC in Taiwan. The association between WFA+-M2BP and histological features was evaluated among those CHC patients underwent liver biopsy. We also aimed to clarify the factors determining the performance of WFA+-M2BP in CHC. A total of 229 CHC patients were consecutively recruited. The mean value of WFA+-M2BP in patients from F0 to F4 was 1.68, 2.23, 3.45, 3.48, 3.77 respectively (linear trend P = 0.008. Linear regression analysis revealed that alanine aminotransferase (odds ratio [OR]: 0.03, 95% confidence intervals [CI]: 0.02–0.05, P < 0.001, AST (OR: −0.1, 95% CI: −0.02 to −0.01, P < 0.001, and liver fibrosis (OR: 0.30, 95% CI: 0.01–0.59, P = 0.043 were the independent factors correlated to serum WFA+-M2BP level. The optimal cutoff values of WFA+-M2BP for fibrosis stages F1, F2, F3, and F4 were 1.42, 1.61, 1.42, and 2.67, respectively. Multivariate analysis revealed that the platelet count (OR/CI: −0.009/0.986–0.996, P = <0.001, r-glutamyl transferase (OR/CI: 0.007/1.000–1.013, P = 0.036, and WFA+-M2BP (OR/CI: 0.187/1.057–1.374, P = 0.005. We concluded that WFA+-M2BP is a competent noninvasive marker for liver fibrosis assessment in CHC patients.

  18. Application of a loading dose of colistin methanesulfonate in critically ill patients: population pharmacokinetics, protein binding, and prediction of bacterial kill.

    Science.gov (United States)

    Mohamed, Ami F; Karaiskos, Ilias; Plachouras, Diamantis; Karvanen, Matti; Pontikis, Konstantinos; Jansson, Britt; Papadomichelakis, Evangelos; Antoniadou, Anastasia; Giamarellou, Helen; Armaganidis, Apostolos; Cars, Otto; Friberg, Lena E

    2012-08-01

    A previous pharmacokinetic study on dosing of colistin methanesulfonate (CMS) at 240 mg (3 million units [MU]) every 8 h indicated that colistin has a long half-life, resulting in insufficient concentrations for the first 12 to 48 h after initiation of treatment. A loading dose would therefore be beneficial. The aim of this study was to evaluate CMS and colistin pharmacokinetics following a 480-mg (6-MU) loading dose in critically ill patients and to explore the bacterial kill following the use of different dosing regimens obtained by predictions from a pharmacokinetic-pharmacodynamic model developed from an in vitro study on Pseudomonas aeruginosa. The unbound fractions of colistin A and colistin B were determined using equilibrium dialysis and considered in the predictions. Ten critically ill patients (6 males; mean age, 54 years; mean creatinine clearance, 82 ml/min) with infections caused by multidrug-resistant Gram-negative bacteria were enrolled in the study. The pharmacokinetic data collected after the first and eighth doses were analyzed simultaneously with the data from the previous study (total, 28 patients) in the NONMEM program. For CMS, a two-compartment model best described the pharmacokinetics, and the half-lives of the two phases were estimated to be 0.026 and 2.2 h, respectively. For colistin, a one-compartment model was sufficient and the estimated half-life was 18.5 h. The unbound fractions of colistin in the patients were 26 to 41% at clinical concentrations. Colistin A, but not colistin B, had a concentration-dependent binding. The predictions suggested that the time to 3-log-unit bacterial kill for a 480-mg loading dose was reduced to half of that for the dose of 240 mg.

  19. Importin alpha binding and nuclear localization of PARP-2 is dependent on lysine 36, which is located within a predicted classical NLS

    Directory of Open Access Journals (Sweden)

    Valovka Taras

    2008-07-01

    Full Text Available Abstract Background The enzymes responsible for the synthesis of poly-ADP-ribose are named poly-ADP-ribose polymerases (PARP. PARP-2 is a nuclear protein, which regulates a variety of cellular functions that are mainly controlled by protein-protein interactions. A previously described non-conventional bipartite nuclear localization sequence (NLS lies in the amino-terminal DNA binding domain of PARP-2 between amino acids 1–69; however, this targeting sequence has not been experimentally examined or validated. Results Using a site-directed mutagenesis approach, we found that lysines 19 and 20, located within a previously described bipartite NLS, are not required for nuclear localization of PARP-2. In contrast, lysine 36, which is located within a predicted classical monopartite NLS, was required for PARP-2 nuclear localization. While wild type PARP-2 interacted with importin α3 and to a very weak extent with importin α1 and importin α5, the mutant PARP-2 (K36R did not interact with importin α3, providing a molecular explanation why PARP-2 (K36R is not targeted to the nucleus. Conclusion Our results provide strong evidence that lysine 36 of PARP-2 is a critical residue for proper nuclear targeting of PARP-2 and consequently for the execution of its biological functions.

  20. Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

    Science.gov (United States)

    Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P. E.; Geerke, Daan P.

    2018-01-01

    Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

  1. A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

    DEFF Research Database (Denmark)

    Pandya, Mital; Rasmussen, Michael; Hansen, Andreas

    2015-01-01

    Major histocompatibility complex (MHC) class I molecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8+ T cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presentation......, and different antigen peptide motifs are associated with specific genetic sequences of class I molecules. Understanding bovine leukocyte antigen (BoLA), peptide-MHC class I binding specificities may facilitate development of vaccines or reagents for quantifying the adaptive immune response to intracellular...... pathogens, such as foot-and-mouth disease virus (FMDV). Six synthetic BoLA class I (BoLA-I) molecules were produced, and the peptide binding motif was generated for five of the six molecules using a combined approach of positional scanning combinatorial peptide libraries (PSCPLs) and neural network...

  2. Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability

    Science.gov (United States)

    The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores th...

  3. CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction.

    Science.gov (United States)

    Xu, Youjun; Wang, Shiwei; Hu, Qiwan; Gao, Shuaishi; Ma, Xiaomin; Zhang, Weilin; Shen, Yihang; Chen, Fangjin; Lai, Luhua; Pei, Jianfeng

    2018-05-10

    CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

  4. Peptide-binding motif prediction by using phage display library for SasaUBA*0301, a resistance haplotype of MHC class I molecule from Atlantic Salmon (Salmo salar)

    DEFF Research Database (Denmark)

    Zhao, Heng; Hermsen, Trudi; Stet, Rene J M

    2008-01-01

    The structure of the peptide-binding specificity of major histocompatibility complex (MHC) class I has been analyzed extensively in human and mouse. For fish, there are no crystallographic models of MHC molecules, neither are there data on the peptide-binding specificity. In this study, we descri...... and there is a significant association between MHC polymorphism and the disease resistance. Therefore, our study might contribute to designing a peptide vaccine against this viral disease....... class I molecule might have a very similar binding motif at the C-terminus compared with a known mouse class I molecule H2-Kb which has L, or I, V, M at p8. Previous work showed that Atlantic Salmon carrying the allele SasaUBA*0301 are resistant to infectious Salmon aneamia virus...

  5. Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

    Science.gov (United States)

    Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

    2007-01-01

    Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method

  6. Α4β2 and α7 nicotinic acetylcholine receptor binding predicts choice preference in two cost benefit decision-making tasks.

    Science.gov (United States)

    Mendez, I A; Damborsky, J C; Winzer-Serhan, U H; Bizon, J L; Setlow, B

    2013-01-29

    Nicotinic receptors have been linked to a wide range of cognitive and behavioral functions, but surprisingly little is known about their involvement in cost benefit decision making. The goal of these experiments was to determine how nicotinic acetylcholine receptor (nAChR) expression is related to two forms of cost benefit decision making. Male Long Evans rats were tested in probability- and delay-discounting tasks, which required discrete trial choices between a small reward and a large reward associated with varying probabilities of omission and varying delays to reward delivery, respectively. Following testing, radioligand binding to α4β2 and α7 nAChR subtypes in brain regions implicated in cost benefit decision making was examined. Significant linear relationships were observed between choice of the large delayed reward in the delay discounting task and α4β2 receptor binding in both the dorsal and ventral hippocampus. Additionally, trends were found suggesting that choice of the large costly reward in both discounting tasks was inversely related to α4β2 receptor binding in the medial prefrontal cortex and nucleus accumbens shell. Similar trends suggested that choice of the large delayed reward in the delay discounting task was inversely related to α4β2 receptor binding in the orbitofrontal cortex, nucleus accumbens core, and basolateral amygdala, as well as to α7 receptor binding in the basolateral amygdala. These data suggest that nAChRs (particularly α4β2) play both unique and common roles in decisions that require consideration of different types of reward costs. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  7. Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data

    DEFF Research Database (Denmark)

    Nielsen, Morten; Connelley, Tim; Ternette, Nicola

    2018-01-01

    Peptide binding to MHC class I molecules is the single most selective step in antigen presentation and the strongest single correlate to peptide cellular immunogenicity. The cost of experimentally characterizing the rules of peptide presentation for a given MHC-I molecule is extensive, and predic...... available at http://www.cbs.dtu.dk/services/NetMHCpan/NetBoLApan . The approach has here been applied to the BoLA-I system, but the pipeline is readily applicable to MHC systems in other species....

  8. Promiscuous prediction and conservancy analysis of CTL binding epitopes of HCV 3a viral proteome from Punjab Pakistan: an In Silico Approach

    Directory of Open Access Journals (Sweden)

    Idrees Muhammad

    2011-02-01

    Full Text Available Abstract Background HCV is a positive sense RNA virus affecting approximately 180 million people world wide and about 10 million Pakistani populations. HCV genotype 3a is the major cause of infection in Pakistani population. One of the major problems of HCV infection especially in the developing countries that limits the limits the antiviral therapy is the long term treatment, high dosage and side effects. Studies of antigenic epitopes of viral sequences of a specific origin can provide an effective way to overcome the mutation rate and to determine the promiscuous binders to be used for epitope based subunit vaccine design. An in silico approach was applied for the analysis of entire HCV proteome of Pakistani origin, aimed to identify the viral epitopes and their conservancy in HCV genotypes 1, 2 and 3 of diverse origin. Results Immunoinformatic tools were applied for the predictive analysis of HCV 3a antigenic epitopes of Pakistani origin. All the predicted epitopes were then subjected for their conservancy analysis in HCV genotypes 1, 2 and 3 of diverse origin (worldwide. Using freely available web servers, 150 MHC II epitopes were predicted as promiscuous binders against 51 subjected alleles. E2 protein represented the 20% of all the predicted MHC II epitopes. 75.33% of the predicted MHC II epitopes were (77-100% conserve in genotype 3; 47.33% and 40.66% in genotype 1 and 2 respectively. 69 MHC I epitopes were predicted as promiscuous binders against 47 subjected alleles. NS4b represented 26% of all the MHC I predicted epitopes. Significantly higher epitope conservancy was represented by genotype 3 i.e. 78.26% and 21.05% for genotype 1 and 2. Conclusions The study revealed comprehensive catalogue of potential HCV derived CTL epitopes from viral proteome of Pakistan origin. A considerable number of predicted epitopes were found to be conserved in different HCV genotype. However, the number of conserved epitopes in HCV genotype 3 was

  9. Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy.

    Science.gov (United States)

    Rahaman, Obaidur; Estrada, Trilce P; Doren, Douglas J; Taufer, Michela; Brooks, Charles L; Armen, Roger S

    2011-09-26

    The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand

  10. Molecular cloning, expression, IgE binding activities and in silico epitope prediction of Per a 9 allergens of the American cockroach

    Science.gov (United States)

    Yang, Haiwei; Chen, Hao; Jin, Min; Xie, Hua; He, Shaoheng; Wei, Ji-Fu

    2016-01-01

    Per a 9 is a major allergen of the American cockroach (CR), which has been recognized as an important cause of imunoglobulin E-mediated type I hypersensitivity worldwide. However, it is not neasy to obtain a substantial quantity of this allergen for use in functional studies. In the present study, the Per a 9 gene was cloned and expressed in Escherichia coli (E. coli) systems. It was found that 13/16 (81.3%) of the sera from patients with allergies caused by the American CR reacted to Per a 9, as assessed by enzyme-linked immunosorbent assay, confirming that Per a 9 is a major allergen of CR. The induction of the expression of CD63 and CCR3 in passively sensitized basophils (from sera of patients with allergies caused by the American CR) by approximately 4.2-fold indicated that recombinant Per a 9 was functionally active. Three immunoinformatics tools, including the DNASTAR Protean system, Bioinformatics Predicted Antigenic Peptides (BPAP) system and the BepiPred 1.0 server were used to predict the potential B cell epitopes, while Net-MHCIIpan-2.0 and NetMHCII-2.2 were used to predict the T cell epitopes of Per a 9. As a result, we predicted 11 peptides (23–28, 39–46, 58–64, 91–118, 131–136, 145–154, 159–165, 176–183, 290–299, 309–320 and 338–344) as potential B cell linear epitopes. In T cell prediction, the Per a 9 allergen was predicted to have 5 potential T cell epitope sequences, 119–127, 194–202, 210–218, 239–250 and 279–290. The findings of our study may prove to be useful in the development of peptide-based vaccines to combat CR-induced allergies. PMID:27840974

  11. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lundegaard, Claus; Blicher, Thomas

    2007-01-01

    BACKGROUND: Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surp...... to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan....... surpassed 1500. Characterizing the specificity of each separately would be a major undertaking. PRINCIPAL FINDINGS: Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account...... successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis. CONCLUSIONS: Encompassing all...

  12. Investigating the binding properties of porous drug delivery systems using nuclear sensors (radiotracers) and positron annihilation lifetime spectroscopy--predicting conditions for optimum performance.

    Science.gov (United States)

    Mume, Eskender; Lynch, Daniel E; Uedono, Akira; Smith, Suzanne V

    2011-06-21

    Understanding how the size, charge and number of available pores in porous material influences the uptake and release properties is important for optimising their design and ultimately their application. Unfortunately there are no standard methods for screening porous materials in solution and therefore formulations must be developed for each encapsulated agent. This study investigates the potential of a library of radiotracers (nuclear sensors) for assessing the binding properties of hollow silica shell materials. Uptake and release of Cu(2+) and Co(2+) and their respective complexes with polyazacarboxylate macrocycles (dota and teta) and a series of hexa aza cages (diamsar, sarar and bis-(p-aminobenzyl)-diamsar) from the hollow silica shells was monitored using their radioisotopic analogues. Coordination chemistry of the metal (M) species, subtle alterations in the molecular architecture of ligands (Ligand) and their resultant complexes (M-Ligand) were found to significantly influence their uptake over pH 3 to 9 at room temperature. Positively charged species were selectively and rapidly (within 10 min) absorbed at pH 7 to 9. Negatively charged species were preferentially absorbed at low pH (3 to 5). Rates of release varied for each nuclear sensor, and time to establish equilibrium varied from minutes to days. The subtle changes in design of the nuclear sensors proved to be a valuable tool for determining the binding properties of porous materials. The data support the development of a library of nuclear sensors for screening porous materials for use in optimising the design of porous materials and the potential of nuclear sensors for high through-put screening of materials.

  13. Genetic polymorphisms in the microRNA binding-sites of the thymidylate synthase gene predict risk and survival in gastric cancer.

    Science.gov (United States)

    Shen, Rong; Liu, Hongliang; Wen, Juyi; Liu, Zhensheng; Wang, Li-E; Wang, Qiming; Tan, Dongfeng; Ajani, Jaffer A; Wei, Qingyi

    2015-09-01

    Thymidylate synthase (TYMS) plays a crucial role in folate metabolism as well as DNA synthesis and repair. We hypothesized that functional polymorphisms in the 3' UTR of TYMS are associated with gastric cancer risk and survival. In the present study, we tested our hypothesis by genotyping three potentially functional (at miRNA binding sites) TYMS SNPs (rs16430 6bp del/ins, rs2790 A>G and rs1059394 C>T) in 379 gastric cancer patients and 431 cancer-free controls. Compared with the rs16430 6bp/6bp + 6bp/0bp genotypes, the 0bp/0bp genotype was associated with significantly increased gastric cancer risk (adjusted OR = 1.72, 95% CI = 1.15-2.58). Similarly, rs2790 GG and rs1059394 TT genotypes were also associated with significantly increased risk (adjusted OR = 2.52, 95% CI = 1.25-5.10 and adjusted OR = 1.57, 95% CI = 1.04-2.35, respectively), compared with AA + AG and CC + CT genotypes, respectively. In the haplotype analysis, the T-G-0bp haplotype was associated with significantly increased gastric cancer risk, compared with the C-A-6bp haplotype (adjusted OR = 1.34, 95% CI = 1.05-1.72). Survival analysis revealed that rs16430 0bp/0bp and rs1059394 TT genotypes were also associated with poor survival in gastric cancer patients who received chemotherapy treatment (adjusted HR = 1.61, 95% CI = 1.05-2.48 and adjusted HR = 1.59, 95% CI = 1.02-2.48, respectively). These results suggest that these three variants in the miRNA binding sites of TYMS may be associated with cancer risk and survival of gastric cancer patients. Larger population studies are warranted to verify these findings. © 2014 Wiley Periodicals, Inc.

  14. Experimental validation of the predicted binding site of Escherichia coli K1 outer membrane protein A to human brain microvascular endothelial cells: identification of critical mutations that prevent E. coli meningitis.

    Science.gov (United States)

    Pascal, Tod A; Abrol, Ravinder; Mittal, Rahul; Wang, Ying; Prasadarao, Nemani V; Goddard, William A

    2010-11-26

    Escherichia coli K1, the most common cause of meningitis in neonates, has been shown to interact with GlcNAc1-4GlcNAc epitopes of Ecgp96 on human brain microvascular endothelial cells (HBMECs) via OmpA (outer membrane protein A). However, the precise domains of extracellular loops of OmpA interacting with the chitobiose epitopes have not been elucidated. We report the loop-barrel model of these OmpA interactions with the carbohydrate moieties of Ecgp96 predicted from molecular modeling. To test this model experimentally, we generated E. coli K1 strains expressing OmpA with mutations of residues predicted to be critical for interaction with the HBMEC and tested E. coli invasion efficiency. For these same mutations, we predicted the interaction free energies (including explicit calculation of the entropy) from molecular dynamics (MD), finding excellent correlation (R(2) = 90%) with experimental invasion efficiency. Particularly important is that mutating specific residues in loops 1, 2, and 4 to alanines resulted in significant inhibition of E. coli K1 invasion in HBMECs, which is consistent with the complete lack of binding found in the MD simulations for these two cases. These studies suggest that inhibition of the interactions of these residues of Loop 1, 2, and 4 with Ecgp96 could provide a therapeutic strategy to prevent neonatal meningitis due to E. coli K1.

  15. Combined serial analysis of gene expression and transcription factor binding site prediction identifies novel-candidate-target genes of Nr2e1 in neocortex development.

    Science.gov (United States)

    Schmouth, Jean-François; Arenillas, David; Corso-Díaz, Ximena; Xie, Yuan-Yun; Bohacec, Slavita; Banks, Kathleen G; Bonaguro, Russell J; Wong, Siaw H; Jones, Steven J M; Marra, Marco A; Simpson, Elizabeth M; Wasserman, Wyeth W

    2015-07-24

    Nr2e1 (nuclear receptor subfamily 2, group e, member 1) encodes a transcription factor important in neocortex development. Previous work has shown that nuclear receptors can have hundreds of target genes, and bind more than 300 co-interacting proteins. However, recognition of the critical role of Nr2e1 in neural stem cells and neocortex development is relatively recent, thus the molecular mechanisms involved for this nuclear receptor are only beginning to be understood. Serial analysis of gene expression (SAGE), has given researchers both qualitative and quantitative information pertaining to biological processes. Thus, in this work, six LongSAGE mouse libraries were generated from laser microdissected tissue samples of dorsal VZ/SVZ (ventricular zone and subventricular zone) from the telencephalon of wild-type (Wt) and Nr2e1-null embryos at the critical development ages E13.5, E15.5, and E17.5. We then used a novel approach, implementing multiple computational methods followed by biological validation to further our understanding of Nr2e1 in neocortex development. In this work, we have generated a list of 1279 genes that are differentially expressed in response to altered Nr2e1 expression during in vivo neocortex development. We have refined this list to 64 candidate direct-targets of NR2E1. Our data suggested distinct roles for Nr2e1 during different neocortex developmental stages. Most importantly, our results suggest a possible novel pathway by which Nr2e1 regulates neurogenesis, which includes Lhx2 as one of the candidate direct-target genes, and SOX9 as a co-interactor. In conclusion, we have provided new candidate interacting partners and numerous well-developed testable hypotheses for understanding the pathways by which Nr2e1 functions to regulate neocortex development.

  16. Computer-assisted prediction of HLA-DR binding and experimental analysis for human promiscuous Th1-cell peptides in the 24 kDa secreted lipoprotein (LppX) of Mycobacterium tuberculosis.

    Science.gov (United States)

    Al-Attiyah, R; Mustafa, A S

    2004-01-01

    The secreted 24 kDa lipoprotein (LppX) is an antigen that is specific for Mycobacterium tuberculosis complex and M. leprae. The present study was carried out to identify the promiscuous T helper 1 (Th1)-cell epitopes of the M. tuberculosis LppX (MT24, Rv2945c) antigen by using 15 overlapping synthetic peptides (25 mers overlapping by 10 residues) covering the sequence of the complete protein. The analysis of Rv2945c sequence for binding to 51 alleles of nine serologically defined HLA-DR molecules, by using a virtual matrix-based prediction program (propred), showed that eight of the 15 peptides of Rv2945c were predicted to bind promiscuously to >/=10 alleles from more than or equal to three serologically defined HLA-DR molecules. The Th1-cell reactivity of all the peptides was assessed in antigen-induced proliferation and interferon-gamma (IFN-gamma)-secretion assays with peripheral blood mononuclear cells (PBMCs) from 37 bacille Calmette-Guérin (BCG)-vaccinated healthy subjects. The results showed that 17 of the 37 donors, which represented an HLA-DR-heterogeneous group, responded to one or more peptides of Rv2945c in the Th1-cell assays. Although each peptide stimulated PBMCs from one or more donors in the above assays, the best positive responses (12/17 (71%) responders) were observed with the peptide p14 (aa 196-220). This suggested a highly promiscuous presentation of p14 to Th1 cells. In addition, the sequence of p14 is completely identical among the LppX of M. tuberculosis, M. bovis and M. leprae, which further supports the usefulness of Rv2945c and p14 in the subunit vaccine design against both tuberculosis and leprosy.

  17. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

    Science.gov (United States)

    Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

    2018-03-14

    It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

  18. To bind or not to bind? Different temporal binding effects from voluntary pressing and releasing actions.

    Science.gov (United States)

    Zhao, Ke; Chen, Yu-Hsin; Yan, Wen-Jing; Fu, Xiaolan

    2013-01-01

    Binding effect refers to the perceptual attraction between an action and an outcome leading to a subjective compression of time. Most studies investigating binding effects exclusively employ the "pressing" action without exploring other types of actions. The present study addresses this issue by introducing another action, releasing action or the voluntary lifting of the finger/wrist, to investigate the differences between voluntary pressing and releasing actions. Results reveal that releasing actions led to robust yet short-lived temporal binding effects, whereas pressing condition had steady temporal binding effects up to super-seconds. The two actions also differ in sensitivity to changes in temporal contiguity and contingency, which could be attributed to the difference in awareness of action. Extending upon current models of "willed action," our results provide insights from a temporal point of view and support the concept of a dual system consisting of predictive motor control and top-down mechanisms.

  19. Solute-vacancy binding in aluminum

    International Nuclear Information System (INIS)

    Wolverton, C.

    2007-01-01

    Previous efforts to understand solute-vacancy binding in aluminum alloys have been hampered by a scarcity of reliable, quantitative experimental measurements. Here, we report a large database of solute-vacancy binding energies determined from first-principles density functional calculations. The calculated binding energies agree well with accurate measurements where available, and provide an accurate predictor of solute-vacancy binding in other systems. We find: (i) some common solutes in commercial Al alloys (e.g., Cu and Mg) possess either very weak (Cu), or even repulsive (Mg), binding energies. Hence, we assert that some previously reported large binding energies for these solutes are erroneous. (ii) Large binding energies are found for Sn, Cd and In, confirming the proposed mechanism for the reduced natural aging in Al-Cu alloys containing microalloying additions of these solutes. (iii) In addition, we predict that similar reduction in natural aging should occur with additions of Si, Ge and Au. (iv) Even larger binding energies are found for other solutes (e.g., Pb, Bi, Sr, Ba), but these solutes possess essentially no solubility in Al. (v) We have explored the physical effects controlling solute-vacancy binding in Al. We find that there is a strong correlation between binding energy and solute size, with larger solute atoms possessing a stronger binding with vacancies. (vi) Most transition-metal 3d solutes do not bind strongly with vacancies, and some are even energetically strongly repelled from vacancies, particularly for the early 3d solutes, Ti and V

  20. Binding lies

    Directory of Open Access Journals (Sweden)

    Avraham eMerzel

    2015-10-01

    Full Text Available Do we feel bound by our own misrepresentations? Does one act of cheating compel the cheater to make subsequent choices that maintain the false image even at a cost? To answer these questions we employed a two-task paradigm such that in the first task the participants could benefit from false reporting of private observations whereas in the second they could benefit from making a prediction in line with their actual, rather than their previously reported observations. Thus, for those participants who inflated their report during the first task, sticking with that report for the second task was likely to lead to a loss, whereas deviating from it would imply that they had lied. Data from three experiments (total N=116 indicate that, having lied, participants were ready to suffer future loss rather than admit, even if implicitly, that they had lied.

  1. Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations

    KAUST Repository

    Evoli, Stefania

    2016-11-10

    Human serum albumin possesses multiple binding sites and transports a wide range of ligands that include the anti-inflammatory drug ibuprofen. A complete map of the binding sites of ibuprofen in albumin is difficult to obtain in traditional experiments, because of the structural adaptability of this protein in accommodating small ligands. In this work, we provide a set of predictions covering the geometry, affinity of binding and protonation state for the pharmaceutically most active form (S-isomer) of ibuprofen to albumin, by using absolute binding free energy calculations in combination with classical molecular dynamics (MD) simulations and molecular docking. The most favorable binding modes correctly reproduce several experimentally identified binding locations, which include the two Sudlow\\'s drug sites (DS2 and DS1) and the fatty acid binding sites 6 and 2 (FA6 and FA2). Previously unknown details of the binding conformations were revealed for some of them, and formerly undetected binding modes were found in other protein sites. The calculated binding affinities exhibit trends which seem to agree with the available experimental data, and drastically degrade when the ligand is modeled in a protonated (neutral) state, indicating that ibuprofen associates with albumin preferentially in its charged form. These findings provide a detailed description of the binding of ibuprofen, help to explain a wide range of results reported in the literature in the last decades, and demonstrate the possibility of using simulation methods to predict ligand binding to albumin.

  2. LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

    KAUST Repository

    Chen, Peng; Huang, Jianhua Z; Gao, Xin

    2014-01-01

    Protein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction

  3. Prediction of the outcome of growth hormone provocative testing in short children by measurement of serum levels of insulin-like growth factor I and insulin-like growth factor binding protein 3

    DEFF Research Database (Denmark)

    Juul, A; Skakkebaek, N E

    1997-01-01

    Serum levels of insulin-like growth factor I (IGF-I) and insulin-like growth factor binding protein 3 (IGFBP-3) reflect the secretion of endogenous growth hormone (GH) in healthy children and exhibit little diurnal variation, which makes them potential candidates for screening of GH deficiency (GHD......). We evaluated serum IGF-I and IGFBP-3 levels in relation to the outcome of GH provocative testing in 203 children and adolescents (111 boys and 92 girls) in whom GHD was suspected. A total of 1030 children served as control subjects. In children less than 10 years of age, IGF-I levels were below...... with a normal GH response (specificity 97.9%). Consequently the predictive value of a positive test result in prepubertal children was 88.8% for IGF-I and 90% for IGFBP-3. In children and adolescents between 10 and 20 years of age, IGF-I levels were below the cutoff limit in 34 of 46 children with GHD...

  4. HLA-DPβ1 Asp84-Lys69 antigen-binding signature predicts event-free survival in childhood B-cell precursor acute lymphoblastic leukaemia: results from the MRC UKALL XI childhood ALL trial.

    Science.gov (United States)

    Taylor, G M; Wade, R; Hussain, A; Thompson, P; Hann, I; Gibson, B; Eden, T; Richards, S

    2012-07-01

    We previously reported that children in the UKALL XI ALL trial with HLA-DP 1 and -DP 3 supertypes had significantly worse event-free survival (EFS) than children with other DP supertypes. As DP 1 and DP 3 share two of four key antigen-binding amino-acid polymorphisms (aspartic acid84-lysine69), we asked whether Asp84-Lys69 or Asp84 alone were independent prognostic indicators in childhood acute lymphoblastic leukemia (ALL). We analysed EFS in 798 UKALL XI patients, stratified by Asp84-Lys69 vs non-Asp84-Lys69, for a median follow-up of 12.5 years. Asp84-Lys69 was associated with a significantly worse EFS than non-Asp84-Lys69 (5-year EFS: Asp84-Lys69: 58.8% (95% CI (confidence of interval): 52.7-64.9%); non-Asp84-Lys69: 67.3% (63.4-71.2%); 2P=0.007). Post-relapse EFS was 10% less in Asp84-Lys69 than non-Asp84-Lys69 patients. EFS was significantly worse (P=0.03) and post-relapse EFS marginally worse (P=0.06) in patients with Asp84 compared with Gly84. These results suggest that Asp84-Lys69 predicted adverse EFS in the context of UKALL XI because of Asp84, and may have influenced post-relapse EFS. We speculate that this may be due to the recruitment of Asp84-Lys69-restricted regulatory T cells in the context of this regimen, leading to the re-emergence of residual disease. However, functional and molecular studies of the prognostic value of this and other HLA molecular signatures in other childhood ALL trials are needed.

  5. Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations

    KAUST Repository

    Evoli, Stefania; Mobley, David L.; Guzzi, Rita; Rizzuti, Bruno

    2016-01-01

    experiments, because of the structural adaptability of this protein in accommodating small ligands. In this work, we provide a set of predictions covering the geometry, affinity of binding and protonation state for the pharmaceutically most active form (S

  6. Total iron binding capacity

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/article/003489.htm Total iron binding capacity To use the sharing features on this page, please enable JavaScript. Total iron binding capacity (TIBC) is a blood test to ...

  7. Feature Binding in Zebrafish

    Directory of Open Access Journals (Sweden)

    P Neri

    2012-07-01

    Full Text Available Binding operations are primarily ascribed to cortex or similarly complex avian structures. My experiments show that the zebrafish, a lower vertebrate lacking cortex, supports visual feature binding of form and motion for the purpose of social behavior. These results challenge the notion that feature binding may require highly evolved neural structures and demonstrate that the nervous system of lower vertebrates can afford unexpectedly complex computations.

  8. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  9. Protein docking prediction using predicted protein-protein interface.

    Science.gov (United States)

    Li, Bin; Kihara, Daisuke

    2012-01-10

    Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  10. Melanin-binding radiopharmaceuticals

    International Nuclear Information System (INIS)

    Packer, S.; Fairchild, R.G.; Watts, K.P.; Greenberg, D.; Hannon, S.J.

    1980-01-01

    The scope of this paper is limited to an analysis of the factors that are important to the relationship of radiopharmaceuticals to melanin. While the authors do not attempt to deal with differences between melanin-binding vs. melanoma-binding, a notable variance is assumed

  11. Competitive protein binding assay

    International Nuclear Information System (INIS)

    Kaneko, Toshio; Oka, Hiroshi

    1975-01-01

    The measurement of cyclic GMP (cGMP) by competitive protein binding assay was described and discussed. The principle of binding assay was represented briefly. Procedures of our method by binding protein consisted of preparation of cGMP binding protein, selection of 3 H-cyclic GMP on market, and measurement procedures. In our method, binding protein was isolated from the chrysalis of silk worm. This method was discussed from the points of incubation medium, specificity of binding protein, the separation of bound cGMP from free cGMP, and treatment of tissue from which cGMP was extracted. cGMP existing in the tissue was only one tenth or one scores of cGMP, and in addition, cGMP competed with cGMP in binding with binding protein. Therefore, Murad's technique was applied to the isolation of cGMP. This method provided the measurement with sufficient accuracy; the contamination by cAMP was within several per cent. (Kanao, N.)

  12. RBPmap: a web server for mapping binding sites of RNA-binding proteins.

    Science.gov (United States)

    Paz, Inbal; Kosti, Idit; Ares, Manuel; Cline, Melissa; Mandel-Gutfreund, Yael

    2014-07-01

    Regulation of gene expression is executed in many cases by RNA-binding proteins (RBPs) that bind to mRNAs as well as to non-coding RNAs. RBPs recognize their RNA target via specific binding sites on the RNA. Predicting the binding sites of RBPs is known to be a major challenge. We present a new webserver, RBPmap, freely accessible through the website http://rbpmap.technion.ac.il/ for accurate prediction and mapping of RBP binding sites. RBPmap has been developed specifically for mapping RBPs in human, mouse and Drosophila melanogaster genomes, though it supports other organisms too. RBPmap enables the users to select motifs from a large database of experimentally defined motifs. In addition, users can provide any motif of interest, given as either a consensus or a PSSM. The algorithm for mapping the motifs is based on a Weighted-Rank approach, which considers the clustering propensity of the binding sites and the overall tendency of regulatory regions to be conserved. In addition, RBPmap incorporates a position-specific background model, designed uniquely for different genomic regions, such as splice sites, 5' and 3' UTRs, non-coding RNA and intergenic regions. RBPmap was tested on high-throughput RNA-binding experiments and was proved to be highly accurate. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  13. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models*

    Science.gov (United States)

    Isakova, Alina; Berset, Yves; Hatzimanikatis, Vassily; Deplancke, Bart

    2016-01-01

    Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers. PMID:26912662

  14. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models.

    Science.gov (United States)

    Isakova, Alina; Berset, Yves; Hatzimanikatis, Vassily; Deplancke, Bart

    2016-05-06

    Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  15. SHBG (Sex Hormone Binding Globulin)

    Science.gov (United States)

    ... Links Patient Resources For Health Professionals Subscribe Search Sex Hormone Binding Globulin (SHBG) Send Us Your Feedback ... As Testosterone-estrogen Binding Globulin TeBG Formal Name Sex Hormone Binding Globulin This article was last reviewed ...

  16. LIBRA: LIgand Binding site Recognition Application.

    Science.gov (United States)

    Hung, Le Viet; Caprari, Silvia; Bizai, Massimiliano; Toti, Daniele; Polticelli, Fabio

    2015-12-15

    In recent years, structural genomics and ab initio molecular modeling activities are leading to the availability of a large number of structural models of proteins whose biochemical function is not known. The aim of this study was the development of a novel software tool that, given a protein's structural model, predicts the presence and identity of active sites and/or ligand binding sites. The algorithm implemented by ligand binding site recognition application (LIBRA) is based on a graph theory approach to find the largest subset of similar residues between an input protein and a collection of known functional sites. The algorithm makes use of two predefined databases for active sites and ligand binding sites, respectively, derived from the Catalytic Site Atlas and the Protein Data Bank. Tests indicate that LIBRA is able to identify the correct binding/active site in 90% of the cases analyzed, 90% of which feature the identified site as ranking first. As far as ligand binding site recognition is concerned, LIBRA outperforms other structure-based ligand binding sites detection tools with which it has been compared. The application, developed in Java SE 7 with a Swing GUI embedding a JMol applet, can be run on any OS equipped with a suitable Java Virtual Machine (JVM), and is available at the following URL: http://www.computationalbiology.it/software/LIBRAv1.zip. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  17. Pharmacokinetic–pharmacodynamic modelling of drug‐induced QTc interval prolongation in man: prediction from in vitro human ether‐à‐go‐go‐related gene binding and functional inhibition assays and conscious dog studies

    Science.gov (United States)

    Dubois, V F S; Casarotto, E; Danhof, M

    2016-01-01

    Background and Purpose Functional measures of human ether‐à‐go‐go‐related gene (hERG; Kv11.1) channel inhibition have been prioritized as an in vitro screening tool for candidate molecules. However, it is unclear how these results can be translated to humans. Here, we explore how data on drug binding and functional inhibition in vitro relate to QT prolongation in vivo. Using cisapride, sotalol and moxifloxacin as paradigm compounds, we assessed the relationship between drug concentrations, binding, functional measures and in vivo effects in preclinical species and humans. Experimental Approach Pharmacokinetic–pharmacodynamic modelling was used to characterize the drug effects in hERG functional patch clamp, hERG radio‐labelled dofetilide displacement experiments and QT interval in conscious dogs. Data were analysed in parallel to identify potential correlations between pharmacological activity in vitro and in vivo. Key Results An Emax model could not be used due to large variability in the functional patch clamp assay. Dofetilide displacement revealed that binding curves are unrelated to the in vivo potency estimates for QTc prolongation in dogs and humans. Mean in vitro potency estimates ranged from 99.9 nM for cisapride to 1030 μM for moxifloxacin. Conclusions and Implications The lack of standardized protocols for in vitro assays leads to significant differences in experimental conditions, making the assessment of in vitro–in vivo correlations unreliable. Identification of an accurate safety window during the screening of candidate molecules requires a quantitative framework that disentangles system‐ from drug‐specific properties under physiological conditions, enabling translation of the results to humans. Similar considerations will be relevant for the comprehensive in vitro pro‐arrhythmia assay initiative. PMID:27427789

  18. Normalized Synergy Predicts That CD8 Co-Receptor Contribution to T Cell Receptor (TCR and pMHC Binding Decreases As TCR Affinity Increases in Human Viral-Specific T Cells

    Directory of Open Access Journals (Sweden)

    Chad M. Williams

    2017-07-01

    Full Text Available The discovery of naturally occurring T cell receptors (TCRs that confer specific, high-affinity recognition of pathogen and cancer-associated antigens remains a major goal in cellular immunotherapies. The contribution of the CD8 co-receptor to the interaction between the TCR and peptide-bound major histocompatibility complex (pMHC has previously been correlated with the activation and responsiveness of CD8+ T cells. However, these studies have been limited to model systems of genetically engineered hybridoma TCRs or transgenic mouse TCRs against either a single epitope or an array of altered peptide ligands. CD8 contribution in a native human antigen-specific T cell response remains elusive. Here, using Hepatitis C Virus-specific precursor CTLs spanning a large range of TCR affinities, we discovered that the functional responsiveness of any given TCR correlated with the contribution of CD8 to TCR/pMHC binding. Furthermore, we found that CD8 contribution to TCR/pMHC binding in the two-dimensional (2D system was more accurately reflected by normalized synergy (CD8 cooperation normalized by total TCR/pMHC bonds rather than synergy (total CD8 cooperation alone. While synergy showed an increasing trend with TCR affinity, normalized synergy was demonstrated to decrease with the increase of TCR affinity. Critically, normalized synergy was shown to correlate with CTL functionality and peptide sensitivity, corroborating three-dimensional (3D analysis of CD8 contribution with respect to TCR affinity. In addition, we identified TCRs that were independent of CD8 for TCR/pMHC binding. Our results resolve the current discrepancy between 2D and 3D analysis on CD8 contribution to TCR/pMHC binding, and demonstrate that naturally occurring high-affinity TCRs are more capable of CD8-independent interactions that yield greater functional responsiveness even with CD8 blocking. Taken together, our data suggest that addition of the normalized synergy parameter to our

  19. CARBOHYDRATE-CONTAINING COMPOUNDS WHICH BIND TO CARBOHYDRATE BINDING RECEPTORS

    DEFF Research Database (Denmark)

    1995-01-01

    Carbohydrate-containing compounds which contain saccharides or derivatives thereof and which bind to carbohydrate binding receptors are useful in pharmaceutical products for treatment of inflammatory diseases and other diseases.......Carbohydrate-containing compounds which contain saccharides or derivatives thereof and which bind to carbohydrate binding receptors are useful in pharmaceutical products for treatment of inflammatory diseases and other diseases....

  20. Prediction of core level binding energies in density functional theory: Rigorous definition of initial and final state contributions and implications on the physical meaning of Kohn-Sham energies.

    Science.gov (United States)

    Pueyo Bellafont, Noèlia; Bagus, Paul S; Illas, Francesc

    2015-06-07

    A systematic study of the N(1s) core level binding energies (BE's) in a broad series of molecules is presented employing Hartree-Fock (HF) and the B3LYP, PBE0, and LC-BPBE density functional theory (DFT) based methods with a near HF basis set. The results show that all these methods give reasonably accurate BE's with B3LYP being slightly better than HF but with both PBE0 and LCBPBE being poorer than HF. A rigorous and general decomposition of core level binding energy values into initial and final state contributions to the BE's is proposed that can be used within either HF or DFT methods. The results show that Koopmans' theorem does not hold for the Kohn-Sham eigenvalues. Consequently, Kohn-Sham orbital energies of core orbitals do not provide estimates of the initial state contribution to core level BE's; hence, they cannot be used to decompose initial and final state contributions to BE's. However, when the initial state contribution to DFT BE's is properly defined, the decompositions of initial and final state contributions given by DFT, with several different functionals, are very similar to those obtained with HF. Furthermore, it is shown that the differences of Kohn-Sham orbital energies taken with respect to a common reference do follow the trend of the properly calculated initial state contributions. These conclusions are especially important for condensed phase systems where our results validate the use of band structure calculations to determine initial state contributions to BE shifts.

  1. Oligosaccharide binding to barley alpha-amylase 1

    DEFF Research Database (Denmark)

    Robert, X.; Haser, R.; Mori, H.

    2005-01-01

    Enzymatic subsite mapping earlier predicted 10 binding subsites in the active site substrate binding cleft of barley alpha-amylase isozymes. The three-dimensional structures of the oligosaccharide complexes with barley alpha-amylase isozyme 1 (AMY1) described here give for the first time a thorough...... in barley alpha-amylase isozyme 2 (AMY2), and the sugar binding modes are compared between the two isozymes. The "sugar tongs" surface binding site discovered in the AMY1-thio-DP4 complex is confirmed in the present work. A site that putatively serves as an entrance for the substrate to the active site...

  2. Enhanced Prediction of Src Homology 2 (SH2) Domain Binding Potentials Using a Fluorescence Polarization-derived c-Met, c-Kit, ErbB, and Androgen Receptor Interactome*

    Science.gov (United States)

    Leung, Kin K.; Hause, Ronald J.; Barkinge, John L.; Ciaccio, Mark F.; Chuu, Chih-Pin; Jones, Richard B.

    2014-01-01

    Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains. PMID:24728074

  3. Sequential memory: Binding dynamics

    Science.gov (United States)

    Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

    2015-10-01

    Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

  4. Cellulose binding domain proteins

    Science.gov (United States)

    Shoseyov, Oded; Shpiegl, Itai; Goldstein, Marc; Doi, Roy

    1998-01-01

    A cellulose binding domain (CBD) having a high affinity for crystalline cellulose and chitin is disclosed, along with methods for the molecular cloning and recombinant production thereof. Fusion products comprising the CBD and a second protein are likewise described. A wide range of applications are contemplated for both the CBD and the fusion products, including drug delivery, affinity separations, and diagnostic techniques.

  5. Binding and Bulgarian

    NARCIS (Netherlands)

    Schürcks-Grozeva, Lilia Lubomirova

    2003-01-01

    In haar proefschrift analyseert Lilia Schürcks de anaforische verschijnselen in de Bulgaarse taal. Het gaat dan om wederkerende aspecten, uitgedrukt bij woorden als ‘zich’ en ‘elkaar’. De situatie in het Bulgaars blijkt moeilijk in te passen in de klassieke Binding Theory van Noam Chomsky. Bron: RUG

  6. Candidate SNP markers of aggressiveness-related complications and comorbidities of genetic diseases are predicted by a significant change in the affinity of TATA-binding protein for human gene promoters.

    Science.gov (United States)

    Chadaeva, Irina V; Ponomarenko, Mikhail P; Rasskazov, Dmitry A; Sharypova, Ekaterina B; Kashina, Elena V; Matveeva, Marina Yu; Arshinova, Tatjana V; Ponomarenko, Petr M; Arkova, Olga V; Bondar, Natalia P; Savinkova, Ludmila K; Kolchanov, Nikolay A

    2016-12-28

    Aggressiveness in humans is a hereditary behavioral trait that mobilizes all systems of the body-first of all, the nervous and endocrine systems, and then the respiratory, vascular, muscular, and others-e.g., for the defense of oneself, children, family, shelter, territory, and other possessions as well as personal interests. The level of aggressiveness of a person determines many other characteristics of quality of life and lifespan, acting as a stress factor. Aggressive behavior depends on many parameters such as age, gender, diseases and treatment, diet, and environmental conditions. Among them, genetic factors are believed to be the main parameters that are well-studied at the factual level, but in actuality, genome-wide studies of aggressive behavior appeared relatively recently. One of the biggest projects of the modern science-1000 Genomes-involves identification of single nucleotide polymorphisms (SNPs), i.e., differences of individual genomes from the reference genome. SNPs can be associated with hereditary diseases, their complications, comorbidities, and responses to stress or a drug. Clinical comparisons between cohorts of patients and healthy volunteers (as a control) allow for identifying SNPs whose allele frequencies significantly separate them from one another as markers of the above conditions. Computer-based preliminary analysis of millions of SNPs detected by the 1000 Genomes project can accelerate clinical search for SNP markers due to preliminary whole-genome search for the most meaningful candidate SNP markers and discarding of neutral and poorly substantiated SNPs. Here, we combine two computer-based search methods for SNPs (that alter gene expression) {i} Web service SNP_TATA_Comparator (DNA sequence analysis) and {ii} PubMed-based manual search for articles on aggressiveness using heuristic keywords. Near the known binding sites for TATA-binding protein (TBP) in human gene promoters, we found aggressiveness-related candidate SNP markers

  7. Prediction of binding modes between protein L-isoaspartyl (D-aspartyl) O-methyltransferase and peptide substrates including isomerized aspartic acid residues using in silico analytic methods for the substrate screening.

    Science.gov (United States)

    Oda, Akifumi; Noji, Ikuhiko; Fukuyoshi, Shuichi; Takahashi, Ohgi

    2015-12-10

    Because the aspartic acid (Asp) residues in proteins are occasionally isomerized in the human body, not only l-α-Asp but also l-β-Asp, D-α-Asp and D-β-Asp are found in human proteins. In these isomerized aspartic acids, the proportion of D-β-Asp is the largest and the proportions of l-β-Asp and D-α-Asp found in human proteins are comparatively small. To explain the proportions of aspartic acid isomers, the possibility of an enzyme able to repair l-β-Asp and D-α-Asp is frequently considered. The protein L-isoaspartyl (D-aspartyl) O-methyltransferase (PIMT) is considered one of the possible repair enzymes for l-β-Asp and D-α-Asp. Human PIMT is an enzyme that recognizes both l-β-Asp and D-α-Asp, and catalyzes the methylation of their side chains. In this study, the binding modes between PIMT and peptide substrates containing l-β-Asp or D-α-Asp residues were investigated using computational protein-ligand docking and molecular dynamics simulations. The results indicate that carboxyl groups of both l-β-Asp and D-α-Asp were recognized in similar modes by PIMT and that the C-terminal regions of substrate peptides were located in similar positions on PIMT for both the l-β-Asp and D-α-Asp peptides. In contrast, for peptides containing l-α-Asp or D-β-Asp residues, which are not substrates of PIMT, the computationally constructed binding modes between PIMT and peptides greatly differed from those between PIMT and substrates. In the nonsubstrate peptides, not inter- but intra-molecular hydrogen bonds were observed, and the conformations of peptides were more rigid than those of substrates. Thus, the in silico analytical methods were able to distinguish substrates from nonsubstrates and the computational methods are expected to complement experimental analytical methods. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

    Directory of Open Access Journals (Sweden)

    Manoranjenni eChetty

    2014-11-01

    Full Text Available This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK models with pharmacodynamics (PD models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PK and PD compared favourably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release formulation (CR were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modelling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

  9. Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

    Science.gov (United States)

    Chetty, Manoranjenni; Rose, Rachel H; Abduljalil, Khaled; Patel, Nikunjkumar; Lu, Gaohua; Cain, Theresa; Jamei, Masoud; Rostami-Hodjegan, Amin

    2014-01-01

    This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

  10. Quantitative Correlation of in Vivo Properties with in Vitro Assay Results: The in Vitro Binding of a Biotin–DNA Analogue Modifier with Streptavidin Predicts the in Vivo Avidin-Induced Clearability of the Analogue-Modified Antibody

    Science.gov (United States)

    Dou, Shuping; Virostko, John; Greiner, Dale L.; Powers, Alvin C.; Liu, Guozheng

    2016-01-01

    Quantitative prediction of in vivo behavior using an in vitro assay would dramatically accelerate pharmaceutical development. However, studies quantitatively correlating in vivo properties with in vitro assay results are rare because of the difficulty in quantitatively understanding the in vivo behavior of an agent. We now demonstrate such a correlation as a case study based on our quantitative understanding of the in vivo chemistry. In an ongoing pretargeting project, we designed a trifunctional antibody (Ab) that concomitantly carried a biotin and a DNA analogue (hereafter termed MORF). The biotin and the MORF were fused into one structure prior to conjugation to the Ab for the concomitant attachment. Because it was known that avidin-bound Ab molecules leave the circulation rapidly, this design would theoretically allow complete clearance by avidin. The clearability of the trifunctional Ab was determined by calculating the blood MORF concentration ratio of avidin-treated Ab to non-avidin-treated Ab using mice injected with these compounds. In theory, any compromised clearability should be due to the presence of impurities. In vitro, we measured the biotinylated percentage of the Ab-reacting (MORF-biotin)⊃-NH2 modifier, by addition of streptavidin to the radiolabeled (MORF-biotin)⊃-NH2 samples and subsequent high-performance liquid chromatography (HPLC) analysis. On the basis of our previous quantitative understanding, we predicted that the clearability of the Ab would be equal to the biotinylation percentage measured via HPLC. We validated this prediction within a 3% difference. In addition to the high avidin-induced clearability of the trifunctional Ab (up to ~95%) achieved by the design, we were able to predict the required quality of the (MORF-biotin)⊃-NH2 modifier for any given in vivo clearability. This approach may greatly reduce the steps and time currently required in pharmaceutical development in the process of synthesis, chemical analysis, in

  11. Accurate and sensitive quantification of protein-DNA binding affinity.

    Science.gov (United States)

    Rastogi, Chaitanya; Rube, H Tomas; Kribelbauer, Judith F; Crocker, Justin; Loker, Ryan E; Martini, Gabriella D; Laptenko, Oleg; Freed-Pastor, William A; Prives, Carol; Stern, David L; Mann, Richard S; Bussemaker, Harmen J

    2018-04-17

    Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes. Copyright © 2018 the Author(s). Published by PNAS.

  12. Modeling Shear Induced Von Willebrand Factor Binding to Collagen

    Science.gov (United States)

    Dong, Chuqiao; Wei, Wei; Morabito, Michael; Webb, Edmund; Oztekin, Alparslan; Zhang, Xiaohui; Cheng, Xuanhong

    2017-11-01

    Von Willebrand factor (vWF) is a blood glycoprotein that binds with platelets and collagen on injured vessel surfaces to form clots. VWF bioactivity is shear flow induced: at low shear, binding between VWF and other biological entities is suppressed; for high shear rate conditions - as are found near arterial injury sites - VWF elongates, activating its binding with platelets and collagen. Based on parameters derived from single molecule force spectroscopy experiments, we developed a coarse-grain molecular model to simulate bond formation probability as a function of shear rate. By introducing a binding criterion that depends on the conformation of a sub-monomer molecular feature of our model, the model predicts shear-induced binding, even for conditions where binding is highly energetically favorable. We further investigate the influence of various model parameters on the ability to predict shear-induced binding (vWF length, collagen site density and distribution, binding energy landscape, and slip/catch bond length) and demonstrate parameter ranges where the model provides good agreement with existing experimental data. Our results may be important for understanding vWF activity and also for achieving targeted drug therapy via biomimetic synthetic molecules. National Science Foundation (NSF),Division of Mathematical Sciences (DMS).

  13. How to deal with multiple binding poses in alchemical relative protein-ligand binding free energy calculations.

    Science.gov (United States)

    Kaus, Joseph W; Harder, Edward; Lin, Teng; Abel, Robert; McCammon, J Andrew; Wang, Lingle

    2015-06-09

    Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the

  14. How To Deal with Multiple Binding Poses in Alchemical Relative Protein–Ligand Binding Free Energy Calculations

    Science.gov (United States)

    2016-01-01

    Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the

  15. Domain-based small molecule binding site annotation

    Directory of Open Access Journals (Sweden)

    Dumontier Michel

    2006-03-01

    Full Text Available Abstract Background Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID, a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB. More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites. Description Using a set of co-crystallized protein-small molecule structures as a starting point, SMID interactions were generated by identifying protein domains that bind to small molecules, using NCBI's Reverse Position Specific BLAST (RPS-BLAST algorithm. SMID records are available for viewing at http://smid.blueprint.org. The SMID-BLAST tool provides accurate transitive annotation of small-molecule binding sites for proteins not found in the PDB. Given a protein sequence, SMID-BLAST identifies domains using RPS-BLAST and then lists potential small molecule ligands based on SMID records, as well as their aligned binding sites. A heuristic ligand score is calculated based on E-value, ligand residue identity and domain entropy to assign a level of confidence to hits found. SMID-BLAST predictions were validated against a set of 793 experimental small molecule interactions from the PDB, of which 472 (60% of predicted interactions identically matched the experimental small molecule and of these, 344 had greater than 80% of the binding site residues correctly identified. Further, we estimate that 45% of predictions which were not observed in the PDB validation set may be true positives. Conclusion By

  16. Dielectric response of molecules in empirical tight-binding theory

    Science.gov (United States)

    Boykin, Timothy B.; Vogl, P.

    2002-01-01

    In this paper we generalize our previous approach to electromagnetic interactions within empirical tight-binding theory to encompass molecular solids and isolated molecules. In order to guarantee physically meaningful results, we rederive the expressions for relevant observables using commutation relations appropriate to the finite tight-binding Hilbert space. In carrying out this generalization, we examine in detail the consequences of various prescriptions for the position and momentum operators in tight binding. We show that attempting to fit parameters of the momentum matrix directly generally results in a momentum operator which is incompatible with the underlying tight-binding model, while adding extra position parameters results in numerous difficulties, including the loss of gauge invariance. We have applied our scheme, which we term the Peierls-coupling tight-binding method, to the optical dielectric function of the molecular solid PPP, showing that this approach successfully predicts its known optical properties even in the limit of isolated molecules.

  17. Carboplatin binding to histidine

    Energy Technology Data Exchange (ETDEWEB)

    Tanley, Simon W. M. [University of Manchester, Brunswick Street, Manchester M13 9PL (United Kingdom); Diederichs, Kay [University of Konstanz, D-78457 Konstanz (Germany); Kroon-Batenburg, Loes M. J. [Utrecht University, Padualaan 8, 3584 CH Utrecht (Netherlands); Levy, Colin [University of Manchester, 131 Princess Street, Manchester M1 7DN (United Kingdom); Schreurs, Antoine M. M. [Utrecht University, Padualaan 8, 3584 CH Utrecht (Netherlands); Helliwell, John R., E-mail: john.helliwell@manchester.ac.uk [University of Manchester, Brunswick Street, Manchester M13 9PL (United Kingdom)

    2014-08-29

    An X-ray crystal structure showing the binding of purely carboplatin to histidine in a model protein has finally been obtained. This required extensive crystallization trials and various novel crystal structure analyses. Carboplatin is a second-generation platinum anticancer agent used for the treatment of a variety of cancers. Previous X-ray crystallographic studies of carboplatin binding to histidine (in hen egg-white lysozyme; HEWL) showed the partial conversion of carboplatin to cisplatin owing to the high NaCl concentration used in the crystallization conditions. HEWL co-crystallizations with carboplatin in NaBr conditions have now been carried out to confirm whether carboplatin converts to the bromine form and whether this takes place in a similar way to the partial conversion of carboplatin to cisplatin observed previously in NaCl conditions. Here, it is reported that a partial chemical transformation takes place but to a transplatin form. Thus, to attempt to resolve purely carboplatin binding at histidine, this study utilized co-crystallization of HEWL with carboplatin without NaCl to eliminate the partial chemical conversion of carboplatin. Tetragonal HEWL crystals co-crystallized with carboplatin were successfully obtained in four different conditions, each at a different pH value. The structural results obtained show carboplatin bound to either one or both of the N atoms of His15 of HEWL, and this particular variation was dependent on the concentration of anions in the crystallization mixture and the elapsed time, as well as the pH used. The structural details of the bound carboplatin molecule also differed between them. Overall, the most detailed crystal structure showed the majority of the carboplatin atoms bound to the platinum centre; however, the four-carbon ring structure of the cyclobutanedicarboxylate moiety (CBDC) remained elusive. The potential impact of the results for the administration of carboplatin as an anticancer agent are described.

  18. Comprehensive human transcription factor binding site map for combinatory binding motifs discovery.

    Directory of Open Access Journals (Sweden)

    Arnoldo J Müller-Molina

    Full Text Available To know the map between transcription factors (TFs and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.

  19. Optical Binding of Nanowires

    Czech Academy of Sciences Publication Activity Database

    Simpson, Stephen Hugh; Zemánek, Pavel; Marago, O.M.; Jones, P.H.; Hanna, S.

    2017-01-01

    Roč. 17, č. 6 (2017), s. 3485-3492 ISSN 1530-6984 R&D Projects: GA ČR GB14-36681G Grant - others:AV ČR(CZ) CNR-16-12 Program:Bilaterální spolupráce Institutional support: RVO:68081731 Keywords : optical binding nanowires * Brownian motion * self-organization * non-equilibrium thermodynamics * non-equilibrium steady state * spin-orbit coupling * emergent phenomena Subject RIV: BH - Optics, Masers, Lasers OBOR OECD: Optics (including laser optics and quantum optics) Impact factor: 12.712, year: 2016

  20. Thermodynamic Characterization of New Positive Allosteric Modulators Binding to the Glutamate Receptor A2 Ligand-Binding Domain

    DEFF Research Database (Denmark)

    Nørholm, Ann-Beth; Francotte, Pierre; Goffin, Eric

    2014-01-01

    , and 5a (5-F) and 5b (6-F) are entropy driven. For 5d (8-F), both quantities were equal in size. Thermodynamic integration (TI) and one-step perturbation (OSP) were used to calculate the relative binding affinity of the modulators. The OSP calculations had a higher predictive power than those from TI......,4-dihydro-2H-1,2,4-benzothiadiazine 1,1-dioxides. Measurements of ligand binding by isothermal titration calorimetry (ITC) showed similar binding affinities for the modulator series at the GluA2 LBD but differences in the thermodynamic driving forces. Binding of 5c (7-F) and 6 (no-F) is enthalpy driven......, and combined with the shorter total simulation time, we found the OSP method to be more effective for this setup. Furthermore, from the molecular dynamics simulations, we extracted the enthalpies and entropies, and along with the ITC data, this suggested that the differences in binding free energies...

  1. IGF binding proteins.

    Science.gov (United States)

    Bach, Leon A

    2017-12-18

    Insulin-like growth factor binding proteins (IGFBPs) 1-6 bind IGFs but not insulin with high affinity. They were initially identified as serum carriers and passive inhibitors of IGF actions. However, subsequent studies showed that, although IGFBPs inhibit IGF actions in many circumstances, they may also potentiate these actions. IGFBPs are widely expressed in most tissues, and they are flexible endocrine and autocrine/paracrine regulators of IGF activity, which is essential for this important physiological system. More recently, individual IGFBPs have been shown to have IGF-independent actions. Mechanisms underlying these actions include (i) interaction with non-IGF proteins in compartments including the extracellular space and matrix, the cell surface and intracellularly; (ii) interaction with and modulation of other growth factor pathways including EGF, TGF- and VEGF; and (iii) direct or indirect transcriptional effects following nuclear entry of IGFBPs. Through these IGF-dependent and IGF-independent actions, IGFBPs modulate essential cellular processes including proliferation, survival, migration, senescence, autophagy and angiogenesis. They have been implicated in a range of disorders including malignant, metabolic, neurological and immune diseases. A more complete understanding of their cellular roles may lead to the development of novel IGFBP-based therapeutic opportunities.

  2. Assessment of algorithms for inferring positional weight matrix motifs of transcription factor binding sites using protein binding microarray data.

    Directory of Open Access Journals (Sweden)

    Yaron Orenstein

    Full Text Available The new technology of protein binding microarrays (PBMs allows simultaneous measurement of the binding intensities of a transcription factor to tens of thousands of synthetic double-stranded DNA probes, covering all possible 10-mers. A key computational challenge is inferring the binding motif from these data. We present a systematic comparison of four methods developed specifically for reconstructing a binding site motif represented as a positional weight matrix from PBM data. The reconstructed motifs were evaluated in terms of three criteria: concordance with reference motifs from the literature and ability to predict in vivo and in vitro bindings. The evaluation encompassed over 200 transcription factors and some 300 assays. The results show a tradeoff between how the methods perform according to the different criteria, and a dichotomy of method types. Algorithms that construct motifs with low information content predict PBM probe ranking more faithfully, while methods that produce highly informative motifs match reference motifs better. Interestingly, in predicting high-affinity binding, all methods give far poorer results for in vivo assays compared to in vitro assays.

  3. Temperature-dependent binding of cyclosporine to an erythrocyte protein

    International Nuclear Information System (INIS)

    Agarwal, R.P.; Threatte, G.A.; McPherson, R.A.

    1987-01-01

    In this competitive binding assay to measure endogenous binding capacity for cyclosporine (CsA) in erythrocyte lysates, a fixed amount of [ 3 H]CsA plus various concentrations of unlabeled CsA is incubated with aliquots of a test hemolysate. Free CsA is then adsorbed onto charcoal and removed by centrifugation; CsA complexed with a cyclosporine-binding protein (CsBP) remains in the supernate. We confirmed the validity of this charcoal-separation mode of binding analysis by comparison with equilibrium dialysis. Scatchard plot analysis of the results at 4 degrees C yielded a straight line with slope corresponding to a binding constant of 1.9 X 10(7) L/mol and a saturation capacity of approximately 4 mumol per liter of packed erythrocytes. Similar analysis of binding data at 24 degrees C and 37 degrees C showed that the binding constant decreased with increasing temperature, but the saturation capacity did not change. CsBP was not membrane bound but appeared to be freely distributed within erythrocytes. 125 I-labeled CsA did not complex with the erythrocyte CsBP. Several antibiotics and other drugs did not inhibit binding between CsA and CsBP. These findings may explain the temperature-dependent uptake of CsA by erythrocytes in whole blood and suggest that measurement of CsBP in erythrocytes or lymphocytes may help predict therapeutic response or toxicity after administration of CsA

  4. Comparison of Transcription Factor Binding Site Models

    KAUST Repository

    Bhuyan, Sharifulislam

    2012-05-01

    Modeling of transcription factor binding sites (TFBSs) and TFBS prediction on genomic sequences are important steps to elucidate transcription regulatory mechanism. Dependency of transcription regulation on a great number of factors such as chemical specificity, molecular structure, genomic and epigenetic characteristics, long distance interaction, makes this a challenging problem. Different experimental procedures generate evidence that DNA-binding domains of transcription factors show considerable DNA sequence specificity. Probabilistic modeling of TFBSs has been moderately successful in identifying patterns from a family of sequences. In this study, we compare performances of different probabilistic models and try to estimate their efficacy over experimental TFBSs data. We build a pipeline to calculate sensitivity and specificity from aligned TFBS sequences for several probabilistic models, such as Markov chains, hidden Markov models, Bayesian networks. Our work, containing relevant statistics and evaluation for the models, can help researchers to choose the most appropriate model for the problem at hand.

  5. 8-anilino-1-naphthaline sulfonate binds at the hemoglobin allosteric regulatory sites: inhibitory analyses

    International Nuclear Information System (INIS)

    Bokut', S.B.; Parul', D.A.; Yachnik, N.N.; Milyutin, A.A.

    2001-01-01

    The present study focused on the localization at least one of the ANS binding sites in the major form of human hemoglobin HbA. High-resolution docking predict ANS binding to the hemoglobin central cavity. Steady-state fluorescence titration data obtained in the absence/presence of natural effector inositol hexaphosphate (IHP) allowed to conclude that IHP competitively inhibited ANS binding to HbA. Thus, we must conclude that one of the ANS binding sites is central cavity, which makes it possible to monitor changes at this region upon ligation/deligation, effector binding and changes in hemoglobin structure

  6. The productive cellulase binding capacity of cellulosic substrates.

    Science.gov (United States)

    Karuna, Nardrapee; Jeoh, Tina

    2017-03-01

    Cellulosic biomass is the most promising feedstock for renewable biofuel production; however, the mechanisms of the heterogeneous cellulose saccharification reaction are still unsolved. As cellulases need to bind isolated molecules of cellulose at the surface of insoluble cellulose fibrils or larger aggregated cellulose structures in order to hydrolyze glycosidic bonds, the "accessibility of cellulose to cellulases" is considered to be a reaction limiting property of cellulose. We have defined the accessibility of cellulose to cellulases as the productive binding capacity of cellulose, that is, the concentration of productive binding sites on cellulose that are accessible for binding and hydrolysis by cellulases. Productive cellulase binding to cellulose results in hydrolysis and can be quantified by measuring hydrolysis rates. In this study, we measured the productive Trichoderma reesei Cel7A (TrCel7A) binding capacity of five cellulosic substrates from different sources and processing histories. Swollen filter paper and bacterial cellulose had higher productive binding capacities of ∼6 µmol/g while filter paper, microcrystalline cellulose, and algal cellulose had lower productive binding capacities of ∼3 µmol/g. Swelling and regenerating filter paper using phosphoric acid increased the initial accessibility of the reducing ends to TrCel7A from 4 to 6 µmol/g. Moreover, this increase in initial productive binding capacity accounted in large part for the difference in the overall digestibility between filter paper and swollen filter paper. We further demonstrated that an understanding of how the productive binding capacity declines over the course of the hydrolysis reaction has the potential to predict overall saccharification time courses. Biotechnol. Bioeng. 2017;114: 533-542. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  7. LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

    KAUST Repository

    Chen, Peng

    2014-12-03

    Background Protein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available. Results In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. We propose a combination technique to reduce the effects of different sliding residue windows in the process of encoding input feature vectors. Moreover, due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we construct several balanced data sets, for each of which a random forest (RF)-based classifier is trained. The ensemble of these RF classifiers forms a sequence-based protein-ligand binding site predictor. Conclusions Experimental results on CASP9 and CASP8 data sets demonstrate that our method compares favorably with the state-of-the-art protein-ligand binding site prediction methods.

  8. Computational identification of antigen-binding antibody fragments.

    Science.gov (United States)

    Burkovitz, Anat; Leiderman, Olga; Sela-Culang, Inbal; Byk, Gerardo; Ofran, Yanay

    2013-03-01

    Determining which parts of the Ab are essential for Ag recognition and binding is crucial for understanding B cell-mediated immunity. Identification of fragments of Abs that maintain specificity to the Ag will also allow for the development of improved Ab-based therapy and diagnostics. In this article, we show that structural analysis of Ab-Ag complexes reveals which fragments of the Ab may bind the Ag on their own. In particular, it is possible to predict whether a given CDR is likely to bind the Ag as a peptide by analyzing the energetic contribution of each CDR to Ag binding and by assessing to what extent the interaction between that CDR and the Ag depends on other CDRs. To demonstrate this, we analyzed five Ab-Ag complexes and predicted for each of them which of the CDRs may bind the Ag on its own as a peptide. We then show that these predictions are in agreement with our experimental analysis and with previously published experimental results. These findings promote our understanding of the modular nature of Ab-Ag interactions and lay the foundation for the rational design of active CDR-derived peptides.

  9. Pictorial binding: endeavor to classify

    Directory of Open Access Journals (Sweden)

    Zinchenko S.

    2015-01-01

    Full Text Available The article is devoted to the classification of bindings of the 1-19th centuries with a unique and untypical book binding decoration technique (encaustic, tempera and oil paintings. Analysis of design features, materials and techniques of art decoration made it possible to identify them as a separate type - pictorial bindings and divide them into four groups. The first group consists of Coptic bindings, decorated with icon-painting images in encaustic technique. The second group is made up of leather Western bindings of the 13-14th centuries, which have the decoration and technique of ornamentation close to iconography. The third group involves parchment bindings, ornamentation technique of which is closer to the miniature. The last group comprises bindings of East Slavic origin of the 15-19th centuries, decorated with icon-painting pictures made in the technique of tempera or oil painting. The proposed classification requires further basic research as several specific kinds of bindings have not yet been investigated

  10. RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins.

    Directory of Open Access Journals (Sweden)

    Hilal Kazan

    2010-07-01

    Full Text Available Metazoan genomes encode hundreds of RNA-binding proteins (RBPs. These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures.

  11. Binding Energy and Equilibrium of Compact Objects

    Directory of Open Access Journals (Sweden)

    Germano M.

    2014-04-01

    Full Text Available The theoretical analysis of the existence of a limit mass for compact astronomic ob- jects requires the solution of the Einstein’s equations of g eneral relativity together with an appropriate equation of state. Analytical solutions exi st in some special cases like the spherically symmetric static object without energy sou rces that is here considered. Solutions, i.e. the spacetime metrics, can have a singular m athematical form (the so called Schwarzschild metric due to Hilbert or a nonsingula r form (original work of Schwarzschild. The former predicts a limit mass and, conse quently, the existence of black holes above this limit. Here it is shown that, the origi nal Schwarzschild met- ric permits compact objects, without mass limit, having rea sonable values for central density and pressure. The lack of a limit mass is also demonst rated analytically just imposing reasonable conditions on the energy-matter densi ty, of positivity and decreas- ing with radius. Finally the ratio between proper mass and to tal mass tends to 2 for high values of mass so that the binding energy reaches the lim it m (total mass seen by a distant observer. As it is known the negative binding energ y reduces the gravitational mass of the object; the limit of m for the binding energy provides a mechanism for stable equilibrium of any amount of mass to contrast the gravitatio nal collapse.

  12. Megalin binds and mediates cellular internalization of folate binding protein

    DEFF Research Database (Denmark)

    Birn, Henrik; Zhai, Xiaoyue; Holm, Jan

    2005-01-01

    Folate is an essential vitamin involved in a number of biological processes. High affinity folate binding proteins (FBPs) exist both as glycosylphosphatidylinositol-linked, membrane associated folate binding proteins and as soluble FBPs in plasma and some secretory fluids such as milk, saliva...... to express high levels of megalin, is inhibitable by excess unlabeled FBP and by receptor associated protein, a known inhibitor of binding to megalin. Immortalized rat yolk sac cells, representing an established model for studying megalin-mediated uptake, reveal (125)I-labeled FBP uptake which is inhibited...

  13. Automated benchmarking of peptide-MHC class I binding predictions

    DEFF Research Database (Denmark)

    Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason

    2015-01-01

    educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All...

  14. QSARs for Plasma Protein Binding: Source Data and Predictions

    Data.gov (United States)

    U.S. Environmental Protection Agency — The dataset has all of the information used to create and evaluate 3 independent QSAR models for the fraction of a chemical unbound by plasma protein (Fub) for...

  15. Sarcomere lattice geometry influences cooperative myosin binding in muscle.

    Directory of Open Access Journals (Sweden)

    Bertrand C W Tanner

    2007-07-01

    Full Text Available In muscle, force emerges from myosin binding with actin (forming a cross-bridge. This actomyosin binding depends upon myofilament geometry, kinetics of thin-filament Ca(2+ activation, and kinetics of cross-bridge cycling. Binding occurs within a compliant network of protein filaments where there is mechanical coupling between myosins along the thick-filament backbone and between actin monomers along the thin filament. Such mechanical coupling precludes using ordinary differential equation models when examining the effects of lattice geometry, kinetics, or compliance on force production. This study uses two stochastically driven, spatially explicit models to predict levels of cross-bridge binding, force, thin-filament Ca(2+ activation, and ATP utilization. One model incorporates the 2-to-1 ratio of thin to thick filaments of vertebrate striated muscle (multi-filament model, while the other comprises only one thick and one thin filament (two-filament model. Simulations comparing these models show that the multi-filament predictions of force, fractional cross-bridge binding, and cross-bridge turnover are more consistent with published experimental values. Furthermore, the values predicted by the multi-filament model are greater than those values predicted by the two-filament model. These increases are larger than the relative increase of potential inter-filament interactions in the multi-filament model versus the two-filament model. This amplification of coordinated cross-bridge binding and cycling indicates a mechanism of cooperativity that depends on sarcomere lattice geometry, specifically the ratio and arrangement of myofilaments.

  16. Nucleos: a web server for the identification of nucleotide-binding sites in protein structures.

    Science.gov (United States)

    Parca, Luca; Ferré, Fabrizio; Ausiello, Gabriele; Helmer-Citterich, Manuela

    2013-07-01

    Nucleos is a web server for the identification of nucleotide-binding sites in protein structures. Nucleos compares the structure of a query protein against a set of known template 3D binding sites representing nucleotide modules, namely the nucleobase, carbohydrate and phosphate. Structural features, clustering and conservation are used to filter and score the predictions. The predicted nucleotide modules are then joined to build whole nucleotide-binding sites, which are ranked by their score. The server takes as input either the PDB code of the query protein structure or a user-submitted structure in PDB format. The output of Nucleos is composed of ranked lists of predicted nucleotide-binding sites divided by nucleotide type (e.g. ATP-like). For each ranked prediction, Nucleos provides detailed information about the score, the template structure and the structural match for each nucleotide module composing the nucleotide-binding site. The predictions on the query structure and the template-binding sites can be viewed directly on the web through a graphical applet. In 98% of the cases, the modules composing correct predictions belong to proteins with no homology relationship between each other, meaning that the identification of brand-new nucleotide-binding sites is possible using information from non-homologous proteins. Nucleos is available at http://nucleos.bio.uniroma2.it/nucleos/.

  17. [3]tetrahydrotrazodone binding. Association with serotonin binding sites

    International Nuclear Information System (INIS)

    Kendall, D.A.; Taylor, D.P.; Enna, S.J.

    1983-01-01

    High (17 nM) and low (603 nM) affinity binding sites for [ 3 ]tetrahydrotrazodone ([ 3 ] THT), a biologically active analogue of trazodone, have been identified in rat brain membranes. The substrate specificity, concentration, and subcellular and regional distributions of these sites suggest that they may represent a component of the serotonin transmitter system. Pharmacological analysis of [ 3 ]THT binding, coupled with brain lesion and drug treatment experiments, revealed that, unlike other antidepressants, [ 3 ] THT does not attach to either a biogenic amine transporter or serotonin binding sites. Rather, it would appear that [ 3 ]THT may be an antagonist ligand for the serotonin binding site. This probe may prove of value in defining the mechanism of action of trazodone and in further characterizing serotonin receptors

  18. Structural and Histone Binding Ability Characterizations of Human PWWP Domains

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Hong; Zeng, Hong; Lam, Robert; Tempel, Wolfram; Amaya, Maria F.; Xu, Chao; Dombrovski, Ludmila; Qiu, Wei; Wang, Yanming; Min, Jinrong (Toronto); (Penn)

    2013-09-25

    The PWWP domain was first identified as a structural motif of 100-130 amino acids in the WHSC1 protein and predicted to be a protein-protein interaction domain. It belongs to the Tudor domain 'Royal Family', which consists of Tudor, chromodomain, MBT and PWWP domains. While Tudor, chromodomain and MBT domains have long been known to bind methylated histones, PWWP was shown to exhibit histone binding ability only until recently. The PWWP domain has been shown to be a DNA binding domain, but sequence analysis and previous structural studies show that the PWWP domain exhibits significant similarity to other 'Royal Family' members, implying that the PWWP domain has the potential to bind histones. In order to further explore the function of the PWWP domain, we used the protein family approach to determine the crystal structures of the PWWP domains from seven different human proteins. Our fluorescence polarization binding studies show that PWWP domains have weak histone binding ability, which is also confirmed by our NMR titration experiments. Furthermore, we determined the crystal structures of the BRPF1 PWWP domain in complex with H3K36me3, and HDGF2 PWWP domain in complex with H3K79me3 and H4K20me3. PWWP proteins constitute a new family of methyl lysine histone binders. The PWWP domain consists of three motifs: a canonical {beta}-barrel core, an insertion motif between the second and third {beta}-strands and a C-terminal {alpha}-helix bundle. Both the canonical {beta}-barrel core and the insertion motif are directly involved in histone binding. The PWWP domain has been previously shown to be a DNA binding domain. Therefore, the PWWP domain exhibits dual functions: binding both DNA and methyllysine histones.

  19. Protein binding of psychotropic agents

    International Nuclear Information System (INIS)

    Hassan, H.A.

    1990-01-01

    Based upon fluorescence measurements, protein binding of some psychotropic agents (chlorpromazine, promethazine, and trifluoperazine) to human IgG and HSA was studied in aqueous cacodylate buffer, PH7. The interaction parameters determined from emission quenching of the proteins. The interaction parameters determined include the equilibrium constant (K), calculated from equations derived by Borazan and coworkers, the number of binding sites (n) available to the monomer molecules on a single protein molecule. The results revealed a high level of affinity, as reflected by high values of K, and the existence of specific binding sites, since a limited number of n values are obtained. 39 tabs.; 37 figs.; 83 refs

  20. WALS Prediction

    NARCIS (Netherlands)

    Magnus, J.R.; Wang, W.; Zhang, Xinyu

    2012-01-01

    Abstract: Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty

  1. Characterization of binding specificities of bovine leucocyte class I molecules: impacts for rational epitope discovery

    DEFF Research Database (Denmark)

    Hansen, Andreas M.; Rasmussen, Michael; Svitek, Nicholas

    2014-01-01

    confirmed experimentally. This study demonstrates how biochemical high-throughput assays combined with immunoinformatics can be used to characterize the peptide-binding motifs of BoLA-I molecules, boosting performance of MHC peptide-binding prediction methods, and empowering rational epitope discovery...

  2. Superresolution microscopy with transient binding.

    Science.gov (United States)

    Molle, Julia; Raab, Mario; Holzmeister, Susanne; Schmitt-Monreal, Daniel; Grohmann, Dina; He, Zhike; Tinnefeld, Philip

    2016-06-01

    For single-molecule localization based superresolution, the concentration of fluorescent labels has to be thinned out. This is commonly achieved by photophysically or photochemically deactivating subsets of molecules. Alternatively, apparent switching of molecules can be achieved by transient binding of fluorescent labels. Here, a diffusing dye yields bright fluorescent spots when binding to the structure of interest. As the binding interaction is weak, the labeling is reversible and the dye ligand construct diffuses back into solution. This approach of achieving superresolution by transient binding (STB) is reviewed in this manuscript. Different realizations of STB are discussed and compared to other localization-based superresolution modalities. We propose the development of labeling strategies that will make STB a highly versatile tool for superresolution microscopy at highest resolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Probing binding hot spots at protein-RNA recognition sites.

    Science.gov (United States)

    Barik, Amita; Nithin, Chandran; Karampudi, Naga Bhushana Rao; Mukherjee, Sunandan; Bahadur, Ranjit Prasad

    2016-01-29

    We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  4. Global analysis of small molecule binding to related protein targets.

    Directory of Open Access Journals (Sweden)

    Felix A Kruger

    2012-01-01

    Full Text Available We report on the integration of pharmacological data and homology information for a large scale analysis of small molecule binding to related targets. Differences in small molecule binding have been assessed for curated pairs of human to rat orthologs and also for recently diverged human paralogs. Our analysis shows that in general, small molecule binding is conserved for pairs of human to rat orthologs. Using statistical tests, we identified a small number of cases where small molecule binding is different between human and rat, some of which had previously been reported in the literature. Knowledge of species specific pharmacology can be advantageous for drug discovery, where rats are frequently used as a model system. For human paralogs, we demonstrate a global correlation between sequence identity and the binding of small molecules with equivalent affinity. Our findings provide an initial general model relating small molecule binding and sequence divergence, containing the foundations for a general model to anticipate and predict within-target-family selectivity.

  5. Binding properties of SUMO-interacting motifs (SIMs) in yeast.

    Science.gov (United States)

    Jardin, Christophe; Horn, Anselm H C; Sticht, Heinrich

    2015-03-01

    Small ubiquitin-like modifier (SUMO) conjugation and interaction play an essential role in many cellular processes. A large number of yeast proteins is known to interact non-covalently with SUMO via short SUMO-interacting motifs (SIMs), but the structural details of this interaction are yet poorly characterized. In the present work, sequence analysis of a large dataset of 148 yeast SIMs revealed the existence of a hydrophobic core binding motif and a preference for acidic residues either within or adjacent to the core motif. Thus the sequence properties of yeast SIMs are highly similar to those described for human. Molecular dynamics simulations were performed to investigate the binding preferences for four representative SIM peptides differing in the number and distribution of acidic residues. Furthermore, the relative stability of two previously observed alternative binding orientations (parallel, antiparallel) was assessed. For all SIMs investigated, the antiparallel binding mode remained stable in the simulations and the SIMs were tightly bound via their hydrophobic core residues supplemented by polar interactions of the acidic residues. In contrary, the stability of the parallel binding mode is more dependent on the sequence features of the SIM motif like the number and position of acidic residues or the presence of additional adjacent interaction motifs. This information should be helpful to enhance the prediction of SIMs and their binding properties in different organisms to facilitate the reconstruction of the SUMO interactome.

  6. Epitope prediction methods

    DEFF Research Database (Denmark)

    Karosiene, Edita

    Analysis. The chapter provides detailed explanations on how to use different methods for T cell epitope discovery research, explaining how input should be given as well as how to interpret the output. In the last chapter, I present the results of a bioinformatics analysis of epitopes from the yellow fever...... peptide-MHC interactions. Furthermore, using yellow fever virus epitopes, we demonstrated the power of the %Rank score when compared with the binding affinity score of MHC prediction methods, suggesting that this score should be considered to be used for selecting potential T cell epitopes. In summary...... immune responses. Therefore, it is of great importance to be able to identify peptides that bind to MHC molecules, in order to understand the nature of immune responses and discover T cell epitopes useful for designing new vaccines and immunotherapies. MHC molecules in humans, referred to as human...

  7. LIGAND-BINDING SITES ON THE MYCOBACTERIUM TUBERCULOSIS UREASE

    Directory of Open Access Journals (Sweden)

    Lisnyak Yu. V.

    2017-10-01

    Full Text Available Introduction. Mycobacterium tuberculosis is the causative agent of tuberculosis that remains a serious medical and social health problem. Despite intensive efforts have been made in the past decade, there are no new efficient anti-tuberculosis drugs today, and that need is growing due to the spread of drug-resistant strains of M.tuberculosis. M. tuberculosis urease (MTU, being an important factor of the bacterium viability and virulence, is an attractive target for anti-tuberculosis drugs acting by inhibition of urease activity. However, the commercially available urease inhibitors are toxic and unstable, that prevent their clinical use. Therefore, new more potent anti-tuberculosis drugs inhibiting new targets are urgently needed. A useful tool for the search of novel inhibitors is a computational drug design. The inhibitor design is significantly easier if binding sites on the enzyme are identified in advance. This paper aimed to determine the probable ligand binding sites on the surface of M. tuberculosis urease. Methods. To identify ligand binding sites on MTU surface, сomputational solvent mapping method FTSite was applied by the use of MTU homology model we have built earlier. The method places molecular probes (small organic molecules containing various functional groups on a dense grid defined around the enzyme, and for each probe finds favorable positions. The selected poses are refined by free energy minimization, the low energy conformations are clustered, and the clusters are ranked on the basis of the average free energy. FTSite server outputs the protein residues delineating a binding sites and the probe molecules representing each cluster. To predict allosteric pockets on MTU, AlloPred and AlloSite servers were applied. AlloPred uses the normal mode analysis (NMA and models how the dynamics of a protein would be altered in the presence of a modulator at a specific pocket. Pockets on the enzyme are predicted using the Fpocket

  8. Analysis of experimental positron-molecule binding energies

    International Nuclear Information System (INIS)

    Danielson, J R; Surko, C M; Young, J A

    2010-01-01

    Experiments show that positron annihilation on molecules frequently occurs via capture into vibrational Feshbach resonances. In these cases, the downshifts in the annihilation spectra from the vibrational mode spectra provide measures of the positron-molecule binding energies. An analysis of these binding energy data is presented in terms of the molecular dipole polarizability, the permanent dipole moment, and the number of π bonds in aromatic molecules. The results of this analysis are in reasonably good agreement with other information about positron-molecule bound states. Predictions for other targets and promising candidate molecules for further investigation are discussed.

  9. Extreme sequence divergence but conserved ligand-binding specificity in Streptococcus pyogenes M protein.

    Directory of Open Access Journals (Sweden)

    2006-05-01

    Full Text Available Many pathogenic microorganisms evade host immunity through extensive sequence variability in a protein region targeted by protective antibodies. In spite of the sequence variability, a variable region commonly retains an important ligand-binding function, reflected in the presence of a highly conserved sequence motif. Here, we analyze the limits of sequence divergence in a ligand-binding region by characterizing the hypervariable region (HVR of Streptococcus pyogenes M protein. Our studies were focused on HVRs that bind the human complement regulator C4b-binding protein (C4BP, a ligand that confers phagocytosis resistance. A previous comparison of C4BP-binding HVRs identified residue identities that could be part of a binding motif, but the extended analysis reported here shows that no residue identities remain when additional C4BP-binding HVRs are included. Characterization of the HVR in the M22 protein indicated that two relatively conserved Leu residues are essential for C4BP binding, but these residues are probably core residues in a coiled-coil, implying that they do not directly contribute to binding. In contrast, substitution of either of two relatively conserved Glu residues, predicted to be solvent-exposed, had no effect on C4BP binding, although each of these changes had a major effect on the antigenic properties of the HVR. Together, these findings show that HVRs of M proteins have an extraordinary capacity for sequence divergence and antigenic variability while retaining a specific ligand-binding function.

  10. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-01-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate...

  11. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery.

    Science.gov (United States)

    Pérot, Stéphanie; Sperandio, Olivier; Miteva, Maria A; Camproux, Anne-Claude; Villoutreix, Bruno O

    2010-08-01

    Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

  12. Metal ion binding to iron oxides

    Science.gov (United States)

    Ponthieu, M.; Juillot, F.; Hiemstra, T.; van Riemsdijk, W. H.; Benedetti, M. F.

    2006-06-01

    The biogeochemistry of trace elements (TE) is largely dependent upon their interaction with heterogeneous ligands including metal oxides and hydrous oxides of iron. The modeling of TE interactions with iron oxides has been pursued using a variety of chemical models. The objective of this work is to show that it is possible to model the adsorption of protons and TE on a crystallized oxide (i.e., goethite) and on an amorphous oxide (HFO) in an identical way. Here, we use the CD-MUSIC approach in combination with valuable and reliable surface spectroscopy information about the nature of surface complexes of the TE. The other objective of this work is to obtain generic parameters to describe the binding of the following elements (Cd, Co, Cu, Ni, Pb, and Zn) onto both iron oxides for the CD-MUSIC approach. The results show that a consistent description of proton and metal ion binding is possible for goethite and HFO with the same set of model parameters. In general a good prediction of almost all the collected experimental data sets corresponding to metal ion binding to HFO is obtained. Moreover, dominant surface species are in agreement with the recently published surface complexes derived from X-ray absorption spectroscopy (XAS) data. Until more detailed information on the structure of the two iron oxides is available, the present option seems a reasonable approximation and can be used to describe complex geochemical systems. To improve our understanding and modeling of multi-component systems we need more data obtained at much lower metal ion to iron oxide ratios in order to be able to account eventually for sites that are not always characterized in spectroscopic studies.

  13. Application of quantitative structure-activity relationship to the determination of binding constant based on fluorescence quenching

    Energy Technology Data Exchange (ETDEWEB)

    Wen Yingying [Department of Applied Chemistry, Yantai University, Yantai 264005 (China); Liu Huitao, E-mail: liuht-ytu@163.co [Department of Applied Chemistry, Yantai University, Yantai 264005 (China); Luan Feng; Gao Yuan [Department of Applied Chemistry, Yantai University, Yantai 264005 (China)

    2011-01-15

    Quantitative structure-activity relationship (QSAR) model was used to predict and explain binding constant (log K) determined by fluorescence quenching. This method allowed us to predict binding constants of a variety of compounds with human serum albumin (HSA) based on their structures alone. Stepwise multiple linear regression (MLR) and nonlinear radial basis function neural network (RBFNN) were performed to build the models. The statistical parameters provided by the MLR model (R{sup 2}=0.8521, RMS=0.2678) indicated satisfactory stability and predictive ability while the RBFNN predictive ability is somewhat superior (R{sup 2}=0.9245, RMS=0.1736). The proposed models were used to predict the binding constants of two bioactive components in traditional Chinese medicines (isoimperatorin and chrysophanol) whose experimental results were obtained in our laboratory and the predicted results were in good agreement with the experimental results. This QSAR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and can be useful in predicting the binding constants of other compounds. - Research Highlights: QSAR models for binding constants of some compounds to HSA were developed. The models provide a simple and straightforward way to predict binding constant. QSAR can give some insight into structural features related to binding behavior.

  14. Climate prediction and predictability

    Science.gov (United States)

    Allen, Myles

    2010-05-01

    Climate prediction is generally accepted to be one of the grand challenges of the Geophysical Sciences. What is less widely acknowledged is that fundamental issues have yet to be resolved concerning the nature of the challenge, even after decades of research in this area. How do we verify or falsify a probabilistic forecast of a singular event such as anthropogenic warming over the 21st century? How do we determine the information content of a climate forecast? What does it mean for a modelling system to be "good enough" to forecast a particular variable? How will we know when models and forecasting systems are "good enough" to provide detailed forecasts of weather at specific locations or, for example, the risks associated with global geo-engineering schemes. This talk will provide an overview of these questions in the light of recent developments in multi-decade climate forecasting, drawing on concepts from information theory, machine learning and statistics. I will draw extensively but not exclusively from the experience of the climateprediction.net project, running multiple versions of climate models on personal computers.

  15. Role of Electrostatics in Protein-RNA Binding: The Global vs the Local Energy Landscape.

    Science.gov (United States)

    Ghaemi, Zhaleh; Guzman, Irisbel; Gnutt, David; Luthey-Schulten, Zaida; Gruebele, Martin

    2017-09-14

    U1A protein-stem loop 2 RNA association is a basic step in the assembly of the spliceosomal U1 small nuclear ribonucleoprotein. Long-range electrostatic interactions due to the positive charge of U1A are thought to provide high binding affinity for the negatively charged RNA. Short range interactions, such as hydrogen bonds and contacts between RNA bases and protein side chains, favor a specific binding site. Here, we propose that electrostatic interactions are as important as local contacts in biasing the protein-RNA energy landscape toward a specific binding site. We show by using molecular dynamics simulations that deletion of two long-range electrostatic interactions (K22Q and K50Q) leads to mutant-specific alternative RNA bound states. One of these states preserves short-range interactions with aromatic residues in the original binding site, while the other one does not. We test the computational prediction with experimental temperature-jump kinetics using a tryptophan probe in the U1A-RNA binding site. The two mutants show the distinct predicted kinetic behaviors. Thus, the stem loop 2 RNA has multiple binding sites on a rough RNA-protein binding landscape. We speculate that the rough protein-RNA binding landscape, when biased to different local minima by electrostatics, could be one way that protein-RNA interactions evolve toward new binding sites and novel function.

  16. Binding energies of cluster ions

    International Nuclear Information System (INIS)

    Parajuli, R.; Matt, S.; Scheier, P.; Echt, O.; Stamatovic, A.; Maerk, T.D.

    2002-01-01

    The binding energy of charged clusters may be measured by analyzing the kinetic energy released in the metastable decay of mass selected parent ions. Using finite heat bath theory to determine the binding energies of argon, neon, krypton, oxygen and nitrogen from their respective average kinetic energy released were carried out. A high-resolution double focussing two-sector mass spectrometer of reversed Nier-Johnson type geometry was used. MIKE ( mass-analysed ion kinetic energy) were measured to investigate decay reactions of mass-selected ions. For the inert gases neon (Ne n + ), argon (Ar n + ) and krypton (Kr n + ), it is found that the binding energies initially decrease with increasing size n and then level off at a value above the enthalpy of vaporization of the condensed phase. Oxygen cluster ions shown a characteristic dependence on cluster size (U-shape) indicating a change in the metastable fragmentation mechanism when going from the dimer to the decamer ion. (nevyjel)

  17. Metal binding by food components

    DEFF Research Database (Denmark)

    Tang, Ning

    for zinc binding by the investigated amino acids, peptides and proteins. The thiol group or imidazole group containing amino acids, peptides and proteins which exhibited strong zinc binding ability were further selected for interacting with zinc salts in relation to zinc absorption. The interactions...... between the above selected food components and zinc citrate or zinc phytate will lead to the enhanced solubility of zinc citrate or zinc phytate. The main driving force for this observed solubility enhancement is the complex formation between zinc and investigated food components as revealed by isothermal...... titration calorimetry and quantum mechanical calculations. This is due to the zinc binding affinity of the relatively softer ligands (investigated food components) will become much stronger than citrate or phytate when they present together in aqueous solution. This mechanism indicates these food components...

  18. Prediction of regulatory elements

    DEFF Research Database (Denmark)

    Sandelin, Albin

    2008-01-01

    Finding the regulatory mechanisms responsible for gene expression remains one of the most important challenges for biomedical research. A major focus in cellular biology is to find functional transcription factor binding sites (TFBS) responsible for the regulation of a downstream gene. As wet......-lab methods are time consuming and expensive, it is not realistic to identify TFBS for all uncharacterized genes in the genome by purely experimental means. Computational methods aimed at predicting potential regulatory regions can increase the efficiency of wet-lab experiments significantly. Here, methods...

  19. Skyrmions with low binding energies

    Energy Technology Data Exchange (ETDEWEB)

    Gillard, Mike, E-mail: m.n.gillard@leeds.ac.uk; Harland, Derek, E-mail: d.g.harland@leeds.ac.uk; Speight, Martin, E-mail: speight@maths.leeds.ac.uk

    2015-06-15

    Nuclear binding energies are investigated in two variants of the Skyrme model: the first replaces the usual Skyrme term with a term that is sixth order in derivatives, and the second includes a potential that is quartic in the pion fields. Solitons in the first model are shown to deviate significantly from ansätze previously assumed in the literature. The binding energies obtained in both models are lower than those obtained from the standard Skyrme model, and those obtained in the second model are close to the experimental values.

  20. Skyrmions with low binding energies

    International Nuclear Information System (INIS)

    Gillard, Mike; Harland, Derek; Speight, Martin

    2015-01-01

    Nuclear binding energies are investigated in two variants of the Skyrme model: the first replaces the usual Skyrme term with a term that is sixth order in derivatives, and the second includes a potential that is quartic in the pion fields. Solitons in the first model are shown to deviate significantly from ansätze previously assumed in the literature. The binding energies obtained in both models are lower than those obtained from the standard Skyrme model, and those obtained in the second model are close to the experimental values

  1. Skyrmions with low binding energies

    Directory of Open Access Journals (Sweden)

    Mike Gillard

    2015-06-01

    Full Text Available Nuclear binding energies are investigated in two variants of the Skyrme model: the first replaces the usual Skyrme term with a term that is sixth order in derivatives, and the second includes a potential that is quartic in the pion fields. Solitons in the first model are shown to deviate significantly from ansätze previously assumed in the literature. The binding energies obtained in both models are lower than those obtained from the standard Skyrme model, and those obtained in the second model are close to the experimental values.

  2. Binding proteins enhance specific uptake rate by increasing the substrate-transporter encounter rate.

    Science.gov (United States)

    Bosdriesz, Evert; Magnúsdóttir, Stefanía; Bruggeman, Frank J; Teusink, Bas; Molenaar, Douwe

    2015-06-01

    Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. Several advantages of using binding proteins in such uptake systems have been proposed. However, a systematic, rigorous and quantitative analysis of the function of binding proteins is lacking. By combining knowledge of selection pressure and physiochemical constraints, we derive kinetic, thermodynamic, and stoichiometric properties of binding-protein dependent transport systems that enable a maximal import activity per amount of transporter. Under the hypothesis that this maximal specific activity of the transport complex is the selection objective, binding protein concentrations should exceed the concentration of both the scarce nutrient and the transporter. This increases the encounter rate of transporter with loaded binding protein at low substrate concentrations, thereby enhancing the affinity and specific uptake rate. These predictions are experimentally testable, and a number of observations confirm them. © 2015 FEBS.

  3. Relationship of nonreturn rates of dairy bulls to binding affinity of heparin to sperm

    International Nuclear Information System (INIS)

    Marks, J.L.; Ax, R.L.

    1985-01-01

    The binding of the glycosaminoglycan [ 3 H] heparin to bull spermatozoa was compared with nonreturn rates of dairy bulls. Semen samples from five bulls above and five below an average 71% nonreturn rate were used. Samples consisted of first and second ejaculates on a single day collected 1 d/wk for up to 5 consecutive wk. Saturation binding assays using [ 3 H] heparin were performed to quantitate the binding characteristics of each sample. Scatchard plot analyses indicated a significant difference in the binding affinity for [ 3 H] heparin between bulls of high and low fertility. Dissociation constants were 69.0 and 119.3 pmol for bulls of high and low fertility, respectively. In contrast, the number of binding sites for [ 3 H] heparin did not differ significantly among bulls. Differences in binding affinity of [ 3 H] heparin to bull sperm might be used to predict relative fertility of dairy bulls

  4. A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

    Directory of Open Access Journals (Sweden)

    Suganthan PN

    2007-09-01

    Full Text Available Abstract Background Odorant binding proteins (OBPs are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC in conjunction with multiple physicochemical properties of amino acids to predict odorant-binding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively. Conclusion Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information.

  5. Real-Time Ligand Binding Pocket Database Search Using Local Surface Descriptors

    Science.gov (United States)

    Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

    2010-01-01

    Due to the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of a particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two dimensional pseudo-Zernike moments or the 3D Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark study employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed. PMID:20455259

  6. Predictions versus high-throughput experiments in T-cell epitope discovery: competition or synergy?

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2012-01-01

    and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible...

  7. Cellulose binding domain fusion proteins

    Science.gov (United States)

    Shoseyov, Oded; Shpiegl, Itai; Goldstein, Marc A.; Doi, Roy H.

    1998-01-01

    A cellulose binding domain (CBD) having a high affinity for crystalline cellulose and chitin is disclosed, along with methods for the molecular cloning and recombinant production thereof. Fusion products comprising the CBD and a second protein are likewise described. A wide range of applications are contemplated for both the CBD and the fusion products, including drug delivery, affinity separations, and diagnostic techniques.

  8. When is protein binding important?

    Science.gov (United States)

    Heuberger, Jules; Schmidt, Stephan; Derendorf, Hartmut

    2013-09-01

    The present paper is an ode to a classic citation by Benet and Hoener (2002. Clin Pharm Ther 71(3):115-121). The now classic paper had a huge impact on drug development and the way the issue of protein binding is perceived and interpreted. Although the authors very clearly pointed out the limitations and underlying assumptions for their delineations, these are too often overlooked and the classic paper's message is misinterpreted by broadening to cases that were not intended. Some members of the scientific community concluded from the paper that protein binding is not important. This was clearly not intended by the authors, as they finished their paper with a paragraph entitled: "When is protein binding important?" Misinterpretation of the underlying assumptions in the classic work can result in major pitfalls in drug development. Therefore, we revisit the topic of protein binding with the intention of clarifying when clinically relevant changes should be considered during drug development. Copyright © 2013 Wiley Periodicals, Inc.

  9. Thermodynamic model of binding of flexible bivalent haptens to antibody

    Energy Technology Data Exchange (ETDEWEB)

    Dembo, M; Goldstein, B

    1978-01-01

    Studies by Wilder et al. of the binding of Fab' fragments to small haptens have shown that the cross-linking constant (the equilibrium constant for binding an additional Fab' fragment to a hapten-Fab' complex) is strongly dependent on the length of the hapten. We present a simple model for predicting the relationship between the intermolecular cross-linking constant and the monovalent hapten-antibody binding constant. In particular we used the model to obtain the dependence of the cross-linking constant on the length of th hapten, the depth to which the hapten fills th Fab' binding site, and the size of the Fab' fragment. To test the model, we devised expressions which allowed us to analyze the data of Wilder et al. From their data we determined the values of two parameters which we took to be unknown in the theory, the size of the Fab' fragment and the depth to which the hapten fills the Fab' binding site. The values arrived at in this way agreed well with published measurements of these parameters.

  10. On the influence of reward on action-effect binding

    Directory of Open Access Journals (Sweden)

    Paul Simon Muhle-Karbe

    2012-11-01

    Full Text Available Ideomotor theory states that the formation of anticipatory representations about the perceptual consequences of an action (i.e. action-effect (A-E binding provides the functional basis of voluntary action control. A host of studies has demonstrated that A-E binding occurs fast and effortlessly, yet only little is known about cognitive and affective factors that influence this learning process. In the present study, we sought to test whether the motivational value of an action modulates the acquisition of A-E associations. To this end, we associated specific actions with monetary incentives during the acquisition of novel A-E mappings. In a subsequent test phase, the degree of binding was assessed by presenting the former effect stimuli as task-irrelevant response primes in a forced-choice response task in the absence of any reward. Binding, as indexed by response priming through the former action effects, was only found for reward-related A-E mappings. Moreover, the degree to which reward associations modulated the binding strength was predicted by individuals’ trait sensitivity to reward. These observations indicate that the association of actions and their immediate outcomes depends on the motivational value of the action during learning, as well as on the motivational disposition of the individual. On a larger scale, these findings also highlight the link between ideomotor theories and reinforcement-learning theories, providing an interesting perspective for future research on anticipatory regulation of behavior.

  11. Target-mediated drug disposition model for drugs with two binding sites that bind to a target with one binding site.

    Science.gov (United States)

    Gibiansky, Leonid; Gibiansky, Ekaterina

    2017-10-01

    The paper extended the TMDD model to drugs with two identical binding sites (2-1 TMDD). The quasi-steady-state (2-1 QSS), quasi-equilibrium (2-1 QE), irreversible binding (2-1 IB), and Michaelis-Menten (2-1 MM) approximations of the model were derived. Using simulations, the 2-1 QSS approximation was compared with the full 2-1 TMDD model. As expected and similarly to the standard TMDD for monoclonal antibodies (mAb), 2-1 QSS predictions were nearly identical to 2-1 TMDD predictions, except for times of fast changes following initiation of dosing, when equilibrium has not yet been reached. To illustrate properties of new equations and approximations, several variations of population PK data for mAbs with soluble (slow elimination of the complex) or membrane-bound (fast elimination of the complex) targets were simulated from a full 2-1 TMDD model and fitted to 2-1 TMDD models, to its approximations, and to the standard (1-1) QSS model. For a mAb with a soluble target, it was demonstrated that the 2-1 QSS model provided nearly identical description of the observed (simulated) free drug and total target concentrations, although there was some minor bias in predictions of unobserved free target concentrations. The standard QSS approximation also provided a good description of the observed data, but was not able to distinguish between free drug concentrations (with no target attached and both binding site free) and partially bound drug concentrations (with one of the binding sites occupied by the target). For a mAb with a membrane-bound target, the 2-1 MM approximation adequately described the data. The 2-1 QSS approximation converged 10 times faster than the full 2-1 TMDD, and its run time was comparable with the standard QSS model.

  12. Defining proximity relationships in the tertiary structure of the dopamine transporter. Identification of a conserved glutamic acid as a third coordinate in the endogenous Zn(2+)-binding site

    DEFF Research Database (Denmark)

    Løland, Claus Juul; Norregaard, L; Gether, U

    1999-01-01

    , high affinity Zn(2+)-binding site. To achieve further insight into the tertiary organization of hDAT, we set out to identify additional residues involved in Zn(2+) binding and subsequently to engineer artificial Zn(2+)-binding sites. Ten aspartic acids and glutamic acids, predicted...

  13. EPR studies of cooperative binding of Cu (II) to hemoglobin

    International Nuclear Information System (INIS)

    Louro, S.R.W.; Tabak, M.

    1983-07-01

    The investigation of the relative affinities of the two pairs of hemoglobin copper sites by monitoring the EPR spectra of the complexes formed by the reaction of copper with deoxyhemoglobin is reported. A model in which two sites are assumed to accept copper ions in a noncooperative way is not able to predict the experimental results. Thus it is conclude that the binding of these ions to hemoglobin is a cooperative phenomenon. (Author) [pt

  14. Discovery and validation of information theory-based transcription factor and cofactor binding site motifs.

    Science.gov (United States)

    Lu, Ruipeng; Mucaki, Eliseos J; Rogan, Peter K

    2017-03-17

    Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, discovered 23 cofactor motifs for 127 TFs and revealed six high-confidence novel motifs. The reliability and accuracy of these iPWMs were determined via four independent validation methods, including the detection of experimentally proven binding sites, explanation of effects of characterized SNPs, comparison with previously published motifs and statistical analyses. We also predict previously unreported TF coregulatory interactions (e.g. TF complexes). These iPWMs constitute a powerful tool for predicting the effects of sequence variants in known binding sites, performing mutation analysis on regulatory SNPs and predicting previously unrecognized binding sites and target genes. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. Fragment-based quantum mechanical calculation of protein-protein binding affinities.

    Science.gov (United States)

    Wang, Yaqian; Liu, Jinfeng; Li, Jinjin; He, Xiao

    2018-04-29

    The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method has been successfully utilized for efficient linear-scaling quantum mechanical (QM) calculation of protein energies. In this work, we applied the EE-GMFCC method for calculation of binding affinity of Endonuclease colicin-immunity protein complex. The binding free energy changes between the wild-type and mutants of the complex calculated by EE-GMFCC are in good agreement with experimental results. The correlation coefficient (R) between the predicted binding energy changes and experimental values is 0.906 at the B3LYP/6-31G*-D level, based on the snapshot whose binding affinity is closest to the average result from the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. The inclusion of the QM effects is important for accurate prediction of protein-protein binding affinities. Moreover, the self-consistent calculation of PB solvation energy is required for accurate calculations of protein-protein binding free energies. This study demonstrates that the EE-GMFCC method is capable of providing reliable prediction of relative binding affinities for protein-protein complexes. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

  16. Context influences on TALE-DNA binding revealed by quantitative profiling.

    Science.gov (United States)

    Rogers, Julia M; Barrera, Luis A; Reyon, Deepak; Sander, Jeffry D; Kellis, Manolis; Joung, J Keith; Bulyk, Martha L

    2015-06-11

    Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE-DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000-20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE-DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design.

  17. Context influences on TALE–DNA binding revealed by quantitative profiling

    Science.gov (United States)

    Rogers, Julia M.; Barrera, Luis A.; Reyon, Deepak; Sander, Jeffry D.; Kellis, Manolis; Joung, J Keith; Bulyk, Martha L.

    2015-01-01

    Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE–DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000–20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE–DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design. PMID:26067805

  18. Informing the Human Plasma Protein Binding of ...

    Science.gov (United States)

    The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18 Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0

  19. The Role of Genome Accessibility in Transcription Factor Binding in Bacteria.

    Directory of Open Access Journals (Sweden)

    Antonio L C Gomes

    2016-04-01

    Full Text Available ChIP-seq enables genome-scale identification of regulatory regions that govern gene expression. However, the biological insights generated from ChIP-seq analysis have been limited to predictions of binding sites and cooperative interactions. Furthermore, ChIP-seq data often poorly correlate with in vitro measurements or predicted motifs, highlighting that binding affinity alone is insufficient to explain transcription factor (TF-binding in vivo. One possibility is that binding sites are not equally accessible across the genome. A more comprehensive biophysical representation of TF-binding is required to improve our ability to understand, predict, and alter gene expression. Here, we show that genome accessibility is a key parameter that impacts TF-binding in bacteria. We developed a thermodynamic model that parameterizes ChIP-seq coverage in terms of genome accessibility and binding affinity. The role of genome accessibility is validated using a large-scale ChIP-seq dataset of the M. tuberculosis regulatory network. We find that accounting for genome accessibility led to a model that explains 63% of the ChIP-seq profile variance, while a model based in motif score alone explains only 35% of the variance. Moreover, our framework enables de novo ChIP-seq peak prediction and is useful for inferring TF-binding peaks in new experimental conditions by reducing the need for additional experiments. We observe that the genome is more accessible in intergenic regions, and that increased accessibility is positively correlated with gene expression and anti-correlated with distance to the origin of replication. Our biophysically motivated model provides a more comprehensive description of TF-binding in vivo from first principles towards a better representation of gene regulation in silico, with promising applications in systems biology.

  20. In silico docking of forchlorfenuron (FCF to septins suggests that FCF interferes with GTP binding.

    Directory of Open Access Journals (Sweden)

    Dimitrios Angelis

    Full Text Available Septins are GTP-binding proteins that form cytoskeleton-like filaments, which are essential for many functions in eukaryotic organisms. Small molecule compounds that disrupt septin filament assembly are valuable tools for dissecting septin functions with high temporal control. To date, forchlorfenuron (FCF is the only compound known to affect septin assembly and functions. FCF dampens the dynamics of septin assembly inducing the formation of enlarged stable polymers, but the underlying mechanism of action is unknown. To investigate how FCF binds and affects septins, we performed in silico simulations of FCF docking to all available crystal structures of septins. Docking of FCF with SEPT2 and SEPT3 indicated that FCF interacts preferentially with the nucleotide-binding pockets of septins. Strikingly, FCF is predicted to form hydrogen bonds with residues involved in GDP-binding, mimicking nucleotide binding. FCF docking with the structure of SEPT2-GppNHp, a nonhydrolyzable GTP analog, and SEPT7 showed that FCF may assume two alternative non-overlapping conformations deeply into and on the outer side of the nucleotide-binding pocket. Surprisingly, FCF was predicted to interact with the P-loop Walker A motif GxxxxGKS/T, which binds the phosphates of GTP, and the GTP specificity motif AKAD, which interacts with the guanine base of GTP, and highly conserved amino acids including a threonine, which is critical for GTP hydrolysis. Thus, in silico FCF exhibits a conserved mechanism of binding, interacting with septin signature motifs and residues involved in GTP binding and hydrolysis. Taken together, our results suggest that FCF stabilizes septins by locking them into a conformation that mimics a nucleotide-bound state, preventing further GTP binding and hydrolysis. Overall, this study provides the first insight into how FCF may bind and stabilize septins, and offers a blueprint for the rational design of FCF derivatives that could target septins with

  1. Probing protein phosphatase substrate binding

    DEFF Research Database (Denmark)

    Højlys-Larsen, Kim B.; Sørensen, Kasper Kildegaard; Jensen, Knud Jørgen

    2012-01-01

    Proteomics and high throughput analysis for systems biology can benefit significantly from solid-phase chemical tools for affinity pull-down of proteins from complex mixtures. Here we report the application of solid-phase synthesis of phosphopeptides for pull-down and analysis of the affinity...... profile of the integrin-linked kinase associated phosphatase (ILKAP), a member of the protein phosphatase 2C (PP2C) family. Phosphatases can potentially dephosphorylate these phosphopeptide substrates but, interestingly, performing the binding studies at 4 °C allowed efficient binding to phosphopeptides......, without the need for phosphopeptide mimics or phosphatase inhibitors. As no proven ILKAP substrates were available, we selected phosphopeptide substrates among known PP2Cδ substrates including the protein kinases: p38, ATM, Chk1, Chk2 and RSK2 and synthesized directly on PEGA solid supports through a BAL...

  2. Human plasminogen binding protein tetranectin

    DEFF Research Database (Denmark)

    Kastrup, J S; Rasmussen, H; Nielsen, B B

    1997-01-01

    The recombinant human plasminogen binding protein tetranectin (TN) and the C-type lectin CRD of this protein (TN3) have been crystallized. TN3 crystallizes in the tetragonal space group P4(2)2(1)2 with cell dimensions a = b = 64.0, c = 75.7 A and with one molecule per asymmetric unit. The crystals...... to at least 2.5 A. A full data set has been collected to 3.0 A. The asymmetric unit contains one monomer of TN. Molecular replacement solutions for TN3 and TN have been obtained using the structure of the C-type lectin CRD of rat mannose-binding protein as search model. The rhombohedral space group indicates...

  3. Ligand binding by PDZ domains

    DEFF Research Database (Denmark)

    Chi, Celestine N.; Bach, Anders; Strømgaard, Kristian

    2012-01-01

    , for example, are particularly rich in these domains. The general function of PDZ domains is to bring proteins together within the appropriate cellular compartment, thereby facilitating scaffolding, signaling, and trafficking events. The many functions of PDZ domains under normal physiological as well...... as pathological conditions have been reviewed recently. In this review, we focus on the molecular details of how PDZ domains bind their protein ligands and their potential as drug targets in this context....

  4. Calcium binding by dietary fibre

    International Nuclear Information System (INIS)

    James, W.P.T.; Branch, W.J.; Southgate, D.A.T.

    1978-01-01

    Dietary fibre from plants low in phytate bound calcium in proportion to its uronic-acid content. This binding by the non-cellulosic fraction of fibre reduces the availability of calcium for small-intestinal absorption, but the colonic microbial digestion of uronic acids liberates the calcium. Thus the ability to maintain calcium balance on high-fibre diets may depend on the adaptive capacity on the colon for calcium. (author)

  5. Material Binding Peptides for Nanotechnology

    Directory of Open Access Journals (Sweden)

    Urartu Ozgur Safak Seker

    2011-02-01

    Full Text Available Remarkable progress has been made to date in the discovery of material binding peptides and their utilization in nanotechnology, which has brought new challenges and opportunities. Nowadays phage display is a versatile tool, important for the selection of ligands for proteins and peptides. This combinatorial approach has also been adapted over the past decade to select material-specific peptides. Screening and selection of such phage displayed material binding peptides has attracted great interest, in particular because of their use in nanotechnology. Phage display selected peptides are either synthesized independently or expressed on phage coat protein. Selected phage particles are subsequently utilized in the synthesis of nanoparticles, in the assembly of nanostructures on inorganic surfaces, and oriented protein immobilization as fusion partners of proteins. In this paper, we present an overview on the research conducted on this area. In this review we not only focus on the selection process, but also on molecular binding characterization and utilization of peptides as molecular linkers, molecular assemblers and material synthesizers.

  6. Anion binding in biological systems

    Energy Technology Data Exchange (ETDEWEB)

    Feiters, Martin C [Department of Organic Chemistry, Institute for Molecules and Materials, Faculty of Science, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen (Netherlands); Meyer-Klaucke, Wolfram [EMBL Hamburg Outstation at DESY, Notkestrasse 85, D-22607 Hamburg (Germany); Kostenko, Alexander V; Soldatov, Alexander V [Faculty of Physics, Southern Federal University, Sorge 5, Rostov-na-Donu, 344090 (Russian Federation); Leblanc, Catherine; Michel, Gurvan; Potin, Philippe [Centre National de la Recherche Scientifique and Universite Pierre et Marie Curie Paris-VI, Station Biologique de Roscoff, Place Georges Teissier, BP 74, F-29682 Roscoff cedex, Bretagne (France); Kuepper, Frithjof C [Scottish Association for Marine Science, Dunstaffnage Marine Laboratory, Oban, Argyll PA37 1QA, Scotland (United Kingdom); Hollenstein, Kaspar; Locher, Kaspar P [Institute of Molecular Biology and Biophysics, ETH Zuerich, Schafmattstrasse 20, Zuerich, 8093 (Switzerland); Bevers, Loes E; Hagedoorn, Peter-Leon; Hagen, Wilfred R, E-mail: m.feiters@science.ru.n [Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft (Netherlands)

    2009-11-15

    We compare aspects of biological X-ray absorption spectroscopy (XAS) studies of cations and anions, and report on some examples of anion binding in biological systems. Brown algae such as Laminaria digitata (oarweed) are effective accumulators of I from seawater, with tissue concentrations exceeding 50 mM, and the vanadate-containing enzyme haloperoxidase is implicated in halide accumulation. We have studied the chemical state of iodine and its biological role in Laminaria at the I K edge, and bromoperoxidase from Ascophyllum nodosum (knotted wrack) at the Br K edge. Mo is essential for many forms of life; W only for certain archaea, such as Archaeoglobus fulgidus and the hyperthermophilic archaeon Pyrococcus furiosus, and some bacteria. The metals are bound and transported as their oxo-anions, molybdate and tungstate, which are similar in size. The transport protein WtpA from P. furiosus binds tungstate more strongly than molybdate, and is related in sequence to Archaeoglobus fulgidus ModA, of which a crystal structure is known. We have measured A. fulgidus ModA with tungstate at the W L{sub 3} (2p{sub 3/2}) edge, and compared the results with the refined crystal structure. XAS studies of anion binding are feasible even if only weak interactions are present, are biologically relevant, and give new insights in the spectroscopy.

  7. Anion binding in biological systems

    International Nuclear Information System (INIS)

    Feiters, Martin C; Meyer-Klaucke, Wolfram; Kostenko, Alexander V; Soldatov, Alexander V; Leblanc, Catherine; Michel, Gurvan; Potin, Philippe; Kuepper, Frithjof C; Hollenstein, Kaspar; Locher, Kaspar P; Bevers, Loes E; Hagedoorn, Peter-Leon; Hagen, Wilfred R

    2009-01-01

    We compare aspects of biological X-ray absorption spectroscopy (XAS) studies of cations and anions, and report on some examples of anion binding in biological systems. Brown algae such as Laminaria digitata (oarweed) are effective accumulators of I from seawater, with tissue concentrations exceeding 50 mM, and the vanadate-containing enzyme haloperoxidase is implicated in halide accumulation. We have studied the chemical state of iodine and its biological role in Laminaria at the I K edge, and bromoperoxidase from Ascophyllum nodosum (knotted wrack) at the Br K edge. Mo is essential for many forms of life; W only for certain archaea, such as Archaeoglobus fulgidus and the hyperthermophilic archaeon Pyrococcus furiosus, and some bacteria. The metals are bound and transported as their oxo-anions, molybdate and tungstate, which are similar in size. The transport protein WtpA from P. furiosus binds tungstate more strongly than molybdate, and is related in sequence to Archaeoglobus fulgidus ModA, of which a crystal structure is known. We have measured A. fulgidus ModA with tungstate at the W L 3 (2p 3/2 ) edge, and compared the results with the refined crystal structure. XAS studies of anion binding are feasible even if only weak interactions are present, are biologically relevant, and give new insights in the spectroscopy.

  8. Anion binding in biological systems

    Science.gov (United States)

    Feiters, Martin C.; Meyer-Klaucke, Wolfram; Kostenko, Alexander V.; Soldatov, Alexander V.; Leblanc, Catherine; Michel, Gurvan; Potin, Philippe; Küpper, Frithjof C.; Hollenstein, Kaspar; Locher, Kaspar P.; Bevers, Loes E.; Hagedoorn, Peter-Leon; Hagen, Wilfred R.

    2009-11-01

    We compare aspects of biological X-ray absorption spectroscopy (XAS) studies of cations and anions, and report on some examples of anion binding in biological systems. Brown algae such as Laminaria digitata (oarweed) are effective accumulators of I from seawater, with tissue concentrations exceeding 50 mM, and the vanadate-containing enzyme haloperoxidase is implicated in halide accumulation. We have studied the chemical state of iodine and its biological role in Laminaria at the I K edge, and bromoperoxidase from Ascophyllum nodosum (knotted wrack) at the Br K edge. Mo is essential for many forms of life; W only for certain archaea, such as Archaeoglobus fulgidus and the hyperthermophilic archaeon Pyrococcus furiosus, and some bacteria. The metals are bound and transported as their oxo-anions, molybdate and tungstate, which are similar in size. The transport protein WtpA from P. furiosus binds tungstate more strongly than molybdate, and is related in sequence to Archaeoglobus fulgidus ModA, of which a crystal structure is known. We have measured A. fulgidus ModA with tungstate at the W L3 (2p3/2) edge, and compared the results with the refined crystal structure. XAS studies of anion binding are feasible even if only weak interactions are present, are biologically relevant, and give new insights in the spectroscopy.

  9. Ligand deconstruction: Why some fragment binding positions are conserved and others are not

    Science.gov (United States)

    Kozakov, Dima; Hall, David R.; Jehle, Stefan; Luo, Lingqi; Ochiana, Stefan O.; Jones, Elizabeth V.; Pollastri, Michael; Allen, Karen N.; Whitty, Adrian; Vajda, Sandor

    2015-01-01

    Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots—regions of the protein where interactions with a ligand contribute substantial binding free energy—the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand. PMID:25918377

  10. Discovery and information-theoretic characterization of transcription factor binding sites that act cooperatively.

    Science.gov (United States)

    Clifford, Jacob; Adami, Christoph

    2015-09-02

    Transcription factor binding to the surface of DNA regulatory regions is one of the primary causes of regulating gene expression levels. A probabilistic approach to model protein-DNA interactions at the sequence level is through position weight matrices (PWMs) that estimate the joint probability of a DNA binding site sequence by assuming positional independence within the DNA sequence. Here we construct conditional PWMs that depend on the motif signatures in the flanking DNA sequence, by conditioning known binding site loci on the presence or absence of additional binding sites in the flanking sequence of each site's locus. Pooling known sites with similar flanking sequence patterns allows for the estimation of the conditional distribution function over the binding site sequences. We apply our model to the Dorsal transcription factor binding sites active in patterning the Dorsal-Ventral axis of Drosophila development. We find that those binding sites that cooperate with nearby Twist sites on average contain about 0.5 bits of information about the presence of Twist transcription factor binding sites in the flanking sequence. We also find that Dorsal binding site detectors conditioned on flanking sequence information make better predictions about what is a Dorsal site relative to background DNA than detection without information about flanking sequence features.

  11. Object-based attention underlies the rehearsal of feature binding in visual working memory.

    Science.gov (United States)

    Shen, Mowei; Huang, Xiang; Gao, Zaifeng

    2015-04-01

    Feature binding is a core concept in many research fields, including the study of working memory (WM). Over the past decade, it has been debated whether keeping the feature binding in visual WM consumes more visual attention than the constituent single features. Previous studies have only explored the contribution of domain-general attention or space-based attention in the binding process; no study so far has explored the role of object-based attention in retaining binding in visual WM. We hypothesized that object-based attention underlay the mechanism of rehearsing feature binding in visual WM. Therefore, during the maintenance phase of a visual WM task, we inserted a secondary mental rotation (Experiments 1-3), transparent motion (Experiment 4), or an object-based feature report task (Experiment 5) to consume the object-based attention available for binding. In line with the prediction of the object-based attention hypothesis, Experiments 1-5 revealed a more significant impairment for binding than for constituent single features. However, this selective binding impairment was not observed when inserting a space-based visual search task (Experiment 6). We conclude that object-based attention underlies the rehearsal of binding representation in visual WM. (c) 2015 APA, all rights reserved.

  12. Ligand deconstruction: Why some fragment binding positions are conserved and others are not.

    Science.gov (United States)

    Kozakov, Dima; Hall, David R; Jehle, Stefan; Jehle, Sefan; Luo, Lingqi; Ochiana, Stefan O; Jones, Elizabeth V; Pollastri, Michael; Allen, Karen N; Whitty, Adrian; Vajda, Sandor

    2015-05-19

    Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots--regions of the protein where interactions with a ligand contribute substantial binding free energy--the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand.

  13. [3H]cytisine binding to nicotinic cholinergic receptors in brain

    International Nuclear Information System (INIS)

    Pabreza, L.A.; Dhawan, S.; Kellar, K.J.

    1991-01-01

    Cytisine, a ganglionic agonist, competes with high affinity for brain nicotinic cholinergic receptors labeled by any of several nicotinic 3 H-agonist ligands. Here we have examined the binding of [ 3 H]cytisine in rat brain homogenates. [ 3 H]Cytisine binds with high affinity (Kd less than 1 nM), and specific binding represented 60-90% of total binding at all concentrations examined up to 15 nM. The nicotinic cholinergic agonists nicotine, acetylcholine, and carbachol compete with high affinity for [ 3 H]cytisine binding sites, whereas among nicotinic receptor antagonists only dihydro-beta-erythroidine competes with high affinity (in the nanomolar range). Comparison of binding in several brain regions showed that [ 3 H]cytisine binding is higher in the thalamus, striatum, and cortex than in the hippocampus, cerebellum, or hypothalamus. The pharmacology and brain regional distribution of [ 3 H]cytisine binding sites are those predicted for neuronal nicotinic receptor agonist recognition sites. The high affinity and low nonspecific binding of [ 3 H]cytisine should make it a very useful ligand for studying neuronal nicotinic receptors

  14. Earthquake prediction

    International Nuclear Information System (INIS)

    Ward, P.L.

    1978-01-01

    The state of the art of earthquake prediction is summarized, the possible responses to such prediction are examined, and some needs in the present prediction program and in research related to use of this new technology are reviewed. Three basic aspects of earthquake prediction are discussed: location of the areas where large earthquakes are most likely to occur, observation within these areas of measurable changes (earthquake precursors) and determination of the area and time over which the earthquake will occur, and development of models of the earthquake source in order to interpret the precursors reliably. 6 figures

  15. Polymeric competitive protein binding adsorbents for radioassay

    International Nuclear Information System (INIS)

    Adams, R.J.

    1976-01-01

    Serum protein comprising specific binding proteins such as antibodies, B 12 intrinsic factor, thyroxin binding globulin and the like may be copolymerized with globulin constituents of serum by the action of ethylchloroformate to form readily packed insoluble precipitates which, following purification as by washing, are eminently suited for employment as competitive binding protein absorbents in radioassay procedures. 10 claims, no drawings

  16. Evolutionarily conserved bias of amino-acid usage refines the definition of PDZ-binding motif

    Directory of Open Access Journals (Sweden)

    Launey Thomas

    2011-06-01

    Full Text Available Abstract Background The interactions between PDZ (PSD-95, Dlg, ZO-1 domains and PDZ-binding motifs play central roles in signal transductions within cells. Proteins with PDZ domains bind to PDZ-binding motifs almost exclusively when the motifs are located at the carboxyl (C- terminal ends of their binding partners. However, it remains little explored whether PDZ-binding motifs show any preferential location at the C-terminal ends of proteins, at genome-level. Results Here, we examined the distribution of the type-I (x-x-S/T-x-I/L/V or type-II (x-x-V-x-I/V PDZ-binding motifs in proteins encoded in the genomes of five different species (human, mouse, zebrafish, fruit fly and nematode. We first established that these PDZ-binding motifs are indeed preferentially present at their C-terminal ends. Moreover, we found specific amino acid (AA bias for the 'x' positions in the motifs at the C-terminal ends. In general, hydrophilic AAs were favored. Our genomics-based findings confirm and largely extend the results of previous interaction-based studies, allowing us to propose refined consensus sequences for all of the examined PDZ-binding motifs. An ontological analysis revealed that the refined motifs are functionally relevant since a large fraction of the proteins bearing the motif appear to be involved in signal transduction. Furthermore, co-precipitation experiments confirmed two new protein interactions predicted by our genomics-based approach. Finally, we show that influenza virus pathogenicity can be correlated with PDZ-binding motif, with high-virulence viral proteins bearing a refined PDZ-binding motif. Conclusions Our refined definition of PDZ-binding motifs should provide important clues for identifying functional PDZ-binding motifs and proteins involved in signal transduction.

  17. Loss of selenium-binding protein 1 decreases sensitivity to clastogens and intracellular selenium content in HeLa cells

    Science.gov (United States)

    Selenium-binding protein 1 (SBP1) is not a selenoprotein but structurally binds selenium. Loss of SBP1 during carcinogenesis usually predicts poor prognosis. Because genome instability is a hallmark of cancer, we hypothesized that loss of SBP1 modulates cellular selenium content and the response of ...

  18. An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency.

    Science.gov (United States)

    Guo, Wei-Li; Huang, De-Shuang

    2017-08-22

    Transcription factors (TFs) are DNA-binding proteins that have a central role in regulating gene expression. Identification of DNA-binding sites of TFs is a key task in understanding transcriptional regulation, cellular processes and disease. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) enables genome-wide identification of in vivo TF binding sites. However, it is still difficult to map every TF in every cell line owing to cost and biological material availability, which poses an enormous obstacle for integrated analysis of gene regulation. To address this problem, we propose a novel computational approach, TFBSImpute, for predicting additional TF binding profiles by leveraging information from available ChIP-seq TF binding data. TFBSImpute fuses the dataset to a 3-mode tensor and imputes missing TF binding signals via simultaneous completion of multiple TF binding matrices with positional consistency. We show that signals predicted by our method achieve overall similarity with experimental data and that TFBSImpute significantly outperforms baseline approaches, by assessing the performance of imputation methods against observed ChIP-seq TF binding profiles. Besides, motif analysis shows that TFBSImpute preforms better in capturing binding motifs enriched in observed data compared with baselines, indicating that the higher performance of TFBSImpute is not simply due to averaging related samples. We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease.

  19. Predictive medicine

    NARCIS (Netherlands)

    Boenink, Marianne; ten Have, Henk

    2015-01-01

    In the last part of the twentieth century, predictive medicine has gained currency as an important ideal in biomedical research and health care. Research in the genetic and molecular basis of disease suggested that the insights gained might be used to develop tests that predict the future health

  20. Molecular cloning, expression analysis and sequence prediction of ...

    African Journals Online (AJOL)

    ajl yemi

    2011-11-28

    Nov 28, 2011 ... prediction of CCAAT/enhancer-binding protein beta ... CCAAT/enhancer-binding protein beta (C/EBPβ), as an essential transcriptional factor, regulates the ... acid area from 274 to 337 was found, concurring with the main ...

  1. Mathematical description of drug-target interactions: application to biologics that bind to targets with two binding sites.

    Science.gov (United States)

    Gibiansky, Leonid; Gibiansky, Ekaterina

    2018-02-01

    The emerging discipline of mathematical pharmacology occupies the space between advanced pharmacometrics and systems biology. A characteristic feature of the approach is application of advance mathematical methods to study the behavior of biological systems as described by mathematical (most often differential) equations. One of the early application of mathematical pharmacology (that was not called this name at the time) was formulation and investigation of the target-mediated drug disposition (TMDD) model and its approximations. The model was shown to be remarkably successful, not only in describing the observed data for drug-target interactions, but also in advancing the qualitative and quantitative understanding of those interactions and their role in pharmacokinetic and pharmacodynamic properties of biologics. The TMDD model in its original formulation describes the interaction of the drug that has one binding site with the target that also has only one binding site. Following the framework developed earlier for drugs with one-to-one binding, this work aims to describe a rigorous approach for working with similar systems and to apply it to drugs that bind to targets with two binding sites. The quasi-steady-state, quasi-equilibrium, irreversible binding, and Michaelis-Menten approximations of the model are also derived. These equations can be used, in particular, to predict concentrations of the partially bound target (RC). This could be clinically important if RC remains active and has slow internalization rate. In this case, introduction of the drug aimed to suppress target activity may lead to the opposite effect due to RC accumulation.

  2. Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

    KAUST Repository

    Wong, Ka-Chun; Peng, Chengbin; Li, Yue

    2016-01-01

    Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

  3. Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

    KAUST Repository

    Wong, Ka-Chun

    2016-02-02

    Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

  4. Binding-site assessment by virtual fragment screening.

    Directory of Open Access Journals (Sweden)

    Niu Huang

    2010-04-01

    Full Text Available The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock approximately 11,000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors.

  5. Cavity Versus Ligand Shape Descriptors: Application to Urokinase Binding Pockets.

    Science.gov (United States)

    Cerisier, Natacha; Regad, Leslie; Triki, Dhoha; Camproux, Anne-Claude; Petitjean, Michel

    2017-11-01

    We analyzed 78 binding pockets of the human urokinase plasminogen activator (uPA) catalytic domain extracted from a data set of crystallized uPA-ligand complexes. These binding pockets were computed with an original geometric method that does NOT involve any arbitrary parameter, such as cutoff distances, angles, and so on. We measured the deviation from convexity of each pocket shape with the pocket convexity index (PCI). We defined a new pocket descriptor called distributional sphericity coefficient (DISC), which indicates to which extent the protein atoms of a given pocket lie on the surface of a sphere. The DISC values were computed with the freeware PCI. The pocket descriptors and their high correspondences with ligand descriptors are crucial for polypharmacology prediction. We found that the protein heavy atoms lining the urokinases binding pockets are either located on the surface of their convex hull or lie close to this surface. We also found that the radii of the urokinases binding pockets and the radii of their ligands are highly correlated (r = 0.9).

  6. Structure of Drosophila Oskar reveals a novel RNA binding protein

    Science.gov (United States)

    Yang, Na; Yu, Zhenyu; Hu, Menglong; Wang, Mingzhu; Lehmann, Ruth; Xu, Rui-Ming

    2015-01-01

    Oskar (Osk) protein plays critical roles during Drosophila germ cell development, yet its functions in germ-line formation and body patterning remain poorly understood. This situation contrasts sharply with the vast knowledge about the function and mechanism of osk mRNA localization. Osk is predicted to have an N-terminal LOTUS domain (Osk-N), which has been suggested to bind RNA, and a C-terminal hydrolase-like domain (Osk-C) of unknown function. Here, we report the crystal structures of Osk-N and Osk-C. Osk-N shows a homodimer of winged-helix–fold modules, but without detectable RNA-binding activity. Osk-C has a lipase-fold structure but lacks critical catalytic residues at the putative active site. Surprisingly, we found that Osk-C binds the 3′UTRs of osk and nanos mRNA in vitro. Mutational studies identified a region of Osk-C important for mRNA binding. These results suggest possible functions of Osk in the regulation of stability, regulation of translation, and localization of relevant mRNAs through direct interaction with their 3′UTRs, and provide structural insights into a novel protein–RNA interaction motif involving a hydrolase-related domain. PMID:26324911

  7. Position specific variation in the rate of evolution intranscription factor binding sites

    Energy Technology Data Exchange (ETDEWEB)

    Moses, Alan M.; Chiang, Derek Y.; Kellis, Manolis; Lander, EricS.; Eisen, Michael B.

    2003-08-28

    The binding sites of sequence specific transcription factors are an important and relatively well-understood class of functional non-coding DNAs. Although a wide variety of experimental and computational methods have been developed to characterize transcription factor binding sites, they remain difficult to identify. Comparison of non-coding DNA from related species has shown considerable promise in identifying these functional non-coding sequences, even though relatively little is known about their evolution. Here we analyze the genome sequences of the budding yeasts Saccharomyces cerevisiae, S. bayanus, S. paradoxus and S. mikataeto study the evolution of transcription factor binding sites. As expected, we find that both experimentally characterized and computationally predicted binding sites evolve slower than surrounding sequence, consistent with the hypothesis that they are under purifying selection. We also observe position-specific variation in the rate of evolution within binding sites. We find that the position-specific rate of evolution is positively correlated with degeneracy among binding sites within S. cerevisiae. We test theoretical predictions for the rate of evolution at positions where the base frequencies deviate from background due to purifying selection and find reasonable agreement with the observed rates of evolution. Finally, we show how the evolutionary characteristics of real binding motifs can be used to distinguish them from artifacts of computational motif finding algorithms. As has been observed for protein sequences, the rate of evolution in transcription factor binding sites varies with position, suggesting that some regions are under stronger functional constraint than others. This variation likely reflects the varying importance of different positions in the formation of the protein-DNA complex. The characterization of the pattern of evolution in known binding sites will likely contribute to the effective use of comparative

  8. Bioinformatic analysis of dihydrofolate reductase predicted in the ...

    African Journals Online (AJOL)

    olayemitoyin

    TASSER was used to predict the 3-D model structure of the protein and potential active binding site residues. ..... co-ordinate file model in PDB format was visualized with Jmol molecular .... evolutionary preservation of the protein dynamics to.

  9. GenProBiS: web server for mapping of sequence variants to protein binding sites.

    Science.gov (United States)

    Konc, Janez; Skrlj, Blaz; Erzen, Nika; Kunej, Tanja; Janezic, Dusanka

    2017-07-03

    Discovery of potentially deleterious sequence variants is important and has wide implications for research and generation of new hypotheses in human and veterinary medicine, and drug discovery. The GenProBiS web server maps sequence variants to protein structures from the Protein Data Bank (PDB), and further to protein-protein, protein-nucleic acid, protein-compound, and protein-metal ion binding sites. The concept of a protein-compound binding site is understood in the broadest sense, which includes glycosylation and other post-translational modification sites. Binding sites were defined by local structural comparisons of whole protein structures using the Protein Binding Sites (ProBiS) algorithm and transposition of ligands from the similar binding sites found to the query protein using the ProBiS-ligands approach with new improvements introduced in GenProBiS. Binding site surfaces were generated as three-dimensional grids encompassing the space occupied by predicted ligands. The server allows intuitive visual exploration of comprehensively mapped variants, such as human somatic mis-sense mutations related to cancer and non-synonymous single nucleotide polymorphisms from 21 species, within the predicted binding sites regions for about 80 000 PDB protein structures using fast WebGL graphics. The GenProBiS web server is open and free to all users at http://genprobis.insilab.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Acid-base and copper-binding properties of three organic matter fractions isolated from a forest floor soil solution

    Science.gov (United States)

    van Schaik, Joris W. J.; Kleja, Dan B.; Gustafsson, Jon Petter

    2010-02-01

    Vast amounts of knowledge about the proton- and metal-binding properties of dissolved organic matter (DOM) in natural waters have been obtained in studies on isolated humic and fulvic (hydrophobic) acids. Although macromolecular hydrophilic acids normally make up about one-third of DOM, their proton- and metal-binding properties are poorly known. Here, we investigated the acid-base and Cu-binding properties of the hydrophobic (fulvic) acid fraction and two hydrophilic fractions isolated from a soil solution. Proton titrations revealed a higher total charge for the hydrophilic acid fractions than for the hydrophobic acid fraction. The most hydrophilic fraction appeared to be dominated by weak acid sites, as evidenced by increased slope of the curve of surface charge versus pH at pH values above 6. The titration curves were poorly predicted by both Stockholm Humic Model (SHM) and NICA-Donnan model calculations using generic parameter values, but could be modelled accurately after optimisation of the proton-binding parameters (pH ⩽ 9). Cu-binding isotherms for the three fractions were determined at pH values of 4, 6 and 9. With the optimised proton-binding parameters, the SHM model predictions for Cu binding improved, whereas the NICA-Donnan predictions deteriorated. After optimisation of Cu-binding parameters, both models described the experimental data satisfactorily. Iron(III) and aluminium competed strongly with Cu for binding sites at both pH 4 and pH 6. The SHM model predicted this competition reasonably well, but the NICA-Donnan model underestimated the effects significantly at pH 6. Overall, the Cu-binding behaviour of the two hydrophilic acid fractions was very similar to that of the hydrophobic acid fraction, despite the differences observed in proton-binding characteristics. These results show that for modelling purposes, it is essential to include the hydrophilic acid fraction in the pool of 'active' humic substances.

  11. A computational method for identification of vaccine targets from protein regions of conserved human leukocyte antigen binding

    DEFF Research Database (Denmark)

    Olsen, Lars Rønn; Simon, Christian; Kudahl, Ulrich J.

    2015-01-01

    Background: Computational methods for T cell-based vaccine target discovery focus on selection of highly conserved peptides identified across pathogen variants, followed by prediction of their binding of human leukocyte antigen molecules. However, experimental studies have shown that T cells ofte...... or proteome using human leukocyte antigen binding predictions and made a web-accessible software implementation freely available at http://met-hilab.cbs.dtu.dk/blockcons/....

  12. Insulin binding to individual rat skeletal muscles

    International Nuclear Information System (INIS)

    Koerker, D.J.; Sweet, I.R.; Baskin, D.G.

    1990-01-01

    Studies of insulin binding to skeletal muscle, performed using sarcolemmal membrane preparations or whole muscle incubations of mixed muscle or typical red (soleus, psoas) or white [extensor digitorum longus (EDL), gastrocnemius] muscle, have suggested that red muscle binds more insulin than white muscle. We have evaluated this hypothesis using cryostat sections of unfixed tissue to measure insulin binding in a broad range of skeletal muscles; many were of similar fiber-type profiles. Insulin binding per square millimeter of skeletal muscle slice was measured by autoradiography and computer-assisted densitometry. We found a 4.5-fold range in specific insulin tracer binding, with heart and predominantly slow-twitch oxidative muscles (SO) at the high end and the predominantly fast-twitch glycolytic (FG) muscles at the low end of the range. This pattern reflects insulin sensitivity. Evaluation of displacement curves for insulin binding yielded linear Scatchard plots. The dissociation constants varied over a ninefold range (0.26-2.06 nM). Binding capacity varied from 12.2 to 82.7 fmol/mm2. Neither binding parameter was correlated with fiber type or insulin sensitivity; e.g., among three muscles of similar fiber-type profile, the EDL had high numbers of low-affinity binding sites, whereas the quadriceps had low numbers of high-affinity sites. In summary, considerable heterogeneity in insulin binding was found among hindlimb muscles of the rat, which can be attributed to heterogeneity in binding affinities and the numbers of binding sites. It can be concluded that a given fiber type is not uniquely associated with a set of insulin binding parameters that result in high or low binding

  13. Binding of matrix metalloproteinase inhibitors to extracellular matrix: 3D-QSAR analysis.

    Science.gov (United States)

    Zhang, Yufen; Lukacova, Viera; Bartus, Vladimir; Nie, Xiaoping; Sun, Guorong; Manivannan, Ethirajan; Ghorpade, Sandeep R; Jin, Xiaomin; Manyem, Shankar; Sibi, Mukund P; Cook, Gregory R; Balaz, Stefan

    2008-10-01

    Binding to the extracellular matrix, one of the most abundant human protein complexes, significantly affects drug disposition. Specifically, the interactions with extracellular matrix determine the free concentrations of small molecules acting in tissues, including signaling peptides, inhibitors of tissue remodeling enzymes such as matrix metalloproteinases, and other drug candidates. The nature of extracellular matrix binding was elucidated for 63 matrix metalloproteinase inhibitors, for which the association constants to an extracellular matrix mimic were reported here. The data did not correlate with lipophilicity as a common determinant of structure-nonspecific, orientation-averaged binding. A hypothetical structure of the binding site of the solidified extracellular matrix surrogate was analyzed using the Comparative Molecular Field Analysis, which needed to be applied in our multi-mode variant. This fact indicates that the compounds bind to extracellular matrix in multiple modes, which cannot be considered as completely orientation-averaged and exhibit structural dependence. The novel comparative molecular field analysis models, exhibiting satisfactory descriptive and predictive abilities, are suitable for prediction of the extracellular matrix binding for the untested chemicals, which are within applicability domains. The results contribute to a better prediction of the pharmacokinetic parameters such as the distribution volume and the tissue-blood partition coefficients, in addition to a more imminent benefit for the development of more effective matrix metalloproteinase inhibitors.

  14. Importance of Accurate Charges in Binding Affinity Calculations: A Case of Neuraminidase Series

    Energy Technology Data Exchange (ETDEWEB)

    Park, Kichul; Kyun, Nack Sung; Cho, Art E. [Korea Univ., Sejong (Korea, Republic of)

    2013-02-15

    It has been shown that calculating atomic charges using quantum mechanical level theory greatly improves the accuracy of docking. A protocol was developed and shown to be effective. That this protocol works is just a manifestation of the fact that electrostatic interactions are important in protein-ligand binding. In order to investigate how the same protocol helps in prediction of binding affinities, we took a series of known cocrystal structures of influenza neuraminidase inhibitors with the receptor and performed docking with Glide SP, Glide XP, and QPLD, the last being a workflow that incorporates QM/MM calculations to replace the fixed atomic charges of force fields with quantum mechanically recalculated ones at a given docking pose, and predicted the binding affinities of each cocrystal. The correlation with experimental binding affinities considerably improved with QPLD compared to Glide SP/XP yielding r{sup 2} = 0.83. The results suggest that for binding sites, such as that of neuraminidase, which are laden with hydrophilic residues, protocols such as QPLD which utilizes QM-based atomic charges can better predict the binding affinities.

  15. Structural motif screening reveals a novel, conserved carbohydrate-binding surface in the pathogenesis-related protein PR-5d

    Directory of Open Access Journals (Sweden)

    Moffatt Barbara A

    2010-08-01

    Full Text Available Abstract Background Aromatic amino acids play a critical role in protein-glycan interactions. Clusters of surface aromatic residues and their features may therefore be useful in distinguishing glycan-binding sites as well as predicting novel glycan-binding proteins. In this work, a structural bioinformatics approach was used to screen the Protein Data Bank (PDB for coplanar aromatic motifs similar to those found in known glycan-binding proteins. Results The proteins identified in the screen were significantly associated with carbohydrate-related functions according to gene ontology (GO enrichment analysis, and predicted motifs were found frequently within novel folds and glycan-binding sites not included in the training set. In addition to numerous binding sites predicted in structural genomics proteins of unknown function, one novel prediction was a surface motif (W34/W36/W192 in the tobacco pathogenesis-related protein, PR-5d. Phylogenetic analysis revealed that the surface motif is exclusive to a subfamily of PR-5 proteins from the Solanaceae family of plants, and is absent completely in more distant homologs. To confirm PR-5d's insoluble-polysaccharide binding activity, a cellulose-pulldown assay of tobacco proteins was performed and PR-5d was identified in the cellulose-binding fraction by mass spectrometry. Conclusions Based on the combined results, we propose that the putative binding site in PR-5d may be an evolutionary adaptation of Solanaceae plants including potato, tomato, and tobacco, towards defense against cellulose-containing pathogens such as species of the deadly oomycete genus, Phytophthora. More generally, the results demonstrate that coplanar aromatic clusters on protein surfaces are a structural signature of glycan-binding proteins, and can be used to computationally predict novel glycan-binding proteins from 3 D structure.

  16. Structural motif screening reveals a novel, conserved carbohydrate-binding surface in the pathogenesis-related protein PR-5d.

    Science.gov (United States)

    Doxey, Andrew C; Cheng, Zhenyu; Moffatt, Barbara A; McConkey, Brendan J

    2010-08-03

    Aromatic amino acids play a critical role in protein-glycan interactions. Clusters of surface aromatic residues and their features may therefore be useful in distinguishing glycan-binding sites as well as predicting novel glycan-binding proteins. In this work, a structural bioinformatics approach was used to screen the Protein Data Bank (PDB) for coplanar aromatic motifs similar to those found in known glycan-binding proteins. The proteins identified in the screen were significantly associated with carbohydrate-related functions according to gene ontology (GO) enrichment analysis, and predicted motifs were found frequently within novel folds and glycan-binding sites not included in the training set. In addition to numerous binding sites predicted in structural genomics proteins of unknown function, one novel prediction was a surface motif (W34/W36/W192) in the tobacco pathogenesis-related protein, PR-5d. Phylogenetic analysis revealed that the surface motif is exclusive to a subfamily of PR-5 proteins from the Solanaceae family of plants, and is absent completely in more distant homologs. To confirm PR-5d's insoluble-polysaccharide binding activity, a cellulose-pulldown assay of tobacco proteins was performed and PR-5d was identified in the cellulose-binding fraction by mass spectrometry. Based on the combined results, we propose that the putative binding site in PR-5d may be an evolutionary adaptation of Solanaceae plants including potato, tomato, and tobacco, towards defense against cellulose-containing pathogens such as species of the deadly oomycete genus, Phytophthora. More generally, the results demonstrate that coplanar aromatic clusters on protein surfaces are a structural signature of glycan-binding proteins, and can be used to computationally predict novel glycan-binding proteins from 3 D structure.

  17. A tool for calculating binding-site residues on proteins from PDB structures

    Directory of Open Access Journals (Sweden)

    Hu Jing

    2009-08-01

    Full Text Available Abstract Background In the research on protein functional sites, researchers often need to identify binding-site residues on a protein. A commonly used strategy is to find a complex structure from the Protein Data Bank (PDB that consists of the protein of interest and its interacting partner(s and calculate binding-site residues based on the complex structure. However, since a protein may participate in multiple interactions, the binding-site residues calculated based on one complex structure usually do not reveal all binding sites on a protein. Thus, this requires researchers to find all PDB complexes that contain the protein of interest and combine the binding-site information gleaned from them. This process is very time-consuming. Especially, combing binding-site information obtained from different PDB structures requires tedious work to align protein sequences. The process becomes overwhelmingly difficult when researchers have a large set of proteins to analyze, which is usually the case in practice. Results In this study, we have developed a tool for calculating binding-site residues on proteins, TCBRP http://yanbioinformatics.cs.usu.edu:8080/ppbindingsubmit. For an input protein, TCBRP can quickly find all binding-site residues on the protein by automatically combining the information obtained from all PDB structures that consist of the protein of interest. Additionally, TCBRP presents the binding-site residues in different categories according to the interaction type. TCBRP also allows researchers to set the definition of binding-site residues. Conclusion The developed tool is very useful for the research on protein binding site analysis and prediction.

  18. Comparison of S. cerevisiae F-BAR domain structures reveals a conserved inositol phosphate binding site

    Science.gov (United States)

    Moravcevic, Katarina; Alvarado, Diego; Schmitz, Karl R.; Kenniston, Jon A.; Mendrola, Jeannine M.; Ferguson, Kathryn M.; Lemmon, Mark A.

    2015-01-01

    SUMMARY F-BAR domains control membrane interactions in endocytosis, cytokinesis, and cell signaling. Although generally thought to bind curved membranes containing negatively charged phospholipids, numerous functional studies argue that differences in lipid-binding selectivities of F-BAR domains are functionally important. Here, we compare membrane-binding properties of the S. cerevisiae F-BAR domains in vitro and in vivo. Whereas some F-BAR domains (such as Bzz1p and Hof1p F-BARs) bind equally well to all phospholipids, the F-BAR domain from the RhoGAP Rgd1p preferentially binds phosphoinositides. We determined X-ray crystal structures of F-BAR domains from Hof1p and Rgd1p, the latter bound to an inositol phosphate. The structures explain phospholipid-binding selectivity differences, and reveal an F-BAR phosphoinositide binding site that is fully conserved in a mammalian RhoGAP called Gmip, and is partly retained in certain other F-BAR domains. Our findings reveal previously unappreciated determinants of F-BAR domain lipid-binding specificity, and provide a basis for its prediction from sequence. PMID:25620000

  19. Aminoglycosylation can enhance the G-quadruplex binding activity of epigallocatechin.

    Directory of Open Access Journals (Sweden)

    Li-Ping Bai

    Full Text Available With the aim of enhancing G-quadruplex binding activity, two new glucosaminosides (16, 18 of penta-methylated epigallocatechin were synthesized by chemical glycosylation. Subsequent ESI-TOF-MS analysis demonstrated that these two glucosaminoside derivatives exhibit much stronger binding activity to human telomeric DNA and RNA G-quadruplexes than their parent structure (i.e., methylated EGC (14 as well as natural epigallocatechin (EGC, 6. The DNA G-quadruplex binding activity of 16 and 18 is even more potent than strong G-quadruplex binder quercetin, which has a more planar structure. These two synthetic compounds also showed a higher binding strength to human telomeric RNA G-quadruplex than its DNA counterpart. Analysis of the structure-activity relationship revealed that the more basic compound, 16, has a higher binding capacity with DNA and RNA G-quadruplexes than its N-acetyl derivative, 18, suggesting the importance of the basicity of the aminoglycoside for G-quadruplex binding activity. Molecular docking simulation predicted that the aromatic ring of 16 π-stacks with the aromatic ring of guanine nucleotides, with the glucosamine moiety residing in the groove of G-quadruplex. This research indicates that glycosylation of natural products with aminosugar can significantly enhance their G-quadruplex binding activities, thus is an effective way to generate small molecules targeting G-quadruplexes in nucleic acids. In addition, this is the first report that green tea catechin can bind to nucleic acid G-quadruplex structures.

  20. Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites.

    Science.gov (United States)

    Marsh, Lorraine

    2015-01-01

    Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ΔG because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ΔG arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where "nonspecific" interactions contribute to biological function.

  1. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

    KAUST Repository

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Wong, Hau-San

    2015-01-01

    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

  2. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

    KAUST Repository

    Wong, Ka-Chun

    2015-06-11

    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

  3. Adaptive evolution of transcription factor binding sites

    Directory of Open Access Journals (Sweden)

    Berg Johannes

    2004-10-01

    Full Text Available Abstract Background The regulation of a gene depends on the binding of transcription factors to specific sites located in the regulatory region of the gene. The generation of these binding sites and of cooperativity between them are essential building blocks in the evolution of complex regulatory networks. We study a theoretical model for the sequence evolution of binding sites by point mutations. The approach is based on biophysical models for the binding of transcription factors to DNA. Hence we derive empirically grounded fitness landscapes, which enter a population genetics model including mutations, genetic drift, and selection. Results We show that the selection for factor binding generically leads to specific correlations between nucleotide frequencies at different positions of a binding site. We demonstrate the possibility of rapid adaptive evolution generating a new binding site for a given transcription factor by point mutations. The evolutionary time required is estimated in terms of the neutral (background mutation rate, the selection coefficient, and the effective population size. Conclusions The efficiency of binding site formation is seen to depend on two joint conditions: the binding site motif must be short enough and the promoter region must be long enough. These constraints on promoter architecture are indeed seen in eukaryotic systems. Furthermore, we analyse the adaptive evolution of genetic switches and of signal integration through binding cooperativity between different sites. Experimental tests of this picture involving the statistics of polymorphisms and phylogenies of sites are discussed.

  4. Structural Fingerprints of Transcription Factor Binding Site Regions

    Directory of Open Access Journals (Sweden)

    Peter Willett

    2009-03-01

    Full Text Available Fourier transforms are a powerful tool in the prediction of DNA sequence properties, such as the presence/absence of codons. We have previously compiled a database of the structural properties of all 32,896 unique DNA octamers. In this work we apply Fourier techniques to the analysis of the structural properties of human chromosomes 21 and 22 and also to three sets of transcription factor binding sites within these chromosomes. We find that, for a given structural property, the structural property power spectra of chromosomes 21 and 22 are strikingly similar. We find common peaks in their power spectra for both Sp1 and p53 transcription factor binding sites. We use the power spectra as a structural fingerprint and perform similarity searching in order to find transcription factor binding site regions. This approach provides a new strategy for searching the genome data for information. Although it is difficult to understand the relationship between specific functional properties and the set of structural parameters in our database, our structural fingerprints nevertheless provide a useful tool for searching for function information in sequence data. The power spectrum fingerprints provide a simple, fast method for comparing a set of functional sequences, in this case transcription factor binding site regions, with the sequences of whole chromosomes. On its own, the power spectrum fingerprint does not find all transcription factor binding sites in a chromosome, but the results presented here show that in combination with other approaches, this technique will improve the chances of identifying functional sequences hidden in genomic data.

  5. Synthetic LPS-Binding Polymer Nanoparticles

    Science.gov (United States)

    Jiang, Tian

    Lipopolysaccharide (LPS), one of the principal components of most gram-negative bacteria's outer membrane, is a type of contaminant that can be frequently found in recombinant DNA products. Because of its strong and even lethal biological effects, selective LPS removal from bioproducts solution is of particular importance in the pharmaceutical and health care industries. In this thesis, for the first time, a proof-of-concept study on preparing LPS-binding hydrogel-like NPs through facile one-step free-radical polymerization was presented. With the incorporation of various hydrophobic (TBAm), cationic (APM, GUA) monomers and cross-linkers (BIS, PEG), a small library of NPs was constructed. Their FITC-LPS binding behaviors were investigated and compared with those of commercially available LPS-binding products. Moreover, the LPS binding selectivity of the NPs was also explored by studying the NPs-BSA interactions. The results showed that all NPs obtained generally presented higher FITC-LPS binding capacity in lower ionic strength buffer than higher ionic strength. However, unlike commercial poly-lysine cellulose and polymyxin B agarose beads' nearly linear increase of FITC-LPS binding with particle concentration, NPs exhibited serious aggregation and the binding quickly saturated or even decreased at high particle concentration. Among various types of NPs, higher FITC-LPS binding capacity was observed for those containing more hydrophobic monomers (TBAm). However, surprisingly, more cationic NPs with higher content of APM exhibited decreased FITC-LPS binding in high ionic strength conditions. Additionally, when new cationic monomer and cross-linker, GUA and PEG, were applied to replace APM and BIS, the obtained NPs showed improved FITC-LPS binding capacity at low NP concentration. But compared with APM- and BIS-containing NPs, the FITC-LPS binding capacity of GUA- and PEG-containing NPs saturated earlier. To investigate the NPs' binding to proteins, we tested the NPs

  6. Sex hormone binding globulin phenotypes

    DEFF Research Database (Denmark)

    Cornelisse, M M; Bennett, Patrick; Christiansen, M

    1994-01-01

    Human sex hormone binding globulin (SHBG) is encoded by a normal and a variant allele. The resulting SHBG phenotypes (the homozygous normal SHBG, the heterozygous SHBG and the homozygous variant SHBG phenotype) can be distinguished by their electrophoretic patterns. We developed a novel detection....... This method of detection was used to determine the distribution of SHBG phenotypes in healthy controls of both sexes and in five different pathological conditions characterized by changes in the SHBG level or endocrine disturbances (malignant and benign ovarian neoplasms, hirsutism, liver cirrhosis...... on the experimental values. Differences in SHBG phenotypes do not appear to have any clinical significance and no sex difference was found in the SHBG phenotype distribution....

  7. DNA Binding Hydroxyl Radical Probes.

    Science.gov (United States)

    Tang, Vicky J; Konigsfeld, Katie M; Aguilera, Joe A; Milligan, Jamie R

    2012-01-01

    The hydroxyl radical is the primary mediator of DNA damage by the indirect effect of ionizing radiation. It is a powerful oxidizing agent produced by the radiolysis of water and is responsible for a significant fraction of the DNA damage associated with ionizing radiation. There is therefore an interest in the development of sensitive assays for its detection. The hydroxylation of aromatic groups to produce fluorescent products has been used for this purpose. We have examined four different chromophores which produce fluorescent products when hydroxylated. Of these, the coumarin system suffers from the fewest disadvantages. We have therefore examined its behavior when linked to a cationic peptide ligand designed to bind strongly to DNA.

  8. Asymmetric cation-binding catalysis

    DEFF Research Database (Denmark)

    Oliveira, Maria Teresa; Lee, Jiwoong

    2017-01-01

    The employment of metal salts is quite limited in asymmetric catalysis, although it would provide an additional arsenal of safe and inexpensive reagents to create molecular functions with high optical purity. Cation chelation by polyethers increases the salts' solubility in conventional organic...... solvents, thus increasing their applicability in synthesis. The expansion of this concept to chiral polyethers led to the emergence of asymmetric cation-binding catalysis, where chiral counter anions are generated from metal salts, particularly using BINOL-based polyethers. Alkali metal salts, namely KF...... highly enantioselective silylation reactions in polyether-generated chiral environments, and leading to a record-high turnover in asymmetric organocatalysis. This can lead to further applications by the asymmetric use of other inorganic salts in various organic transformations....

  9. The helical structure of DNA facilitates binding

    International Nuclear Information System (INIS)

    Berg, Otto G; Mahmutovic, Anel; Marklund, Emil; Elf, Johan

    2016-01-01

    The helical structure of DNA imposes constraints on the rate of diffusion-limited protein binding. Here we solve the reaction–diffusion equations for DNA-like geometries and extend with simulations when necessary. We find that the helical structure can make binding to the DNA more than twice as fast compared to a case where DNA would be reactive only along one side. We also find that this rate advantage remains when the contributions from steric constraints and rotational diffusion of the DNA-binding protein are included. Furthermore, we find that the association rate is insensitive to changes in the steric constraints on the DNA in the helix geometry, while it is much more dependent on the steric constraints on the DNA-binding protein. We conclude that the helical structure of DNA facilitates the nonspecific binding of transcription factors and structural DNA-binding proteins in general. (paper)

  10. Consensus of sample-balanced classifiers for identifying ligand-binding residue by co-evolutionary physicochemical characteristics of amino acids

    KAUST Repository

    Chen, Peng

    2013-01-01

    Protein-ligand binding is an important mechanism for some proteins to perform their functions, and those binding sites are the residues of proteins that physically bind to ligands. So far, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available. In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. Due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets, for each of which a random forest (RF)-based classifier was trained. The ensemble of these RF classifiers formed a sequence-based protein-ligand binding site predictor. Experimental results on CASP9 targets demonstrated that our method compared favorably with the state-of-the-art. © Springer-Verlag Berlin Heidelberg 2013.

  11. Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome.

    Science.gov (United States)

    Dresch, Jacqueline M; Zellers, Rowan G; Bork, Daniel K; Drewell, Robert A

    2016-01-01

    A long-standing objective in modern biology is to characterize the molecular components that drive the development of an organism. At the heart of eukaryotic development lies gene regulation. On the molecular level, much of the research in this field has focused on the binding of transcription factors (TFs) to regulatory regions in the genome known as cis-regulatory modules (CRMs). However, relatively little is known about the sequence-specific binding preferences of many TFs, especially with respect to the possible interdependencies between the nucleotides that make up binding sites. A particular limitation of many existing algorithms that aim to predict binding site sequences is that they do not allow for dependencies between nonadjacent nucleotides. In this study, we use a recently developed computational algorithm, MARZ, to compare binding site sequences using 32 distinct models in a systematic and unbiased approach to explore nucleotide dependencies within binding sites for 15 distinct TFs known to be critical to Drosophila development. Our results indicate that many of these proteins have varying levels of nucleotide interdependencies within their DNA recognition sequences, and that, in some cases, models that account for these dependencies greatly outperform traditional models that are used to predict binding sites. We also directly compare the ability of different models to identify the known KRUPPEL TF binding sites in CRMs and demonstrate that a more complex model that accounts for nucleotide interdependencies performs better when compared with simple models. This ability to identify TFs with critical nucleotide interdependencies in their binding sites will lead to a deeper understanding of how these molecular characteristics contribute to the architecture of CRMs and the precise regulation of transcription during organismal development.

  12. Prediction Markets

    DEFF Research Database (Denmark)

    Horn, Christian Franz; Ivens, Bjørn Sven; Ohneberg, Michael

    2014-01-01

    In recent years, Prediction Markets gained growing interest as a forecasting tool among researchers as well as practitioners, which resulted in an increasing number of publications. In order to track the latest development of research, comprising the extent and focus of research, this article...... provides a comprehensive review and classification of the literature related to the topic of Prediction Markets. Overall, 316 relevant articles, published in the timeframe from 2007 through 2013, were identified and assigned to a herein presented classification scheme, differentiating between descriptive...... works, articles of theoretical nature, application-oriented studies and articles dealing with the topic of law and policy. The analysis of the research results reveals that more than half of the literature pool deals with the application and actual function tests of Prediction Markets. The results...

  13. Quaternary Benzyltriethylammonium Ion Binding to the Na,K-ATPase: a Tool to Investigate Extracellular K+ Binding Reactions†

    Science.gov (United States)

    Peluffo, R. Daniel; González-Lebrero, Rodolfo M.; Kaufman, Sergio B.; Kortagere, Sandhya; Orban, Branly; Rossi, Rolando C.; Berlin, Joshua R.

    2009-01-01

    This study examined how the quaternary organic ammonium ion, benzyltriethylamine (BTEA), binds to the Na,K-ATPase to produce membrane potential (VM)-dependent inhibition and tested the prediction that such a VM-dependent inhibitor would display electrogenic binding kinetics. BTEA competitively inhibited K+ activation of Na,K-ATPase activity and steady-state 86Rb+ occlusion. The initial rate of 86Rb+ occlusion was decreased by BTEA to a similar degree whether it was added to the enzyme prior to or simultaneously with Rb+, a demonstration that BTEA inhibits the Na,K-ATPase without being occluded. Several BTEA structural analogues reversibly inhibited Na,K-pump current, but none blocked current in a VM-dependent manner except BTEA and its para-nitro derivative, pNBTEA. Under conditions that promoted electroneutral K+-K+ exchange by the Na,K-ATPase, step changes in VM elicited pNBTEA-activated ouabain-sensitive transient currents that had similarities to those produced with the K+ congener, Tl+. pNBTEA- and Tl+-dependent transient currents both displayed saturation of charge moved at extreme negative and positive VM, equivalence of charge moved during and after step changes in VM, and similar apparent valence. The rate constant (ktot) for Tl+-dependent transient current asymptotically approached a minimum value at positive VM. In contrast, ktot for pNBTEA-dependent transient current was a “U”-shaped function of VM with a minimum value near 0 mV. Homology models of the Na,K-ATPase alpha subunit suggested that quaternary amines can bind to two extracellularly-accessible sites, one of them located at K+ binding sites positioned between transmembrane helices 4, 5, and 6. Altogether, these data revealed important information about electrogenic ion binding reactions of the Na,K-ATPase that are not directly measurable during ion transport by this enzyme. PMID:19621894

  14. Interpretation of Ocular Melanin Drug Binding Assays. Alternatives to the Model of Multiple Classes of Independent Sites.

    Science.gov (United States)

    Manzanares, José A; Rimpelä, Anna-Kaisa; Urtti, Arto

    2016-04-04

    Melanin has a high binding affinity for a wide range of drugs. The determination of the melanin binding capacity and its binding affinity are important, e.g., in the determination of the ocular drug distribution, the prediction of drug effects in the eye, and the trans-scleral drug delivery. The binding parameters estimated from a given data set vary significantly when using different isotherms or different nonlinear fitting methods. In this work, the commonly used bi-Langmuir isotherm, which assumes two classes of independent sites, is confronted with the Sips isotherm. Direct, log-log, and Scatchard plots are used, and the interpretation of the binding curves in the latter is critically analyzed. In addition to the goodness of fit, the emphasis is placed on the physical meaning of the binding parameters. The bi-Langmuir model imposes a bimodal distribution of binding energies for the sites on the melanin granules, but the actual distribution is most likely continuous and unimodal, as assumed by the Sips isotherm. Hence, the latter describes more accurately the distribution of binding energies and also the experimental results of melanin binding to drugs and metal ions. Simulations are used to show that the existence of two classes of sites cannot be confirmed on the sole basis of the shape of the binding curve in the Scatchard plot, and that serious doubts may appear on the meaning of the binding parameters of the bi-Langmuir model. Experimental results of melanin binding to chloroquine and metoprolol are used to illustrate the importance of the choice of the binding isotherm and of the method used to evaluate the binding parameters.

  15. Fundamental considerations in ski binding analysis.

    Science.gov (United States)

    Mote, C D; Hull, M L

    1976-01-01

    1. The static adjustment of a ski binding by hand or by available machines is only an adjustment and is neither a static nor a dynamic evaluation of the binding design. Bindings of different design with identical static adjustments will perform differently in environments in which the forces are static or dynamic. 2. The concept of binding release force is a useful measure of binding adjustment, but it is inappropriate as a criterion for binding evaluation. First, it does not direct attention toward the injury causing mechanism, strain, or displacement in the leg. Second, it is only part of the evaluation in dynamic problems. 3. The binding release decision in present bindings is displacement controlled. The relative displacement of the boot and ski is the system variable. For any specified relative displacement the binding force can be any of an infinite number of possibilities determined by the loading path. 4. The response of the leg-ski system to external impulses applied to the ski is independent of the boot-ski relative motion as long as the boot recenters quickly in the binding. Response is dependent upon the external impulse plus system inertia, damping and stiffness. 5. When tested under half sinusoidal forces applied to a test ski, all bindings will demonstrate static and impulse loading regions. In the static region the force drives the binding to a relative release displacement. In the impulse region the initial velocity of the ski drives the binding to a release displacement. 6. The transition between the static and impulse loading regions is determined by the binding's capacity to store and dissipate energy along the principal loading path. Increased energy capacity necessitates larger external impulses to produce release. 7. In all bindings examined to date, the transmitted leg displacement or strain at release under static loading exceeds leg strain under dynamic or impact loading. Because static loading is responsible for many injuries, a skier

  16. Lead-Binding Proteins: A Review

    Directory of Open Access Journals (Sweden)

    Harvey C. Gonick

    2011-01-01

    Full Text Available Lead-binding proteins are a series of low molecular weight proteins, analogous to metallothionein, which segregate lead in a nontoxic form in several organs (kidney, brain, lung, liver, erythrocyte. Whether the lead-binding proteins in every organ are identical or different remains to be determined. In the erythrocyte, delta-aminolevulinic acid dehydratase (ALAD isoforms have commanded the greatest attention as proteins and enzymes that are both inhibitable and inducible by lead. ALAD-2, although it binds lead to a greater degree than ALAD-1, appears to bind lead in a less toxic form. What may be of greater significance is that a low molecular weight lead-binding protein, approximately 10 kDa, appears in the erythrocyte once blood lead exceeds 39 μg/dL and eventually surpasses the lead-binding capacity of ALAD. In brain and kidney of environmentally exposed humans and animals, a cytoplasmic lead-binding protein has been identified as thymosin β4, a 5 kDa protein. In kidney, but not brain, another lead-binding protein has been identified as acyl-CoA binding protein, a 9 kDa protein. Each of these proteins, when coincubated with liver ALAD and titrated with lead, diminishes the inhibition of ALAD by lead, verifying their ability to segregate lead in a nontoxic form.

  17. Drug binding properties of neonatal albumin

    DEFF Research Database (Denmark)

    Brodersen, R; Honoré, B

    1989-01-01

    Neonatal and adult albumin was isolated by gel chromatography on Sephacryl S-300, from adult and umbilical cord serum, respectively. Binding of monoacetyl-diamino-diphenyl sulfone, warfarin, sulfamethizole, and diazepam was studied by means of equilibrium dialysis and the binding data were analyzed...... by the method of several acceptable fitted curves. It was found that the binding affinity to neonatal albumin is less than to adult albumin for monoacetyl-diamino-diphenyl sulfone and warfarin. Sulfamethizole binding to the neonatal protein is similarly reduced when more than one molecule of the drug is bound...

  18. Bitopic Ligands and Metastable Binding Sites

    DEFF Research Database (Denmark)

    Fronik, Philipp; Gaiser, Birgit I; Sejer Pedersen, Daniel

    2017-01-01

    of orthosteric binding sites. Bitopic ligands have been employed to address the selectivity problem by combining (linking) an orthosteric ligand with an allosteric modulator, theoretically leading to high-affinity subtype selective ligands. However, it remains a challenge to identify suitable allosteric binding...... that have been reported to date, this type of bitopic ligands would be composed of two identical pharmacophores. Herein, we outline the concept of bitopic ligands, review metastable binding sites, and discuss their potential as a new source of allosteric binding sites....

  19. PRISM offers a comprehensive genomic approach to transcription factor function prediction

    KAUST Repository

    Wenger, A. M.; Clarke, S. L.; Guturu, H.; Chen, J.; Schaar, B. T.; McLean, C. Y.; Bejerano, G.

    2013-01-01

    The human genome encodes 1500-2000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context-relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high-quality nonredundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (predicting regulatory information from single motifs) approach obtains 2543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells.

  20. PRISM offers a comprehensive genomic approach to transcription factor function prediction

    KAUST Repository

    Wenger, A. M.

    2013-02-04

    The human genome encodes 1500-2000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context-relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high-quality nonredundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (predicting regulatory information from single motifs) approach obtains 2543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells.

  1. Retinoid-binding proteins: similar protein architectures bind similar ligands via completely different ways.

    Directory of Open Access Journals (Sweden)

    Yu-Ru Zhang

    Full Text Available BACKGROUND: Retinoids are a class of compounds that are chemically related to vitamin A, which is an essential nutrient that plays a key role in vision, cell growth and differentiation. In vivo, retinoids must bind with specific proteins to perform their necessary functions. Plasma retinol-binding protein (RBP and epididymal retinoic acid binding protein (ERABP carry retinoids in bodily fluids, while cellular retinol-binding proteins (CRBPs and cellular retinoic acid-binding proteins (CRABPs carry retinoids within cells. Interestingly, although all of these transport proteins possess similar structures, the modes of binding for the different retinoid ligands with their carrier proteins are different. METHODOLOGY/PRINCIPAL FINDINGS: In this work, we analyzed the various retinoid transport mechanisms using structure and sequence comparisons, binding site analyses and molecular dynamics simulations. Our results show that in the same family of proteins and subcellular location, the orientation of a retinoid molecule within a binding protein is same, whereas when different families of proteins are considered, the orientation of the bound retinoid is completely different. In addition, none of the amino acid residues involved in ligand binding is conserved between the transport proteins. However, for each specific binding protein, the amino acids involved in the ligand binding are conserved. The results of this study allow us to propose a possible transport model for retinoids. CONCLUSIONS/SIGNIFICANCE: Our results reveal the differences in the binding modes between the different retinoid-binding proteins.

  2. Molecular simulations and Markov state modeling reveal the structural diversity and dynamics of a theophylline-binding RNA aptamer in its unbound state.

    Directory of Open Access Journals (Sweden)

    Becka M Warfield

    Full Text Available RNA aptamers are oligonucleotides that bind with high specificity and affinity to target ligands. In the absence of bound ligand, secondary structures of RNA aptamers are generally stable, but single-stranded and loop regions, including ligand binding sites, lack defined structures and exist as ensembles of conformations. For example, the well-characterized theophylline-binding aptamer forms a highly stable binding site when bound to theophylline, but the binding site is unstable and disordered when theophylline is absent. Experimental methods have not revealed at atomic resolution the conformations that the theophylline aptamer explores in its unbound state. Consequently, in the present study we applied 21 microseconds of molecular dynamics simulations to structurally characterize the ensemble of conformations that the aptamer adopts in the absence of theophylline. Moreover, we apply Markov state modeling to predict the kinetics of transitions between unbound conformational states. Our simulation results agree with experimental observations that the theophylline binding site is found in many distinct binding-incompetent states and show that these states lack a binding pocket that can accommodate theophylline. The binding-incompetent states interconvert with binding-competent states through structural rearrangement of the binding site on the nanosecond to microsecond timescale. Moreover, we have simulated the complete theophylline binding pathway. Our binding simulations supplement prior experimental observations of slow theophylline binding kinetics by showing that the binding site must undergo a large conformational rearrangement after the aptamer and theophylline form an initial complex, most notably, a major rearrangement of the C27 base from a buried to solvent-exposed orientation. Theophylline appears to bind by a combination of conformational selection and induced fit mechanisms. Finally, our modeling indicates that when Mg2+ ions are

  3. Predicting unpredictability

    Science.gov (United States)

    Davis, Steven J.

    2018-04-01

    Analysts and markets have struggled to predict a number of phenomena, such as the rise of natural gas, in US energy markets over the past decade or so. Research shows the challenge may grow because the industry — and consequently the market — is becoming increasingly volatile.

  4. Altered SPECT 123I iomazenil Binding in the Cingulate Cortex of Children with Anorexia Nervosa

    Directory of Open Access Journals (Sweden)

    Shinichiro eNagamitsu

    2016-02-01

    Full Text Available Several lines of evidence suggest that anxiety plays a key role in the development and maintenance of anorexia nervosa (AN in children. The purpose of this study was to examine cortical GABA(A-benzodiazepine receptor binding before and after treatment in children beginning intensive AN treatment. Brain single photon emission computed tomography (SPECT measurements using 123I iomazenil, which binds to GABA(A-benzodiazepine receptors, was performed in 26 participants with AN who were enrolled in a multimodal treatment program. Sixteen of the 26 participants underwent a repeat SPECT scan immediately before discharge at conclusion of the intensive treatment program. Eating behavior and mood disturbances were assessed using Eating Attitudes Test with 26 items (EAT-26 and the short form of the Profile of Mood States (POMS. Clinical outcome scores were evaluated after a 1-year period. We examined association between relative iomazenil binding activity in cortical regions of interest (ROIs and psychometric profiles, and determined which psychometric profiles show interaction effects with brain regions. Further, we determined if binding activity could predict clinical outcome and treatment changes. Higher EAT-26 scores were significantly associated with lower iomazenil binding activity in the anterior posterior cingulate cortex (ACC. Higher POMS subscale scores were significantly associated with lower iomazenil binding activity in the left frontal, parietal cortex, and posterior cingulate cortex (PCC. Depression-Dejection, and Confusion POMS subscale scores, and total POMS score, showed interaction effects with brain regions in iomazenil binding activity. Decreased binding in the ACC and left parietal cortex was associated with poor clinical outcomes. Relative binding increases throughout the PCC and occipital gyrus were observed after weight gain in children with AN. These findings suggest that cortical GABAergic receptor binding is altered in children

  5. Exploring the composition of protein-ligand binding sites on a large scale.

    Directory of Open Access Journals (Sweden)

    Nickolay A Khazanov

    Full Text Available The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant "valid" ligands from "invalid" small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition.

  6. Altered SPECT 123I-iomazenil Binding in the Cingulate Cortex of Children with Anorexia Nervosa

    Science.gov (United States)

    Nagamitsu, Shinichiro; Sakurai, Rieko; Matsuoka, Michiko; Chiba, Hiromi; Ozono, Shuichi; Tanigawa, Hitoshi; Yamashita, Yushiro; Kaida, Hayato; Ishibashi, Masatoshi; Kakuma, Tatsuki; Croarkin, Paul E.; Matsuishi, Toyojiro

    2016-01-01

    Several lines of evidence suggest that anxiety plays a key role in the development and maintenance of anorexia nervosa (AN) in children. The purpose of this study was to examine cortical GABA(A)-benzodiazepine receptor binding before and after treatment in children beginning intensive AN treatment. Brain single-photon emission computed tomography (SPECT) measurements using 123I-iomazenil, which binds to GABA(A)-benzodiazepine receptors, was performed in 26 participants with AN who were enrolled in a multimodal treatment program. Sixteen of the 26 participants underwent a repeat SPECT scan immediately before discharge at conclusion of the intensive treatment program. Eating behavior and mood disturbances were assessed using Eating Attitudes Test with 26 items (EAT-26) and the short form of the Profile of Mood States (POMS). Clinical outcome scores were evaluated after a 1-year period. We examined association between relative iomazenil-binding activity in cortical regions of interest and psychometric profiles and determined which psychometric profiles show interaction effects with brain regions. Further, we determined if binding activity could predict clinical outcome and treatment changes. Higher EAT-26 scores were significantly associated with lower iomazenil-binding activity in the anterior and posterior cingulate cortex. Higher POMS subscale scores were significantly associated with lower iomazenil-binding activity in the left frontal, parietal cortex, and posterior cingulate cortex (PCC). “Depression–Dejection” and “Confusion” POMS subscale scores, and total POMS score showed interaction effects with brain regions in iomazenil-binding activity. Decreased binding in the anterior cingulate cortex and left parietal cortex was associated with poor clinical outcomes. Relative binding increases throughout the PCC and occipital gyrus were observed after weight gain in children with AN. These findings suggest that cortical GABAergic receptor binding is altered

  7. Detecting Local Ligand-Binding Site Similarity in Non-Homologous Proteins by Surface Patch Comparison

    Science.gov (United States)

    Sael, Lee; Kihara, Daisuke

    2012-01-01

    Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, each of which is characterized by its geometrical shape, the electrostatic potential, the hydrophobicity, and the concaveness. Representing a pocket by a set of patches is effective to absorb difference of global pocket shape while capturing local similarity of pockets. The shape and the physicochemical properties of surface patches are represented using the 3D Zernike descriptor, which is a series expansion of mathematical 3D function. Two pockets are compared using a modified weighted bipartite matching algorithm, which matches similar patches from the two pockets. Patch-Surfer was benchmarked on three datasets, which consist in total of 390 proteins that bind to one of 21 ligands. Patch-Surfer showed superior performance to existing methods including a global pocket comparison method, Pocket-Surfer, which we have previously introduced. Particularly, as intended, the accuracy showed large improvement for flexible ligand molecules, which bind to pockets in different conformations. PMID:22275074

  8. Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

    Science.gov (United States)

    Sael, Lee; Kihara, Daisuke

    2012-04-01

    Functional elucidation of proteins is one of the essential tasks in biology. Function of a protein, specifically, small ligand molecules that bind to a protein, can be predicted by finding similar local surface regions in binding sites of known proteins. Here, we developed an alignment free local surface comparison method for predicting a ligand molecule which binds to a query protein. The algorithm, named Patch-Surfer, represents a binding pocket as a combination of segmented surface patches, each of which is characterized by its geometrical shape, the electrostatic potential, the hydrophobicity, and the concaveness. Representing a pocket by a set of patches is effective to absorb difference of global pocket shape while capturing local similarity of pockets. The shape and the physicochemical properties of surface patches are represented using the 3D Zernike descriptor, which is a series expansion of mathematical 3D function. Two pockets are compared using a modified weighted bipartite matching algorithm, which matches similar patches from the two pockets. Patch-Surfer was benchmarked on three datasets, which consist in total of 390 proteins that bind to one of 21 ligands. Patch-Surfer showed superior performance to existing methods including a global pocket comparison method, Pocket-Surfer, which we have previously introduced. Particularly, as intended, the accuracy showed large improvement for flexible ligand molecules, which bind to pockets in different conformations. Copyright © 2011 Wiley Periodicals, Inc.

  9. Effect of dipole polarizability on positron binding by strongly polar molecules

    International Nuclear Information System (INIS)

    Gribakin, G F; Swann, A R

    2015-01-01

    A model for positron binding to polar molecules is considered by combining the dipole potential outside the molecule with a strongly repulsive core of a given radius. Using existing experimental data on binding energies leads to unphysically small core radii for all of the molecules studied. This suggests that electron–positron correlations neglected in the simple model play a large role in determining the binding energy. We account for these by including the polarization potential via perturbation theory and non-perturbatively. The perturbative model makes reliable predictions of binding energies for a range of polar organic molecules and hydrogen cyanide. The model also agrees with the linear dependence of the binding energies on the polarizability inferred from the experimental data (Danielson et al 2009 J. Phys. B: At. Mol. Opt. Phys. 42 235203). The effective core radii, however, remain unphysically small for most molecules. Treating molecular polarization non-perturbatively leads to physically meaningful core radii for all of the molecules studied and enables even more accurate predictions of binding energies to be made for nearly all of the molecules considered. (paper)

  10. New DNA-binding radioprotectors

    Science.gov (United States)

    Martin, Roger

    The normal tissue damage associated with cancer radiotherapy has motivated the development at Peter Mac of a new class of DNA-binding radioprotecting drugs that could be applied top-ically to normal tissues at risk. Methylproamine (MP), the lead compound, reduces radiation induced cell kill at low concentrations. For example, experiments comparing the clonogenic survival of transformed human keratinocytes treated with 30 micromolar MP before and dur-ing various doses of ionising radiation, with the radiation dose response for untreated cells, indicate a dose reduction factor (DRF) of 2. Similar survival curve experiments using various concentrations of MP, with parallel measurements of uptake of MP into cell nuclei, have en-abled the relationship between drug uptake and extent of radioprotection to be established. Radioprotection has also been demonstrated after systemic administration to mice, for three different endpoints, namely lung, jejunum and bone marrow (survival at 30 days post-TBI). The results of pulse radiolysis studies indicated that the drugs act by reduction of transient radiation-induced oxidative species on DNA. This hypothesis was substantiated by the results of experiments in which MP radioprotection of radiation-induced DNA double-strand breaks, assessed as -H2AX foci, in the human keratinocyte cell line. For both endpoints, the extent of radioprotection increased with MP concentration up to a maximal value. These results are consistent with the hypothesis that radioprotection by MP is mediated by attenuation of the extent of initial DNA damage. However, although MP is a potent radioprotector, it becomes cytotoxic at higher concentrations. This limitation has been addressed in an extensive program of lead optimisation and some promising analogues have emerged from which the next lead will be selected. Given the clinical potential of topical radioprotection, the new analogues are being assessed in terms of delivery to mouse oral mucosa. This is

  11. Prediction of Biomolecular Complexes

    KAUST Repository

    Vangone, Anna

    2017-04-12

    Almost all processes in living organisms occur through specific interactions between biomolecules. Any dysfunction of those interactions can lead to pathological events. Understanding such interactions is therefore a crucial step in the investigation of biological systems and a starting point for drug design. In recent years, experimental studies have been devoted to unravel the principles of biomolecular interactions; however, due to experimental difficulties in solving the three-dimensional (3D) structure of biomolecular complexes, the number of available, high-resolution experimental 3D structures does not fulfill the current needs. Therefore, complementary computational approaches to model such interactions are necessary to assist experimentalists since a full understanding of how biomolecules interact (and consequently how they perform their function) only comes from 3D structures which provide crucial atomic details about binding and recognition processes. In this chapter we review approaches to predict biomolecular complexesBiomolecular complexes, introducing the concept of molecular dockingDocking, a technique which uses a combination of geometric, steric and energetics considerations to predict the 3D structure of a biological complex starting from the individual structures of its constituent parts. We provide a mini-guide about docking concepts, its potential and challenges, along with post-docking analysis and a list of related software.

  12. Prediction of Biomolecular Complexes

    KAUST Repository

    Vangone, Anna; Oliva, Romina; Cavallo, Luigi; Bonvin, Alexandre M. J. J.

    2017-01-01

    Almost all processes in living organisms occur through specific interactions between biomolecules. Any dysfunction of those interactions can lead to pathological events. Understanding such interactions is therefore a crucial step in the investigation of biological systems and a starting point for drug design. In recent years, experimental studies have been devoted to unravel the principles of biomolecular interactions; however, due to experimental difficulties in solving the three-dimensional (3D) structure of biomolecular complexes, the number of available, high-resolution experimental 3D structures does not fulfill the current needs. Therefore, complementary computational approaches to model such interactions are necessary to assist experimentalists since a full understanding of how biomolecules interact (and consequently how they perform their function) only comes from 3D structures which provide crucial atomic details about binding and recognition processes. In this chapter we review approaches to predict biomolecular complexesBiomolecular complexes, introducing the concept of molecular dockingDocking, a technique which uses a combination of geometric, steric and energetics considerations to predict the 3D structure of a biological complex starting from the individual structures of its constituent parts. We provide a mini-guide about docking concepts, its potential and challenges, along with post-docking analysis and a list of related software.

  13. Binding of (/sup 3/H)imipramine to human platelet membranes with compensation for saturable binding to filters and its implication for binding studies with brain membranes

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, O.M.; Wood, K.M.; Williams, D.C.

    1984-08-01

    Apparent specific binding of (/sup 3/H)imipramine to human platelet membranes at high concentrations of imipramine showed deviation from that expected of a single binding site, a result consistent with a low-affinity binding site. The deviation was due to displaceable, saturable binding to the glass fibre filters used in the assays. Imipramine, chloripramine, desipramine, and fluoxetine inhibited binding to filters whereas 5-hydroxytryptamine and ethanol were ineffective. Experimental conditions were developed that eliminated filter binding, allowing assay of high- and low-affinity binding to membranes. Failure to correct for filter binding may lead to overestimation of binding parameters, Bmax and KD for high-affinity binding to membranes, and may also be misinterpreted as indicating a low-affinity binding component in both platelet and brain membranes. Low-affinity binding (KD less than 2 microM) of imipramine to human platelet membranes was demonstrated and its significance discussed.

  14. Structural and binding properties of two paralogous fatty acid binding proteins of Taenia solium metacestode.

    Directory of Open Access Journals (Sweden)

    Seon-Hee Kim

    Full Text Available BACKGROUND: Fatty acid (FA binding proteins (FABPs of helminths are implicated in acquisition and utilization of host-derived hydrophobic substances, as well as in signaling and cellular interactions. We previously demonstrated that secretory hydrophobic ligand binding proteins (HLBPs of Taenia solium metacestode (TsM, a causative agent of neurocysticercosis (NC, shuttle FAs in the surrounding host tissues and inwardly transport the FAs across the parasite syncytial membrane. However, the protein molecules responsible for the intracellular trafficking and assimilation of FAs have remained elusive. METHODOLOGY/PRINCIPAL FINDINGS: We isolated two novel TsMFABP genes (TsMFABP1 and TsMFABP2, which encoded 133- and 136-amino acid polypeptides with predicted molecular masses of 14.3 and 14.8 kDa, respectively. They shared 45% sequence identity with each other and 15-95% with other related-members. Homology modeling demonstrated a characteristic β-barrel composed of 10 anti-parallel β-strands and two α-helices. TsMFABP2 harbored two additional loops between β-strands two and three, and β-strands six and seven, respectively. TsMFABP1 was secreted into cyst fluid and surrounding environments, whereas TsMFABP2 was intracellularly confined. Partially purified native proteins migrated to 15 kDa with different isoelectric points of 9.2 (TsMFABP1 and 8.4 (TsMFABP2. Both native and recombinant proteins bound to 11-([5-dimethylaminonaphthalene-1-sulfonyl]aminoundecannoic acid, dansyl-DL-α-amino-caprylic acid, cis-parinaric acid and retinol, which were competitively inhibited by oleic acid. TsMFABP1 exhibited high affinity toward FA analogs. TsMFABPs showed weak binding activity to retinol, but TsMFABP2 showed relatively high affinity. Isolation of two distinct genes from an individual genome strongly suggested their paralogous nature. Abundant expression of TsMFABP1 and TsMFABP2 in the canal region of worm matched well with the histological distributions

  15. Sequence2Vec: A novel embedding approach for modeling transcription factor binding affinity landscape

    KAUST Repository

    Dai, Hanjun

    2017-07-26

    Motivation: An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Results: Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model (HMM) which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these HMMs into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA data sets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.

  16. PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

    Directory of Open Access Journals (Sweden)

    Ribarics Reiner

    2011-06-01

    Full Text Available Abstract Background The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions? Results Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1 selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2 that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders. Conclusion We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

  17. Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

    Science.gov (United States)

    Pan, Yuliang; Wang, Zixiang; Zhan, Weihua; Deng, Lei

    2018-05-01

    Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. The PrabHot webserver is freely available at http://denglab.org/PrabHot/. leideng@csu.edu.cn. Supplementary data are available at Bioinformatics online.

  18. Thermodynamics of ligand binding to acyl-coenzyme A binding protein studied by titration calorimetry

    DEFF Research Database (Denmark)

    Færgeman, Nils J.; Sigurskjold, B W; Kragelund, B B

    1996-01-01

    Ligand binding to recombinant bovine acyl-CoA binding protein (ACBP) was examined using isothermal microcalorimetry. Microcalorimetric measurements confirm that the binding affinity of acyl-CoA esters for ACBP is strongly dependent on the length of the acyl chain with a clear preference for acyl-...

  19. Impact of germline and somatic missense variations on drug binding sites.

    Science.gov (United States)

    Yan, C; Pattabiraman, N; Goecks, J; Lam, P; Nayak, A; Pan, Y; Torcivia-Rodriguez, J; Voskanian, A; Wan, Q; Mazumder, R

    2017-03-01

    Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein-drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein-drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein-drug binding sites. Using this method we identified 12 993 amino acid-drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid-drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid-drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid-drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein-drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein-drug binding predicted drug target proteins and prevalence of both somatic and

  20. Identification of Glossina morsitans morsitans odorant binding ...

    African Journals Online (AJOL)

    Tsetse flies are vectors of trypanosome parasites, causative agents of Trypanosomiasis in humans and animals. Odorant Binding Proteins (OBPs) are critical in insect olfaction as they bind volatile odours from the environment and transport them to receptors within olfactory receptor neurons for processing providing critical ...

  1. Triazatriangulene as binding group for molecular electronics

    DEFF Research Database (Denmark)

    Wei, Zhongming; Wang, Xintai; Borges, Anders

    2014-01-01

    The triazatriangulene (TATA) ring system was investigated as a binding group for tunnel junctions of molecular wires on gold surfaces. Self-assembled monolayers (SAMs) of TATA platforms with three different lengths of phenylene wires were fabricated, and their electrical conductance was recorded ...... with its high stability and directionality make this binding group very attractive for molecular electronic measurements and devices. (Figure Presented)....

  2. Localization-enhanced biexciton binding in semiconductors

    DEFF Research Database (Denmark)

    Langbein, Wolfgang Werner; Hvam, Jørn Märcher

    1999-01-01

    The influence of excitonic localization on the binding energy of biexcitons is investigated for quasi-three-dimensional and quasi-two-dimensional AlxGa1-xAs structures. An increase of the biexciton binding energy is observed for localization energies comparable to or larger than the free biexcito...

  3. Binding of corroded ions to human saliva.

    Science.gov (United States)

    Mueller, H J

    1985-05-01

    Employing equilibrium dialysis, the binding abilities of Cu, Al, Co and Cr ions from corroded Cu-Al and Co-Cr dental casting alloys towards human saliva and two of its gel chromatographic fractions were determined. Results indicate that both Cu and Co bind to human saliva i.e. 0.045 and 0.027 mg/mg protein, respectively. Besides possessing the largest binding ability, Cu also possessed the largest binding capacity. The saturation of Cu binding was not reached up to the limit of 0.35 mg protein/ml employed in the tests, while Co reached full saturation at about 0.2 mg protein/ml. Chromium showed absolutely no binding to human saliva while Al ions did not pass through the dialysis membranes. Compared to the binding with solutions that were synthetically made up to contain added salivary-type proteins, it is shown that the binding to human saliva is about 1 order of magnitude larger, at least for Cu ions.

  4. Intentional binding of visual effects.

    Science.gov (United States)

    Ruess, Miriam; Thomaschke, Roland; Kiesel, Andrea

    2018-04-01

    When an action produces an effect, the effect is perceived earlier in time compared to a stimulus without preceding action. This temporal bias is called intentional binding (IB) and serves as an implicit measure of sense of agency. Typically, IB is investigated by presenting a rotating clock hand while participants execute an action and perceive a resulting tone. Participants are asked to estimate the time point of tone onset by referring to the clock hand position. This time point estimate is compared to a time point estimate of a tone in a condition in which the tone occurs without preceding action. Studies employing this classic clock paradigm employed auditory action effects. We modified this paradigm to investigate potential IB of visual action effects, and, additionally, to investigate how IB differs for visual action effects (Experiment 1) in comparison to auditory action effects (Experiment 2). Our results show that, like the IB of an auditory effect, the time point of a visual action effect is shifted toward the causing action, and that the size of the IB depends on the delay duration of the effect. Comparable to auditory action effects, earlier action effects showed stronger IB compared to later action effects. Yet overall IB of the visual effects was weaker than IB of the auditory effects. As IB is seen as an indicator of sense of agency, this may have important implications for the design of human-machine interfaces.

  5. Binding of cyclic carboxylates to octa-acid deep-cavity cavitand

    Science.gov (United States)

    Gibb, Corinne L. D.; Gibb, Bruce C.

    2014-04-01

    As part of the fourth statistical assessment of modeling of proteins and ligands (sampl.eyesopen.com) prediction challenge, the strength of association of nine guests ( 1- 9) binding to octa-acid host was determined by a combination of 1H NMR and isothermal titration calorimetry. Association constants in sodium tetraborate buffered (pH 9.2) aqueous solution ranged from 5.39 × 102 M-1 in the case of benzoate 1, up to 3.82 × 105 M-1 for trans-4-methylcyclohexanoate 7. Overall, the free energy difference between the free energies of complexation of these weakest and strongest binding guests was ΔΔG° = 3.88 kcal mol-1. Based on a multitude of previous studies, the anticipated order of strength of binding was close to that which was actually obtained. However, the binding of guest 3 (4-ethylbenzoate) was considerably stronger than initially estimated.

  6. Porcine major histocompatibility complex (MHC) class I molecules and analysis of their peptide-binding specificities

    DEFF Research Database (Denmark)

    Pedersen, Lasse Eggers; Harndahl, Mikkel; Rasmussen, Michael

    2011-01-01

    a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules....... A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data......In all vertebrate animals, CD8+ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC...

  7. Toward Improved Force-Field Accuracy through Sensitivity Analysis of Host-Guest Binding Thermodynamics

    Science.gov (United States)

    Yin, Jian; Fenley, Andrew T.; Henriksen, Niel M.; Gilson, Michael K.

    2015-01-01

    Improving the capability of atomistic computer models to predict the thermodynamics of noncovalent binding is critical for successful structure-based drug design, and the accuracy of such calculations remains limited by non-optimal force field parameters. Ideally, one would incorporate protein-ligand affinity data into force field parametrization, but this would be inefficient and costly. We now demonstrate that sensitivity analysis can be used to efficiently tune Lennard-Jones parameters of aqueous host-guest systems for increasingly accurate calculations of binding enthalpy. These results highlight the promise of a comprehensive use of calorimetric host-guest binding data, along with existing validation data sets, to improve force field parameters for the simulation of noncovalent binding, with the ultimate goal of making protein-ligand modeling more accurate and hence speeding drug discovery. PMID:26181208

  8. Binding Energy, Vapor Pressure and Melting Point of Semiconductor Nanoparticles

    International Nuclear Information System (INIS)

    H. H. Farrell; C. D. Van Siclen

    2007-01-01

    Current models for the cohesive energy of nanoparticles generally predict a linear dependence on the inverse particle diameter for spherical clusters, or, equivalently, on the inverse of the cube root of the number of atoms in the cluster. Although this is generally true for metals, we find that for the group IV semiconductors, C, Si and Ge, this linear dependence does not hold. Instead, using first principles, density functional theory calculations to calculate the binding energy of these materials, we find a quadratic dependence on the inverse of the particle size. Similar results have also been obtained for the metallic group IV elements Sn and Pb. This is in direct contradiction to current assumptions. Further, as a consequence of this quadratic behavior, the vapor pressure of semiconductor nanoparticles rises more slowly with decreasing size than would be expected. In addition, the melting point of these nanoparticles will experience less suppression than experienced by metal nanoparticles with comparable bulk binding energies. This non-linearity also affects sintering or Ostwald ripening behavior of these nanoparticles as well as other physical properties that depend on the nanoparticle binding energy. The reason for this variation in size dependence involves the covalent nature of the bonding in semiconductors, and even in the 'poor' metals. Therefore, it is expected that this result will hold for compound semiconductors as well as the elemental semiconductors

  9. Study on dopamine D2 binding capacity in vascular parkinsonism

    International Nuclear Information System (INIS)

    Terashi, Hiroo; Nagata, Ken; Hirata, Yutaka; Hatazawa, Jun; Utsumi, Hiroya

    2001-01-01

    To investigate whether the striatal dopamine receptor function is involved in the development of vascular parkinsonism (VP), a positron emission tomography (PET) study was conducted on 9 patients with VP by using [ 11 C] N-methylspiperone as the tracer. The rate of binding availability in the striatal dopamine D 2 receptor (k 3 ) was determined semiquantitatively, and the values were compared to the predicted normal values based on the results from 7 normal volunteers. Of 9 patients with VP, the normalized D 2 receptor binding [%k 3 ] was more than 90% in 5 patients, 89 to 87% in 3, and 75% in one. These values showed no evident correlation with the Hoehn and Yahr stage. The laterality of the striatal %k 3 did not correspond to that of the parkinsonism. Thus, the striatal dopamine D 2 receptor binding was not severely impaired and did not correlate with the neurological status in patients with VP. This may indicate that striatal dopamine D 2 receptor function is not primarily associated with the development of the parkinsonism in VP. (author)

  10. Cloud computing for protein-ligand binding site comparison.

    Science.gov (United States)

    Hung, Che-Lun; Hua, Guan-Jie

    2013-01-01

    The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery.

  11. Characterization of chlorophyll binding to LIL3.

    Science.gov (United States)

    Mork-Jansson, Astrid Elisabeth; Eichacker, Lutz Andreas

    2018-01-01

    The light harvesting like protein 3 (LIL 3) from higher plants, has been linked to functions in chlorophyll and tocopherol biosynthesis, photo-protection and chlorophyll transfer. However, the binding of chlorophyll to LIL3 is unclear. We present a reconstitution protocol for chlorophyll binding to LIL3 in DDM micelles. It is shown in the absence of lipids and carotenoids that reconstitution of chlorophyll binding to in vitro expressed LIL3 requires pre-incubation of reaction partners at room temperature. We show chlorophyll a but not chlorophyll b binding to LIL3 at a molar ratio of 1:1. Neither dynamic light scattering nor native PAGE, enabled a discrimination between binding of chlorophyll a and/or b to LIL3.

  12. (TH) diazepam binding to human granulocytes

    Energy Technology Data Exchange (ETDEWEB)

    Bond, P.A.; Cundall, R.L.; Rolfe, B.

    1985-07-08

    (TH)-diazepam binds to sites on human granulocyte membranes, with little or no binding to platelets or lymphocytes. These (TH)-diazepam binding sites are of the peripheral type, being strongly inhibited by R05-4864 (Ki=6.23nM) but only weakly by clonazepam (Ki=14 M). Binding of (TH) diazepam at 0 is saturable, specific and stereoselective. Scatchard analysis indicates a single class of sites with Bmax of 109 +/- 17f moles per mg of protein and K/sub D/ of 3.07 +/- 0.53nM. Hill plots of saturation experiments gave straight lines with a mean Hill coefficient of 1.03 +/- 0.014. Binding is time dependent and reversible and it varies linearly with granulocyte protein concentration over the range 0.025-0.300 mg of protein. 11 references, 3 figures, 1 table.

  13. Factor VIIa binding and internalization in hepatocytes

    DEFF Research Database (Denmark)

    Hjortoe, G; Sorensen, B B; Petersen, L C

    2005-01-01

    The liver is believed to be the primary clearance organ for coagulation proteases, including factor VIIa (FVIIa). However, at present, clearance mechanisms for FVIIa in liver are unknown. To obtain information on the FVIIa clearance mechanism, we investigated the binding and internalization...... no effect. HEPG2 cells internalized FVIIa with a rate of 10 fmol 10(-5) cells h(-1). In contrast to HEPG2 cells, FVIIa binding to primary rat hepatocytes was completely independent of TF, and excess unlabeled FVIIa partly reduced the binding of 125I-FVIIa to rat hepatocytes. Further, compared with HEPG2...... cells, three- to fourfold more FVIIa bound to rat primary hepatocytes, and the bound FVIIa was internalized at a faster rate. Similar FVIIa binding and internalization profiles were observed in primary human hepatocytes. Plasma inhibitors had no effect on FVIIa binding and internalization in hepatocytes...

  14. Unification predictions

    International Nuclear Information System (INIS)

    Ghilencea, D.; Ross, G.G.; Lanzagorta, M.

    1997-07-01

    The unification of gauge couplings suggests that there is an underlying (supersymmetric) unification of the strong, electromagnetic and weak interactions. The prediction of the unification scale may be the first quantitative indication that this unification may extend to unification with gravity. We make a precise determination of these predictions for a class of models which extend the multiplet structure of the Minimal Supersymmetric Standard Model to include the heavy states expected in many Grand Unified and/or superstring theories. We show that there is a strong cancellation between the 2-loop and threshold effects. As a result the net effect is smaller than previously thought, giving a small increase in both the unification scale and the value of the strong coupling at low energies. (author). 15 refs, 5 figs

  15. The involvement of coordinative interactions in the binding of dihydrolipoamide dehydrogenase to titanium dioxide-Localization of a putative binding site.

    Science.gov (United States)

    Dayan, Avraham; Babin, Gilad; Ganoth, Assaf; Kayouf, Nivin Samir; Nitoker Eliaz, Neta; Mukkala, Srijana; Tsfadia, Yossi; Fleminger, Gideon

    2017-08-01

    Titanium (Ti) and its alloys are widely used in orthodontic and orthopedic implants by virtue to their high biocompatibility, mechanical strength, and high resistance to corrosion. Biointegration of the implants with the tissue requires strong interactions, which involve biological molecules, proteins in particular, with metal oxide surfaces. An exocellular high-affinity titanium dioxide (TiO 2 )-binding protein (TiBP), purified from Rhodococcus ruber, has been previously studied in our lab. This protein was shown to be homologous with the orthologous cytoplasmic rhodococcal dihydrolipoamide dehydrogenase (rhDLDH). We have found that rhDLDH and its human homolog (hDLDH) share the TiO 2 -binding capabilities with TiBP. Intrigued by the unique TiO 2 -binding properties of hDLDH, we anticipated that it may serve as a molecular bridge between Ti-based medical structures and human tissues. The objective of the current study was to locate the region and the amino acids of the protein that mediate the protein-TiO 2 surface interaction. We demonstrated the role of acidic amino acids in the nonelectrostatic enzyme/dioxide interactions at neutral pH. The observation that the interaction of DLDH with various metal oxides is independent of their isoelectric values strengthens this notion. DLDH does not lose its enzymatic activity upon binding to TiO 2 , indicating that neither the enzyme undergoes major conformational changes nor the TiO 2 binding site is blocked. Docking predictions suggest that both rhDLDH and hDLDH bind TiO 2 through similar regions located far from the active site and the dimerization sites. The putative TiO 2 -binding regions of both the bacterial and human enzymes were found to contain a CHED (Cys, His, Glu, Asp) motif, which has been shown to participate in metal-binding sites in proteins. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Analysis of functional importance of binding sites in the Drosophila gap gene network model.

    Science.gov (United States)

    Kozlov, Konstantin; Gursky, Vitaly V; Kulakovskiy, Ivan V; Dymova, Arina; Samsonova, Maria

    2015-01-01

    The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions. We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions. The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

  17. Methods and systems for identifying ligand-protein binding sites

    KAUST Repository

    Gao, Xin

    2016-05-06

    The invention provides a novel integrated structure and system-based approach for drug target prediction that enables the large-scale discovery of new targets for existing drugs Novel computer-readable storage media and computer systems are also provided. Methods and systems of the invention use novel sequence order-independent structure alignment, hierarchical clustering, and probabilistic sequence similarity techniques to construct a probabilistic pocket ensemble (PPE) that captures even promiscuous structural features of different binding sites for a drug on known targets. The drug\\'s PPE is combined with an approximation of the drug delivery profile to facilitate large-scale prediction of novel drug- protein interactions with several applications to biological research and drug development.

  18. Acetylcholine-Binding Protein Engineered to Mimic the α4-α4 Binding Pocket in α4β2 Nicotinic Acetylcholine Receptors Reveals Interface Specific Interactions Important for Binding and Activity

    DEFF Research Database (Denmark)

    Shahsavar, Azadeh; Ahring, Philip K; Olsen, Jeppe A

    2015-01-01

    Neuronal α4β2 nicotinic acetylcholine receptors are attractive drug targets for psychiatric and neurodegenerative disorders and smoking cessation aids. Recently, a third agonist binding site between two α4 subunits in the (α4)(3)(β2)(2) receptor subpopulation was discovered. In particular, three......-yl)-1,4-diazepane], highlights the roles of the three residues in determining binding affinities and functional properties of ligands at the α4-α4 interface. Confirmed by mutational studies, our structures suggest a unique ligand-specific role of residue H142 on the α4 subunit. In the cocrystal...... that could not be predicted based on wild-type Ls-AChBP structures in complex with the same agonists. The results show that an unprecedented correlation between binding in engineered AChBPs and functional receptors can be obtained and provide new opportunities for structure-based design of drugs targeting...

  19. Human Leukocyte Antigen (HLA) Class I Restricted Epitope Discovery in Yellow Fewer and Dengue Viruses: Importance of HLA Binding Strength

    DEFF Research Database (Denmark)

    Lund, Ole; Nascimento, Eduardo J. M.; Maciel, Milton, Jr

    2011-01-01

    Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV...... inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding...

  20. MHC Class II epitope predictive algorithms

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole; Buus, S

    2010-01-01

    Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been...... developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason...

  1. Computational predictions of zinc oxide hollow structures

    Science.gov (United States)

    Tuoc, Vu Ngoc; Huan, Tran Doan; Thao, Nguyen Thi

    2018-03-01

    Nanoporous materials are emerging as potential candidates for a wide range of technological applications in environment, electronic, and optoelectronics, to name just a few. Within this active research area, experimental works are predominant while theoretical/computational prediction and study of these materials face some intrinsic challenges, one of them is how to predict porous structures. We propose a computationally and technically feasible approach for predicting zinc oxide structures with hollows at the nano scale. The designed zinc oxide hollow structures are studied with computations using the density functional tight binding and conventional density functional theory methods, revealing a variety of promising mechanical and electronic properties, which can potentially find future realistic applications.

  2. A hybrid approach for predicting promiscuous MHC class I restricted ...

    Indian Academy of Sciences (India)

    Prakash

    2006-09-15

    Sep 15, 2006 ... with existing MHC binder prediction methods for alleles studied by both ... in locating the promiscuous MHC binding regions from antigen sequence. ... Artificial neural network; MHC class I alleles; promiscuous binders; ... this problem by developing methods for prediction for ... In case equal number of.

  3. Bacterial periplasmic sialic acid-binding proteins exhibit a conserved binding site

    Energy Technology Data Exchange (ETDEWEB)

    Gangi Setty, Thanuja [Institute for Stem Cell Biology and Regenerative Medicine, NCBS Campus, GKVK Post, Bangalore, Karnataka 560 065 (India); Cho, Christine [Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109 (United States); Govindappa, Sowmya [Institute for Stem Cell Biology and Regenerative Medicine, NCBS Campus, GKVK Post, Bangalore, Karnataka 560 065 (India); Apicella, Michael A. [Carver College of Medicine, University of Iowa, Iowa City, IA 52242-1109 (United States); Ramaswamy, S., E-mail: ramas@instem.res.in [Institute for Stem Cell Biology and Regenerative Medicine, NCBS Campus, GKVK Post, Bangalore, Karnataka 560 065 (India)

    2014-07-01

    Structure–function studies of sialic acid-binding proteins from F. nucleatum, P. multocida, V. cholerae and H. influenzae reveal a conserved network of hydrogen bonds involved in conformational change on ligand binding. Sialic acids are a family of related nine-carbon sugar acids that play important roles in both eukaryotes and prokaryotes. These sialic acids are incorporated/decorated onto lipooligosaccharides as terminal sugars in multiple bacteria to evade the host immune system. Many pathogenic bacteria scavenge sialic acids from their host and use them for molecular mimicry. The first step of this process is the transport of sialic acid to the cytoplasm, which often takes place using a tripartite ATP-independent transport system consisting of a periplasmic binding protein and a membrane transporter. In this paper, the structural characterization of periplasmic binding proteins from the pathogenic bacteria Fusobacterium nucleatum, Pasteurella multocida and Vibrio cholerae and their thermodynamic characterization are reported. The binding affinities of several mutations in the Neu5Ac binding site of the Haemophilus influenzae protein are also reported. The structure and the thermodynamics of the binding of sugars suggest that all of these proteins have a very well conserved binding pocket and similar binding affinities. A significant conformational change occurs when these proteins bind the sugar. While the C1 carboxylate has been identified as the primary binding site, a second conserved hydrogen-bonding network is involved in the initiation and stabilization of the conformational states.

  4. Bacterial periplasmic sialic acid-binding proteins exhibit a conserved binding site

    International Nuclear Information System (INIS)

    Gangi Setty, Thanuja; Cho, Christine; Govindappa, Sowmya; Apicella, Michael A.; Ramaswamy, S.

    2014-01-01

    Structure–function studies of sialic acid-binding proteins from F. nucleatum, P. multocida, V. cholerae and H. influenzae reveal a conserved network of hydrogen bonds involved in conformational change on ligand binding. Sialic acids are a family of related nine-carbon sugar acids that play important roles in both eukaryotes and prokaryotes. These sialic acids are incorporated/decorated onto lipooligosaccharides as terminal sugars in multiple bacteria to evade the host immune system. Many pathogenic bacteria scavenge sialic acids from their host and use them for molecular mimicry. The first step of this process is the transport of sialic acid to the cytoplasm, which often takes place using a tripartite ATP-independent transport system consisting of a periplasmic binding protein and a membrane transporter. In this paper, the structural characterization of periplasmic binding proteins from the pathogenic bacteria Fusobacterium nucleatum, Pasteurella multocida and Vibrio cholerae and their thermodynamic characterization are reported. The binding affinities of several mutations in the Neu5Ac binding site of the Haemophilus influenzae protein are also reported. The structure and the thermodynamics of the binding of sugars suggest that all of these proteins have a very well conserved binding pocket and similar binding affinities. A significant conformational change occurs when these proteins bind the sugar. While the C1 carboxylate has been identified as the primary binding site, a second conserved hydrogen-bonding network is involved in the initiation and stabilization of the conformational states

  5. Structure-function analysis of peroxisomal ATP-binding cassette transporters using chimeric dimers

    NARCIS (Netherlands)

    Geillon, Flore; Gondcaille, Catherine; Charbonnier, Soëli; van Roermund, Carlo W.; Lopez, Tatiana E.; Dias, Alexandre M. M.; Pais de Barros, Jean-Paul; Arnould, Christine; Wanders, Ronald J.; Trompier, Doriane; Savary, Stéphane

    2014-01-01

    ABCD1 and ABCD2 are two closely related ATP-binding cassette half-transporters predicted to homodimerize and form peroxisomal importers for fatty acyl-CoAs. Available evidence has shown that ABCD1 and ABCD2 display a distinct but overlapping substrate specificity, although much remains to be learned

  6. footprintDB: a database of transcription factors with annotated cis elements and binding interfaces.

    Science.gov (United States)

    Sebastian, Alvaro; Contreras-Moreira, Bruno

    2014-01-15

    Traditional and high-throughput techniques for determining transcription factor (TF) binding specificities are generating large volumes of data of uneven quality, which are scattered across individual databases. FootprintDB integrates some of the most comprehensive freely available libraries of curated DNA binding sites and systematically annotates the binding interfaces of the corresponding TFs. The first release contains 2422 unique TF sequences, 10 112 DNA binding sites and 3662 DNA motifs. A survey of the included data sources, organisms and TF families was performed together with proprietary database TRANSFAC, finding that footprintDB has a similar coverage of multicellular organisms, while also containing bacterial regulatory data. A search engine has been designed that drives the prediction of DNA motifs for input TFs, or conversely of TF sequences that might recognize input regulatory sequences, by comparison with database entries. Such predictions can also be extended to a single proteome chosen by the user, and results are ranked in terms of interface similarity. Benchmark experiments with bacterial, plant and human data were performed to measure the predictive power of footprintDB searches, which were able to correctly recover 10, 55 and 90% of the tested sequences, respectively. Correctly predicted TFs had a higher interface similarity than the average, confirming its diagnostic value. Web site implemented in PHP,Perl, MySQL and Apache. Freely available from http://floresta.eead.csic.es/footprintdb.

  7. Specific insulin binding in bovine chromaffin cells; demonstration of preferential binding to adrenalin-storing cells

    International Nuclear Information System (INIS)

    Serck-Hanssen, G.; Soevik, O.

    1987-01-01

    Insulin binding was studied in subpopulations of bovine chromaffin cells enriched in adrenalin-producing cells (A-cells) or noradrenalin-producing cells (NA-cells). Binding of 125 I-insulin was carried out at 15 0 C for 3 hrs in the absence or presence of excess unlabeled hormone. Four fractions of cells were obtained by centrifugation on a stepwise bovine serum albumin gradient. The four fractions were all shown to bind insulin in a specific manner and the highest binding was measured in the cell layers of higher densities, containing mainly A-cells. The difference in binding of insulin to the four subpopulations of chromaffin cells seemed to be related to differences in numbers of receptors as opposed to receptor affinities. The authors conclude that bovine chromaffin cells possess high affinity binding sites for insulin and that these binding sites are mainly confined to A-cells. 24 references, 2 figures, 1 table

  8. Resolving the fast kinetics of cooperative binding: Ca2+ buffering by calretinin.

    Directory of Open Access Journals (Sweden)

    Guido C Faas

    2007-11-01

    Full Text Available Cooperativity is one of the most important properties of molecular interactions in biological systems. It is the ability to influence ligand binding at one site of a macromolecule by previous ligand binding at another site of the same molecule. As a consequence, the affinity of the macromolecule for the ligand is either decreased (negative cooperativity or increased (positive cooperativity. Over the last 100 years, O2 binding to hemoglobin has served as the paradigm for cooperative ligand binding and allosteric modulation, and four practical models were developed to quantitatively describe the mechanism: the Hill, the Adair-Klotz, the Monod-Wyman-Changeux, and the Koshland-Némethy-Filmer models. The predictions of these models apply under static conditions when the binding reactions are at equilibrium. However, in a physiological setting, e.g., inside a cell, the timing and dynamics of the binding events are essential. Hence, it is necessary to determine the dynamic properties of cooperative binding to fully understand the physiological implications of cooperativity. To date, the Monod-Wyman-Changeux model was applied to determine the kinetics of cooperative binding to biologically active molecules. In this model, cooperativity is established by postulating two allosteric isoforms with different binding properties. However, these studies were limited to special cases, where transition rates between allosteric isoforms are much slower than the binding rates or where binding and unbinding rates could be measured independently. For all other cases, the complex mathematical description precludes straightforward interpretations. Here, we report on calculating for the first time the fast dynamics of a cooperative binding process, the binding of Ca2+ to calretinin. Calretinin is a Ca2+-binding protein with four cooperative binding sites and one independent binding site. The Ca2+ binding to calretinin was assessed by measuring the decay of free Ca2

  9. Chiral Cliffs: Investigating the Influence of Chirality on Binding Affinity.

    Science.gov (United States)

    Schneider, Nadine; Lewis, Richard A; Fechner, Nikolas; Ertl, Peter

    2018-05-11

    Chirality is understood by many as a binary concept: a molecule is either chiral or it is not. In terms of the action of a structure on polarized light, this is indeed true. When examined through the prism of molecular recognition, the answer becomes more nuanced. In this work, we investigated chiral behavior on protein-ligand binding: when does chirality make a difference in binding activity? Chirality is a property of the 3D structure, so recognition also requires an appreciation of the conformation. In many situations, the bioactive conformation is undefined. We set out to address this by defining and using several novel 2D descriptors to capture general characteristic features of the chiral center. Using machine-learning methods, we built different predictive models to estimate if a chiral pair (a set of two enantiomers) might exhibit a chiral cliff in a binding assay. A set of about 3800 chiral pairs extracted from the ChEMBL23 database was used to train and test our models. By achieving an accuracy of up to 75 %, our models provide good performance in discriminating chiral cliffs from non-cliffs. More importantly, we were able to derive some simple guidelines for when one can reasonably use a racemate and when an enantiopure compound is needed in an assay. We critically discuss our results and show detailed examples of using our guidelines. Along with this publication we provide our dataset, our novel descriptors, and the Python code to rebuild the predictive models. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Human liver aldehyde dehydrogenase: coenzyme binding

    International Nuclear Information System (INIS)

    Kosley, L.L.; Pietruszko, R.

    1987-01-01

    The binding of [U- 14 C] NAD to mitochondrial (E2) and cytoplasmin(E1) aldehyde dehydrogenase was measured by gel filtration and sedimentation techniques. The binding data for NAD and (E1) yielded linear Scatchard plots giving a dissociation constant of 25 (+/- 8) uM and the stoichiometry of 2 mol of NAD bound per mol of E1. The binding data for NAD and (E2) gave nonlinear Scatchard plots. The binding of NADH to E2 was measured via fluorescence enhancement; this could not be done with E1 because there was no signal. The dissociation constant for E2 by this technique was 0.7 (+/- 0.4) uM and stoichiometry of 1.0 was obtained. The binding of [U- 14 C] NADH to (E1) and (E2) was also measured by the sedimentation technique. The binding data for (E1) and NADH gave linear Scatchard plots giving a dissociation constant of 13 (+/- 6) uM and the stoichiometry of 2.0. The binding data for NADH to (E2) gave nonlinear Scatchard plots. With (E1), the dissociation constants for both NAD and NADH are similar to those determined kinetically, but the stoichiometry is only half of that found by stopped flow technique. With (E2) the dissociation constant by fluorometric procedure was 2 orders of magnitude less than that from catalytic reaction

  11. Increased thyrotropin binding in hyperfunctioning thyroid nodules.

    Science.gov (United States)

    Müller-Gärtner, H W; Schneider, C; Bay, V; Tadt, A; Rehpenning, W; de Heer, K; Jessel, M

    1987-08-01

    The object of this study was to investigate TSH receptors in hyperfunctioning thyroid nodules (HFN). In HFN, obtained from seven patients, 125-I-TSH binding as determined by equilibrium binding analysis on particulate membrane preparations, was found to be significantly increased as compared with normal thyroid tissues (five patients; P less than 0.001). Scatchard analysis of TSH-binding revealed two kinds of binding sites for both normal thyroid tissue and HFN, and displayed significantly increased association constants of high- and low-affinity binding sites in HFN (Ka = 11.75 +/- 6.8 10(9) M-1, P less than 0.001 and Ka = 2.1 +/- 1.0 10(7) M-1, P less than 0.025; x +/- SEM) as compared with normal thyroid tissue (Ka = 0.25 +/- 0.06 10(9) M-1, Ka = 0.14 +/- 0.03 10(7) M-1; x +/- SEM). The capacity of the high-affinity binding sites in HFN was found to be decreased (1.8 +/- 1.1 pmol/mg protein, x +/- SEM) in comparison with normal thyroid tissue (4.26 +/- 1.27 pmol/mg protein; x +/- SEM). TSH-receptor autoradiography applied to cryostatic tissue sections confirmed increased TSH binding of the follicular epithelium in HFN. These data suggest that an increased affinity of TSH-receptor sites in HFN in iodine deficient areas may be an important event in thyroid autonomy.

  12. Rational redesign of neutral endopeptidase binding to merlin and moesin proteins

    Science.gov (United States)

    Niv, Masha Y; Iida, Katsuyuki; Zheng, Rong; Horiguchi, Akio; Shen, Ruoqian; Nanus, David M

    2009-01-01

    Neutral endopeptidase (NEP) is a 90- to 110-kDa cell-surface peptidase that is normally expressed by numerous tissues but whose expression is lost or reduced in a variety of malignancies. The anti-tumorigenic function of NEP is mediated not only by its catalytic activity but also through direct protein–protein interactions of its cytosolic region with several binding partners, including Lyn kinase, PTEN, and ezrin/radixin/moesin (ERM) proteins. We have previously shown that mutation of the K19K20K21 basic cluster in NEPs' cytosolic region to residues QNI disrupts binding to the ERM proteins. Here we show that the ERM-related protein merlin (NF2) does not bind NEP or its cytosolic region. Using experimental data, threading, and sequence analysis, we predicted the involvement of moesin residues E159Q160 in binding to the NEP cytosolic domain. Mutation of these residues to NL (to mimic the corresponding N159L160 residues in the nonbinder merlin) disrupted moesin binding to NEP. Mutation of residues N159L160Y161K162M163 in merlin to the corresponding moesin residues resulted in NEP binding to merlin. This engineered NEP peptide–merlin interaction was diminished by the QNI mutation in NEP, supporting the role of the NEP basic cluster in binding. We thus identified the region of interaction between NEP and moesin, and engineered merlin into a NEP-binding protein. These data form the basis for further exploration of the details of NEP-ERM binding and function. PMID:19388049

  13. Characterization and quantitation of concanavalin A binding by plasma membrane enriched fractions from soybean root

    International Nuclear Information System (INIS)

    Berkowitz, R.L.; Travis, R.L.

    1981-01-01

    The binding of concanavalin A (Con A) to soybean root membranes in plasma membrane enriched fractions (recovered from the 34/45% interface of simplified discontinuous sucrose density gradients) was studied using a radiochemical assay employing tritated ( 3 H)-Con A. The effect of lectin concentration, time, and membrane protein concentration on the specific binding of 3 H-Con A by the membranes was evaluated. Kinetic analyses showed that Con A will react with membranes in that fraction in a characteristic and predictable manner. The parameters for an optimal and standard binding assay were established. Maximal binding occurred with Con A concentrations in the range of 8 to 16% of the total membrane protein with incubation times greater than 40 min at 22 C. Approximately 10 15 molecules of 3 H-Con A were bound per microgram of membrane protein at saturation. Binding was reversible. Greater than 92% of the total Con A bound at saturation was released by addition of α-methyl mannoside. A major peak of 3 H-Con A binding was also observed in fractions recovered from the 25/30% interface of a complex discontinuous sucrose density gradient when membranes were isolated in the absence of Mg 2+ . When high Mg 2+ was present in the isolation and gradient media, the peak was shifted to a fraction recovered from the 34/38% sucrose interface. These results suggest that Con A binding sites are also present on membranes of the endoplasmic reticulum. The amount of Con A bound by endoplasmic reticulum membranes was at least twice the amount bound by membranes in plasma membrane enriched fractions when binding was compared on a per unit membrane protein basis. In contrast, mitochondrial inner membranes, which equilibrate at the same density as plasma membranes, had little ability to bind the lectin

  14. A microscopic insight from conformational thermodynamics to functional ligand binding in proteins.

    Science.gov (United States)

    Sikdar, Samapan; Chakrabarti, J; Ghosh, Mahua

    2014-12-01

    We show that the thermodynamics of metal ion-induced conformational changes aid to understand the functions of protein complexes. This is illustrated in the case of a metalloprotein, alpha-lactalbumin (aLA), a divalent metal ion binding protein. We use the histograms of dihedral angles of the protein, generated from all-atom molecular dynamics simulations, to calculate conformational thermodynamics. The thermodynamically destabilized and disordered residues in different conformational states of a protein are proposed to serve as binding sites for ligands. This is tested for β-1,4-galactosyltransferase (β4GalT) binding to the Ca(2+)-aLA complex, in which the binding residues are known. Among the binding residues, the C-terminal residues like aspartate (D) 116, glutamine (Q) 117, tryptophan (W) 118 and leucine (L) 119 are destabilized and disordered and can dock β4GalT onto Ca(2+)-aLA. No such thermodynamically favourable binding residues can be identified in the case of the Mg(2+)-aLA complex. We apply similar analysis to oleic acid binding and predict that the Ca(2+)-aLA complex can bind to oleic acid through the basic histidine (H) 32 of the A2 helix and the hydrophobic residues, namely, isoleucine (I) 59, W60 and I95, of the interfacial cleft. However, the number of destabilized and disordered residues in Mg(2+)-aLA are few, and hence, the oleic acid binding to Mg(2+)-bound aLA is less stable than that to the Ca(2+)-aLA complex. Our analysis can be generalized to understand the functionality of other ligand bound proteins.

  15. NADP+ binding to the regulatory subunit of methionine adenosyltransferase II increases intersubunit binding affinity in the hetero-trimer.

    Directory of Open Access Journals (Sweden)

    Beatriz González

    Full Text Available Mammalian methionine adenosyltransferase II (MAT II is the only hetero-oligomer in this family of enzymes that synthesize S-adenosylmethionine using methionine and ATP as substrates. Binding of regulatory β subunits and catalytic α2 dimers is known to increase the affinity for methionine, although scarce additional information about this interaction is available. This work reports the use of recombinant α2 and β subunits to produce oligomers showing kinetic parameters comparable to MAT II purified from several tissues. According to isothermal titration calorimetry data and densitometric scanning of the stained hetero-oligomer bands on denatured gels, the composition of these oligomers is that of a hetero-trimer with α2 dimers associated to single β subunits. Additionally, the regulatory subunit is able to bind NADP(+ with a 1:1 stoichiometry, the cofactor enhancing β to α2-dimer binding affinity. Mutants lacking residues involved in NADP(+ binding and N-terminal truncations of the β subunit were able to oligomerize with α2-dimers, although the kinetic properties appeared altered. These data together suggest a role for both parts of the sequence in the regulatory role exerted by the β subunit on catalysis. Moreover, preparation of a structural model for the hetero-oligomer, using the available crystal data, allowed prediction of the regions involved in β to α2-dimer interaction. Finally, the implications that the presence of different N-terminals in the β subunit could have on MAT II behavior are discussed in light of the recent identification of several splicing forms of this subunit in hepatoma cells.

  16. Isothermal titration calorimetric and computational studies on the binding of chitooligosaccharides to pumpkin (Cucurbita maxima) phloem exudate lectin.

    Science.gov (United States)

    Narahari, Akkaladevi; Singla, Hitesh; Nareddy, Pavan Kumar; Bulusu, Gopalakrishnan; Surolia, Avadhesha; Swamy, Musti J

    2011-04-14

    The interaction of chitooligosaccharides [(GlcNAc)(2-6)] with pumpkin phloem exudate lectin (PPL) was investigated by isothermal titration calorimetry and computational methods. The dimeric PPL binds to (GlcNAc)(3-5) with binding constants of 1.26-1.53 × 10(5) M(-1) at 25 °C, whereas chitobiose exhibits approximately 66-fold lower affinity. Interestingly, chitohexaose shows nearly 40-fold higher affinity than chitopentaose with a binding constant of 6.16 × 10(6) M(-1). The binding stoichiometry decreases with an increase in the oligosaccharide size from 2.26 for chitobiose to 1.08 for chitohexaose. The binding reaction was essentially enthalpy driven with negative entropic contribution, suggesting that hydrogen bonds and van der Waals' interactions are the main factors that stabilize PPL-saccharide association. The three-dimensional structure of PPL was predicted by homology modeling, and binding of chitooligosaccharides was investigated by molecular docking and molecular dynamics simulations, which showed that the protein binding pocket can accommodate up to three GlcNAc residues, whereas additional residues in chitotetraose and chitopentaose did not exhibit any interactions with the binding pocket. Docking studies with chitohexaose indicated that the two triose units of the molecule could interact with different protein binding sites, suggesting formation of higher order complexes by the higher oligomers of GlcNAc by their simultaneous interaction with two protein molecules.

  17. Computational analysis of protein-protein interfaces involving an alpha helix: insights for terphenyl-like molecules binding.

    Science.gov (United States)

    Isvoran, Adriana; Craciun, Dana; Martiny, Virginie; Sperandio, Olivier; Miteva, Maria A

    2013-06-14

    Protein-Protein Interactions (PPIs) are key for many cellular processes. The characterization of PPI interfaces and the prediction of putative ligand binding sites and hot spot residues are essential to design efficient small-molecule modulators of PPI. Terphenyl and its derivatives are small organic molecules known to mimic one face of protein-binding alpha-helical peptides. In this work we focus on several PPIs mediated by alpha-helical peptides. We performed computational sequence- and structure-based analyses in order to evaluate several key physicochemical and surface properties of proteins known to interact with alpha-helical peptides and/or terphenyl and its derivatives. Sequence-based analysis revealed low sequence identity between some of the analyzed proteins binding alpha-helical peptides. Structure-based analysis was performed to calculate the volume, the fractal dimension roughness and the hydrophobicity of the binding regions. Besides the overall hydrophobic character of the binding pockets, some specificities were detected. We showed that the hydrophobicity is not uniformly distributed in different alpha-helix binding pockets that can help to identify key hydrophobic hot spots. The presence of hydrophobic cavities at the protein surface with a more complex shape than the entire protein surface seems to be an important property related to the ability of proteins to bind alpha-helical peptides and low molecular weight mimetics. Characterization of similarities and specificities of PPI binding sites can be helpful for further development of small molecules targeting alpha-helix binding proteins.

  18. Measuring Binding Affinity of Protein-Ligand Interaction Using Spectrophotometry: Binding of Neutral Red to Riboflavin-Binding Protein

    Science.gov (United States)

    Chenprakhon, Pirom; Sucharitakul, Jeerus; Panijpan, Bhinyo; Chaiyen, Pimchai

    2010-01-01

    The dissociation constant, K[subscript d], of the binding of riboflavin-binding protein (RP) with neutral red (NR) can be determined by titrating RP to a fixed concentration of NR. Upon adding RP to the NR solution, the maximum absorption peak of NR shifts to 545 nm from 450 nm for the free NR. The change of the absorption can be used to determine…

  19. The moral ties that bind . . . Even to out-groups: the interactive effect of moral identity and the binding moral foundations.

    Science.gov (United States)

    Smith, Isaac H; Aquino, Karl; Koleva, Spassena; Graham, Jesse

    2014-08-01

    Throughout history, principles such as obedience, loyalty, and purity have been instrumental in binding people together and helping them thrive as groups, tribes, and nations. However, these same principles have also led to in-group favoritism, war, and even genocide. Does adhering to the binding moral foundations that underlie such principles unavoidably lead to the derogation of out-group members? We demonstrated that for people with a strong moral identity, the answer is "no," because they are more likely than those with a weak moral identity to extend moral concern to people belonging to a perceived out-group. Across three studies, strongly endorsing the binding moral foundations indeed predicted support for the torture of out-group members (Studies 1a and 1b) and withholding of necessary help from out-group members (Study 2), but this relationship was attenuated among participants who also had a strong moral identity. © The Author(s) 2014.

  20. Hardware device binding and mutual authentication

    Science.gov (United States)

    Hamlet, Jason R; Pierson, Lyndon G

    2014-03-04

    Detection and deterrence of device tampering and subversion by substitution may be achieved by including a cryptographic unit within a computing device for binding multiple hardware devices and mutually authenticating the devices. The cryptographic unit includes a physically unclonable function ("PUF") circuit disposed in or on the hardware device, which generates a binding PUF value. The cryptographic unit uses the binding PUF value during an enrollment phase and subsequent authentication phases. During a subsequent authentication phase, the cryptographic unit uses the binding PUF values of the multiple hardware devices to generate a challenge to send to the other device, and to verify a challenge received from the other device to mutually authenticate the hardware devices.

  1. Peptide binding specificity of the chaperone calreticulin

    DEFF Research Database (Denmark)

    Sandhu, N.; Duus, K.; Jorgensen, C.S.

    2007-01-01

    Calreticulin is a molecular chaperone with specificity for polypeptides and N-linked monoglucosylated glycans. In order to determine the specificity of polypeptide binding, the interaction of calreticulin with polypeptides was investigated using synthetic peptides of different length and composit......Calreticulin is a molecular chaperone with specificity for polypeptides and N-linked monoglucosylated glycans. In order to determine the specificity of polypeptide binding, the interaction of calreticulin with polypeptides was investigated using synthetic peptides of different length...... than 5 amino acids showed binding and a clear correlation with hydrophobicity was demonstrated for oligomers of different hydrophobic amino acids. Insertion of hydrophilic amino acids in a hydrophobic sequence diminished or abolished binding. In conclusion our results show that calreticulin has...

  2. Fatty Acid Binding Proteins in Prostate Cancer

    National Research Council Canada - National Science Library

    Jett, Marti

    2000-01-01

    We have shown that there is a distinct pattern of fatty acid binding protein (FAEP) expression in prostate cancer vs normal cells and that finding has be confirmed in patient samples of biopsy specimens...

  3. Kaempferol-human serum albumin interaction: Characterization of the induced chirality upon binding by experimental circular dichroism and TDDFT calculations

    Science.gov (United States)

    Matei, Iulia; Ionescu, Sorana; Hillebrand, Mihaela

    2012-10-01

    The experimental induced circular dichroism (ICD) and absorption spectra of the achiral flavonoid kaempferol upon binding to human serum albumin (HSA) were correlated to electronic CD and UV-vis spectra theoretically predicted by time-dependent density functional theory (TDDFT). The neutral and four anionic species of kaempferol in various conformations were considered in the calculations. The appearance of the experimental ICD signal was rationalized in terms of kaempferol binding to HSA in a distorted, chiral, rigid conformation. The comparison between the experimental and simulated spectra allowed for the identification of the kaempferol species that binds to HSA, namely the anion generated by deprotonation of the hydroxyl group in position 7. This approach constitutes a convenient method for evidencing the binding species and for determining its conformation in the binding pocket of the protein. Its main advantage over the UV-vis absorption method lays in the fact that only the bound ligand species gives an ICD signal.

  4. A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana

    DEFF Research Database (Denmark)

    Lindemose, Søren; Jensen, Michael Krogh; de Velde, Jan Van

    2014-01-01

    regulatory networks of 12 NAC transcription factors. Our data offer specific single-base resolution fingerprints for most TFs studied and indicate that NAC DNA-binding specificities might be predicted from their DNA-binding domain's sequence. The developed methodology, including the application......Target gene identification for transcription factors is a prerequisite for the systems wide understanding of organismal behaviour. NAM-ATAF1/2-CUC2 (NAC) transcription factors are amongst the largest transcription factor families in plants, yet limited data exist from unbiased approaches to resolve...... the DNA-binding preferences of individual members. Here, we present a TF-target gene identification workflow based on the integration of novel protein binding microarray data with gene expression and multi-species promoter sequence conservation to identify the DNA-binding specificities and the gene...

  5. Locations of the three primary binding sites for long-chain fatty acids on bovine serum albumin

    International Nuclear Information System (INIS)

    Hamilton, J.A.; Era, S.; Bhamidipati, S.P.; Reed, R.G.

    1991-01-01

    Binding of 13 C-enriched oleic acid to bovine serum albumin and to three large proteolytic fragments of albumin - two complementary fragments corresponding to the two halved of albumin and one fragment corresponding to the carboxyl-terminal domain - yielded unique patterns of NMR resonances (chemical shifts and relative intensities) that were used to identify the locations of binding of the first 5 mol of oleic acid to the multidomain albumin molecule. The first 3 mol of oleic acid added to intact albumin generated three distinct NMR resonances as a result of simultaneous binding of oleic acid to three heterogeneous sites (primary sites). This distribution suggests albumin to be a less symmetrical binding molecule than theoretical models predict. This work also demonstrates the power of NMR for the study of microenvironments of individual fatty acid binding sites in specific domain

  6. Bilirubin Binding Capacity in the Preterm Neonate.

    Science.gov (United States)

    Amin, Sanjiv B

    2016-06-01

    Total serum/plasma bilirubin (TB), the biochemical measure currently used to evaluate and manage hyperbilirubinemia, is not a useful predictor of bilirubin-induced neurotoxicity in premature infants. Altered bilirubin-albumin binding in premature infants limits the usefulness of TB in premature infants. In this article, bilirubin-albumin binding, a modifying factor for bilirubin-induced neurotoxicity, in premature infants is reviewed. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Exciton Binding Energy of Monolayer WS2

    Science.gov (United States)

    Zhu, Bairen; Chen, Xi; Cui, Xiaodong

    2015-03-01

    The optical properties of monolayer transition metal dichalcogenides (TMDC) feature prominent excitonic natures. Here we report an experimental approach to measuring the exciton binding energy of monolayer WS2 with linear differential transmission spectroscopy and two-photon photoluminescence excitation spectroscopy (TP-PLE). TP-PLE measurements show the exciton binding energy of 0.71 +/- 0.01 eV around K valley in the Brillouin zone.

  8. P-shell hyperon binding energies

    International Nuclear Information System (INIS)

    Koetsier, D.; Amos, K.

    1991-01-01

    A shell model for lambda hypernuclei has been used to determine the binding energy of the hyperon in nuclei throughout the p shell. Conventional (Cohen and Kurath) potential energies for nucleon-nucleon interactions were used with hyperon-nucleon interactions taken from Nijmegen one boson exchange potentials. The hyperon binding energies calculated from these potentials compare well with measured values. 7 refs., 2 figs

  9. Specificity of anion-binding in the substrate-pocket ofbacteriorhodopsin

    Energy Technology Data Exchange (ETDEWEB)

    Facciotti, Marc T.; Cheung, Vincent S.; Lunde, Christopher S.; Rouhani, Shahab; Baliga, Nitin S.; Glaeser, Robert M.

    2003-08-30

    The structure of the D85S mutant of bacteriorhodopsin with a nitrate anion bound in the Schiff-base binding site, and the structure of the anion-free protein have been obtained in the same crystal form. Together with the previously solved structures of this anion pump, in both the anion-free state and bromide-bound state, these new structures provide insight into how this mutant of bacteriorhodopsin is able to bind a variety of different anions in the same binding pocket. The structural analysis reveals that the main structural change that accommodates different anions is the repositioning of the polar side-chain of S85. On the basis of these x-ray crystal structures, the prediction is then made that the D85S/D212N double mutant might bind similar anions and do so over a broader pH range than does the single mutant. Experimental comparison of the dissociation constants, K{sub d}, for a variety of anions confirms this prediction and demonstrates, in addition, that the binding affinity is dramatically improved by the D212N substitution.

  10. Genome-wide conserved consensus transcription factor binding motifs are hyper-methylated

    Directory of Open Access Journals (Sweden)

    Down Thomas A

    2010-09-01

    Full Text Available Abstract Background DNA methylation can regulate gene expression by modulating the interaction between DNA and proteins or protein complexes. Conserved consensus motifs exist across the human genome ("predicted transcription factor binding sites": "predicted TFBS" but the large majority of these are proven by chromatin immunoprecipitation and high throughput sequencing (ChIP-seq not to be biological transcription factor binding sites ("empirical TFBS". We hypothesize that DNA methylation at conserved consensus motifs prevents promiscuous or disorderly transcription factor binding. Results Using genome-wide methylation maps of the human heart and sperm, we found that all conserved consensus motifs as well as the subset of those that reside outside CpG islands have an aggregate profile of hyper-methylation. In contrast, empirical TFBS with conserved consensus motifs have a profile of hypo-methylation. 40% of empirical TFBS with conserved consensus motifs resided in CpG islands whereas only 7% of all conserved consensus motifs were in CpG islands. Finally we further identified a minority subset of TF whose profiles are either hypo-methylated or neutral at their respective conserved consensus motifs implicating that these TF may be responsible for establishing or maintaining an un-methylated DNA state, or whose binding is not regulated by DNA methylation. Conclusions Our analysis supports the hypothesis that at least for a subset of TF, empirical binding to conserved consensus motifs genome-wide may be controlled by DNA methylation.

  11. Extracellular and intracellular steroid binding proteins

    International Nuclear Information System (INIS)

    Wagner, R.K.

    1978-01-01

    Steroid hormone binding proteins can be measured, after the removal of endogenous steroids, as specific complexes with radio-labelled hormones. In this study all the requirements for a quantitative determination of steroid hormone binding proteins are defined. For different methods, agargel electrophoresis, density gradient centrifugation, equilibrium dialysis and polyacrylamide electrophoresis have been evaluated. Agar electrophoresis at low temperature was found to be the simplest and most useful procedure. With this method the dissociation rates of high affinity complexes can be assessed and absolute binding protein concentrations can be determined. The dissociation rates of the oestradiol-oestrogen receptor complex and the R-5020-progestin receptor complex are low (1-2% per h run time.) In contrast, that of complexes between androgen receptor and dihydrotestosterone (17β-hydroxy-5α-androstan-3-one (DHT), progestin receptor and progesterone, corticosteroid binding globulin (CBG) and cortisol or progesterone and sex hormone binding globulin (SHBG) and DHT were hign (16-27% per h run time). Target tissue extracts (cytosols) contain, besides soluble tissue proteins, large amounts of plasma proteins. The extent of this plasma contamination can be determined by measuring the albumin concentration in cytosols by immunodiffusion. In cytosols of 4 different human target tissues the albumin content varied from 20-30% corresponding to an even higher whole plasma concentration. Steroid binding plasma proteins, such as CBG and SHBG are constituents of this containment. (author)

  12. The readiness potential reflects intentional binding

    Directory of Open Access Journals (Sweden)

    Han-Gue eJo

    2014-06-01

    Full Text Available When a voluntary action is causally linked with a sensory outcome, the action and its consequent effect are perceived as being closer together in time. This effect is called intentional binding. Although many experiments were conducted on this phenomenon, the underlying neural mechanisms are not well understood. While intentional binding is specific to voluntary action, we presumed that preconscious brain activity (the readiness potential, RP, which occurs before an action is made, might play an important role in this binding effect. In this study, the brain dynamics were recorded with electroencephalography (EEG and analyzed in single-trials in order to estimate whether intentional binding is correlated with the early neural processes. Moreover, we were interested in different behavioral performance between meditators and non-meditators since meditators are expected to be able to keep attention more consistently on a task. Thus, we performed the intentional binding paradigm with twenty mindfulness meditators and compared them to matched controls. Although, we did not observe a group effect on either behavioral data or EEG recordings, we found that self-initiated movements following ongoing negative deflections of slow cortical potentials (SCPs result in a stronger binding effect compared to positive potentials, especially regarding the perceived time of the consequent effect. Our results provide the first direct evidence that the early neural activity within the range of SCPs affects perceived time of a sensory outcome that is caused by intentional action.

  13. DNA-Aptamers Binding Aminoglycoside Antibiotics

    Directory of Open Access Journals (Sweden)

    Nadia Nikolaus

    2014-02-01

    Full Text Available Aptamers are short, single stranded DNA or RNA oligonucleotides that are able to bind specifically and with high affinity to their non-nucleic acid target molecules. This binding reaction enables their application as biorecognition elements in biosensors and assays. As antibiotic residues pose a problem contributing to the emergence of antibiotic-resistant pathogens and thereby reducing the effectiveness of the drug to fight human infections, we selected aptamers targeted against the aminoglycoside antibiotic kanamycin A with the aim of constructing a robust and functional assay that can be used for water analysis. With this work we show that aptamers that were derived from a Capture-SELEX procedure targeting against kanamycin A also display binding to related aminoglycoside antibiotics. The binding patterns differ among all tested aptamers so that there are highly substance specific aptamers and more group specific aptamers binding to a different variety of aminoglycoside antibiotics. Also the region of the aminoglycoside antibiotics responsible