WorldWideScience

Sample records for binding holo-structure prediction

  1. Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin.

    Science.gov (United States)

    Sridhar, Akshay; Ross, Gregory A; Biggin, Philip C

    2017-01-01

    Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.

  2. ProBiS-CHARMMing: Web Interface for Prediction and Optimization of Ligands in Protein Binding Sites.

    Science.gov (United States)

    Konc, Janez; Miller, Benjamin T; Štular, Tanja; Lešnik, Samo; Woodcock, H Lee; Brooks, Bernard R; Janežič, Dušanka

    2015-11-23

    Proteins often exist only as apo structures (unligated) in the Protein Data Bank, with their corresponding holo structures (with ligands) unavailable. However, apoproteins may not represent the amino-acid residue arrangement upon ligand binding well, which is especially problematic for molecular docking. We developed the ProBiS-CHARMMing web interface by connecting the ProBiS ( http://probis.cmm.ki.si ) and CHARMMing ( http://www.charmming.org ) web servers into one functional unit that enables prediction of protein-ligand complexes and allows for their geometry optimization and interaction energy calculation. The ProBiS web server predicts ligands (small compounds, proteins, nucleic acids, and single-atom ligands) that may bind to a query protein. This is achieved by comparing its surface structure against a nonredundant database of protein structures and finding those that have binding sites similar to that of the query protein. Existing ligands found in the similar binding sites are then transposed to the query according to predictions from ProBiS. The CHARMMing web server enables, among other things, minimization and potential energy calculation for a wide variety of biomolecular systems, and it is used here to optimize the geometry of the predicted protein-ligand complex structures using the CHARMM force field and to calculate their interaction energies with the corresponding query proteins. We show how ProBiS-CHARMMing can be used to predict ligands and their poses for a particular binding site, and minimize the predicted protein-ligand complexes to obtain representations of holoproteins. The ProBiS-CHARMMing web interface is freely available for academic users at http://probis.nih.gov.

  3. Computational Prediction of RNA-Binding Proteins and Binding Sites.

    Science.gov (United States)

    Si, Jingna; Cui, Jing; Cheng, Jin; Wu, Rongling

    2015-01-01

    Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%-8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein-RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein-RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

  4. Computational Prediction of RNA-Binding Proteins and Binding Sites

    Directory of Open Access Journals (Sweden)

    Jingna Si

    2015-11-01

    Full Text Available Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%–8% of all proteins are RNA-binding proteins (RBPs. Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein–RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein–RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

  5. Predicting zinc binding at the proteome level

    Directory of Open Access Journals (Sweden)

    Rosato Antonio

    2007-02-01

    Full Text Available Abstract Background Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for structural purposes. Metal-binding properties remain difficult to predict as well as to investigate experimentally at the whole-proteome level. Consequently, the current knowledge about metalloproteins is only partial. Results The present work reports on the development of a machine learning method for the prediction of the zinc-binding state of pairs of nearby amino-acids, using predictors based on support vector machines. The predictor was trained using chains containing zinc-binding sites and non-metalloproteins in order to provide positive and negative examples. Results based on strong non-redundancy tests prove that (1 zinc-binding residues can be predicted and (2 modelling the correlation between the binding state of nearby residues significantly improves performance. The trained predictor was then applied to the human proteome. The present results were in good agreement with the outcomes of previous, highly manually curated, efforts for the identification of human zinc-binding proteins. Some unprecedented zinc-binding sites could be identified, and were further validated through structural modelling. The software implementing the predictor is freely available at: http://zincfinder.dsi.unifi.it Conclusion The proposed approach constitutes a highly automated tool for the identification of metalloproteins, which provides results of comparable quality with respect to highly manually refined predictions. The ability to model correlations between pairwise residues allows it to obtain a significant improvement over standard 1D based approaches. In addition, the method permits the identification of unprecedented metal sites, providing important hints for the work of experimentalists.

  6. Predicted metal binding sites for phytoremediation.

    Science.gov (United States)

    Sharma, Ashok; Roy, Sudeep; Tripathi, Kumar Parijat; Roy, Pratibha; Mishra, Manoj; Khan, Feroz; Meena, Abha

    2009-09-05

    Metal ion binding domains are found in proteins that mediate transport, buffering or detoxification of metal ions. The objective of the study is to design and analyze metal binding motifs against the genes involved in phytoremediation. This is being done on the basis of certain pre-requisite amino-acid residues known to bind metal ions/metal complexes in medicinal and aromatic plants (MAP's). Earlier work on MAP's have shown that heavy metals accumulated by aromatic and medicinal plants do not appear in the essential oil and that some of these species are able to grow in metal contaminated sites. A pattern search against the UniProtKB/Swiss-Prot and UniProtKB/TrEMBL databases yielded true positives in each case showing the high specificity of the motifs designed for the ions of nickel, lead, molybdenum, manganese, cadmium, zinc, iron, cobalt and xenobiotic compounds. Motifs were also studied against PDB structures. Results of the study suggested the presence of binding sites on the surface of protein molecules involved. PDB structures of proteins were finally predicted for the binding sites functionality in their respective phytoremediation usage. This was further validated through CASTp server to study its physico-chemical properties. Bioinformatics implications would help in designing strategy for developing transgenic plants with increased metal binding capacity. These metal binding factors can be used to restrict metal update by plants. This helps in reducing the possibility of metal movement into the food chain.

  7. Predicting binding free energies in solution

    CERN Document Server

    Jensen, Jan H

    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 for others. In paper I summarize some of the many factors that could easily contribute 1-3 kcal/mol errors at 298 K: three-body dispersion effects, molecular symmetry, anharmonicity, spurious imaginary frequencies, insufficient conformational sampling, wrong or changing ionization states, errors in the solvation free energy of ions, and explicit solvent (and ion) effects that are not well-represented by continuum models. While the paper is primarily a synthesis of previously published work there are two new results: the adaptation of Legendre transformed free energies to electronic structure theory and a use of water clusters that maximizes error cancellation in binding free energies computed using explicit solvent molecules. While I focus on binding free energies in aqueous solution the approach also a...

  8. Predicted metal binding sites for phytoremediation

    OpenAIRE

    Sharma, Ashok; Roy, Sudeep; Tripathi, Kumar Parijat; Roy, Pratibha; Mishra, Manoj; Khan, Feroz; Meena, Abha

    2009-01-01

    Metal ion binding domains are found in proteins that mediate transport, buffering or detoxification of metal ions. The objective of the study is to design and analyze metal binding motifs against the genes involved in phytoremediation. This is being done on the basis of certain pre-requisite amino-acid residues known to bind metal ions/metal complexes in medicinal and aromatic plants (MAP's). Earlier work on MAP's have shown that heavy metals accumulated by aromatic and medicinal plants do no...

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

    DEFF Research Database (Denmark)

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

    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...... 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...

  10. Influence of binding energies of electrons on nuclear mass predictions

    Science.gov (United States)

    Tang, Jing; Niu, Zhong-Ming; Guo, Jian-You

    2016-07-01

    Nuclear mass contains a wealth of nuclear structure information, and has been widely employed to extract the nuclear effective interactions. The known nuclear mass is usually extracted from the experimental atomic mass by subtracting the masses of electrons and adding the binding energy of electrons in the atom. However, the binding energies of electrons are sometimes neglected in extracting the known nuclear masses. The influence of binding energies of electrons on nuclear mass predictions are carefully investigated in this work. If the binding energies of electrons are directly subtracted from the theoretical mass predictions, the rms deviations of nuclear mass predictions with respect to the known data are increased by about 200 keV for nuclei with Z, N ⩾ 8. Furthermore, by using the Coulomb energies between protons to absorb the binding energies of electrons, their influence on the rms deviations is significantly reduced to only about 10 keV for nuclei with Z, N ⩾ 8. However, the binding energies of electrons are still important for the heavy nuclei, about 150 keV for nuclei around Z = 100 and up to about 500 keV for nuclei around Z = 120. Therefore, it is necessary to consider the binding energies of electrons to reliably predict the masses of heavy nuclei at an accuracy of hundreds of keV. Supported by National Natural Science Foundation of China (11205004)

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

    Science.gov (United States)

    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.

  12. Prediction of MHC class I binding peptides, using SVMHC

    Directory of Open Access Journals (Sweden)

    Elofsson Arne

    2002-09-01

    Full Text Available Abstract Background T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. Results Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. Conclusions Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences.

  13. Predicting where small molecules bind at protein-protein interfaces.

    Directory of Open Access Journals (Sweden)

    Peter Walter

    Full Text Available Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP complexes and protein-ligand (PL complexes with known three-dimensional structures for which (1 one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2 the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10,000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.

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

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    Xiang Zhou

    Full Text Available BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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

  15. Predicting bioactive conformations and binding modes of macrocycles

    Science.gov (United States)

    Anighoro, Andrew; de la Vega de León, Antonio; Bajorath, Jürgen

    2016-10-01

    Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein-protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.

  16. Oxypred: Prediction and Classification of Oxygen-Binding Proteins

    Institute of Scientific and Technical Information of China (English)

    S.; Muthukrishnan; Aarti; Garg; G.P.S.; Raghava

    2007-01-01

    This study describes a method for predicting and classifying oxygen-binding pro- teins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding pro- teins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Sec- ondly, an SVM module was developed based on amino acid composition, classify- ing the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemo- cyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins(available from http://www.imtech.res.in/raghava/oxypred/).

  17. DBD-Hunter: a knowledge-based method for the prediction of DNA-protein interactions.

    Science.gov (United States)

    Gao, Mu; Skolnick, Jeffrey

    2008-07-01

    The structures of DNA-protein complexes have illuminated the diversity of DNA-protein binding mechanisms shown by different protein families. This lack of generality could pose a great challenge for predicting DNA-protein interactions. To address this issue, we have developed a knowledge-based method, DNA-binding Domain Hunter (DBD-Hunter), for identifying DNA-binding proteins and associated binding sites. The method combines structural comparison and the evaluation of a statistical potential, which we derive to describe interactions between DNA base pairs and protein residues. We demonstrate that DBD-Hunter is an accurate method for predicting DNA-binding function of proteins, and that DNA-binding protein residues can be reliably inferred from the corresponding templates if identified. In benchmark tests on approximately 4000 proteins, our method achieved an accuracy of 98% and a precision of 84%, which significantly outperforms three previous methods. We further validate the method on DNA-binding protein structures determined in DNA-free (apo) state. We show that the accuracy of our method is only slightly affected on apo-structures compared to the performance on holo-structures cocrystallized with DNA. Finally, we apply the method to approximately 1700 structural genomics targets and predict that 37 targets with previously unknown function are likely to be DNA-binding proteins. DBD-Hunter is freely available at http://cssb.biology.gatech.edu/skolnick/webservice/DBD-Hunter/.

  18. PRBP: Prediction of RNA-Binding Proteins Using a Random Forest Algorithm Combined with an RNA-Binding Residue Predictor.

    Science.gov (United States)

    Ma, Xin; Guo, Jing; Xiao, Ke; Sun, Xiao

    2015-01-01

    The prediction of RNA-binding proteins is an incredibly challenging problem in computational biology. Although great progress has been made using various machine learning approaches with numerous features, the problem is still far from being solved. In this study, we attempt to predict RNA-binding proteins directly from amino acid sequences. A novel approach, PRBP predicts RNA-binding proteins using the information of predicted RNA-binding residues in conjunction with a random forest based method. For a given protein, we first predict its RNA-binding residues and then judge whether the protein binds RNA or not based on information from that prediction. If the protein cannot be identified by the information associated with its predicted RNA-binding residues, then a novel random forest predictor is used to determine if the query protein is a RNA-binding protein. We incorporated features of evolutionary information combined with physicochemical features (EIPP) and amino acid composition feature to establish the random forest predictor. Feature analysis showed that EIPP contributed the most to the prediction of RNA-binding proteins. The results also showed that the information from the RNA-binding residue prediction improved the overall performance of our RNA-binding protein prediction. It is anticipated that the PRBP method will become a useful tool for identifying RNA-binding proteins. A PRBP Web server implementation is freely available at http://www.cbi.seu.edu.cn/PRBP/.

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

    Indian Academy of Sciences (India)

    Sumedha Roy; Shayoni Dutta; Kanika Khanna; Shruti Singla; Durai Sundar

    2012-07-01

    Zinc finger proteins interact via their individual fingers to three base pair subsites on the target DNA. The four key residue positions −1, 2, 3 and 6 on the alpha-helix of the zinc fingers have hydrogen bond interactions with the DNA. Mutating these key residues enables generation of a plethora of combinatorial possibilities that can bind to any DNA stretch of interest. Exploiting the binding specificity and affinity of the interaction between the zinc fingers and the respective DNA can help to generate engineered zinc fingers for therapeutic purposes involving genome targeting. Exploring the structure–function relationships of the existing zinc finger–DNA complexes can aid in predicting the probable zinc fingers that could bind to any target DNA. Computational tools ease the prediction of such engineered zinc fingers by effectively utilizing information from the available experimental data. A study of literature reveals many approaches for predicting DNA-binding specificity in zinc finger proteins. However, an alternative approach that looks into the physico-chemical properties of these complexes would do away with the difficulties of designing unbiased zinc fingers with the desired affinity and specificity. We present a physico-chemical approach that exploits the relative strengths of hydrogen bonding between the target DNA and all combinatorially possible zinc fingers to select the most optimum zinc finger protein candidate.

  20. Predicting accurate absolute binding energies in aqueous solution

    DEFF Research Database (Denmark)

    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....

  1. Prediction of chloride ingress and binding in cement paste

    DEFF Research Database (Denmark)

    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...

  2. QSAR Models for the Prediction of Plasma Protein Binding

    Directory of Open Access Journals (Sweden)

    Zeshan Amin

    2013-02-01

    Full Text Available Introduction: The prediction of plasma protein binding (ppb is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure – Activity Relationships (QSAR for the prediction of plasma protein binding. Methods: Protein binding values for 794 compounds were collated from literature. The data was partitioned into a training set of 662 compounds and an external validation set of 132 compounds. Physicochemical and molecular descriptors were calculated for each compound using ACD labs/logD, MOE (Chemical Computing Group and Symyx QSAR software packages. Several data mining tools were employed for the construction of models. These included stepwise regression analysis, Classification and Regression Trees (CART, Boosted trees and Random Forest. Results: Several predictive models were identified; however, one model in particular produced significantly superior prediction accuracy for the external validation set as measured using mean absolute error and correlation coefficient. The selected model was a boosted regression tree model which had the mean absolute error for training set of 13.25 and for validation set of 14.96. Conclusion: Plasma protein binding can be modeled using simple regression trees or multiple linear regressions with reasonable model accuracies. These interpretable models were able to identify the governing molecular factors for a high ppb that included hydrophobicity, van der Waals surface area parameters, and aromaticity. On the other hand, the more complicated ensemble method of boosted regression trees produced the most accurate ppb estimations for the external validation set.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...... 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...

  4. AH-DB: collecting protein structure pairs before and after binding.

    Science.gov (United States)

    Chang, Darby Tien-Hao; Yao, Tsung-Ju; Fan, Chen-Yu; Chiang, Chih-Yun; Bai, Yi-Han

    2012-01-01

    This work presents the Apo-Holo DataBase (AH-DB, http://ahdb.ee.ncku.edu.tw/ and http://ahdb.csbb.ntu.edu.tw/), which provides corresponding pairs of protein structures before and after binding. Conformational transitions are commonly observed in various protein interactions that are involved in important biological functions. For example, copper-zinc superoxide dismutase (SOD1), which destroys free superoxide radicals in the body, undergoes a large conformational transition from an 'open' state (apo structure) to a 'closed' state (holo structure). Many studies have utilized collections of apo-holo structure pairs to investigate the conformational transitions and critical residues. However, the collection process is usually complicated, varies from study to study and produces a small-scale data set. AH-DB is designed to provide an easy and unified way to prepare such data, which is generated by identifying/mapping molecules in different Protein Data Bank (PDB) entries. Conformational transitions are identified based on a refined alignment scheme to overcome the challenge that many structures in the PDB database are only protein fragments and not complete proteins. There are 746,314 apo-holo pairs in AH-DB, which is about 30 times those in the second largest collection of similar data. AH-DB provides sophisticated interfaces for searching apo-holo structure pairs and exploring conformational transitions from apo structures to the corresponding holo structures.

  5. Imputation for transcription factor binding predictions based on deep learning

    Science.gov (United States)

    Qin, Qian

    2017-01-01

    Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding. PMID:28234893

  6. Development of predictive models for predicting binding affinity of endocrine disrupting chemicals to fish sex hormone-binding globulin.

    Science.gov (United States)

    Liu, Huihui; Yang, Xianhai; Yin, Cen; Wei, Mengbi; He, Xiao

    2017-02-01

    Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) exerting disrupting endocrine function. However, this mechanism has not received enough attention compared with that of hormones receptors and synthetase. Recently, we have explored the interaction between EDCs and sex hormone-binding globulin of human (hSHBG). In this study, interactions between EDCs and sex hormone-binding globulin of eight fish species (fSHBG) were investigated by employing classification methods and quantitative structure-activity relationships (QSAR). In the modeling, the relative binding affinity (RBA) of a chemical with 17β-estradiol binding to fSHBG was selected as the endpoint. Classification models were developed for two fish species, while QSAR models were established for the other six fish species. Statistical results indicated that the models had satisfactory goodness of fit, robustness and predictive ability, and that application domain covered a large number of endogenous and exogenous steroidal and non-steroidal chemicals. Additionally, by comparing the log RBA values, it was found that the same chemical may have different affinities for fSHBG from different fish species, thus species diversity should be taken into account. However, the affinity of fSHBG showed a high correlation for fishes within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes and Siluriformes), thus the fSHBG binding data for one fish species could be used to extrapolate other fish species in the same Order.

  7. Predicting accurate absolute binding energies in aqueous solution

    DEFF Research Database (Denmark)

    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...... mol(-1) errors at 298 K: three-body dispersion effects, molecular symmetry, anharmonicity, spurious imaginary frequencies, insufficient conformational sampling, wrong or changing ionization states, errors in the solvation free energy of ions, and explicit solvent (and ion) effects that are not well......-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....

  8. Prediction of Protein-DNA binding by Monte Carlo method

    Science.gov (United States)

    Deng, Yuefan; Eisenberg, Moises; Korobka, Alex

    1997-08-01

    We present an analysis and prediction of protein-DNA binding specificity based on the hydrogen bonding between DNA, protein, and auxillary clusters of water molecules. Zif268, glucocorticoid receptor, λ-repressor mutant, HIN-recombinase, and tramtrack protein-DNA complexes are studied. Hydrogen bonds are approximated by the Lennard-Jones potential with a cutoff distance between the hydrogen and the acceptor atoms set to 3.2 Åand an angular component based on a dipole-dipole interaction. We use a three-stage docking algorithm: geometric hashing that matches pairs of hydrogen bonding sites; (2) least-squares minimization of pairwise distances to filter out insignificant matches; and (3) Monte Carlo stochastic search to minimize the energy of the system. More information can be obtained from our first paper on this subject [Y.Deng et all, J.Computational Chemistry (1995)]. Results show that the biologically correct base pair is selected preferentially when there are two or more strong hydrogen bonds (with LJ potential lower than -0.20) that bind it to the protein. Predicted sequences are less stable in the case of weaker bonding sites. In general the inclusion of water bridges does increase the number of base pairs for which correct specificity is predicted.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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

  11. BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins.

    Science.gov (United States)

    Paz, Inbal; Kligun, Efrat; Bengad, Barak; Mandel-Gutfreund, Yael

    2016-07-08

    Gene expression is a multi-step process involving many layers of regulation. The main regulators of the pathway are DNA and RNA binding proteins. While over the years, a large number of DNA and RNA binding proteins have been identified and extensively studied, it is still expected that many other proteins, some with yet another known function, are awaiting to be discovered. Here we present a new web server, BindUP, freely accessible through the website http://bindup.technion.ac.il/, for predicting DNA and RNA binding proteins using a non-homology-based approach. Our method is based on the electrostatic features of the protein surface and other general properties of the protein. BindUP predicts nucleic acid binding function given the proteins three-dimensional structure or a structural model. Additionally, BindUP provides information on the largest electrostatic surface patches, visualized on the server. The server was tested on several datasets of DNA and RNA binding proteins, including proteins which do not possess DNA or RNA binding domains and have no similarity to known nucleic acid binding proteins, achieving very high accuracy. BindUP is applicable in either single or batch modes and can be applied for testing hundreds of proteins simultaneously in a highly efficient manner.

  12. 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.

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

    DEFF Research Database (Denmark)

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

    2004-01-01

    prediction methods exist only for alleles were the binding pattern have been deduced from peptide motifs. Using empirical knowledge of important anchor positions within the binding peptides dramatically reduces the number of peptides needed for reliable predictions. We here present a general method...... for predicting peptides binding to specific MHC class I alleles. The method combines advanced automatic scoring matrix generation with empirical position specific differential anchor weighting. The method leads to predictions with a comparable or higher accuracy than other established prediction servers, even...

  14. Medial Temporal Lobe Activity Predicts Successful Relational Memory Binding

    Science.gov (United States)

    Hannula, Deborah E.; Ranganath, Charan

    2009-01-01

    Previous neuropsychological findings have implicated medial temporal lobe (MTL) structures in retaining object-location relations over the course of short delays, but MTL effects have not always been reported in neuroimaging investigations with similar short-term memory requirements. Here, we used event-related functional magnetic resonance imaging to test the hypothesis that the hippocampus and related MTL structures support accurate retention of relational memory representations, even across short delays. On every trial, four objects were presented, each in one of nine possible locations of a three-dimensional grid. Participants were to mentally rotate the grid and then maintain the rotated representation in anticipation of a test stimulus: a rendering of the grid, rotated 90° from the original viewpoint. The test stimulus was either a “match” display, in which object-location relations were intact, or a “mismatch” display, in which one object occupied a new, previously unfilled location (mismatch position), or two objects had swapped locations (mismatch swap). Encoding phase activation in anterior and posterior regions of the left hippocampus, and in bilateral perirhinal cortex, predicted subsequent accuracy on the short-term memory decision, as did bilateral posterior hippocampal activity after the test stimulus. Notably, activation in these posterior hippocampal regions was also sensitive to the degree to which object-location bindings were preserved in the test stimulus; activation was greatest for match displays, followed by mismatch-position displays, and finally mismatch-swap displays. These results indicate that the hippocampus and related MTL structures contribute to successful encoding and retrieval of relational information in visual short-term memory. PMID:18171929

  15. Late Pregnancy Thyroid-Binding Globulin Predicts Perinatal Depression

    Science.gov (United States)

    Pedersen, Cort; Leserman, Jane; Garcia, Nacire; Stansbury, Melissa; Meltzer-Brody, Samantha; Johnson, Jacqueline

    2016-01-01

    Previously we found that late pregnancy total and free thyroxine (TT4, FT4) concentrations were negatively related to greater pre and/or postpartum depressive symptoms. In a much larger cohort, the current study examined whether these thyroid indices measured earlier in the third trimester (31-33 weeks) predict subsequent perinatal depression and anxiety ratings as well as syndromal depression. Thyroid-binding globulin (TBG) concentrations increase markedly during pregnancy and may be an index of sensitivity to elevated estrogen levels. TBG was examined in this study because prior findings suggest that postpartum depression is related to sensitivity to mood destabilization by elevated sex hormone concentrations during pregnancy. Our cohort was 199 euthyroid women recruited from a public health obstetrics clinic (63.8% Hispanic, 21.6% Black). After screening and blood draws for hormone measures at pregnancy weeks 31-33, subjects were evaluated during home visits at pregnancy weeks 35-36 as well as postpartum weeks 6 and 12. Evaluations included psychiatric interviews for current and life-time DSM-IV psychiatric history (M.I.N.I.-Plus), subject self-ratings and interviewer ratings for depression and anxiety (Edinburgh Postnatal Depression Scale, Montgomery-Ǻsberg Depression Rating Scale; Spielberger State-Trait Anxiety Inventory, Hamilton Anxiety Inventory), as well as a standardized interview to obtain life-time trauma history. Numerous covariates were included in all regression analyses. Trauma and major depression history were robustly significant predictors of depression and anxiety ratings over the study period when these variables were analyzed individually or in a combined model including FT4 or TBG (pdepression and anxiety ratings (pdepression history, were significant individual predictors of syndromal depression during the study period (pdepression history, FT4 and TBG generally were not significantly predictive of depression or anxiety ratings, and FT4

  16. Predictive model of cationic surfactant binding to humic substances

    NARCIS (Netherlands)

    Ishiguro, M.; Koopal, L.K.

    2011-01-01

    The humic substances (HS) have a high reactivity with other components in the natural environment. An important factor for the reactivity of HS is their negative charge. Cationic surfactants bind strongly to HS by electrostatic and specific interaction. Therefore, a surfactant binding model is devel

  17. A 3D-QSAR-driven approach to binding mode and affinity prediction

    DEFF Research Database (Denmark)

    Tosco, Paolo; Balle, Thomas

    2012-01-01

    A method for predicting the binding mode of a series of ligands is proposed. The procedure relies on three-dimensional quantitative structure-activity relationships (3D-QSAR) and does not require structural knowledge of the binding site. Candidate alignments are automatically built and ranked...... according to a consensus scoring function. 3D-QSAR analysis based on the selected binding mode enables affinity prediction of new drug candidates having less than 10 rotatable bonds....

  18. 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

  19. High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder.

    Science.gov (United States)

    Peng, Zhenling; Kurgan, Lukasz

    2015-10-15

    Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under physiological conditions in-vitro, are common in eukaryotes, and facilitate interactions with RNA, DNA and proteins. Current methods for prediction of IDPs and IDRs do not provide insights into their functions, except for a handful of methods that address predictions of protein-binding regions. We report first-of-its-kind computational method DisoRDPbind for high-throughput prediction of RNA, DNA and protein binding residues located in IDRs from protein sequences. DisoRDPbind is implemented using a runtime-efficient multi-layered design that utilizes information extracted from physiochemical properties of amino acids, sequence complexity, putative secondary structure and disorder and sequence alignment. Empirical tests demonstrate that it provides accurate predictions that are competitive with other predictors of disorder-mediated protein binding regions and complementary to the methods that predict RNA- and DNA-binding residues annotated based on crystal structures. Application in Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila melanogaster proteomes reveals that RNA- and DNA-binding proteins predicted by DisoRDPbind complement and overlap with the corresponding known binding proteins collected from several sources. Also, the number of the putative protein-binding regions predicted with DisoRDPbind correlates with the promiscuity of proteins in the corresponding protein-protein interaction networks. Webserver: http://biomine.ece.ualberta.ca/DisoRDPbind/.

  20. Prediction of calcium-binding sites by combining loop-modeling with machine learning

    Directory of Open Access Journals (Sweden)

    Altman Russ B

    2009-12-01

    Full Text Available Abstract Background Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information. Results We report a hybrid approach for recognizing calcium-binding sites in disordered regions. Our approach combines loop modeling with a machine learning method (FEATURE for structure-based site recognition. For validation, we compared the performance of our method on known calcium-binding sites for which there are both holo and apo structures. When loops in the apo structures are rebuilt using modeling methods, FEATURE identifies 14 out of 20 crystallographically proven calcium-binding sites. It only recognizes 7 out of 20 calcium-binding sites in the initial apo crystal structures. We applied our method to unstructured loops in proteins from SCOP families known to bind calcium in order to discover potential cryptic calcium binding sites. We built 2745 missing loops and evaluated them for potential calcium binding. We made 102 predictions of calcium-binding sites. Ten predictions are consistent with independent experimental verifications. We found indirect experimental evidence for 14 other predictions. The remaining 78 predictions are novel predictions, some with intriguing potential biological significance. In particular, we see an enrichment of beta-sheet folds with predicted calcium binding sites in the connecting loops on the surface that may be important for calcium-mediated function switches. Conclusion Protein crystal structures are a potentially rich source of functional information. When loops are missing in these structures, we may be losing important information about binding sites and active

  1. 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/.

  2. 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.

  3. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  4. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  5. Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation.

    Science.gov (United States)

    Mentes, Ahmet; Deng, Nan-Jie; Vijayan, R S K; Xia, Junchao; Gallicchio, Emilio; Levy, Ronald M

    2016-05-10

    Molecular dynamics modeling of complex biological systems is limited by finite simulation time. The simulations are often trapped close to local energy minima separated by high energy barriers. Here, we introduce Hamiltonian replica exchange (H-REMD) with torsional flattening in the Binding Energy Distribution Analysis Method (BEDAM), to reduce energy barriers along torsional degrees of freedom and accelerate sampling of intramolecular degrees of freedom relevant to protein-ligand binding. The method is tested on a standard benchmark (T4 Lysozyme/L99A/p-xylene complex) and on a library of HIV-1 integrase complexes derived from the SAMPL4 blind challenge. We applied the torsional flattening strategy to 26 of the 53 known binders to the HIV Integrase LEDGF site found to have a binding energy landscape funneled toward the crystal structure. We show that our approach samples the conformational space more efficiently than the original method without flattening when starting from a poorly docked pose with incorrect ligand dihedral angle conformations. In these unfavorable cases convergence to a binding pose within 2-3 Å from the crystallographic pose is obtained within a few nanoseconds of the Hamiltonian replica exchange simulation. We found that torsional flattening is insufficient in cases where trapping is due to factors other than torsional energy, such as the formation of incorrect intramolecular hydrogen bonds and stacking. Work is in progress to generalize the approach to handle these cases and thereby make it more widely applicable.

  6. Predicting DNA-binding sites of proteins based on sequential and 3D structural information.

    Science.gov (United States)

    Li, Bi-Qing; Feng, Kai-Yan; Ding, Juan; Cai, Yu-Dong

    2014-06-01

    Protein-DNA interactions play important roles in many biological processes. To understand the molecular mechanisms of protein-DNA interaction, it is necessary to identify the DNA-binding sites in DNA-binding proteins. In the last decade, computational approaches have been developed to predict protein-DNA-binding sites based solely on protein sequences. In this study, we developed a novel predictor based on support vector machine algorithm coupled with the maximum relevance minimum redundancy method followed by incremental feature selection. We incorporated not only features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, solvent accessibility, but also five three-dimensional (3D) structural features calculated from PDB data to predict the protein-DNA interaction sites. Feature analysis showed that 3D structural features indeed contributed to the prediction of DNA-binding site and it was demonstrated that the prediction performance was better with 3D structural features than without them. It was also shown via analysis of features from each site that the features of DNA-binding site itself contribute the most to the prediction. Our prediction method may become a useful tool for identifying the DNA-binding sites and the feature analysis described in this paper may provide useful insights for in-depth investigations into the mechanisms of protein-DNA interaction.

  7. Genome-wide prediction, display and refinement of binding sites with information theory-based models

    Directory of Open Access Journals (Sweden)

    Leeder J Steven

    2003-09-01

    Full Text Available Abstract Background We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with refined matrices as a measure of the strengths of the corresponding protein-nucleic acid interactions. The software can then be used to predict novel sites by rescanning the genome with the refined matrices. Results Parameters for genome scans are entered using a Java-based GUI interface and backend scripts in Perl. Multi-processor CPU load-sharing minimized the average response time for scans of different chromosomes. Scans of human genome assemblies required 4–6 hours for transcription factor binding sites and 10–19 hours for splice sites, respectively, on 24- and 3-node Mosix and Beowulf clusters. Individual binding sites are displayed either as high-resolution sequence walkers or in low-resolution custom tracks in the UCSC genome browser. For large datasets, we applied a data reduction strategy that limited displays of binding sites exceeding a threshold information content to specific chromosomal regions within or adjacent to genes. An HTML document is produced listing binding sites ranked by binding site strength or chromosomal location hyperlinked to the UCSC custom track, other annotation databases and binding site sequences. Post-genome scan tools parse binding site annotations of selected chromosome intervals and compare the results of genome scans using different weight matrices. Comparisons of multiple genome scans can display binding sites that are unique to each scan and identify sites with significantly altered binding strengths

  8. Spatial distribution of predicted transcription factor binding sites in Drosophila ChIP peaks.

    Science.gov (United States)

    Pettie, Kade P; Dresch, Jacqueline M; Drewell, Robert A

    2016-08-01

    In the development of the Drosophila embryo, gene expression is directed by the sequence-specific interactions of a large network of protein transcription factors (TFs) and DNA cis-regulatory binding sites. Once the identity of the typically 8-10bp binding sites for any given TF has been determined by one of several experimental procedures, the sequences can be represented in a position weight matrix (PWM) and used to predict the location of additional TF binding sites elsewhere in the genome. Often, alignments of large (>200bp) genomic fragments that have been experimentally determined to bind the TF of interest in Chromatin Immunoprecipitation (ChIP) studies are trimmed under the assumption that the majority of the binding sites are located near the center of all the aligned fragments. In this study, ChIP/chip datasets are analyzed using the corresponding PWMs for the well-studied TFs; CAUDAL, HUNCHBACK, KNIRPS and KRUPPEL, to determine the distribution of predicted binding sites. All four TFs are critical regulators of gene expression along the anterio-posterior axis in early Drosophila development. For all four TFs, the ChIP peaks contain multiple binding sites that are broadly distributed across the genomic region represented by the peak, regardless of the prediction stringency criteria used. This result suggests that ChIP peak trimming may exclude functional binding sites from subsequent analyses.

  9. 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.

  10. Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation.

    Science.gov (United States)

    Ishikawa, Takeshi

    2016-10-01

    Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

  11. A Prediction Method of Binding Free Energy of Protein and Ligand

    Science.gov (United States)

    Yang, Kun; Wang, Xicheng

    2010-05-01

    Predicting the binding free energy is an important problem in bimolecular simulation. Such prediction would be great benefit in understanding protein functions, and may be useful for computational prediction of ligand binding strengths, e.g., in discovering pharmaceutical drugs. Free energy perturbation (FEP)/thermodynamics integration (TI) is a classical method to explicitly predict free energy. However, this method need plenty of time to collect datum, and that attempts to deal with some simple systems and small changes of molecular structures. Another one for estimating ligand binding affinities is linear interaction energy (LIE) method. This method employs averages of interaction potential energy terms from molecular dynamics simulations or other thermal conformational sampling techniques. Incorporation of systematic deviations from electrostatic linear response, derived from free energy perturbation studies, into the absolute binding free energy expression significantly enhances the accuracy of the approach. However, it also is time-consuming work. In this paper, a new prediction method based on steered molecular dynamics (SMD) with direction optimization is developed to compute binding free energy. Jarzynski's equality is used to derive the PMF or free-energy. The results for two numerical examples are presented, showing that the method has good accuracy and efficiency. The novel method can also simulate whole binding proceeding and give some important structural information about development of new drugs.

  12. 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.

  13. Major histocompatibility complex class I binding predictions as a tool in epitope discovery

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Buus, Søren;

    2010-01-01

    Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available...... for any human leucocyte antigen (HLA) -A or -B molecule with known protein sequence. Furthermore, peptide binding to MHC molecules from several non-human primates, mouse strains and other mammals can now be predicted. In this review, a number of different prediction methods are briefly explained...

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

    Directory of Open Access Journals (Sweden)

    Bjoern Peters

    2006-06-01

    Full Text Available 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.

  15. 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 web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community.

  16. 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

  17. Computational protocol for predicting the binding affinities of zinc containing metalloprotein-ligand complexes.

    Science.gov (United States)

    Jain, Tarun; Jayaram, B

    2007-06-01

    Zinc is one of the most important metal ions found in proteins performing specific functions associated with life processes. Coordination geometry of the zinc ion in the active site of the metalloprotein-ligand complexes poses a challenge in determining ligand binding affinities accurately in structure-based drug design. We report here an all atom force field based computational protocol for estimating rapidly the binding affinities of zinc containing metalloprotein-ligand complexes, considering electrostatics, van der Waals, hydrophobicity, and loss in conformational entropy of protein side chains upon ligand binding along with a nonbonded approach to model the interactions of the zinc ion with all the other atoms of the complex. We examined the sensitivity of the binding affinity predictions to the choice of Lennard-Jones parameters, partial atomic charges, and dielectric treatments adopted for system preparation and scoring. The highest correlation obtained was R2 = 0.77 (r = 0.88) for the predicted binding affinity against the experiment on a heterogenous dataset of 90 zinc containing metalloprotein-ligand complexes consisting of five unique protein targets. Model validation and parameter analysis studies underscore the robustness and predictive ability of the scoring function. The high correlation obtained suggests the potential applicability of the methodology in designing novel ligands for zinc-metalloproteins. The scoring function has been web enabled for free access at www.scfbio-iitd.res.in/software/drugdesign/bapplz.jsp as BAPPL-Z server (Binding Affinity Prediction of Protein-Ligand complexes containing Zinc metal ions).

  18. MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets

    Science.gov (United States)

    Bandyopadhyay, Sanghamitra; Ghosh, Dip; Mitra, Ramkrishna; Zhao, Zhongming

    2015-01-01

    MicroRNA (miRNA) regulates gene expression by binding to specific sites in the 3'untranslated regions of its target genes. Machine learning based miRNA target prediction algorithms first extract a set of features from potential binding sites (PBSs) in the mRNA and then train a classifier to distinguish targets from non-targets. However, they do not consider whether the PBSs are functional or not, and consequently result in high false positive rates. This substantially affects the follow up functional validation by experiments. We present a novel machine learning based approach, MBSTAR (Multiple instance learning of Binding Sites of miRNA TARgets), for accurate prediction of true or functional miRNA binding sites. Multiple instance learning framework is adopted to handle the lack of information about the actual binding sites in the target mRNAs. Biologically validated 9531 interacting and 973 non-interacting miRNA-mRNA pairs are identified from Tarbase 6.0 and confirmed with PAR-CLIP dataset. It is found that MBSTAR achieves the highest number of binding sites overlapping with PAR-CLIP with maximum F-Score of 0.337. Compared to the other methods, MBSTAR also predicts target mRNAs with highest accuracy. The tool and genome wide predictions are available at http://www.isical.ac.in/~bioinfo_miu/MBStar30.htm.

  19. 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/.

  20. Modeling Nonlinear Adsorption with a Single Chemical Parameter: Predicting Chemical Median Langmuir Binding Constants.

    Science.gov (United States)

    Davis, Craig Warren; Di Toro, Dominic M

    2015-07-07

    Procedures for accurately predicting linear partition coefficients onto various sorbents (e.g., organic carbon, soils, clay) are reliable and well established. However, similar procedures for the prediction of sorption parameters of nonlinear isotherm models are not. The purpose of this paper is to present a procedure for predicting nonlinear isotherm parameters, specifically the median Langmuir binding constants, K̃L, obtained utilizing the single-chemical parameter log-normal Langmuir isotherm developed in the accompanying work. A reduced poly parameter linear free energy relationship (pp-LFER) is able to predict median Langmuir binding constants for graphite, charcoal, and Darco granular activated carbon (GAC) adsorption data. For the larger F400 GAC data set, a single pp-LFER model was insufficient, as a plateau is observed for the median Langmuir binding constants of larger molecular volume sorbates. This volumetric cutoff occurs in proximity to the median pore diameter for F400 GAC. A log-linear relationship exists between the aqueous solubility of these large compounds and their median Langmuir binding constants. Using this relationship for the chemicals above the volumetric cutoff and the pp-LFER below the cutoff, the median Langmuir binding constants can be predicted with a root-mean square error for graphite (n = 13), charcoal (n = 11), Darco GAC (n = 14), and F400 GAC (n = 44) of 0.129, 0.307, 0.407, and 0.424, respectively.

  1. Accuracy of binding mode prediction with a cascadic stochastic tunneling method.

    Science.gov (United States)

    Fischer, Bernhard; Basili, Serena; Merlitz, Holger; Wenzel, Wolfgang

    2007-07-01

    We investigate the accuracy of the binding modes predicted for 83 complexes of the high-resolution subset of the ASTEX/CCDC receptor-ligand database using the atomistic FlexScreen approach with a simple forcefield-based scoring function. The median RMS deviation between experimental and predicted binding mode was just 0.83 A. Over 80% of the ligands dock within 2 A of the experimental binding mode, for 60 complexes the docking protocol locates the correct binding mode in all of ten independent simulations. Most docking failures arise because (a) the experimental structure clashed in our forcefield and is thus unattainable in the docking process or (b) because the ligand is stabilized by crystal water.

  2. A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction

    Directory of Open Access Journals (Sweden)

    Wang Renxiao

    2010-04-01

    Full Text Available Abstract Background Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants. Results In our study, an automatic algorithm was developed for summarizing key protein-ligand interactions as a pharmacophore model and identifying the reference complex with a maximal similarity to the query complex. Our KGS strategy was evaluated in combination with two scoring functions (X-Score and PLP on three test sets, containing 112 HIV protease complexes, 44 carbonic anhydrase complexes, and 73 trypsin complexes, respectively. Our results obtained on crystal structures as well as computer-generated docking poses indicated that application of the KGS strategy produced more accurate predictions especially when X-Score or PLP alone did not perform well. Conclusions Compared to other targeted scoring functions, our KGS strategy does not require any re-parameterization or modification on current scoring methods, and its application is not tied to certain systems. The effectiveness of our KGS strategy is in theory proportional to the ever-increasing knowledge of experimental protein-ligand binding data. Our KGS strategy may serve as a more practical remedy for current scoring functions to improve their

  3. A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs.

    Directory of Open Access Journals (Sweden)

    Zhichao Miao

    2015-12-01

    Full Text Available Computational prediction of nucleic acid binding sites in proteins are necessary to disentangle functional mechanisms in most biological processes and to explore the binding mechanisms. Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i the definition of nucleic acid binding sites; ii the training and test datasets; iii the algorithmic methods for the prediction strategies; iv the performance measures and v the distribution and availability of the prediction programs. Here we report a large-scale assessment of 19 web servers and 3 stand-alone programs on 41 datasets including more than 5000 proteins derived from 3D structures of protein-nucleic acid complexes. Well-defined binary assessment criteria (specificity, sensitivity, precision, accuracy… are applied. We found that i the tools have been greatly improved over the years; ii some of the approaches suffer from theoretical defects and there is still room for sorting out the essential mechanisms of binding; iii RNA binding and DNA binding appear to follow similar driving forces and iv dataset bias may exist in some methods.

  4. Development of a protein-ligand-binding site prediction method based on interaction energy and sequence conservation.

    Science.gov (United States)

    Tsujikawa, Hiroto; Sato, Kenta; Wei, Cao; Saad, Gul; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

    2016-09-01

    We present a new method for predicting protein-ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction.

  5. The predicted 3D structure of the human D2 dopamine receptor and the binding site and binding affinities for agonists and antagonists

    Science.gov (United States)

    Kalani, M. Yashar S.; Vaidehi, Nagarajan; Hall, Spencer E.; Trabanino, Rene J.; Freddolino, Peter L.; Kalani, Maziyar A.; Floriano, Wely B.; Tak Kam, Victor Wai; Goddard, William A., III

    2004-03-01

    Dopamine neurotransmitter and its receptors play a critical role in the cell signaling process responsible for information transfer in neurons functioning in the nervous system. Development of improved therapeutics for such disorders as Parkinson's disease and schizophrenia would be significantly enhanced with the availability of the 3D structure for the dopamine receptors and of the binding site for dopamine and other agonists and antagonists. We report here the 3D structure of the long isoform of the human D2 dopamine receptor, predicted from primary sequence using first-principles theoretical and computational techniques (i.e., we did not use bioinformatic or experimental 3D structural information in predicting structures). The predicted 3D structure is validated by comparison of the predicted binding site and the relative binding affinities of dopamine, three known dopamine agonists (antiparkinsonian), and seven known antagonists (antipsychotic) in the D2 receptor to experimentally determined values. These structures correctly predict the critical residues for binding dopamine and several antagonists, identified by mutation studies, and give relative binding affinities that correlate well with experiments. The predicted binding site for dopamine and agonists is located between transmembrane (TM) helices 3, 4, 5, and 6, whereas the best antagonists bind to a site involving TM helices 2, 3, 4, 6, and 7 with minimal contacts to TM helix 5. We identify characteristic differences between the binding sites of agonists and antagonists.

  6. 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.

  7. 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.

  8. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

    Directory of Open Access Journals (Sweden)

    John A Capra

    2009-12-01

    Full Text Available Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/.

  9. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

    Science.gov (United States)

    Capra, John A; Laskowski, Roman A; Thornton, Janet M; Singh, Mona; Funkhouser, Thomas A

    2009-12-01

    Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

  10. Calciomics:prediction and analysis of EF-hand calcium binding proteins by protein engineering

    Institute of Scientific and Technical Information of China (English)

    YANG; Jenny; Jie

    2010-01-01

    Ca2+ plays a pivotal role in the physiology and biochemistry of prokaryotic and mammalian organisms.Viruses also utilize the universal Ca2+ signal to create a specific cellular environment to achieve coexistence with the host,and to propagate.In this paper we first describe our development of a grafting approach to understand site-specific Ca2+ binding properties of EF-hand proteins with a helix-loop-helix Ca2+ binding motif,then summarize our prediction and identification of EF-hand Ca2+ binding sites on a genome-wide scale in bacteria and virus,and next report the application of the grafting approach to probe the metal binding capability of predicted EF-hand motifs within the streptococcal hemoprotein receptor(Shr) of Streptococcus pyrogenes and the nonstructural protein 1(nsP1) of Sindbis virus.When methods such as the grafting approach are developed in conjunction with prediction algorithms we are better able to probe continuous Ca2+-binding sites that have been previously underrepresented due to the limitation of conventional methodology.

  11. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules.

    Directory of Open Access Journals (Sweden)

    Lianming Zhang

    Full Text Available MOTIVATION: Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules. METHOD: We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE. RESULT: The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in

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

    DEFF Research Database (Denmark)

    Hoof, Ilka; Peters, B; Sidney, J;

    2009-01-01

    .0, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I...

  13. STarMir Tools for Prediction of microRNA binding sites

    Science.gov (United States)

    Kanoria, Shaveta; Rennie, William; Liu, Chaochun; Carmack, C. Steven; Lu, Jun; Ding, Ye

    2017-01-01

    MicroRNAs (miRNAs) are a class of endogenous short non-coding RNAs that regulate gene expression by targeting messenger RNAs (mRNAs), which results in translational repression and/or mRNA degradation. As regulatory molecules, miRNAs are involved in many mammalian biological processes and also in the manifestation of certain human diseases. As miRNAs play central role in the regulation of gene expression, understanding miRNA-binding patterns is essential to gain an insight of miRNA mediated gene regulation and also holds promise for therapeutic applications. Computational prediction of miRNA binding sites on target mRNAs facilitates experimental investigation of miRNA functions. This chapter provides protocols for using the STarMir web server for improved predictions of miRNA binding sites on a target mRNA. As an application module of the Sfold RNA package, the current version of STarMir is an implementation of logistic prediction models developed with high throughput miRNA binding data from crosslinking immuno-precipitation (CLIP) studies. The models incorporated comprehensive thermodynamic, structural and sequence features, and were found to make improved predictions of both seed and seedless sites, in comparison to the established algorithms [1]. Their broad applicability was indicated by their good performance in cross-species validation. STarMir is freely available at http://sfold.wadsworth.org/starmir.html PMID:27665594

  14. Description and prediction of peptide-MHC binding: the 'human MHC project'

    DEFF Research Database (Denmark)

    Buus, S

    1999-01-01

    MHC molecules are crucially involved in controlling the specific immune system. They are highly polymorphic receptors sampling peptides from the cellular environment and presenting these peptides for scrutiny by immune cells. Recent advances in combinatorial peptide chemistry have improved the de...... the description and prediction of peptide-MHC binding. It is envisioned that a complete mapping of human immune reactivities will be possible....

  15. 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.

  16. 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...... benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set. Results: We found that cross-validated performances systematically overestimated performance on the blind set. This was found not to be due...

  17. Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams

    Directory of Open Access Journals (Sweden)

    Fernandez-Fuentes Narcis

    2011-08-01

    Full Text Available Abstract Background Protein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i which proteins interact and (ii how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces. Results Here we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance have been analyzed in order to maximize the performance of the method. Conclusions The integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.

  18. Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

    Science.gov (United States)

    Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

    2015-12-15

    Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

  19. 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.

  20. DEPTH: a web server to compute depth and predict small-molecule binding cavities in proteins.

    Science.gov (United States)

    Tan, Kuan Pern; Varadarajan, Raghavan; Madhusudhan, M S

    2011-07-01

    Depth measures the extent of atom/residue burial within a protein. It correlates with properties such as protein stability, hydrogen exchange rate, protein-protein interaction hot spots, post-translational modification sites and sequence variability. Our server, DEPTH, accurately computes depth and solvent-accessible surface area (SASA) values. We show that depth can be used to predict small molecule ligand binding cavities in proteins. Often, some of the residues lining a ligand binding cavity are both deep and solvent exposed. Using the depth-SASA pair values for a residue, its likelihood to form part of a small molecule binding cavity is estimated. The parameters of the method were calibrated over a training set of 900 high-resolution X-ray crystal structures of single-domain proteins bound to small molecules (molecular weight structures. Users have the option of tuning several parameters to detect cavities of different sizes, for example, geometrically flat binding sites. The input to the server is a protein 3D structure in PDB format. The users have the option of tuning the values of four parameters associated with the computation of residue depth and the prediction of binding cavities. The computed depths, SASA and binding cavity predictions are displayed in 2D plots and mapped onto 3D representations of the protein structure using Jmol. Links are provided to download the outputs. Our server is useful for all structural analysis based on residue depth and SASA, such as guiding site-directed mutagenesis experiments and small molecule docking exercises, in the context of protein functional annotation and drug discovery.

  1. Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods

    Science.gov (United States)

    Roos, Katarina; Hogner, Anders; Ogg, Derek; Packer, Martin J.; Hansson, Eva; Granberg, Kenneth L.; Evertsson, Emma; Nordqvist, Anneli

    2015-12-01

    In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R2 = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R2 = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

  2. Predicting Polymerase Ⅱ Core Promoters by Cooperating Transcription Factor Binding Sites in Eukaryotic Genes

    Institute of Scientific and Technical Information of China (English)

    Xiao-Tu MA; Min-Ping QIAN; Hai-Xu TANG

    2004-01-01

    Several discriminate functions for predicting core promoters that based on the potential cooperation between transcription factor binding sites (TFBSs) are discussed. It is demonstrated that the promoter predicting accuracy is improved when the cooperation among TFBSs is taken into consideration.The core promoter region of a newly discovered gene CKLFSF1 is predicted to locate more than 1.5 kb far away from the 5′ end of the transcript and in the last intron of its upstream gene, which is experimentally confirmed later. The core promoters of 3402 human RefSeq sequences, obtained by extending the mRNAs in human genome sequences, are predicted by our algorithm, and there are about 60% of the predicted core promoters locating within the ± 500 bp region relative to the annotated transcription start site.

  3. 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/.

  4. 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

  5. Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.

    Science.gov (United States)

    Brylinski, Michal

    2013-11-25

    A common strategy for virtual screening considers a systematic docking of a large library of organic compounds into the target sites in protein receptors with promising leads selected based on favorable intermolecular interactions. Despite a continuous progress in the modeling of protein-ligand interactions for pharmaceutical design, important challenges still remain, thus the development of novel techniques is required. In this communication, we describe eSimDock, a new approach to ligand docking and binding affinity prediction. eSimDock employs nonlinear machine learning-based scoring functions to improve the accuracy of ligand ranking and similarity-based binding pose prediction, and to increase the tolerance to structural imperfections in the target structures. In large-scale benchmarking using the Astex/CCDC data set, we show that 53.9% (67.9%) of the predicted ligand poses have RMSD of <2 Å (<3 Å). Moreover, using binding sites predicted by recently developed eFindSite, eSimDock models ligand binding poses with an RMSD of 4 Å for 50.0-39.7% of the complexes at the protein homology level limited to 80-40%. Simulations against non-native receptor structures, whose mean backbone rearrangements vary from 0.5 to 5.0 Å Cα-RMSD, show that the ratio of docking accuracy and the estimated upper bound is at a constant level of ∼0.65. Pearson correlation coefficient between experimental and predicted by eSimDock Ki values for a large data set of the crystal structures of protein-ligand complexes from BindingDB is 0.58, which decreases only to 0.46 when target structures distorted to 3.0 Å Cα-RMSD are used. Finally, two case studies demonstrate that eSimDock can be customized to specific applications as well. These encouraging results show that the performance of eSimDock is largely unaffected by the deformations of ligand binding regions, thus it represents a practical strategy for across-proteome virtual screening using protein models. eSimDock is freely

  6. 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.

  7. 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...... 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...

  8. Resolving the problem of trapped water in binding cavities: prediction of host-guest binding free energies in the SAMPL5 challenge by funnel metadynamics

    Science.gov (United States)

    Bhakat, Soumendranath; Söderhjelm, Pär

    2017-01-01

    The funnel metadynamics method enables rigorous calculation of the potential of mean force along an arbitrary binding path and thereby evaluation of the absolute binding free energy. A problem of such physical paths is that the mechanism characterizing the binding process is not always obvious. In particular, it might involve reorganization of the solvent in the binding site, which is not easily captured with a few geometrically defined collective variables that can be used for biasing. In this paper, we propose and test a simple method to resolve this trapped-water problem by dividing the process into an artificial host-desolvation step and an actual binding step. We show that, under certain circumstances, the contribution from the desolvation step can be calculated without introducing further statistical errors. We apply the method to the problem of predicting host-guest binding free energies in the SAMPL5 blind challenge, using two octa-acid hosts and six guest molecules. For one of the hosts, well-converged results are obtained and the prediction of relative binding free energies is the best among all the SAMPL5 submissions. For the other host, which has a narrower binding pocket, the statistical uncertainties are slightly higher; longer simulations would therefore be needed to obtain conclusive results.

  9. Combining features in a graphical model to predict protein binding sites.

    Science.gov (United States)

    Wierschin, Torsten; Wang, Keyu; Welter, Marlon; Waack, Stephan; Stanke, Mario

    2015-05-01

    Large efforts have been made in classifying residues as binding sites in proteins using machine learning methods. The prediction task can be translated into the computational challenge of assigning each residue the label binding site or non-binding site. Observational data comes from various possibly highly correlated sources. It includes the structure of the protein but not the structure of the complex. The model class of conditional random fields (CRFs) has previously successfully been used for protein binding site prediction. Here, a new CRF-approach is presented that models the dependencies of residues using a general graphical structure defined as a neighborhood graph and thus our model makes fewer independence assumptions on the labels than sequential labeling approaches. A novel node feature "change in free energy" is introduced into the model, which is then denoted by ΔF-CRF. Parameters are trained with an online large-margin algorithm. Using the standard feature class relative accessible surface area alone, the general graph-structure CRF already achieves higher prediction accuracy than the linear chain CRF of Li et al. ΔF-CRF performs significantly better on a large range of false positive rates than the support-vector-machine-based program PresCont of Zellner et al. on a homodimer set containing 128 chains. ΔF-CRF has a broader scope than PresCont since it is not constrained to protein subgroups and requires no multiple sequence alignment. The improvement is attributed to the advantageous combination of the novel node feature with the standard feature and to the adopted parameter training method.

  10. 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.

  11. SNP2TFBS – a database of regulatory SNPs affecting predicted transcription factor binding site affinity

    Science.gov (United States)

    Kumar, Sunil; Ambrosini, Giovanna; Bucher, Philipp

    2017-01-01

    SNP2TFBS is a computational resource intended to support researchers investigating the molecular mechanisms underlying regulatory variation in the human genome. The database essentially consists of a collection of text files providing specific annotations for human single nucleotide polymorphisms (SNPs), namely whether they are predicted to abolish, create or change the affinity of one or several transcription factor (TF) binding sites. A SNP's effect on TF binding is estimated based on a position weight matrix (PWM) model for the binding specificity of the corresponding factor. These data files are regenerated at regular intervals by an automatic procedure that takes as input a reference genome, a comprehensive SNP catalogue and a collection of PWMs. SNP2TFBS is also accessible over a web interface, enabling users to view the information provided for an individual SNP, to extract SNPs based on various search criteria, to annotate uploaded sets of SNPs or to display statistics about the frequencies of binding sites affected by selected SNPs. Homepage: http://ccg.vital-it.ch/snp2tfbs/. PMID:27899579

  12. Accurate prediction of DnaK-peptide binding via homology modelling and experimental data.

    Directory of Open Access Journals (Sweden)

    Joost Van Durme

    2009-08-01

    Full Text Available Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides, refolding and degradation of misfolded proteins, and activation of a wide range of client proteins. The prokaryotic heat-shock protein DnaK is the E. coli representative of the ubiquitous Hsp70 family, which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides. Accurate prediction of DnaK binding sites in E. coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins. In order to map DnaK binding sites in protein sequences, we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling. We show that this combination significantly outperforms either single approach. The final predictor had a Matthews correlation coefficient (MCC of 0.819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives. To test the robustness of the learning set, we have conducted a simulated cross-validation, where we omit sequences from the learning sets and calculate the rate of repredicting them. This resulted in a surprisingly good MCC of 0.703. The algorithm was also able to perform equally well on a blind test set of binders and non-binders, of which there was no prior knowledge in the learning sets. The algorithm is freely available at http://limbo.vib.be.

  13. 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

    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...... 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...

  14. 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.

  15. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding

    DEFF Research Database (Denmark)

    Zhang, H.; Lund, Ole; Nielsen, M.

    2009-01-01

    of the specificities of MHC molecules in this library weighted by the similarity of their pocket-residues to the query. This PickPocket method is demonstrated to accurately predict MHC-peptide binding for a broad range of MHC alleles, including human and non-human species. In contrast to neural network-based pan......-specific methods, PickPocket was shown to be robust both when data is scarce and when the similarity to MHC molecules with characterized binding specificity is low. A consensus method combining the PickPocket and NetMHCpan methods was shown to achieve superior predictive performance. This study demonstrates how...

  16. Novel predicted RNA-binding domains associated with the translation machinery.

    Science.gov (United States)

    Aravind, L; Koonin, E V

    1999-03-01

    Two previously undetected domains were identified in a variety of RNA-binding proteins, particularly RNA-modifying enzymes, using methods for sequence profile analysis. A small domain consisting of 60-65 amino acid residues was detected in the ribosomal protein S4, two families of pseudouridine synthases, a novel family of predicted RNA methylases, a yeast protein containing a pseudouridine synthetase and a deaminase domain, bacterial tyrosyl-tRNA synthetases, and a number of uncharacterized, small proteins that may be involved in translation regulation. Another novel domain, designated PUA domain, after PseudoUridine synthase and Archaeosine transglycosylase, was detected in archaeal and eukaryotic pseudouridine synthases, archaeal archaeosine synthases, a family of predicted ATPases that may be involved in RNA modification, a family of predicted archaeal and bacterial rRNA methylases. Additionally, the PUA domain was detected in a family of eukaryotic proteins that also contain a domain homologous to the translation initiation factor eIF1/SUI1; these proteins may comprise a novel type of translation factors. Unexpectedly, the PUA domain was detected also in bacterial and yeast glutamate kinases; this is compatible with the demonstrated role of these enzymes in the regulation of the expression of other genes. We propose that the S4 domain and the PUA domain bind RNA molecules with complex folded structures, adding to the growing collection of nucleic acid-binding domains associated with DNA and RNA modification enzymes. The evolution of the translation machinery components containing the S4, PUA, and SUI1 domains must have included several events of lateral gene transfer and gene loss as well as lineage-specific domain fusions.

  17. Exploiting structural and topological information to improve prediction of RNA-protein binding sites

    Directory of Open Access Journals (Sweden)

    Yuan Zheng

    2009-10-01

    Full Text Available Abstract Background RNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy. Results We have quantified the impact of structural information on the prediction accuracy in comparison to the purely sequence based approach using two machine learning techniques: Naïve Bayes classifiers and Support Vector Machines. The highest AUC of 0.83 was achieved by a Support Vector Machine, exploiting PSI-BLAST profile, accessible surface area, betweenness-centrality and retention coefficient as input features. Taking into account that our results are based on a larger non-redundant data set, the prediction accuracy is considerably higher than reported in previous, comparable studies. A protein-RNA interface predictor (PRIP and the data set have been made available at http://www.qfab.org/PRIP. Conclusion Graph-theoretic properties of residue contact maps derived from protein structures such as betweenness-centrality can supplement sequence or structure features to improve the prediction accuracy for binding residues in RNA-protein interactions. While Support Vector Machines perform better on this task, Naïve Bayes classifiers also have been found to achieve good prediction accuracies but require much less training time and are an attractive choice for large scale predictions.

  18. 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

    MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes...... 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/....

  19. Ligand-induced conformational changes: Improved predictions of ligand binding conformations and affinities

    DEFF Research Database (Denmark)

    Frimurer, T.M.; Peters, Günther H.J.; Iversen, L.F.

    2003-01-01

    A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein...... tyrosine phosphatase 1 B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal...... predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side...

  20. 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.

  1. Predicting a DNA-binding protein using random forest with multiple mathematical features.

    Science.gov (United States)

    Guan, Changge; Niu, Xiaohui; Shi, Feng; Yang, Kun; Li, Nana

    2015-01-01

    DNA-binding proteins are involved and play a crucial role in a lot of important biological processes. Hence, the identification of the DNA-binding proteins is a challenging and significant problem. In order to reveal the intrinsic information correlated to DNA-binding, nine classes of candidate features based on different mathematical fields are applied to construct the prediction model with random forest. They are fractal dimension, conjoint triad feature, Hilbert-Huang Transformation, amino acid composition, dipeptide composition, chaos game representation, and the corresponding information entropies. These mathematical expressions are evaluated with 5-fold cross validation test. The results of numerical simulations show that the mathematical features consisted of amino acid composition, fractal dimension and information entropies of amino acid and chaos game representation achieve the best performance. Its accuracy is 0.8157, and Matthew's correlation coefficient (MCC) achieves 0.5968 on the benchmark dataset from DNA-Prot. By analyzing the components of top combination of the nine candidate features, the concepts of fractal dimension and information entropy are the effective and vital features, which can provide complementary sequence-order information on the basis of amino acid composition.

  2. Quantitative predictions of binding free energy changes in drug-resistant influenza neuraminidase.

    Directory of Open Access Journals (Sweden)

    Daniel R Ripoll

    Full Text Available Quantitatively predicting changes in drug sensitivity associated with residue mutations is a major challenge in structural biology. By expanding the limits of free energy calculations, we successfully identified mutations in influenza neuraminidase (NA that confer drug resistance to two antiviral drugs, zanamivir and oseltamivir. We augmented molecular dynamics (MD with Hamiltonian Replica Exchange and calculated binding free energy changes for H274Y, N294S, and Y252H mutants. Based on experimental data, our calculations achieved high accuracy and precision compared with results from established computational methods. Analysis of 15 micros of aggregated MD trajectories provided insights into the molecular mechanisms underlying drug resistance that are at odds with current interpretations of the crystallographic data. Contrary to the notion that resistance is caused by mutant-induced changes in hydrophobicity of the binding pocket, our simulations showed that drug resistance mutations in NA led to subtle rearrangements in the protein structure and its dynamics that together alter the active-site electrostatic environment and modulate inhibitor binding. Importantly, different mutations confer resistance through different conformational changes, suggesting that a generalized mechanism for NA drug resistance is unlikely.

  3. Advances and applications of binding affinity prediction methods in drug discovery.

    Science.gov (United States)

    Parenti, Marco Daniele; Rastelli, Giulio

    2012-01-01

    Nowadays, the improvement of R&D productivity is the primary commitment in pharmaceutical research, both in big pharma and smaller biotech companies. To reduce costs, to speed up the discovery process and to increase the chance of success, advanced methods of rational drug design are very helpful, as demonstrated by several successful applications. Among these, computational methods able to predict the binding affinity of small molecules to specific biological targets are of special interest because they can accelerate the discovery of new hit compounds. Here we provide an overview of the most widely used methods in the field of binding affinity prediction, as well as of our own work in developing BEAR, an innovative methodology specifically devised to overtake some limitations in existing approaches. The BEAR method was successfully validated against different biological targets, and proved its efficacy in retrieving active compounds from virtual screening campaigns. The results obtained so far indicate that BEAR may become a leading tool in the drug discovery pipeline. We primarily discuss advantages and drawbacks of each technique and show relevant examples and applications in drug discovery.

  4. 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.

  5. 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.

  6. Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor-stratified calibrations

    DEFF Research Database (Denmark)

    Lauemøller, S L; Holm, A; Hilden, J;

    2001-01-01

    predictions, we have measured the MHC class I binding of a large number of peptides. In an attempt to further improve predictions and to include sequence dependency, we subdivided the panel of peptides according to whether the peptides had zero, one or two primary anchor residues. This allowed us to define......Peptides are key immune targets. They are generated by fragmentation of antigenic proteins, selected by major histocompatibility complex (MHC) molecules and subsequently presented to T cells. One of the most selective requirements is that of peptide binding to MHC. Accurate descriptions...... and predictions of peptide-MHC interactions are therefore important. Quantitative matrices representing MHC class I specificity can be used to search any query protein for the presence of MHC binding peptides. Assuming that each peptide residue contributes to binding in an additive and sequence independent manner...

  7. Predicting copper-, iron- and zinc-binding proteins in pathogenic species of the Paracoccidioides genus

    Directory of Open Access Journals (Sweden)

    Gabriel B Tristao

    2015-01-01

    Full Text Available Approximately one-third of all proteins have been estimated to contain at least one metal cofactor, and these proteins are referred to as metalloproteins. These represent one of the most diverse classes of proteins, containing metal ions that bind to specific sites to perform catalytic, regulatory and structural functions. Bioinformatic tools have been developed to predict metalloproteins encoded by an organism based only on its genome sequence. Its function and the type of metal binder can also be predicted via a bioinformatics approach. Paracoccidioides complex includes termodimorphic pathogenic fungi that are found as saprobic mycelia in the environment and as yeast, the parasitic form, in host tissues. They are the etiologic agents of Paracoccidioidomycosis, a prevalent systemic mycosis in Latin America. Many metalloproteins are important for the virulence of several pathogenic microorganisms. Accordingly, the present work aimed to predict the cooper, iron and zinc proteins encoded by the genomes of three phylogenetic species of Paracoccidioides (Pb01, Pb03 and Pb18. The metalloproteins were identified using bioinformatics approaches based on structure, annotation and domains. Cu-, Fe- and Zn-binding proteins represent 7% of the total proteins encoded by Paracoccidioides spp. genomes. Zinc proteins were the most abundant metalloproteins, representing 5.7% of the fungus proteome, whereas copper and iron proteins represent 0.3% and 1.2%, respectively. Functional classification revealed that metalloproteins are related to many cellular processes. Furthermore, it was observed that many of these metalloproteins serve as virulence factors in the biology of the fungus. Thus, it is concluded that the Cu, Fe and Zn metalloproteomes of the Paracoccidioides spp. are of the utmost importance for the biology and virulence of these particular human pathogens.

  8. 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...

  9. 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

  10. Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols.

    Science.gov (United States)

    Chakavorty, Arghya; Li, Lin; Alexov, Emil

    2016-10-30

    Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson-Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔGelec ) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔGelec . We argue that the sensitivity of the absolute ΔΔGelec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM-27, AMBER-94, and OPLS-AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔGelec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔGelec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.

  11. 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.

  12. 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

  13. 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

    from pathogens or cancer proteomes involve either reverse immunology or high-throughput mass spectrometry (HTMS). Reverse immunology approaches involve pre-screening of proteomes by computational algorithms, followed by experimental validation of selected targets ( [Mora et al., 2006], [De Groot et al......). Hundreds of naturally processed HLA class I associated peptides have been identified in individual studies using HTMS in normal (Escobar et al., 2008), cancer ( [Antwi et al., 2009] and [Bassani-Sternberg et al., 2010]), autoimmunity-related (Ben Dror et al., 2010), and infected samples (Wahl et al, 2010......). Computational algorithms are essential steps in high-throughput identification of T-cell epitope candidates using both reverse immunology and HTMS approaches. Peptide binding to MHC molecules is the single most selective step in defining T cell epitope and the accuracy of computational algorithms for prediction...

  14. 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.

  15. Prediction on the binding domain between human interleukin-6 and its receptor

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Based on the spatial conformations of human interleukin-6 (hIL-6) derived from nuclear magnetic resonance analysis and human interleukin-6 receptor (hIL-6R) modeled with homology modeling method using human growth hormone receptor as template, the interaction between hIL-6 and its receptor (hIL-6R) is studied with docking program according to the surface electrostatic potential analysis and spatial conformation complement. The stable region structure composed of hIL-6 and hIL-6R is obtained on the basis of molecular mechanism optimization and molecular dynamics simulation. The binding domain between hIL-6 and hIL-6R is predicted theoretically. Furthermore, the especial binding sites that influence the interaction between hIL-6 and hIL-6R are confirmed. The results lay a theoretical foundation for confirming the active regions of hIL-6 and designing novel antagonist with computer-guided techniques.

  16. Prediction on the binding domain between human interleukin-6 and its receptor

    Institute of Scientific and Technical Information of China (English)

    冯健男; 任蕴芳; 沈倍奋

    2000-01-01

    Based on the spatial conformations of human interleukin-6 (hlL-6) derived from nuclear magnetic resonance analysis and human interleukin-6 receptor (hlL-6R) modeled with homology modeling method using human growth hormone receptor as template, the interaction between hlL-6 and its receptor (hIL-6R) is studied with docking program according to the surface electrostatic potential analysis and spatial conformation complement. The stable region structure composed of hlL-6 and hlL-6R is obtained on the basis of molecular mechanism optimization and molecular dynamics simulation. The binding domain between hIL-6 and hIL-6R is predicted theoretically. Furthermore, the especial binding sites that influence the interaction between hlL-6 and hlL-6R are confirmed. The results lay a theoretical foundation for confirming the active regions of hlL-6 and designing novel antagonist with computer-guided techniques.

  17. Identification of co-regulated genes through Bayesian clustering of predicted regulatory binding sites.

    Science.gov (United States)

    Qin, Zhaohui S; McCue, Lee Ann; Thompson, William; Mayerhofer, Linda; Lawrence, Charles E; Liu, Jun S

    2003-04-01

    The identification of co-regulated genes and their transcription-factor binding sites (TFBS) are key steps toward understanding transcription regulation. In addition to effective laboratory assays, various computational approaches for the detection of TFBS in promoter regions of coexpressed genes have been developed. The availability of complete genome sequences combined with the likelihood that transcription factors and their cognate sites are often conserved during evolution has led to the development of phylogenetic footprinting. The modus operandi of this technique is to search for conserved motifs upstream of orthologous genes from closely related species. The method can identify hundreds of TFBS without prior knowledge of co-regulation or coexpression. Because many of these predicted sites are likely to be bound by the same transcription factor, motifs with similar patterns can be put into clusters so as to infer the sets of co-regulated genes, that is, the regulons. This strategy utilizes only genome sequence information and is complementary to and confirmative of gene expression data generated by microarray experiments. However, the limited data available to characterize individual binding patterns, the variation in motif alignment, motif width, and base conservation, and the lack of knowledge of the number and sizes of regulons make this inference problem difficult. We have developed a Gibbs sampling-based Bayesian motif clustering (BMC) algorithm to address these challenges. Tests on simulated data sets show that BMC produces many fewer errors than hierarchical and K-means clustering methods. The application of BMC to hundreds of predicted gamma-proteobacterial motifs correctly identified many experimentally reported regulons, inferred the existence of previously unreported members of these regulons, and suggested novel regulons.

  18. Love to win or hate to lose? Asymmetry of dopamine D2 receptor binding predicts sensitivity to reward vs. punishment

    Science.gov (United States)

    Tomer, Rachel; Slagter, Heleen A; Christian, Bradley T; Fox, Andrew S; King, Carlye R; Murali, Dhanabalan; Gluck, Mark A; Davidson, Richard J

    2014-01-01

    Humans show consistent differences in the extent to which their behavior reflects a bias towards appetitive approach-related behavior or avoidance of aversive stimuli (Elliot, 2008). We examined the hypothesis that in healthy subjects this motivational bias (assessed by self-report and by a probabilistic learning task that allows direct comparison of the relative sensitivity to reward and punishment) reflects lateralization of dopamine signaling. Using [F-18]fallypride to measure D2/D3 binding , we found that self-reported motivational bias was predicted by the asymmetry of frontal D2 binding. Similarly, striatal and frontal asymmetries in D2 dopamine receptor binding, rather than absolute binding levels, predicted individual differences in learning from reward vs. punishment. These results suggest that normal variation in asymmetry of dopamine signaling may, in part, underlie human personality and cognition. PMID:24345165

  19. Genome wide prediction of HNF4alpha functional binding sites by the use of local and global sequence context.

    Science.gov (United States)

    Kel, Alexander E; Niehof, Monika; Matys, Volker; Zemlin, Rüdiger; Borlak, Jürgen

    2008-01-01

    We report an application of machine learning algorithms that enables prediction of the functional context of transcription factor binding sites in the human genome. We demonstrate that our method allowed de novo identification of hepatic nuclear factor (HNF)4alpha binding sites and significantly improved an overall recognition of faithful HNF4alpha targets. When applied to published findings, an unprecedented high number of false positives were identified. The technique can be applied to any transcription factor.

  20. Model-based Comparative Prediction of Transcription-Factor Binding Motifs in Anabolic Responses in Bone

    Institute of Scientific and Technical Information of China (English)

    Andy; B.; Chen; Kazunori; Hamamura; Guohua; Wang; Weirong; Xing; Subburaman; Mohan; Hiroki; Yokota; Yunlong; Liu

    2007-01-01

    Understanding the regulatory mechanism that controls the alteration of global gene expression patterns continues to be a challenging task in computational biology. We previously developed an ant algorithm, a biologically-inspired computational technique for microarray data, and predicted putative transcription-factor binding motifs (TFBMs) through mimicking interactive behaviors of natural ants. Here we extended the algorithm into a set of web-based software, Ant Modeler, and applied it to investigate the transcriptional mechanism underlying bone formation. Mechanical loading and administration of bone morphogenic proteins (BMPs) are two known treatments to strengthen bone. We addressed a question: Is there any TFBM that stimulates both "anabolic responses of mechanical loading" and "BMP-mediated osteogenic signaling"? Although there is no significant overlap among genes in the two responses, a comparative model-based analysis suggests that the two independent osteogenic processes employ common TFBMs, such as a stress responsive element and a motif for peroxisome proliferator-activated recep- tor (PPAR). The post-modeling in vitro analysis using mouse osteoblast cells sup- ported involvements of the predicted TFBMs such as PPAR, Ikaros 3, and LMO2 in response to mechanical loading. Taken together, the results would be useful to derive a set of testable hypotheses and examine the role of specific regulators in complex transcriptional control of bone formation.

  1. 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.

  2. Interactome-wide prediction of protein-protein binding sites reveals effects of protein sequence variation in Arabidopsis thaliana.

    Directory of Open Access Journals (Sweden)

    Felipe Leal Valentim

    Full Text Available The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.

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

    Directory of Open Access Journals (Sweden)

    Huixiao Hong

    2016-03-01

    Full Text Available 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.

  4. 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.

  5. 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.

  6. 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

  7. 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...

  8. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lundegaard, Claus; Blicher, Thomas

    2008-01-01

    class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method...

  9. MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence

    OpenAIRE

    Passerini, A; Lippi, M.; P. Frasconi

    2011-01-01

    MetalDetector identifies CYS and HIS involved in transition metal protein binding sites, starting from sequence alone. A major new feature of release 2.0 is the ability to predict which residues are jointly involved in the coordination of the same metal ion. The server is available at http://metaldetector.dsi.unifi.it/v2.0/.

  10. 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 predict...

  11. 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.

  12. Protein function annotation by local binding site surface similarity.

    Science.gov (United States)

    Spitzer, Russell; Cleves, Ann E; Varela, Rocco; Jain, Ajay N

    2014-04-01

    Hundreds of protein crystal structures exist for proteins whose function cannot be confidently determined from sequence similarity. Surflex-PSIM, a previously reported surface-based protein similarity algorithm, provides an alternative method for hypothesizing function for such proteins. The method now supports fully automatic binding site detection and is fast enough to screen comprehensive databases of protein binding sites. The binding site detection methodology was validated on apo/holo cognate protein pairs, correctly identifying 91% of ligand binding sites in holo structures and 88% in apo structures where corresponding sites existed. For correctly detected apo binding sites, the cognate holo site was the most similar binding site 87% of the time. PSIM was used to screen a set of proteins that had poorly characterized functions at the time of crystallization, but were later biochemically annotated. Using a fully automated protocol, this set of 8 proteins was screened against ∼60,000 ligand binding sites from the PDB. PSIM correctly identified functional matches that predated query protein biochemical annotation for five out of the eight query proteins. A panel of 12 currently unannotated proteins was also screened, resulting in a large number of statistically significant binding site matches, some of which suggest likely functions for the poorly characterized proteins.

  13. 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.

  14. PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences

    Science.gov (United States)

    Murakami, Yoichi; Spriggs, Ruth V.; Nakamura, Haruki; Jones, Susan

    2010-01-01

    The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity as features. The server allows the submission of up to 10 protein sequences, and the predictions for each sequence are provided on a web page and via email. The prediction results are provided in sequence format with predicted RBRs highlighted, in text format with the SVM threshold score indicated and as a graph which enables users to quickly identify those residues above any specific SVM threshold. The graph effectively enables the increase or decrease of the false positive rate. When tested on a non-redundant data set of 42 protein sequences not used in training, the PiRaNhA server achieved an accuracy of 85%, specificity of 90% and a Matthews correlation coefficient of 0.41 and outperformed other publicly available servers. The PiRaNhA prediction server is freely available at http://www.bioinformatics.sussex.ac.uk/PIRANHA. PMID:20507911

  15. 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.

  16. Dopamine transporter comparative molecular modeling and binding site prediction using the LeuT(Aa) leucine transporter as a template.

    Science.gov (United States)

    Indarte, Martín; Madura, Jeffry D; Surratt, Christopher K

    2008-02-15

    Pharmacological and behavioral studies indicate that binding of cocaine and the amphetamines by the dopamine transporter (DAT) protein is principally responsible for initiating the euphoria and addiction associated with these drugs. The lack of an X-ray crystal structure for the DAT or any other member of the neurotransmitter:sodium symporter (NSS) family has hindered understanding of psychostimulant recognition at the atomic level; structural information has been obtained largely from mutagenesis and biophysical studies. The recent publication of a crystal structure for the bacterial leucine transporter LeuT(Aa), a distantly related NSS family homolog, provides for the first time a template for three-dimensional comparative modeling of NSS proteins. A novel computational modeling approach using the capabilities of the Molecular Operating Environment program MOE 2005.06 in conjunction with other comparative modeling servers generated the LeuT(Aa)-directed DAT model. Probable dopamine and amphetamine binding sites were identified within the DAT model using multiple docking approaches. Binding sites for the substrate ligands (dopamine and amphetamine) overlapped substantially with the analogous region of the LeuT(Aa) crystal structure for the substrate leucine. The docking predictions implicated DAT side chains known to be critical for high affinity ligand binding and suggest novel mutagenesis targets in elucidating discrete substrate and inhibitor binding sites. The DAT model may guide DAT ligand QSAR studies, and rational design of novel DAT-binding therapeutics.

  17. 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....

  18. 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.

  19. 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...... to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools....

  20. Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.

    Science.gov (United States)

    Brender, Jeffrey R; Zhang, Yang

    2015-10-01

    The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.

  1. Flanking p10 contribution and sequence bias in matrix based epitope prediction: revisiting the assumption of independent binding pockets

    Directory of Open Access Journals (Sweden)

    Parry Christian S

    2008-10-01

    Full Text Available Abstract Background Eluted natural peptides from major histocompatibility molecules show patterns of conserved residues. Crystallographic structures show that the bound peptide in class II major histocompatibility complex adopts a near uniform polyproline II-like conformation. This way allele-specific favoured residues are able to anchor into pockets in the binding groove leaving other peptide side chains exposed for recognition by T cells. The anchor residues form a motif. This sequence pattern can be used to screen large sequences for potential epitopes. Quantitative matrices extend the motif idea to include the contribution of non-anchor peptide residues. This report examines two new matrices that extend the binding register to incorporate the polymorphic p10 pocket of human leukocyte antigen DR1. Their performance is quantified against experimental binding measurements and against the canonical nine-residue register matrix. Results One new matrix shows significant improvement over the base matrix; the other does not. The new matrices differ in the sequence of the peptide library. Conclusion One of the extended quantitative matrices showed significant improvement in prediction over the original nine residue matrix and over the other extended matrix. Proline in the sequence of the peptide library of the better performing matrix presumably stabilizes the peptide conformation through neighbour interactions. Such interactions may influence epitope prediction in this test of quantitative matrices. This calls into question the assumption of the independent contribution of individual binding pockets.

  2. Prediction of Peptide Binding to Major Histocompatibility II Receptors with Molecular Mechanics and Semi-Empirical Quantum Mechanics Methods

    Directory of Open Access Journals (Sweden)

    James A Platts

    2012-02-01

    Full Text Available Methods for prediction of the binding of peptides to major histocompatibility complex (MHC II receptors are examined, using literature values of IC50 as a benchmark. Two sets of IC50 data for closely structurally related peptides based on hen egg lysozyme (HEL and myelin basic protein (MBP are reported first. This shows that methods based on both molecular mechanics and semi-empirical quantum mechanics can predict binding with good-to-reasonable accuracy, as long as a suitable method for estimation of solvation effects is included. A more diverse set of 22 peptides bound to HLA-DR1 provides a tougher test of such methods, especially since no crystal structure is available for these peptide-MHC complexes. We therefore use sequence based methods such as SYFPEITHI and SVMHC to generate possible binding poses, using a consensus approach to determine the most likely anchor residues, which are then mapped onto the crystal structure of an unrelated peptide bound to the same receptor. This analysis shows that the MM/GBVI method performs particularly well, as does the AMBER94 forcefield with Born solvation model. Indeed, MM/GBVI can be used as an alternative to sequence based methods in generating binding poses, leading to still better accuracy.

  3. Love to win or hate to Lose? Asymmetry of dopamine D2 receptor binding predicts sensitivity to reward versus punishment.

    Science.gov (United States)

    Tomer, Rachel; Slagter, Heleen A; Christian, Bradley T; Fox, Andrew S; King, Carlye R; Murali, Dhanabalan; Gluck, Mark A; Davidson, Richard J

    2014-05-01

    Humans show consistent differences in the extent to which their behavior reflects a bias toward appetitive approach-related behavior or avoidance of aversive stimuli [Elliot, A. J. Approach and avoidance motivation. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation (pp. 3-14). New York: Psychology Press, 2008]. We examined the hypothesis that in healthy participants this motivational bias (assessed by self-report and by a probabilistic learning task that allows direct comparison of the relative sensitivity to reward and punishment) reflects lateralization of dopamine signaling. Using [F-18]fallypride to measure D2/D3 binding, we found that self-reported motivational bias was predicted by the asymmetry of frontal D2 binding. Similarly, striatal and frontal asymmetries in D2 dopamine receptor binding, rather than absolute binding levels, predicted individual differences in learning from reward versus punishment. These results suggest that normal variation in asymmetry of dopamine signaling may, in part, underlie human personality and cognition.

  4. Development of molecular docking-based binding energy to predict the joint effect of BPA and its analogs.

    Science.gov (United States)

    Zhang, Hong-Chang; Hu, Xia-Lin; Yin, Da-Qiang; Lin, Zhi-Fen

    2011-04-01

    A general proposal for predicting the joint effect of endocrine disrupting chemicals by examining binding energy models was developed in this study. 2,2-bis(4-hydroxyphenyl)propane (BPA) and 11 of its analogs were chosen, and the estrogenic activity of each compound was measured by determining its EC50 value using a recombinant gene yeast assay. Binding energies (BEs) were calculated using Surflex-Docking software. The analysis of the relationship between EC50 values and BEs showed that there is a linear correlation between the BEs and EC50 values. Furthermore, the analysis of the given binary and quaternary mixtures of BPA and three of its analogs showed that the joint effects of the mixtures were affected by the proportions of the chemicals in each mixture and their relative binding energy. The correlation between the joint effects of mixtures and the binding energy of the individual compounds has been described using one formula, which can be used to predict the joint effects of other mixtures.

  5. THUMP--a predicted RNA-binding domain shared by 4-thiouridine, pseudouridine synthases and RNA methylases.

    Science.gov (United States)

    Aravind, L; Koonin, E V

    2001-04-01

    Sequence profile searches were used to identify an ancient domain in ThiI-like thiouridine synthases, conserved RNA methylases, archaeal pseudouridine synthases and several uncharacterized proteins. We predict that this domain is an RNA-binding domain that adopts an alpha/beta fold similar to that found in the C-terminal domain of translation initiation factor 3 and ribosomal protein S8.

  6. BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

    Science.gov (United States)

    Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

    2016-10-01

    Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc.

  7. Asymmetry of /sup 3/H- imipramine binding may predict psychiatric illness

    Energy Technology Data Exchange (ETDEWEB)

    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 /sup 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.

  8. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors

    Energy Technology Data Exchange (ETDEWEB)

    Bennion, B J; Kulp, K S; Cosman, M; Lightstone, F C

    2005-08-26

    Many carcinogens have been shown to cause tissue specific tumors in animal models. The mechanism for this specificity has not been fully elucidated and is usually attributed to differences in organ metabolism. For heterocyclic amines, potent carcinogens that are formed in well-done meat, the ability to either bind to the estrogen receptor and activate or inhibit an estrogenic response will have a major impact on carcinogenicity. Here we describe our work with the human estrogen receptor alpha (hERa) and the mutagenic/carcinogenic heterocyclic amines PhIP, MeIQx, IFP, and the hydroxylated metabolite of PhIP, N2-hydroxy-PhIP. We found that PhIP, in contrast to the other heterocyclic amines, increased cell-proliferation in MCF-7 human breast cancer cells and activated the hERa receptor. We show mechanistic data supporting this activation both computationally by homology modeling and docking, and by NMR confirmation that PhIP binds with the ligand binding domain (LBD). This binding competes with estradiol (E2) in the native E2 binding cavity of the receptor. We also find that other heterocyclic amines and N2-hydroxy-PhIP inhibit ER activation presumably by binding into another cavity on the LBD. Moreover, molecular dynamics simulations of inhibitory heterocyclic amines reveal a disruption of the surface of the receptor protein involved with protein-protein signaling. We therefore propose that the mechanism for the tissue specific carcinogenicity seen in the rat breast tumors and the presumptive human breast cancer associated with the consumption of well-done meat maybe mediated by this receptor activation.

  9. Predicting the Strength of Anion-π Interactions of Substituted Benzenes: the Development of Anion-π Binding Substituent Constants.

    Science.gov (United States)

    Bagwill, Christina; Anderson, Christa; Sullivan, Elizabeth; Manohara, Varun; Murthy, Prithvi; Kirkpatrick, Charles C; Stalcup, Apryll; Lewis, Michael

    2016-11-23

    A computational study aimed at accurately predicting the strength of the anion-π binding of substituted benzenes is presented. The anion-π binding energies (Ebind) of 37 substituted benzenes and the parent benzene, with chloride or bromide were investigated at the MP2(full)/6-311++G** level of theory. In addition, energy decomposition analysis was performed on 27 selected chloride-arene complexes via symmetry adapted perturbation theory (SAPT), using the SAPT2+ approach. Initial efforts aimed to correlate the anion-π Ebind values with the sum of the Hammett constants σp (Σσp) or σm (Σσm), as done by others. This proved a decent approach for predicting the binding strength of aromatics with electron-withdrawing substituents. For the Cl(-)-substituted benzene Ebind values, the correlation with the Σσp and Σσm values of aromatics with electron-withdrawing groups had r(2) values of 0.89 and 0.87 respectively. For the Br(-)-substituted benzene Ebind values, the correlation with the Σσp and Σσm values of aromatics with electron-withdrawing groups had r(2) values of 0.90 and 0.87. However, adding aromatics with electron-donating substituents to the investigation caused the correlation to deteriorate. For the Cl(-)-substituted benzene complexes the correlation between Ebind values and the Hammett constants had r(2) = 0.81 for Σσp and r(2) = 0.84 for Σσm. For the Br(-)-substituted benzene complexes, the respective r(2) values were 0.71 for Σσp and 0.79 for Σσm. The deterioration in correlation upon consideration of substituted benzenes with electron-donating substituents is due to the anion-π binding energies becoming more attractive regardless of what type of substituent is added to the aromatic. A similar trend has been reported for parallel face-to-face substituted benzene-benzene binding. This is certainly counter to what electrostatic arguments would predict for trends in anion-π binding energies, and this discrepancy is further highlighted

  10. pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model

    Directory of Open Access Journals (Sweden)

    Schneider Georg

    2007-01-01

    Full Text Available Abstract Background Protein kinase A (cAMP-dependent kinase, PKA is a serine/threonine kinase, for which ca. 150 substrate proteins are known. Based on a refinement of the recognition motif using the available experimental data, we wished to apply the simplified substrate protein binding model for accurate prediction of PKA phosphorylation sites, an approach that was previously successful for the prediction of lipid posttranslational modifications and of the PTS1 peroxisomal translocation signal. Results Approximately 20 sequence positions flanking the phosphorylated residue on both sides have been found to be restricted in their sequence variability (region -18...+23 with the site at position 0. The conserved physical pattern can be rationalized in terms of a qualitative binding model with the catalytic cleft of the protein kinase A. Positions -6...+4 surrounding the phosphorylation site are influenced by direct interaction with the kinase in a varying degree. This sequence stretch is embedded in an intrinsically disordered region composed preferentially of hydrophilic residues with flexible backbone and small side chain. This knowledge has been incorporated into a simplified analytical model of productive binding of substrate proteins with PKA. Conclusion The scoring function of the pkaPS predictor can confidently discriminate PKA phosphorylation sites from serines/threonines with non-permissive sequence environments (sensitivity of ~96% at a specificity of ~94%. The tool "pkaPS" has been applied on the whole human proteome. Among new predicted PKA targets, there are entirely uncharacterized protein groups as well as apparently well-known families such as those of the ribosomal proteins L21e, L22 and L6. Availability The supplementary data as well as the prediction tool as WWW server are available at http://mendel.imp.univie.ac.at/sat/pkaPS. Reviewers Erik van Nimwegen (Biozentrum, University of Basel, Switzerland, Sandor Pongor (International

  11. 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 ...

  12. Individual Differences in the Multisensory Temporal Binding Window Predict Susceptibility to Audiovisual Illusions

    Science.gov (United States)

    Stevenson, Ryan A.; Zemtsov, Raquel K.; Wallace, Mark T.

    2012-01-01

    Human multisensory systems are known to bind inputs from the different sensory modalities into a unified percept, a process that leads to measurable behavioral benefits. This integrative process can be observed through multisensory illusions, including the McGurk effect and the sound-induced flash illusion, both of which demonstrate the ability of…

  13. Predicting treatment response in schizophrenia: The role of striatal and frontal dopamine D2/D3 receptor binding potential

    DEFF Research Database (Denmark)

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

    Predicting treatment response in schizophrenia: the role of striatal and frontal dopamine D2/D3 receptor binding potential Authors: Henrik Nørbak-Emig, Sanne Wulff, Mette Ø. Nielsen, Hans Rasmussen, Egill Rostrup, Nikolaj Bak, Lars Pinborg, Claus Svarer, Lars Thorbjørn Jensen, Bjørn H. Ebdrup......, Birte Y. Glenthøj Background: One of the best validated findings in schizophrenia is an association between increased presynaptic striatal dopaminergic activity and psychotic symptoms. Preclinical studies suggest an inverse relationship between frontal and striatal dopamine activity. This activity can...... indirectly be expressed by the binding potential (BP) of dopamine receptors using Single Photon Emission Computed Tomography (SPECT) where low striatal BP is believed to reflect high dopamine availability. Aim to assess the association between D2 receptor BPs in antipsychotic-naïve first...

  14. 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 procedure....... In particular, no specific property of the binary system is explicitly required in the fitting database. The present model is proved to be accurate and highly transferable for electronic, magnetic, and energetic properties of a large variety of structural and chemical environments: surfaces, interfaces......, embedded clusters, and the whole compositional range of the binary alloy. The occurrence of noncollinear magnetic configurations caused by magnetic frustrations is successfully predicted. The present tight-binding approach can apply to other binary magnetic transition-metal alloys. It is expected...

  15. A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

    Directory of Open Access Journals (Sweden)

    Zaslavskiy Mikhail

    2010-02-01

    Full Text Available Abstract Background Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets. Results We propose a new method to quantify the similarity between binding pockets, and explore its relevance for ligand prediction. We represent each pocket by a cloud of atoms, and assess the similarity between two pockets by aligning their atoms in the 3D space and comparing the resulting configurations with a convolution kernel. Pocket alignment and comparison is possible even when the corresponding proteins share no sequence or overall structure similarities. In order to predict ligands for a given target pocket, we compare it to an ensemble of pockets with known ligands to identify the most similar pockets. We discuss two criteria to evaluate the performance of a binding pocket similarity measure in the context of ligand prediction, namely, area under ROC curve (AUC scores and classification based scores. We show that the latter is better suited to evaluate the methods with respect to ligand prediction, and demonstrate the relevance of our new binding site similarity compared to existing similarity measures. Conclusions This study demonstrates the relevance of the proposed method to identify ligands binding to known binding pockets. We also provide a new benchmark for future work in this field. The new method and the benchmark are available at http://cbio.ensmp.fr/paris/.

  16. Immunohistochemical determination of calcium-calmodulin binding predicts neuronal damage after global ischemia.

    Science.gov (United States)

    Picone, C M; Grotta, J C; Earls, R; Strong, R; Dedman, J

    1989-12-01

    Since ionic Ca2+ binds with intracellular calmodulin (CaM) before activating proteases, kinases, and phospholipases, demonstration of persistent Ca2+-CaM binding in neurons destined to show ischemic cellular injury would support the concept that elevated intracellular Ca2+ plays a causative role in ischemic neuronal damage. In order to characterize Ca2+-CaM binding, we used a sheep anti-CaM antibody (CaM-Ab) which recognizes CaM that is not bound to Ca2+ or brain target proteins. Therefore, immunohistochemical staining of brain sections by labeled CaM-Ab represented only unbound CaM. Six normal rats were compared to 15 animals rendered ischemic for 30 min by a modification of the four-vessel occlusion model. Animals were killed immediately after ischemia, and after 2 and 24 h of reperfusion. Brain sections through hippocampus were incubated in CaM-Ab, and a diaminobenzadiene labeled anti-sheep secondary antibody was added to stain the CaM-Ab. Staining in the endal limb of dentate, dorsal CA1, lateral CA3, and parietal cortex was graded on a 4-point scale. All normal animals had grade 4 staining indicating the presence of unbound CaM in all four brain regions. Ischemic animals demonstrated reduced (grade 0 to 2) staining in the CA1 and CA3 regions immediately and 2 and 24 h after ischemia (p less than 0.01 for both regions at all three time intervals) indicating persistent binding of CaM with Ca2+ and target proteins in these regions. Staining decreased in dentate and cortex up to 2 h after ischemia (p = 0.02 for both regions) but returned toward normal by 24 h.(ABSTRACT TRUNCATED AT 250 WORDS)

  17. Individual Differences in the Multisensory Temporal Binding Window Predict Susceptibility to Audiovisual Illusions

    OpenAIRE

    Stevenson, Ryan A.; Raquel K Zemtsov; Wallace, Mark T.

    2012-01-01

    Human multisensory systems are known to bind inputs from the different sensory modalities into a unified percept, a process that leads to measurable behavioral benefits. This integrative process can be observed through multisensory illusions, including the McGurk effect and the sound-induced flash illusion, both of which demonstrate the ability of one sensory modality to modulate perception in a second modality. Such multisensory integration is highly dependent upon the temporal relationship ...

  18. Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

    DEFF Research Database (Denmark)

    Zhang, Hao; Lund, Ole; Nielsen, Morten;

    2010-01-01

    on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical...... methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results...

  19. 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

    -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......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...

  20. Predicting the right spacing between protein immobilization sites on self-assembled monolayers to optimize ligand binding.

    Science.gov (United States)

    Perez, Javier Batista; Tyagi, Deependra; Yang, Mo; Calvo, Loany; Perez, Rolando; Moreno, Ernesto; Zhu, Jinsong

    2015-09-01

    Self-assembled monolayers designed to immobilize capture antibodies are usually prepared using a mixture of functional and inactive linkers. Here, using low molar ratios (1:1 to 1:100) of the two linkers resulted in loss of binding capability of the anti-EGFR (epidermal growth factor receptor) antibody nimotuzumab, as assessed by surface plasmon resonance imaging. We then developed a simple theoretical model to predict the optimal surface density of the functional linker, taking into account the antibody size and linker diameter. A high (1:1000) dilution of the functional linker yielded the best results. As an advantage, this approach does not require chemical modification of the protein.

  1. Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis

    DEFF Research Database (Denmark)

    Sette, A; Buus, S; Appella, E;

    1989-01-01

    We have previously experimentally analyzed the structural requirements for interaction between peptide antigens and mouse major histocompatibility complex (MHC) molecules of the d haplotype. We describe here two procedures devised to predict specifically the capacity of peptide molecules to inter......We have previously experimentally analyzed the structural requirements for interaction between peptide antigens and mouse major histocompatibility complex (MHC) molecules of the d haplotype. We describe here two procedures devised to predict specifically the capacity of peptide molecules...

  2. 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

  3. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    Energy Technology Data Exchange (ETDEWEB)

    Politi, Regina [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, NC 27599 (United States); Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599 (United States); Rusyn, Ivan, E-mail: iir@unc.edu [Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599 (United States); Tropsha, Alexander, E-mail: alex_tropsha@unc.edu [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, NC 27599 (United States)

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  4. Structure prediction of LDLR-HNP1 complex based on docking enhanced by LDLR binding 3D motif.

    Science.gov (United States)

    Esmaielbeiki, Reyhaneh; Naughton, Declan P; Nebel, Jean-Christophe

    2012-04-01

    Human antimicrobial peptides (AMPs), including defensins, have come under intense scrutiny owing to their key multiple roles as antimicrobial agents. Not only do they display direct action on microbes, but also recently they have been shown to interact with the immune system to increase antimicrobial activity. Unfortunately, since mechanisms involved in the binding of AMPs to mammalian cells are largely unknown, their potential as novel anti-infective agents cannot be exploited yet. Following the reported interaction of Human Neutrophil Peptide 1 dimer (HNP1) with a low density lipoprotein receptor (LDLR), a computational study was conducted to discover their putative mode of interaction. State-of-the-art docking software produced a set of LDLR-HNP1 complex 3D models. Creation of a 3D motif capturing atomic interactions of the LDLR binding interface allowed selection of the most plausible configurations. Eventually, only two models were in agreement with the literature. Binding energy estimations revealed that only one of them is particularly stable, but also interaction with LDLR weakens significantly bonds within the HNP1 dimer. This may be significant since it suggests a mechanism for internalisation of HNP1 in mammalian cells. In addition to a novel approach for complex structure prediction, this study proposes a 3D model of the LDLR-HNP1 complex which highlights the key residues which are involved in the interactions. The putative identification of the receptor binding mechanism should inform the future design of synthetic HNPs to afford maximum internalisation, which could lead to novel anti-infective drugs.

  5. Relative binding affinity does not predict biological response to xenoestrogens in rat endometrial adenocarcinoma cells.

    Science.gov (United States)

    Strunck, E; Stemmann, N; Hopert, A; Wünsche, W; Frank, K; Vollmer, G

    2000-10-01

    The possible adverse effects of the so-called environmental estrogens have raised considerable concern. Developmental, endocrine and reproductive disorders in wildlife animals have been linked to high exposure to persistent environmental chemicals with estrogen-like activity (xenoestrogens); yet, the potential impact of environmental estrogens on human health is currently under debate also due to lack of data. A battery of in vitro assays exist for identifying compounds with estrogenic activity, but only a few models are available to assess estrogenic potency in a multiparametric analysis. We have recently established the endometrial adenocarcinoma cell line RUCA-I; it enables us to compare estrogenic effects both in vitro and in vivo as these cells are estrogen responsive in vitro and grow estrogen sensitive tumors if inoculated in syngeneic animals in vivo. Here we report in vitro data concerning (a) the relative binding affinity of the selected synthetic chemicals Bisphenol A, nonylphenol, p-tert-octylphenol, and o,p-DDT to the estrogen receptor of RUCA-I cells and (b) the relative potency of these compounds in inducing increased production of complement C3, an endogenous estrogen-responsive gene. Competitive Scatchard analysis revealed that xenoestrogens bound with an at least 1000-fold lower affinity to the estrogen receptor of RUCA-I cells than estradiol itself, thereby exhibiting the following affinity ranking, estradiol>nonylphenol>bisphenol A approximately p-tert-octylphenol>o,p-DDT. Despite these low binding affinities, bisphenol A, nonylphenol and p-tert-octylphenol increased production of complement C3 in a dose dependent manner. Compared with estradiol, only 100-fold higher concentrations were needed for all the compounds to achieve similar levels of induction, except o,p-DDT which was by far less potent. Northern blot analyses demonstrated that the increased production of complement C3 was mediated by an increased transcription. In summary, cultured

  6. Predicting protein-binding RNA nucleotides using the feature-based removal of data redundancy and the interaction propensity of nucleotide triplets.

    Science.gov (United States)

    Choi, Sungwook; Han, Kyungsook

    2013-11-01

    Several learning approaches have been used to predict RNA-binding amino acids in a protein sequence, but there has been little attempt to predict protein-binding nucleotides in an RNA sequence. One of the reasons is that the differences between nucleotides in their interaction propensity are much smaller than those between amino acids. Another reason is that RNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding RNA nucleotides is much harder than predicting RNA-binding amino acids. We developed a new method that removes data redundancy in a training set of sequences based on their features. The new method constructs a larger and more informative training set than the standard redundancy removal method based on sequence similarity, and the constructed dataset is guaranteed to be redundancy-free. We computed the interaction propensity (IP) of nucleotide triplets by applying a new definition of IP to an extensive dataset of protein-RNA complexes, and developed a support vector machine (SVM) model to predict protein binding sites in RNA sequences. In a 5-fold cross-validation with 812 RNA sequences, the SVM model predicted protein-binding nucleotides with an accuracy of 86.4%, an F-measure of 84.8%, and a Matthews correlation coefficient of 0.66. With an independent dataset of 56 RNA sequences that were not used in training, the resulting accuracy was 68.1% with an F-measure of 71.7% and a Matthews correlation coefficient of 0.35. To the best of our knowledge, this is the first attempt to predict protein-binding RNA nucleotides in a given RNA sequence from the sequence data alone. The SVM model and datasets are freely available for academics at http://bclab.inha.ac.kr/primer.

  7. Accurate microRNA target prediction using detailed binding site accessibility and machine learning on proteomics data

    Directory of Open Access Journals (Sweden)

    Martin eReczko

    2012-01-01

    Full Text Available MicroRNAs (miRNAs are a class of small regulatory genes regulating gene expression by targetingmessenger RNA. Though computational methods for miRNA target prediction are the prevailingmeans to analyze their function, they still miss a large fraction of the targeted genes and additionallypredict a large number of false positives. Here we introduce a novel algorithm called DIANAmicroT-ANN which combines multiple novel target site features through an artificial neural network(ANN and is trained using recently published high-throughput data measuring the change of proteinlevels after miRNA overexpression, providing positive and negative targeting examples. The featurescharacterizing each miRNA recognition element include binding structure, conservation level and aspecific profile of structural accessibility. The ANN is trained to integrate the features of eachrecognition element along the 3’ untranslated region into a targeting score, reproducing the relativerepression fold change of the protein. Tested on two different sets the algorithm outperforms otherwidely used algorithms and also predicts a significant number of unique and reliable targets notpredicted by the other methods. For 542 human miRNAs DIANA-microT-ANN predicts 120,000targets not provided by TargetScan 5.0. The algorithm is freely available athttp://microrna.gr/microT-ANN.

  8. Physics-based enzyme design: predicting binding affinity and catalytic activity.

    Science.gov (United States)

    Sirin, Sarah; Pearlman, David A; Sherman, Woody

    2014-12-01

    Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.

  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. 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/.

  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. 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.

    2013-01-01

    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. PMID:23892291

  13. 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; Brewer, J B

    2011-04-01

    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.

  14. Predicting the effects of amino acid replacements in peptide hormones on their binding affinities for class B GPCRs and application to the design of secretin receptor antagonists

    Science.gov (United States)

    Te, Jerez A.; Dong, Maoqing; Miller, Laurence J.; Bordner, Andrew J.

    2012-07-01

    Computational prediction of the effects of residue changes on peptide-protein binding affinities, followed by experimental testing of the top predicted binders, is an efficient strategy for the rational structure-based design of peptide inhibitors. In this study we apply this approach to the discovery of competitive antagonists for the secretin receptor, the prototypical member of class B G protein-coupled receptors (GPCRs). Proteins in this family are involved in peptide hormone-stimulated signaling and are implicated in several human diseases, making them potential therapeutic targets. We first validated our computational method by predicting changes in the binding affinities of several peptides to their cognate class B GPCRs due to alanine replacement and compared the results with previously published experimental values. Overall, the results showed a significant correlation between the predicted and experimental ΔΔG values. Next, we identified candidate inhibitors by applying this method to a homology model of the secretin receptor bound to an N-terminal truncated secretin peptide. Predictions were made for single residue replacements to each of the other nineteen naturally occurring amino acids at peptide residues within the segment binding the receptor N-terminal domain. Amino acid replacements predicted to most enhance receptor binding were then experimentally tested by competition-binding assays. We found two residue changes that improved binding affinities by almost one log unit. Furthermore, a peptide combining both of these favorable modifications resulted in an almost two log unit improvement in binding affinity, demonstrating the approximately additive effect of these changes on binding. In order to further investigate possible physical effects of these residue changes on receptor binding affinity, molecular dynamics simulations were performed on representatives of the successful peptide analogues (namely A17I, G25R, and A17I/G25R) in bound and

  15. 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.

  16. Predicting the binding free energy of the inclusion process of 2-hydroxypropyl-β-cyclodextrin and small molecules by means of the MM/3D-RISM method

    Science.gov (United States)

    Sugita, Masatake; Hirata, Fumio

    2016-09-01

    A protocol to calculate the binding free energy of a host-guest system is proposed based on the MM/3D-RISM method, taking cyclodextrin derivatives and their ligands as model systems. The protocol involves the procedure to identify the most probable binding mode (MPBM) of receptors and ligands by means of the umbrella sampling method. The binding free energies calculated by the MM/3D-RISM method for the complexes of the seven ligands with the MPBM of the cyclodextrin, and with the fluctuated structures around it, are in agreement with the corresponding experimental data in a semi-quantitative manner. It suggests that the protocol proposed here is promising for predicting the binding affinity of a small ligand to a relatively rigid receptor such as cyclodextrin.

  17. Predicting important residues and interaction pathways in proteins using Gaussian Network Model: binding and stability of HLA proteins.

    Directory of Open Access Journals (Sweden)

    Turkan Haliloglu

    Full Text Available A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed.

  18. Acrosin-binding protein (ACRBP) and triosephosphate isomerase (TPI) are good markers to predict boar sperm freezing capacity.

    Science.gov (United States)

    Vilagran, Ingrid; Castillo, Judit; Bonet, Sergi; Sancho, Sílvia; Yeste, Marc; Estanyol, Josep M; Oliva, Rafael

    2013-09-15

    Sperm cryopreservation is the most efficient method for storing boar sperm samples for a long time. However, one of the inconveniences of this method is the large variation between and within boars in the cryopreservation success of their sperm. The aim of the present work was thus to find reliable and useful predictive biomarkers of the good and poor capacity to withstand the freeze-thawing process in boar ejaculates. To find these biomarkers, the amount of proteins present in the total proteome in sperm cells were compared between good freezability ejaculates (GFE) and poor freezability ejaculates (PFE) using the two-dimensional difference gel electrophoresis technique. Samples were classified as GFE and PFE using progressive motility and viability of the sperm at 30 and 240 minutes after thawing, and the proteomes from each group, before starting cryopreservation protocols, were compared. Because two proteins, acrosin binding protein (ACRBP) and triosephosphate isomerase (TPI), presented the highest significant differences between GFE and PFE groups in two-dimensional difference gel electrophoresis assessment, Western blot analyses for ACRBP and TPI were also performed for validation. ACRBP normalized content was significantly lower in PFE than in GFE (P sperm viability and motility was confirmed using Pearson's linear correlation. In conclusion, ACRBP and TPI can be used as markers of boar sperm freezability before starting the cryopreservation procedure, thereby avoiding unnecessary costs involved in this practice.

  19. Structure–property reduced order model for viscosity prediction in single-component CO 2 -binding organic liquids

    Energy Technology Data Exchange (ETDEWEB)

    Cantu, David C.; Malhotra, Deepika; Koech, Phillip K.; Heldebrant, David J.; Zheng, Feng (Richard); Freeman, Charles J.; Rousseau, Roger; Glezakou, Vassiliki-Alexandra

    2016-01-01

    CO2 capture from power generation with aqueous solvents remains energy intensive due to the high water content of the current technology, or the high viscosity of non-aqueous alternatives. Quantitative reduced models, connecting molecular structure to bulk properties, are key for developing structure-property relationships that enable molecular design. In this work, we describe such a model that quantitatively predicts viscosities of CO2 binding organic liquids (CO2BOLs) based solely on molecular structure and the amount of bound CO2. The functional form of the model correlates the viscosity with the CO2 loading and an electrostatic term describing the charge distribution between the CO2-bearing functional group and the proton-receiving amine. Molecular simulations identify the proton shuttle between these groups within the same molecule to be the critical indicator of low viscosity. The model, developed to allow for quick screening of solvent libraries, paves the way towards the rational design of low viscosity non-aqueous solvent systems for post-combustion CO2 capture. Following these theoretical recommendations, synthetic efforts of promising candidates and viscosity measurement provide experimental validation and verification.

  20. ¹³C NMR-distance matrix descriptors: optimal abstract 3D space granularity for predicting estrogen binding.

    Science.gov (United States)

    Slavov, Svetoslav H; Geesaman, Elizabeth L; Pearce, Bruce A; Schnackenberg, Laura K; Buzatu, Dan A; Wilkes, Jon G; Beger, Richard D

    2012-07-23

    An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ×10 ppm × Z Å (Z = 0.5, ..., 2.5 Å). It was hypothesized that this optimum depends on the chemical shifts' estimation error (±4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.

  1. Holo- And Apo- Structures of Bacterial Periplasmic Heme Binding Proteins

    Energy Technology Data Exchange (ETDEWEB)

    Ho, W.W.; Li, H.; Eakanunkul, S.; Tong, Y.; Wilks, A.; Guo, M.; Poulos, T.L.

    2009-06-01

    An essential component of heme transport in Gram-negative bacterial pathogens is the periplasmic protein that shuttles heme between outer and inner membranes. We have solved the first crystal structures of two such proteins, ShuT from Shigella dysenteriae and PhuT from Pseudomonas aeruginosa. Both share a common architecture typical of Class III periplasmic binding proteins. The heme binds in a narrow cleft between the N- and C-terminal binding domains and is coordinated by a Tyr residue. A comparison of the heme-free (apo) and -bound (holo) structures indicates little change in structure other than minor alterations in the heme pocket and movement of the Tyr heme ligand from an 'in' position where it can coordinate the heme iron to an 'out' orientation where it points away from the heme pocket. The detailed architecture of the heme pocket is quite different in ShuT and PhuT. Although Arg{sup 228} in PhuT H-bonds with a heme propionate, in ShuT a peptide loop partially takes up the space occupied by Arg{sup 228}, and there is no Lys or Arg H-bonding with the heme propionates. A comparison of PhuT/ShuT with the vitamin B{sub 12}-binding protein BtuF and the hydroxamic-type siderophore-binding protein FhuD, the only two other structurally characterized Class III periplasmic binding proteins, demonstrates that PhuT/ShuT more closely resembles BtuF, which reflects the closer similarity in ligands, heme and B{sub 12}, compared with ligands for FhuD, a peptide siderophore.

  2. 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.

  3. 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/.

  4. Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding

    DEFF Research Database (Denmark)

    Stryhn, A; Pedersen, L O; Romme, T

    1996-01-01

    size are distributed into positional scanning combinatorial peptide libraries (PSCPL) to develop a highly efficient, universal and unbiased approach to address MHC specificity. The PSCPL approach appeared qualitatively and quantitatively superior to other currently used strategies. The average effect...... of any amino acid in each position was quantitated, allowing a detailed description of extended peptide binding motifs including primary and secondary anchor residues. It also identified disfavored residues which were found to be surprisingly important in shaping MHC class I specificity. Assuming...

  5. gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence.

    Science.gov (United States)

    Zhang, Yan-Ping; Wuyunqiqige; Zheng, Wei; Liu, Shuyi; Zhao, Chunguang

    2016-10-01

    DNA-binding proteins are the functional proteins in cells, which play an important role in various essential biological activities. An effective and fast computational method gDNA-Prot is proposed to predict DNA-binding proteins in this paper, which is a DNA-binding predictor that combines the support vector machine classifier and a novel kind of feature called graphical representation. The DNA-binding protein sequence information was described with the 20 probabilities of amino acids and the 23 new numerical graphical representation features of a protein sequence, based on 23 physicochemical properties of 20 amino acids. The Principal Components Analysis (PCA) was employed as feature selection method for removing the irrelevant features and reducing redundant features. The Sigmod function and Min-max normalization methods for PCA were applied to accelerate the training speed and obtain higher accuracy. Experiments demonstrated that the Principal Components Analysis with Sigmod function generated the best performance. The gDNA-Prot method was also compared with the DNAbinder, iDNA-Prot and DNA-Prot. The results suggested that gDNA-Prot outperformed the DNAbinder and iDNA-Prot. Although the DNA-Prot outperformed gDNA-Prot, gDNA-Prot was faster and convenient to predict the DNA-binding proteins. Additionally, the proposed gNDA-Prot method is available at http://sourceforge.net/projects/gdnaprot.

  6. Biopartitioning micellar chromatography with sodium dodecyl sulfate as a pseudo α(1)-acid glycoprotein to the prediction of protein-drug binding.

    Science.gov (United States)

    Hadjmohammadi, Mohammadreza; Salary, Mina

    2013-01-01

    A simple and fast method is of urgent need to measure protein-drug binding affinity in order to meet the rapid development of new drugs. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography (MLC) using micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug-protein interactions. In this study, sodium dodecyl sulfate-micellar liquid chromatography (SDS-MLC) was used for the prediction of protein-drug binding based on the similar property of SDS micelles to α(1)-acid glycoprotein (AGP). The relationships between the BMC retention data of a heterogeneous set of 14 basic and neutral drugs and their plasma protein binding parameter were studied and the predictive ability of models was evaluated. Modeling of logk(BMC) of these compounds was established by multiple linear regression (MLR) and second-order polynomial models obtained in two different concentrations (0.07 and 0.09M) of SDS. The developed MLR models were characterized by both the descriptive and predictive ability (R(2)=0.882, R(CV)(2)=0.832 and R(2)=0.840, R(CV)(2)=0.765 for 0.07 and 0.09M SDS, respectively). The p values <0.01 also indicated that the relationships between the protein-drug binding and the logk(BMC) values were statistically significant at the 99% confidence level. The standard error of estimation showed the standard deviation of the regression to be 11.89 and 13.87 for 0.07 and 0.09M, respectively. The application of the developed model to a prediction set demonstrated that the model was also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values were in good agreement with the experimental value.

  7. Interactome-Wide Prediction of Protein-Protein Binding Sites Reveals Effects of Protein Sequence Variation in Arabidopsis thaliana

    NARCIS (Netherlands)

    Valentim, F.L.; Neven, F.; Boyen, P.; Dijk, van A.D.J.

    2012-01-01

    The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in thos

  8. An improved method for TAL effectors DNA-binding sites prediction reveals functional convergence in TAL repertoires of Xanthomonas oryzae strains.

    Directory of Open Access Journals (Sweden)

    Alvaro L Pérez-Quintero

    Full Text Available Transcription Activators-Like Effectors (TALEs belong to a family of virulence proteins from the Xanthomonas genus of bacterial plant pathogens that are translocated into the plant cell. In the nucleus, TALEs act as transcription factors inducing the expression of susceptibility genes. A code for TALE-DNA binding specificity and high-resolution three-dimensional structures of TALE-DNA complexes were recently reported. Accurate prediction of TAL Effector Binding Elements (EBEs is essential to elucidate the biological functions of the many sequenced TALEs as well as for robust design of artificial TALE DNA-binding domains in biotechnological applications. In this work a program with improved EBE prediction performances was developed using an updated specificity matrix and a position weight correction function to account for the matching pattern observed in a validation set of TALE-DNA interactions. To gain a systems perspective on the large TALE repertoires from X. oryzae strains, this program was used to predict rice gene targets for 99 sequenced family members. Integrating predictions and available expression data in a TALE-gene network revealed multiple candidate transcriptional targets for many TALEs as well as several possible instances of functional convergence among TALEs.

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

    DEFF Research Database (Denmark)

    Follin, Elna; Karlsson, Maria; Lundegaard, Claus

    2013-01-01

    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......, 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....

  10. 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

    2017-01-01

    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, r2 = 0.928–0.988,  = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 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.

  11. [Predicting the cadmium bioavailability in the soil of sugarcane field based on the diffusive gradients in thin films with binding phase of sodium polyacrylate].

    Science.gov (United States)

    Wang, Fang-Li; Song, Ning-Ning; Zhao, Yu-Jie; Zhang, Chang-Bo; Shen, Yue; Liu, Zhong-Qi

    2012-10-01

    The diffusive gradients in thin films (DGT) technique with solid-state binding phases has been widely used for in situ collection and measurement of available heavy metals in waters, soils or sediments, whereas DGT with liquid binding phase is primarily used in the in situ analysis of heavy metals in waters. In this paper, rhizosphere soils of sugarcane were collected in Guangxi and the concentrations of cadmium (Cd) were determined by DGT with a solid-state binding phase of chelex100 (chelex100-DGT) and modified DGT with a liquid binding phase of sodium polyacrylate (CDM-PAAS-DGT). The result showed that the Cd contents in soils measured by DGT with both binding phases and Cd in the roots, leaves and unpolished stems of sugarcane had significant positive correlation. The extraction ability of the CDM-PAAAS-DGT was much higher than that of the chelex100-DGT. In addition, multivariate analyses were used to assess the impact of pH, cation exchange capacity (CEC), soil organic matter (OM) and texture. Two principal components were extracted and the linear regression models were established. The Cd bioavailability in soils could be accurately predicted by the CDM-PAAAS-DGT technique, which expanded its applicable area.

  12. Predicting the Structure-Activity Relationship of Hydroxyapatite-Binding Peptides by Enhanced-Sampling Molecular Simulation.

    Science.gov (United States)

    Zhao, Weilong; Xu, Zhijun; Cui, Qiang; Sahai, Nita

    2016-07-12

    Understanding the molecular structural and energetic basis of the interactions between peptides and inorganic surfaces is critical to their applications in tissue engineering and biomimetic material synthesis. Despite recent experimental progresses in the identification and functionalization of hydroxyapatite (HAP)-binding peptides, the molecular mechanisms of their interactions with HAP surfaces are yet to be explored. In particular, the traditional method of molecular dynamics (MD) simulation suffers from insufficient sampling at the peptide-inorganic interface that renders the molecular-level observation dubious. Here we demonstrate that an integrated approach combining bioinformatics, MD, and metadynamics provides a powerful tool for investigating the structure-activity relationship of HAP-binding peptides. Four low charge density peptides, previously identified by phage display, have been considered. As revealed by bioinformatics and MD, the binding conformation of the peptides is controlled by both the sequence and the amino acid composition. It was found that formation of hydrogen bonds between lysine residue and phosphate ions on the surface dictates the binding of positively charged peptide to HAP. The binding affinities of the peptides to the surface are estimated by free energy calculation using parallel-tempering metadynamics, and the results compare favorably to measurements reported in previous experimental studies. The calculation suggests that the charge density of the peptide primarily controls the binding affinity to the surface, while the backbone secondary structure that may restrain side chain orientation toward the surface plays a minor role. We also report that the application of enhanced-sampling metadynamics effects a major advantage over the steered MD method by significantly improving the reliability of binding free energy calculation. In general, our novel integration of diverse sampling techniques should contribute to the rational

  13. Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules.

    Science.gov (United States)

    Paricharak, Shardul; Cortés-Ciriano, Isidro; IJzerman, Adriaan P; Malliavin, Thérèse E; Bender, Andreas

    2015-01-01

    The rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining in silico target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency/affinity. The proposed pipeline was evaluated on the retrospective discovery of Plasmodium falciparum DHFR inhibitors. The qualitative in silico target prediction model comprised 553,084 ligand-target associations (a total of 262,174 compounds), covering 3,481 protein targets and used protein domain annotations to extrapolate predictions across species. The prediction of bioactivities for plasmodial DHFR led to a recall value of 79% and a precision of 100%, where the latter high value arises from the structural similarity of plasmodial DHFR inhibitors and T. gondii DHFR inhibitors in the training set. Quantitative PCM models were then trained on a dataset comprising 20 eukaryotic, protozoan and bacterial DHFR sequences, and 1,505 distinct compounds (in total 3,099 data points). The most predictive PCM model exhibited R (2) 0 test and RMSEtest values of 0.79 and 0.59 pIC50 units respectively, which was shown to outperform models based exclusively on compound (R (2) 0 test/RMSEtest = 0.63/0.78) and target information (R (2) 0 test/RMSEtest = 0.09/1.22), as well as inductive transfer knowledge between targets, with respective R (2) 0 test and RMSEtest values of 0.76 and 0.63 pIC50 units. Finally, both methods were integrated to predict the protein

  14. Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations.

    Science.gov (United States)

    Misini Ignjatović, Majda; Caldararu, Octav; Dong, Geng; Muñoz-Gutierrez, Camila; Adasme-Carreño, Francisco; Ryde, Ulf

    2016-09-01

    We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.

  15. Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

    Science.gov (United States)

    Misini Ignjatović, Majda; Caldararu, Octav; Dong, Geng; Muñoz-Gutierrez, Camila; Adasme-Carreño, Francisco; Ryde, Ulf

    2016-09-01

    We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.

  16. Measurement of binding of basic drugs to acidic phospholipids using surface plasmon resonance and incorporation of the data into mechanistic tissue composition equations to predict steady-state volume of distribution.

    Science.gov (United States)

    Small, Helen; Gardner, Iain; Jones, Hannah M; Davis, John; Rowland, Malcolm

    2011-10-01

    Acidic phospholipid binding plays an important role in determining the tissue distribution of basic drugs. This article describes the use of surface plasmon resonance to measure binding affinity (K(D)) of three basic drugs to phosphatidylserine, a major tissue acidic phospholipid. The data are incorporated into mechanistic tissue composition equations to allow prediction of the steady-state volume of distribution (V(ss)). The prediction accuracy of V(ss) using this approach is compared with the original methodology described by Rodgers et al. (J Pharm Sci 94:1259-1276), in which the binding to acidic phospholipids is calculated from the blood/plasma concentration ratio (BPR). The compounds used in this study [amlodipine, propranolol, and 3-dimethylaminomethyl-4-(4-methylsulfanyl-phenoxy)-benzenesulfonamide (UK-390957)] showed higher affinity binding to phosphatidylserine than to phosphatidylcholine. When the binding affinity to phosphatidylserine was incorporated into mechanistic tissue composition equations, the V(ss) was more accurately predicted for all three compounds by using the surface plasmon resonance measurement than by using the BPR to estimate acidic phospholipid binding affinity. The difference was particularly marked for UK-390957, a sulfonamide that has a high BPR due to binding to carbonic anhydrase. The novel approach described in this article allows the binding affinity of drugs to an acidic phospholipid (phosphatidylserine) to be measured directly and demonstrates the utility of the binding data in the prediction of V(ss).

  17. 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.

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

    Science.gov (United States)

    Marchiori, Alessandro; Capece, Luciana; Giorgetti, Alejandro; Gasparini, Paolo; Behrens, Maik; Carloni, Paolo; Meyerhof, Wolfgang

    2013-01-01

    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.

  19. 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...

  20. Predicting Allosteric Effects from Orthosteric Binding in Hsp90-Ligand Interactions: Implications for Fragment-Based Drug Design

    Science.gov (United States)

    Larsson, Andreas; Nordlund, Paer; Jansson, Anna; Anand, Ganesh S.

    2016-01-01

    A key question in mapping dynamics of protein-ligand interactions is to distinguish changes at binding sites from those associated with long range conformational changes upon binding at distal sites. This assumes a greater challenge when considering the interactions of low affinity ligands (dissociation constants, KD, in the μM range or lower). Amide hydrogen deuterium Exchange mass spectrometry (HDXMS) is a robust method that can provide both structural insights and dynamics information on both high affinity and transient protein-ligand interactions. In this study, an application of HDXMS for probing the dynamics of low affinity ligands to proteins is described using the N-terminal ATPase domain of Hsp90. Comparison of Hsp90 dynamics between high affinity natural inhibitors (KD ~ nM) and fragment compounds reveal that HDXMS is highly sensitive in mapping the interactions of both high and low affinity ligands. HDXMS reports on changes that reflect both orthosteric effects and allosteric changes accompanying binding. Orthosteric sites can be identified by overlaying HDXMS onto structural information of protein-ligand complexes. Regions distal to orthosteric sites indicate long range conformational changes with implications for allostery. HDXMS, thus finds powerful utility as a high throughput method for compound library screening to identify binding sites and describe allostery with important implications for fragment-based ligand discovery (FBLD). PMID:27253209

  1. Predicting Allosteric Effects from Orthosteric Binding in Hsp90-Ligand Interactions: Implications for Fragment-Based Drug Design.

    Directory of Open Access Journals (Sweden)

    Arun Chandramohan

    2016-06-01

    Full Text Available A key question in mapping dynamics of protein-ligand interactions is to distinguish changes at binding sites from those associated with long range conformational changes upon binding at distal sites. This assumes a greater challenge when considering the interactions of low affinity ligands (dissociation constants, KD, in the μM range or lower. Amide hydrogen deuterium Exchange mass spectrometry (HDXMS is a robust method that can provide both structural insights and dynamics information on both high affinity and transient protein-ligand interactions. In this study, an application of HDXMS for probing the dynamics of low affinity ligands to proteins is described using the N-terminal ATPase domain of Hsp90. Comparison of Hsp90 dynamics between high affinity natural inhibitors (KD ~ nM and fragment compounds reveal that HDXMS is highly sensitive in mapping the interactions of both high and low affinity ligands. HDXMS reports on changes that reflect both orthosteric effects and allosteric changes accompanying binding. Orthosteric sites can be identified by overlaying HDXMS onto structural information of protein-ligand complexes. Regions distal to orthosteric sites indicate long range conformational changes with implications for allostery. HDXMS, thus finds powerful utility as a high throughput method for compound library screening to identify binding sites and describe allostery with important implications for fragment-based ligand discovery (FBLD.

  2. Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures.

    Science.gov (United States)

    Mlinsek, G; Novic, M; Hodoscek, M; Solmajer, T

    2001-01-01

    Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor-enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.

  3. Positive priming and intentional binding: eye-blink rate predicts reward information effects on the sense of agency.

    Science.gov (United States)

    Aarts, Henk; Bijleveld, Erik; Custers, Ruud; Dogge, Myrthel; Deelder, Merel; Schutter, Dennis; Haren, Neeltje E M van

    2012-01-01

    Human society is strongly rooted in people's experiences of agency; that is, the pervasive feeling that one engages in voluntary behavior and causes one's own actions and resulting outcomes. Rewards and positive affect play an important role in the control of voluntary action. However, the role of positive reward signals in the sense of agency is poorly understood. This study examined effects of reward-related information on the sense of agency by employing the intentional binding paradigm. This paradigm measures the extent to which actions and their effects subjectively shift together across time, reflecting a crucial component of people's sense of agency. Results showed that intentional binding is stronger when participants are primed with reward-related information via brief exposure to positive pictures. Interestingly, this positive priming effect was moderated by baseline eye-blink rates (an indirect marker of striatal dopaminergic functioning); reward-related information increased intentional binding mainly for participants displaying higher baseline eye-blink rates. These findings suggest a possible role for striatal dopamine activity in the process by which reward-related information shapes the way people see themselves as agents.

  4. 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.

  5. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    Science.gov (United States)

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

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

    OpenAIRE

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

    2005-01-01

    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 fu...

  7. Murine hyperglycemic vasculopathy and cardiomyopathy: whole-genome gene expression analysis predicts cellular targets and regulatory networks influenced by mannose binding lectin

    Directory of Open Access Journals (Sweden)

    Chenhui eZou

    2012-02-01

    Full Text Available Hyperglycemia, in the absence of type 1 or 2 diabetes, is an independent risk factor for cardiovascular disease. We have previously demonstrated a central role for mannose binding lectin (MBL-mediated cardiac dysfunction in acute hyperglycemic mice. In this study, we applied whole genome microarray data analysis to investigate MBL’s role in systematic gene expression changes. The data predict possible intracellular events taking place in multiple cellular compartments such as enhanced insulin signaling pathway sensitivity, promoted mitochondrial respiratory function, improved cellular energy expenditure and protein quality control, improved cytoskeleton structure and facilitated intracellular trafficking, all of which may contribute to the organismal health of MBL null mice against acute hyperglycemia. Our data show a tight association between gene expression profile and tissue function which might be a very useful tool in predicting cellular targets and regulatory networks connected with in vivo observations, providing clues for further mechanistic studies.

  8. Predicting the sensitivity of fishes to dioxin-like compounds: possible role of the aryl hydrocarbon receptor (AhR) ligand binding domain.

    Science.gov (United States)

    Doering, Jon A; Giesy, John P; Wiseman, Steve; Hecker, Markus

    2013-03-01

    Dioxin-like compounds are chronically toxic to most vertebrates. However, dramatic differences in sensitivity to these chemicals exist both within and among vertebrate classes. A recent study found that in birds, critical amino acid residues in the aryl hydrocarbon receptor (AhR) ligand binding domain are predictive of sensitivity to dioxin-like compounds in a range of species. It is currently unclear whether similar predictive relationships exist for fishes, a group of animals at risk of exposure to dioxin-like compounds. Effects of dioxin-like compounds are mediated through the AhR in fishes and birds. However, AhR dynamics are more complex among fishes. Fishes possess AhRs that can be grouped within at least three distinct clades (AhR1, AhR2, AhR3) with each clade possibly containing multiple isoforms. AhR2 has been shown to be the active form in most teleosts, with AhR1 not binding dioxin-like compounds. The role of AhR3 in dioxin-like toxicity has not been established to date and this clade is only known to be expressed in some cartilaginous fishes. Furthermore, multiple mechanisms of sensitivity to dioxin-like compounds that are not relevant in birds could exist among fishes. Although, at this time, deficiencies exist for the development of such a predictive relationship for application to fishes, successfully establishing such relationships would offer a substantial improvement in assessment of risks of dioxin-like compounds for this class of vertebrates. Elucidation of such relationships would provide a mechanistic foundation for extrapolation among species to allow the identification of the most sensitive fishes, with the ultimate goal of the prediction of risk posed to endangered species that are not easily studied.

  9. High-Throughput Melanin-Binding Affinity and In Silico Methods to Aid in the Prediction of Drug Exposure in Ocular Tissue.

    Science.gov (United States)

    Reilly, John; Williams, Sarah L; Forster, Cornelia J; Kansara, Viral; End, Peter; Serrano-Wu, Michael H

    2015-12-01

    Drugs possessing the ability to bind to melanin-rich tissue, such as the eye, are linked with higher ocular exposure, and therefore have the potential to affect the efficacy and safety profiles of therapeutics. A high-throughput melanin chromatographic affinity assay has been developed and validated, which has allowed the rapid melanin affinity assessment for a large number of compounds. Melanin affinity of compounds can be quickly assigned as low, medium, or high melanin binders. A high-throughput chromatographic method has been developed and fully validated to assess melanin affinity of pharmaceuticals and has been useful in predicting ocular tissue distribution in vivo studies. The high-throughput experimental approach has also allowed for a specific training set of 263 molecules for a quantitative structure-affinity relationships (QSAR) method to be developed, which has also been shown to be a predictor of ocular tissue exposure. Previous studies have reported the development of in silico QSAR models based on training sets of relatively small and mostly similar compounds; this model covers a broader range of melanin-binding affinities than what has been previously published and identified several physiochemical descriptors to be considered in the design of compounds where melanin-binding modulation is desired.

  10. Structural model of a putrescine-cadaverine permease from Trypanosoma cruzi predicts residues vital for transport and ligand binding.

    Science.gov (United States)

    Soysa, Radika; Venselaar, Hanka; Poston, Jacqueline; Ullman, Buddy; Hasne, Marie-Pierre

    2013-06-15

    The TcPOT1.1 gene from Trypanosoma cruzi encodes a high affinity putrescine-cadaverine transporter belonging to the APC (amino acid/polyamine/organocation) transporter superfamily. No experimental three-dimensional structure exists for any eukaryotic member of the APC family, and thus the structural determinants critical for function of these permeases are unknown. To elucidate the key residues involved in putrescine translocation and recognition by this APC family member, a homology model of TcPOT1.1 was constructed on the basis of the atomic co-ordinates of the Escherichia coli AdiC arginine/agmatine antiporter crystal structure. The TcPOT1.1 homology model consisted of 12 transmembrane helices with the first ten helices organized in two V-shaped antiparallel domains with discontinuities in the helical structures of transmembrane spans 1 and 6. The model suggests that Trp241 and a Glu247-Arg403 salt bridge participate in a gating system and that Asn245, Tyr148 and Tyr400 contribute to the putrescine-binding pocket. To test the validity of the model, 26 site-directed mutants were created and tested for their ability to transport putrescine and to localize to the parasite cell surface. These results support the robustness of the TcPOT1.1 homology model and reveal the importance of specific aromatic residues in the TcPOT1.1 putrescine-binding pocket.

  11. Prediction and validation of protein–protein interactors from genome-wide DNA-binding data using a knowledge-based machine-learning approach

    Science.gov (United States)

    Homan, Bernou; Mohamed, Stephanie; Harvey, Richard P.; Bouveret, Romaric

    2016-01-01

    The ability to accurately predict the DNA targets and interacting cofactors of transcriptional regulators from genome-wide data can significantly advance our understanding of gene regulatory networks. NKX2-5 is a homeodomain transcription factor that sits high in the cardiac gene regulatory network and is essential for normal heart development. We previously identified genomic targets for NKX2-5 in mouse HL-1 atrial cardiomyocytes using DNA-adenine methyltransferase identification (DamID). Here, we apply machine learning algorithms and propose a knowledge-based feature selection method for predicting NKX2-5 protein : protein interactions based on motif grammar in genome-wide DNA-binding data. We assessed model performance using leave-one-out cross-validation and a completely independent DamID experiment performed with replicates. In addition to identifying previously described NKX2-5-interacting proteins, including GATA, HAND and TBX family members, a number of novel interactors were identified, with direct protein : protein interactions between NKX2-5 and retinoid X receptor (RXR), paired-related homeobox (PRRX) and Ikaros zinc fingers (IKZF) validated using the yeast two-hybrid assay. We also found that the interaction of RXRα with NKX2-5 mutations found in congenital heart disease (Q187H, R189G and R190H) was altered. These findings highlight an intuitive approach to accessing protein–protein interaction information of transcription factors in DNA-binding experiments. PMID:27683156

  12. Dispersion-correcting potentials can significantly improve the bond dissociation enthalpies and noncovalent binding energies predicted by density-functional theory

    Energy Technology Data Exchange (ETDEWEB)

    DiLabio, Gino A., E-mail: Gino.DiLabio@nrc.ca [National Institute for Nanotechnology, National Research Council of Canada, 11421 Saskatchewan Drive, Edmonton, Alberta T6G 2M9 (Canada); Department of Chemistry, University of British Columbia, Okanagan, 3333 University Way, Kelowna, British Columbia V1V 1V7 (Canada); Koleini, Mohammad [National Institute for Nanotechnology, National Research Council of Canada, 11421 Saskatchewan Drive, Edmonton, Alberta T6G 2M9 (Canada); Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2V4 (Canada)

    2014-05-14

    Dispersion-correcting potentials (DCPs) are atom-centered Gaussian functions that are applied in a manner that is similar to effective core potentials. Previous work on DCPs has focussed on their use as a simple means of improving the ability of conventional density-functional theory methods to predict the binding energies of noncovalently bonded molecular dimers. We show in this work that DCPs developed for use with the LC-ωPBE functional along with 6-31+G(2d,2p) basis sets are capable of simultaneously improving predicted noncovalent binding energies of van der Waals dimer complexes and covalent bond dissociation enthalpies in molecules. Specifically, the DCPs developed herein for the C, H, N, and O atoms provide binding energies for a set of 66 noncovalently bonded molecular dimers (the “S66” set) with a mean absolute error (MAE) of 0.21 kcal/mol, which represents an improvement of more than a factor of 10 over unadorned LC-ωPBE/6-31+G(2d,2p) and almost a factor of two improvement over LC-ωPBE/6-31+G(2d,2p) used in conjunction with the “D3” pairwise dispersion energy corrections. In addition, the DCPs reduce the MAE of calculated X-H and X-Y (X,Y = C, H, N, O) bond dissociation enthalpies for a set of 40 species from 3.2 kcal/mol obtained with unadorned LC-ωPBE/6-31+G(2d,2p) to 1.6 kcal/mol. Our findings demonstrate that broad improvements to the performance of DFT methods may be achievable through the use of DCPs.

  13. 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.

  14. Alkaloid metabolite profiles by GC/MS and acetylcholinesterase inhibitory activities with binding-mode predictions of five Amaryllidaceae plants.

    Science.gov (United States)

    Cortes, Natalie; Alvarez, Rafael; Osorio, Edison H; Alzate, Fernando; Berkov, Strahil; Osorio, Edison

    2015-01-01

    Acetylcholinesterase (AChE) enzymatic inhibition is an important target for the management of Alzheimer disease (AD) and AChE inhibitors are the mainstay drugs for its treatment. In order to discover new sources of potent AChE inhibitors, a combined strategy is presented based on AChE-inhibitory activity and chemical profiles by GC/MS, together with in silico studies. The combined strategy was applied on alkaloid extracts of five Amaryllidaceae species that grow in Colombia. Fifty-seven alkaloids were detected using GC/MS, and 21 of them were identified by comparing their mass-spectral fragmentation patterns with standard reference spectra in commercial and private library databases. The alkaloid extracts of Zephyranthes carinata exhibited a high level of inhibitory activity (IC50 = 5.97 ± 0.24 μg/mL). Molecular modeling, which was performed using the structures of some of the alkaloids present in this extract and the three-dimensional crystal structures of AChE derived from Torpedo californica, disclosed their binding configuration in the active site of this AChE. The results suggested that the alkaloids 3-epimacronine and lycoramine might be of interest for AChE inhibition. Although the galanthamine group is known for its potential utility in treating AD, the tazettine-type alkaloids should be evaluated to find more selective compounds of potential benefit for AD.

  15. Prediction

    CERN Document Server

    Sornette, Didier

    2010-01-01

    This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...

  16. Monitoring of urinary L-type fatty acid-binding protein predicts histological severity of acute kidney injury.

    Science.gov (United States)

    Negishi, Kousuke; Noiri, Eisei; Doi, Kent; Maeda-Mamiya, Rui; Sugaya, Takeshi; Portilla, Didier; Fujita, Toshiro

    2009-04-01

    The present study aimed to evaluate whether levels of urinary L-type fatty acid-binding protein (L-FABP) could be used to monitor histological injury in acute kidney injury (AKI) induced by cis-platinum (CP) injection and ischemia reperfusion (IR). Different degrees of AKI severity were induced by several renal insults (CP dose and ischemia time) in human L-FABP transgenic mice. Renal histological injury scores increased with both CP dose and ischemic time. In CP-induced AKI, urinary L-FABP levels increased exponentially even in the lowest dose group as early as 2 hours, whereas blood urea nitrogen (BUN) levels increased at 48 hours. In IR-induced AKI, BUN levels increased only in the 30-minute ischemia group 24 hours after reperfusion; however, urinary L-FABP levels increased more than 100-fold, even in the 5-minute ischemia group after 1 hour. In both AKI models, urinary L-FABP levels showed a better correlation with final histological injury scores and glomerular filtration rates measured by fluorescein isothiocyanate-labeled inulin injection than with levels of BUN and urinary N-acetyl-D-glucosaminidase, especially at earlier time points. Receiver operating characteristic curve analysis demonstrated that urinary L-FABP was superior to other biomarkers for the detection of significant histological injuries and functional declines. In conclusion, urinary L-FABP levels are better suited to allow the accurate and earlier detection of both histological and functional insults in ischemic and nephrotoxin-induced AKI compared with conventional renal markers.

  17. Serum lipopolysaccharide binding protein levels predict severity of lung injury and mortality in patients with severe sepsis.

    Directory of Open Access Journals (Sweden)

    Jesús Villar

    Full Text Available BACKGROUND: There is a need for biomarkers insuring identification of septic patients at high-risk for death. We performed a prospective, multicenter, observational study to investigate the time-course of lipopolysaccharide binding protein (LBP serum levels in patients with severe sepsis and examined whether serial serum levels of LBP could be used as a marker of outcome. METHODOLOGY/PRINCIPAL FINDINGS: LBP serum levels at study entry, at 48 hours and at day-7 were measured in 180 patients with severe sepsis. Data regarding the nature of infections, disease severity, development of acute lung injury (ALI and acute respiratory distress syndrome (ARDS, and intensive care unit (ICU outcome were recorded. LBP serum levels were similar in survivors and non-survivors at study entry (117.4+/-75.7 microg/mL vs. 129.8+/-71.3 microg/mL, P = 0.249 but there were significant differences at 48 hours (77.2+/-57.0 vs. 121.2+/-73.4 microg/mL, P<0.0001 and at day-7 (64.7+/-45.8 vs. 89.7+/-61.1 microg/ml, p = 0.017. At 48 hours, LBP levels were significantly higher in ARDS patients than in ALI patients (112.5+/-71.8 microg/ml vs. 76.6+/-55.9 microg/ml, P = 0.0001. An increase of LBP levels at 48 hours was associated with higher mortality (odds ratio 3.97; 95%CI: 1.84-8.56; P<0.001. CONCLUSIONS/SIGNIFICANCE: Serial LBP serum measurements may offer a clinically useful biomarker for identification of patients with severe sepsis having the worst outcomes and the highest probability of developing sepsis-induced ARDS.

  18. 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

    information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using...... this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space.Conclusions: We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor...... 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....

  19. A new size-independent score for pairwise protein structure alignment and its application to structure classification and nucleic-acid binding prediction.

    Science.gov (United States)

    Yang, Yuedong; Zhan, Jian; Zhao, Huiying; Zhou, Yaoqi

    2012-08-01

    A structure alignment program aligns two structures by optimizing a scoring function that measures structural similarity. It is highly desirable that such scoring function is independent of the sizes of proteins in comparison so that the significance of alignment across different sizes of the protein regions aligned is comparable. Here, we developed a new score called SP-score that fixes the cutoff distance at 4 Å and removed the size dependence using a normalization prefactor. We further built a program called SPalign that optimizes SP-score for structure alignment. SPalign was applied to recognize proteins within the same structure fold and having the same function of DNA or RNA binding. For fold discrimination, SPalign improves sensitivity over TMalign for the chain-level comparison by 12% and over DALI for the domain-level comparison by 13% at the same specificity of 99.6%. The difference between TMalign and SPalign at the chain level is due to the inability of TMalign to detect single domain similarity between multidomain proteins. For recognizing nucleic acid binding proteins, SPalign consistently improves over TMalign by 12% and DALI by 31% in average value of Mathews correlation coefficients for four datasets. SPalign with default setting is 14% faster than TMalign. SPalign is expected to be useful for function prediction and comparing structures with or without domains defined. The source code for SPalign and the server are available at http://sparks.informatics.iupui.edu.

  20. 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

  1. Self-consistent tight-binding method for the prediction of magnetic spin structures in solids: Application to MnF{sub 2} and MnO{sub 2}

    Energy Technology Data Exchange (ETDEWEB)

    Zhuang, M.; Halley, J. W.

    2001-07-01

    We introduce a self-consistent tight-binding approach to the modeling and prediction of magnetic structure in solids. The method is similar to a charge self-consistent tight-binding method which we introduced earlier, but here we add information concerning the dependence of the ion energy on the total ion spin in the on-site matrix elements of the tight-binding Hamiltonian. We self-consistently determine both spins and charges of the ions during calculation. We illustrate with studies of MnF{sub 2} and the rutile form of MnO{sub 2}. In the first case we find without adjustment that the well-known two sublattice spin structure is predicted. In the second case we find that a disordered spin phase is predicted, contrary to experimental evidence, but a small adjustment of the parametrization yields the spiral spin structure suggested by experiments.

  2. Density functional theory based tight binding study on theoretical prediction of low-density nanoporous phases ZnO semiconductor materials

    Science.gov (United States)

    Tuoc, Vu Ngoc; Doan Huan, Tran; Viet Minh, Nguyen; Thi Thao, Nguyen

    2016-06-01

    Polymorphs or phases - different inorganic solids structures of the same composition usually have widely differing properties and applications, thereby synthesizing or predicting new classes of polymorphs for a certain compound is of great significance and has been gaining considerable interest. Herein, we perform a density functional theory based tight binding (DFTB) study on theoretical prediction of several new phases series of II-VI semiconductor material ZnO nanoporous phases from their bottom-up building blocks. Among these, three phases are reported for the first time, which could greatly expand the family of II- VI compound nanoporous phases. We also show that all these generally can be categorized similarly to the aluminosilicate zeolites inorganic open-framework materials. The hollow cage structure of the corresponding building block ZnkOk (k= 9, 12, 16) is well preserved in all of them, which leads to their low-density nanoporous and high flexibility. Additionally the electronic wide-energy gap of the individual ZnkOk is also retained. Our study reveals that they are all semiconductor materials with a large band gap. Further, this study is likely to be the common for II-VI semiconductor compounds and will be helpful for extending their range of properties and applications.

  3. Prediction of altered 3'- UTR miRNA-binding sites from RNA-Seq data: the swine leukocyte antigen complex (SLA as a model region.

    Directory of Open Access Journals (Sweden)

    Marie-Laure Endale Ahanda

    Full Text Available THE SLA (swine leukocyte antigen, MHC: SLA genes are the most important determinants of immune, infectious disease and vaccine response in pigs; several genetic associations with immunity and swine production traits have been reported. However, most of the current knowledge on SLA is limited to gene coding regions. MicroRNAs (miRNAs are small molecules that post-transcriptionally regulate the expression of a large number of protein-coding genes in metazoans, and are suggested to play important roles in fine-tuning immune mechanisms and disease responses. Polymorphisms in either miRNAs or their gene targets may have a significant impact on gene expression by abolishing, weakening or creating miRNA target sites, possibly leading to phenotypic variation. We explored the impact of variants in the 3'-UTR miRNA target sites of genes within the whole SLA region. The combined predictions by TargetScan, PACMIT and TargetSpy, based on different biological parameters, empowered the identification of miRNA target sites and the discovery of polymorphic miRNA target sites (poly-miRTSs. Predictions for three SLA genes characterized by a different range of sequence variation provided proof of principle for the analysis of poly-miRTSs from a total of 144 M RNA-Seq reads collected from different porcine tissues. Twenty-four novel SNPs were predicted to affect miRNA-binding sites in 19 genes of the SLA region. Seven of these genes (SLA-1, SLA-6, SLA-DQA, SLA-DQB1, SLA-DOA, SLA-DOB and TAP1 are linked to antigen processing and presentation functions, which is reminiscent of associations with disease traits reported for altered miRNA binding to MHC genes in humans. An inverse correlation in expression levels was demonstrated between miRNAs and co-expressed SLA targets by exploiting a published dataset (RNA-Seq and small RNA-Seq of three porcine tissues. Our results support the resource value of RNA-Seq collections to identify SNPs that may lead to altered mi

  4. Prediction of altered 3'- UTR miRNA-binding sites from RNA-Seq data: the swine leukocyte antigen complex (SLA) as a model region.

    Science.gov (United States)

    Endale Ahanda, Marie-Laure; Fritz, Eric R; Estellé, Jordi; Hu, Zhi-Liang; Madsen, Ole; Groenen, Martien A M; Beraldi, Dario; Kapetanovic, Ronan; Hume, David A; Rowland, Robert R R; Lunney, Joan K; Rogel-Gaillard, Claire; Reecy, James M; Giuffra, Elisabetta

    2012-01-01

    THE SLA (swine leukocyte antigen, MHC: SLA) genes are the most important determinants of immune, infectious disease and vaccine response in pigs; several genetic associations with immunity and swine production traits have been reported. However, most of the current knowledge on SLA is limited to gene coding regions. MicroRNAs (miRNAs) are small molecules that post-transcriptionally regulate the expression of a large number of protein-coding genes in metazoans, and are suggested to play important roles in fine-tuning immune mechanisms and disease responses. Polymorphisms in either miRNAs or their gene targets may have a significant impact on gene expression by abolishing, weakening or creating miRNA target sites, possibly leading to phenotypic variation. We explored the impact of variants in the 3'-UTR miRNA target sites of genes within the whole SLA region. The combined predictions by TargetScan, PACMIT and TargetSpy, based on different biological parameters, empowered the identification of miRNA target sites and the discovery of polymorphic miRNA target sites (poly-miRTSs). Predictions for three SLA genes characterized by a different range of sequence variation provided proof of principle for the analysis of poly-miRTSs from a total of 144 M RNA-Seq reads collected from different porcine tissues. Twenty-four novel SNPs were predicted to affect miRNA-binding sites in 19 genes of the SLA region. Seven of these genes (SLA-1, SLA-6, SLA-DQA, SLA-DQB1, SLA-DOA, SLA-DOB and TAP1) are linked to antigen processing and presentation functions, which is reminiscent of associations with disease traits reported for altered miRNA binding to MHC genes in humans. An inverse correlation in expression levels was demonstrated between miRNAs and co-expressed SLA targets by exploiting a published dataset (RNA-Seq and small RNA-Seq) of three porcine tissues. Our results support the resource value of RNA-Seq collections to identify SNPs that may lead to altered miRNA regulation patterns.

  5. Urinary Tissue Inhibitor of Metalloproteinase-2 (TIMP-2 • Insulin-Like Growth Factor-Binding Protein 7 (IGFBP7 Predicts Adverse Outcome in Pediatric Acute Kidney Injury.

    Directory of Open Access Journals (Sweden)

    Jens H Westhoff

    Full Text Available The G1 cell cycle inhibitors tissue inhibitor of metalloproteinase-2 (TIMP-2 and insulin-like growth factor-binding protein 7 (IGFBP7 have been identified as promising biomarkers for the prediction of adverse outcomes including renal replacement therapy (RRT and mortality in critically ill adult patients who develop acute kidney injury (AKI. However, the prognostic value of urinary TIMP-2 and IGFBP7 in neonatal and pediatric AKI for adverse outcome has not been investigated yet.The product of the urinary concentration of TIMP-2 and IGFBP7 ([TIMP-2]•[IGFBP7] was assessed by a commercially available immunoassay (NephroCheck™ in a prospective cohort study in 133 subjects aged 0-18 years including 46 patients with established AKI according to pRIFLE criteria, 27 patients without AKI (non-AKI group I and 60 apparently healthy neonates and children (non-AKI group II. AKI etiologies were: dehydration/hypovolemia (n = 7, hemodynamic instability (n = 7, perinatal asphyxia (n = 9, septic shock (n = 7, typical hemolytic-uremic syndrome (HUS; n = 5, interstitial nephritis (n = 5, vasculitis (n = 4, nephrotoxic injury (n = 1 and renal vein thrombosis (n = 1.When AKI patients were classified into pRIFLE criteria, 6/46 (13% patients fulfilled the criteria for the category "Risk", 13/46 (28% for "Injury", 26/46 (57% for "Failure" and 1/46 (2% for "Loss". Patients in the "Failure" stage had a median 3.7-fold higher urinary [TIMP-2]•[IGFBP7] compared to non-AKI subjects (P<0.001. When analyzed for AKI etiology, highest [TIMP-2]•[IGFBP7] values were found in patients with septic shock (P<0.001 vs. non-AKI I+II. Receiver operating characteristic (ROC curve analyses in the AKI group revealed good performance of [TIMP-2]•[IGFBP7] in predicting 30-day (area under the curve (AUC 0.79; 95% CI, 0.61-0.97 and 3-month mortality (AUC 0.84; 95% CI, 0.67-0.99 and moderate performance in predicting RRT (AUC 0.67; 95% CI, 0.50-0.84.This study shows that urinary [TIMP

  6. Experimental determination of water activity for binary aqueous cerium(III) ionic solutions: application to an assessment of the predictive capability of the binding mean spherical approximation model.

    Science.gov (United States)

    Ruas, Alexandre; Simonin, Jean-Pierre; Turq, Pierre; Moisy, Philippe

    2005-12-08

    This work is aimed at a description of the thermodynamic properties of actinide salt solutions at high concentration. The predictive capability of the binding mean spherical approximation (BIMSA) theory to describe the thermodynamic properties of electrolytes is assessed in the case of aqueous solutions of lanthanide(III) nitrate and chloride salts. Osmotic coefficients of cerium(III) nitrate and chloride were calculated from other lanthanide(III) salts properties. In parallel, concentrated binary solutions of cerium nitrate were prepared in order to measure experimentally its water activity and density as a function of concentration, at 25 degrees C. Water activities of several binary solutions of cerium chloride were also measured to check existing data on this salt. Then, the properties of cerium chloride and cerium nitrate solutions were compared within the BIMSA model. Osmotic coefficient values for promethium nitrate and promethium chloride given by this theory are proposed. Finally, water activity measurements were made to examine the fact that the ternary system Ce(NO3)3/HNO3/H2O and the quaternary system Ce(NO3)3/HNO3/N2H5NO3/H2O may be regarded as "simple solutions" (in the sense of Zdanovskii and Mikulin).

  7. Performance of the TPSS Functional on Predicting Core Level Binding Energies of Main Group Elements Containing Molecules: A Good Choice for Molecules Adsorbed on Metal Surfaces.

    Science.gov (United States)

    Pueyo Bellafont, Noèlia; Viñes, Francesc; Illas, Francesc

    2016-01-12

    Here we explored the performance of Hartree-Fock (HF), Perdew-Burke-Ernzerhof (PBE), and Tao-Perdew-Staroverov-Scuseria (TPSS) functionals in predicting core level 1s binding energies (BEs) and BE shifts (ΔBEs) for a large set of 68 molecules containing a wide variety of functional groups for main group elements B → F and considering up to 185 core levels. A statistical analysis comparing with X-ray photoelectron spectroscopy (XPS) experiments shows that BEs estimations are very accurate, TPSS exhibiting the best performance. Considering ΔBEs, the three methods yield very similar and excellent results, with mean absolute deviations of ∼0.25 eV. When considering relativistic effects, BEs deviations drop approaching experimental values. So, the largest mean percentage deviation is of 0.25% only. Linear trends among experimental and estimated values have been found, gaining offsets with respect to ideality. By adding relativistic effects to offsets, HF and TPSS methods underestimate experimental values by solely 0.11 and 0.05 eV, respectively, well within XPS chemical precision. TPSS is posed as an excellent choice for the characterization, by XPS, of molecules on metal solid substrates, given its suitability in describing metal substrates bonds and atomic and/or molecular orbitals.

  8. Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations.

    Science.gov (United States)

    Zhou, Hongyi; Zhang, Chi; Liu, Song; Zhou, Yaoqi

    2005-07-01

    We have developed the following web servers for protein structural modeling and analysis at http://theory.med.buffalo.edu: THUMBUP, UMDHMM(TMHP) and TUPS, predictors of transmembrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMM(TMHP)) and their combinations (TUPS); SPARKS 2.0 and SP3, two profile-profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP3); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein-protein/peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively.

  9. Prediction of the Occurrence of the ADP-binding βαβ-fold in Proteins, Using an Amino Acid Sequence Fingerprint

    NARCIS (Netherlands)

    Wierenga, Rik K.; Terpstra, Peter; Hol, Wim G.J.

    1986-01-01

    An amino acid sequence "fingerprint” has been derived that can be used to test if a particular sequence will fold into a βαβ-unit with ADP-binding properties. It was deduced from a careful analysis of the known three-dimensional structures of ADP-binding βαβ-folds. This fingerprint is in fact a set

  10. 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...

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

    Science.gov (United States)

    Major histocompatibility complex (MHC) class I molecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8positive T-cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presenta...

  12. 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...

  13. Clinical Usefulness of Urinary Fatty Acid Binding Proteins in Assessing the Severity and Predicting Treatment Response of Pneumonia in Critically Ill Patients: A Cross-Sectional Study.

    Science.gov (United States)

    Tsao, Tsung-Cheng; Tsai, Han-Chen; Chang, Shi-Chuan

    2016-05-01

    To investigate the clinical relevance of urinary fatty acid binding proteins (FABPs), including intestinal-FABP, adipocyte-FABP, liver-FABP, and heart-FABP in pneumonia patients required admission to respiratory intensive care unit (RICU).Consecutive pneumonia patients who admitted to RICU from September 2013 to October 2014 were enrolled except for those with pneumonia for more than 24 h before admission to RICU. Pneumonia patients were further divided into with and without septic shock subgroups. Twelve patients without infection were enrolled to serve as control group. Urine samples were collected on days 1 and 7 after admission to RICU for measuring FABPs and inflammatory cytokines. Clinical and laboratory data were collected and compared between pneumonia and control groups, and between the pneumonia patients with and without septic shock.There were no significant differences in urinary levels of various FABPs and inflammatory cytokines measured on day 1 between control and pneumonia groups. Urinary values of intestine-FABP (P = 0.020), adipocyte-FABP (P = 0.005), heart-FABP (P = 0.025), and interleukin-6 (P = 0.019) were significantly higher and arterial oxygen tension/fraction of inspired oxygen (PaO2/FiO2, P/F) ratio (P = 0.024) was significantly lower in pneumonia patients with septic shock on day 1 than in those without septic shock. After multivariate analysis, adipocyte-FABP was the independent factor (P = 0.026). Urinary levels of FABPs measured on day 7 of pneumonia patients were significantly lower in the improved than in nonimproved groups (P = 0.030 for intestine-FABP, P = 0.003 for adipocyte-FABP, P = 0.010 for heart-FABP, and P = 0.008 for liver-FABP, respectively). After multivariate analysis, adipocyte-FABP was the independent factor (P = 0.023).For pneumonia patients required admission to RICU, urinary levels of adipocyte-FABP on days 1 and 7 after admission to RICU may be valuable in assessing the

  14. Detection of C3d-Binding Donor-Specific Anti-HLA Antibodies at Diagnosis of Humoral Rejection Predicts Renal Graft Loss

    Science.gov (United States)

    Sicard, Antoine; Ducreux, Stéphanie; Rabeyrin, Maud; Couzi, Lionel; McGregor, Brigitte; Badet, Lionel; Scoazec, Jean Yves; Bachelet, Thomas; Lepreux, Sébastien; Visentin, Jonathan; Merville, Pierre; Fremeaux-Bacchi, Véronique; Morelon, Emmanuel; Taupin, Jean-Luc; Dubois, Valérie

    2015-01-01

    Antibody-mediated rejection (AMR) is a major cause of kidney graft loss, yet assessment of individual risk at diagnosis is impeded by the lack of a reliable prognosis assay. Here, we tested whether the capacity of anti-HLA antibodies to bind complement components allows accurate risk stratification at the time of AMR diagnosis. Among 938 kidney transplant recipients for whom a graft biopsy was performed between 2004 and 2012 at the Lyon University Hospitals, 69 fulfilled the diagnosis criteria for AMR and were enrolled. Sera banked at the time of the biopsy were screened for the presence of donor-specific anti-HLA antibodies (DSAs) and their ability to bind C1q and C3d using flow bead assays. In contrast with C4d graft deposition, the presence of C3d-binding DSA was associated with a higher risk of graft loss (P<0.001). Despite similar trend, the difference did not reach significance with a C1q-binding assay (P=0.06). The prognostic value of a C3d-binding assay was further confirmed in an independent cohort of 39 patients with AMR (P=0.04). Patients with C3d-binding antibodies had worse eGFR and higher DSA mean fluorescence intensity. In a multivariate analysis, only eGFR<30 ml/min per 1.73 m2 (hazard ratio [HR], 3.56; 95% confidence interval [CI], 1.46 to 8.70; P=0.005) and the presence of circulating C3d-binding DSA (HR, 2.80; 95% CI, 1.12 to 6.95; P=0.03) were independent predictors for allograft loss at AMR diagnosis. We conclude that assessment of the C3d-binding capacity of DSA at the time of AMR diagnosis allows for identification of patients at risk for allograft loss. PMID:25125383

  15. Protein Binding Pocket Dynamics.

    Science.gov (United States)

    Stank, Antonia; Kokh, Daria B; Fuller, Jonathan C; Wade, Rebecca C

    2016-05-17

    The dynamics of protein binding pockets are crucial for their interaction specificity. Structural flexibility allows proteins to adapt to their individual molecular binding partners and facilitates the binding process. This implies the necessity to consider protein internal motion in determining and predicting binding properties and in designing new binders. Although accounting for protein dynamics presents a challenge for computational approaches, it expands the structural and physicochemical space for compound design and thus offers the prospect of improved binding specificity and selectivity. A cavity on the surface or in the interior of a protein that possesses suitable properties for binding a ligand is usually referred to as a binding pocket. The set of amino acid residues around a binding pocket determines its physicochemical characteristics and, together with its shape and location in a protein, defines its functionality. Residues outside the binding site can also have a long-range effect on the properties of the binding pocket. Cavities with similar functionalities are often conserved across protein families. For example, enzyme active sites are usually concave surfaces that present amino acid residues in a suitable configuration for binding low molecular weight compounds. Macromolecular binding pockets, on the other hand, are located on the protein surface and are often shallower. The mobility of proteins allows the opening, closing, and adaptation of binding pockets to regulate binding processes and specific protein functionalities. For example, channels and tunnels can exist permanently or transiently to transport compounds to and from a binding site. The influence of protein flexibility on binding pockets can vary from small changes to an already existent pocket to the formation of a completely new pocket. Here, we review recent developments in computational methods to detect and define binding pockets and to study pocket dynamics. We introduce five

  16. Molecular cloning and 3D structure prediction of the first raw-starch-degrading glucoamylase without a separate starch-binding domain.

    Science.gov (United States)

    Hostinová, Eva; Solovicová, Adriana; Dvorský, Radovan; Gasperík, Juraj

    2003-03-15

    Raw-starch-degrading glucoamylases have been known as multidomain enzymes consisting of a catalytic domain connected to a starch-binding domain (SBD) by an O-glycosylated linker region. A molecular genetics approach has been chosen to find structural differences between two related glucoamylases, raw-starch-degrading Glm and nondegrading Glu, from the yeasts Saccharomycopsis fibuligera IFO 0111 and HUT 7212, respectively. We have found that Glm and Glu show a high primary (77%) and tertiary structure similarity. Glm, although possessing a good ability for raw starch degradation, did not show consensus amino acid residues to any SBD found in glucoamylases or other amylolytic enzymes. Raw starch binding and digestion by Glm must thus depend on the existence of a site(s) lying within the intact protein which lacks a separate SBD. The enzyme represents a structurally new type of raw-starch-degrading glucoamylase.

  17. Molecular modeling of human APOBEC3G to predict the binding modes of the inhibitor compounds IMB26 and IMB35

    Directory of Open Access Journals (Sweden)

    Zhixin Zhang

    2013-07-01

    Full Text Available APOBEC3G(A3G is a host cytidine deaminase that incorporates into HIV-1 virions and efficiently inhibits viral replication. The virally encoded protein Vif binds to A3G and induces its degradation, thereby counteracting the antiviral activity of A3G. Vif-mediated A3G degradation clearly represents a potential target for anti-HIV drug development. Currently, there is an urgent need for understanding the three dimensional structure of full-length A3G. In this work, we use a homology modeling approach to propose a structure for A3G based on the crystal structure of APOBEC2 (APO2 and the catalytic domain structure of A3G. Two compounds, IMB26 and IMB35, which have been shown to bind to A3G and block degradation by Vif, were docked into the A3G model and the binding modes were generated for further analysis. The results may be used to design or optimize molecules targeting Vif–A3G interaction, and lead to the development of novel anti-HIV drugs.

  18. Molecular cloning, expression pattern, and 3D structural prediction of the cold inducible RNA-binding protein (CIRP) in Japanese flounder ( Paralichthys olivaceus)

    Science.gov (United States)

    Yang, Xiao; Gao, Jinning; Ma, Liman; Li, Zan; Wang, Wenji; Wang, Zhongkai; Yu, Haiyang; Qi, Jie; Wang, Xubo; Wang, Zhigang; Zhang, Quanqi

    2015-02-01

    Cold-inducible RNA-binding protein (CIRP) is a kind of RNA binding proteins that plays important roles in many physiological processes. The CIRP has been widely studied in mammals and amphibians since it was first cloned from mammals. On the contrary, there are little reports in teleosts. In this study, the Po CIRP gene of the Japanese flounder was cloned and sequenced. The genomic sequence consists of seven exons and six introns. The putative PoCIRP protein of flounder was 198 amino acid residues long containing the RNA recognition motif (RRM). Phylogenetic analysis showed that the flounder PoCIRP is highly conserved with other teleost CIRPs. The 5' flanking sequence was cloned by genome walking and many transcription factor binding sites were identified. There is a CpGs region located in promoter and exon I region and the methylation state is low. Quantitative real-time PCR analysis uncovered that Po CIRP gene was widely expressed in adult tissues with the highest expression level in the ovary. The mRNA of the Po CIRP was maternally deposited and the expression level of the gene was regulated up during the gastrula and neurula stages. In order to gain the information how the protein interacts with mRNA, we performed the modeling of the 3D structure of the flounder PoCIRP. The results showed a cleft existing the surface of the molecular. Taken together, the results indicate that the CIRP is a multifunctional molecular in teleosts and the findings about the structure provide valuable information for understanding the basis of this protein's function.

  19. Molecular Cloning, Expression Pattern, and 3D Structural Prediction of the Cold Inducible RNA - Binding Protein (CIRP) in Japanese Flounder (Paralichthys olivaceus)

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao; WANG Zhigang; ZHANG Quanqi; GAO Jinning; MA Liman; LI Zan; WANG Wenji; WANG Zhongkai; YU Haiyang; QI Jie; WANG Xubo

    2015-01-01

    Cold-inducible RNA-binding protein (CIRP) is a kind of RNA binding proteins that plays important roles in many physiological processes. The CIRP has been widely studied in mammals and amphibians since it was first cloned from mammals. On the contrary, there are little reports in teleosts. In this study, the PoCIRP gene of the Japanese flounder was cloned and sequenced. The genomic sequence consists of seven exons and six introns. The putative PoCIRP protein of flounder was 198 amino acid residues long containing the RNA recognition motif (RRM). Phylogenetic analysis showed that the flounder PoCIRP is highly conserved with other teleost CIRPs. The 5’ flanking sequence was cloned by genome walking and many transcription factor binding sites were iden-tified. There is a CpGs region located in promoter and exon I region and the methylation state is low. Quantitative real-time PCR analysis uncovered that PoCIRP gene was widely expressed in adult tissues with the highest expression level in the ovary. The mRNA of the PoCIRP was maternally deposited and the expression level of the gene was regulated up during the gastrula and neu-rula stages. In order to gain the information how the protein interacts with mRNA, we performed the modeling of the 3D structure of the flounder PoCIRP. The results showed a cleft existing the surface of the molecular. Taken together, the results indicate that the CIRP is a multifunctional molecular in teleosts and the findings about the structure provide valuable information for understanding the basis of this protein’s function.

  20. Binding Procurement

    Science.gov (United States)

    Rao, Gopalakrishna M.; Vaidyanathan, Hari

    2007-01-01

    This viewgraph presentation reviews the use of the binding procurement process in purchasing Aerospace Flight Battery Systems. NASA Engineering and Safety Center (NESC) requested NASA Aerospace Flight Battery Systems Working Group to develop a set of guideline requirements document for Binding Procurement Contracts.

  1. In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines

    Directory of Open Access Journals (Sweden)

    Yonghua Wang

    2010-09-01

    Full Text Available Over the years development of selective estrogen receptor (ER ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship (QSAR methods based on the two linear (multiple linear regression, MLR, partial least squares regression, PLSR and a nonlinear statistical method (Bayesian regularized neural network, BRNN were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ERα and ERβ, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties (ERα: for MLR, Rtr2 = 0.72, Qte2 = 0.63; for PLSR, Rtr2 = 0.92, Qte2 = 0.84. ERβ: for MLR, Rtr2 = 0.75, Qte2 = 0.75; for PLSR, Rtr2 = 0.98, Qte2 = 0.80. The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in Rtr2 = 0.74 and Qte2 = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ERα and ERβ is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity

  2. 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

  3. 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

  4. The Binding Mode Prediction and Similar Ligand Potency in the Active Site of Vitamin D Receptor with QM/MM Interaction, MESP, and MD Simulation.

    Science.gov (United States)

    Selvaraman, Nagamani; Selvam, Saravana Kumar; Muthusamy, Karthikeyan

    2016-08-01

    Non-secosteroidal ligands are well-known vitamin D receptor (VDR) agonists. In this study, we described a combined QM/MM to define the protein-ligand interaction energy a strong positive correlation in both QM-MM interaction energy and binding free energy against the biological activity. The molecular dynamics simulation study was performed, and specific interactions were extensively studied. The molecular docking results and surface analysis shed light on steric and electrostatic complementarities of these non-secosteroidal ligands to VDR. Finally, the drug likeness properties were also calculated and found within the acceptable range. The results show that bulky group substitutions in side chain decrease the VDR activity, whereas a small substitution increased it. Functional analyses of H393A and H301A mutations substantiate their roles in the VDR agonistic and antagonistic activities. Apart from the His393 and His301, two other amino acids in the hinge region viz. Ser233 and Arg270 acted as an electron donor/acceptor specific to the agonist in the distinct ligand potency. The results from this study disclose the binding mechanism of VDR agonists and structural modifications required to improve the selectivity.

  5. 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

    -terminus. Meanwhile, phage display peptide library encoding random 12mer peptides was also screened against beta(2)m/SasaUBA*0301. Eighty-five percentages of the corresponding peptides have an enrichment of leucine, methionine, valine, or isoleucine at the C-terminus. We predict that this particular allele of Salmon...

  6. 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

    2016-12-01

    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.

  7. Mapping of the Neisseria meningitidis NadA cell-binding site: relevance of predicted {alpha}-helices in the NH2-terminal and dimeric coiled-coil regions.

    Science.gov (United States)

    Tavano, Regina; Capecchi, Barbara; Montanari, Paolo; Franzoso, Susanna; Marin, Oriano; Sztukowska, Maryta; Cecchini, Paola; Segat, Daniela; Scarselli, Maria; Aricò, Beatrice; Papini, Emanuele

    2011-01-01

    NadA is a trimeric autotransporter protein of Neisseria meningitidis belonging to the group of oligomeric coiled-coil adhesins. It is implicated in the colonization of the human upper respiratory tract by hypervirulent serogroup B N. meningitidis strains and is part of a multiantigen anti-serogroup B vaccine. Structure prediction indicates that NadA is made by a COOH-terminal membrane anchor (also necessary for autotranslocation to the bacterial surface), an intermediate elongated coiled-coil-rich stalk, and an NH(2)-terminal region involved in cell interaction. Electron microscopy analysis and structure prediction suggest that the apical region of NadA forms a compact and globular domain. Deletion studies proved that the NH(2)-terminal sequence (residues 24 to 87) is necessary for cell adhesion. In this study, to better define the NadA cell binding site, we exploited (i) a panel of NadA mutants lacking sequences along the coiled-coil stalk and (ii) several oligoclonal rabbit antibodies, and their relative Fab fragments, directed to linear epitopes distributed along the NadA ectodomain. We identified two critical regions for the NadA-cell receptor interaction with Chang cells: the NH(2) globular head domain and the NH(2) dimeric intrachain coiled-coil α-helices stemming from the stalk. This raises the importance of different modules within the predicted NadA structure. The identification of linear epitopes involved in receptor binding that are able to induce interfering antibodies reinforces the importance of NadA as a vaccine antigen.

  8. 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.

  9. Candidate SNP Markers of Chronopathologies Are Predicted by a Significant Change in the Affinity of TATA-Binding Protein for Human Gene Promoters

    Directory of Open Access Journals (Sweden)

    Petr Ponomarenko

    2016-01-01

    Full Text Available Variations in human genome (e.g., single nucleotide polymorphisms, SNPs may be associated with hereditary diseases, their complications, comorbidities, and drug responses. Using Web service SNP_TATA_Comparator presented in our previous paper, here we analyzed immediate surroundings of known SNP markers of diseases and identified several candidate SNP markers that can significantly change the affinity of TATA-binding protein for human gene promoters, with circadian consequences. For example, rs572527200 may be related to asthma, where symptoms are circadian (worse at night, and rs367732974 may be associated with heart attacks that are characterized by a circadian preference (early morning. By the same method, we analyzed the 90 bp proximal promoter region of each protein-coding transcript of each human gene of the circadian clock core. This analysis yielded 53 candidate SNP markers, such as rs181985043 (susceptibility to acute Q fever in male patients, rs192518038 (higher risk of a heart attack in patients with diabetes, and rs374778785 (emphysema and lung cancer in smokers. If they are properly validated according to clinical standards, these candidate SNP markers may turn out to be useful for physicians (to select optimal treatment for each patient and for the general population (to choose a lifestyle preventing possible circadian complications of diseases.

  10. Missense mutations in the TP53 DNA-binding domain predict outcomes in patients with advanced oral cavity squamous cell carcinoma.

    Science.gov (United States)

    Lapke, Nina; Lu, Yen-Jung; Liao, Chun-Ta; Lee, Li-Yu; Lin, Chien-Yu; Wang, Hung-Ming; Ng, Shu-Hang; Chen, Shu-Jen; Yen, Tzu-Chen

    2016-07-12

    TP53 mutations have been linked to reduced survival in patients with oral cavity squamous cell carcinoma (OSCC). However, the impact of different types of TP53 mutations remains unclear. Here, we demonstrate that the carriage of missense mutations in the TP53 DNA binding domain (DBD missense mutations) is associated with decreased disease-specific survival (DSS) compared with wild-type TP53 (P=0.002) in a cohort of 345 OSCC patients. In contrast, DSS of patients bearing all of the remaining TP53 mutations did not differ from that observed in wild-type TP53 patients (P=0.955). Our classification method for TP53 mutations was superior to previously reported approaches (disruptive, truncating, Evolutionary Action score, mutations in L2/L3/LSH) for distinguishing between low- and high-risk patients. When analyzed in combination with traditional clinicopathological factors, TP53 DBD missense mutations were an independent prognostic factor for shorter DSS (P=0.014) alongside with advanced AJCC T- and N-classifications and the presence of extracapsular spread. A scoring system that included the four independent prognostic factors allowed a reliable patient stratification into distinct risk groups (high-risk patients, 16.2%). Our results demonstrate the usefulness of TP53 DBD missense mutations combined with clinicopathological factors for improving the prognostic stratification of OSCC patients.

  11. Increase in skin autofluorescence and release of heart-type fatty acid binding protein in plasma predicts mortality of hemodialysis patients.

    Science.gov (United States)

    Arsov, Stefan; Trajceska, Lada; van Oeveren, Wim; Smit, Andries J; Dzekova, Pavlina; Stegmayr, Bernd; Sikole, Aleksandar; Rakhorst, Gerhard; Graaff, Reindert

    2013-07-01

    Advanced glycation end-products (AGEs) are uremic toxins that accumulate progressively in hemodialysis (HD) patients. The aim of this study was to assess the 1-year increase in skin autofluorescence (ΔAF), a measure of AGEs accumulation and plasma markers, as predictors of mortality in HD patients. One hundred sixty-nine HD patients were enrolled in this study. Skin autofluorescence was measured twice, 1 year apart using an AGE Reader (DiagnOptics Technologies BV, Groningen, The Netherlands). Besides routine blood chemistry, additional plasma markers including superoxide dismutase, myeloperoxydase, intercellular adhesion molecule 1 (ICAM-1), C-reactive protein (hs-CRP), heart-type fatty acid binding protein (H-FABP), and von Willebrand factor were measured at baseline. The mortality of HD patients was followed for 36 months. Skin autofluorescence values of the HD patients at the two time points were significantly higher (P < 0.001) than those of healthy subjects of the same age. Mean 1-year ΔAF of HD patients was 0.16 ± 0.06, which was around seven- to ninefold higher than 1-year ΔAF in healthy subjects. Multivariate Cox regression showed that age, hypertension, 1-year ΔAF, hs-CRP, ICAM-1, and H-FABP were independent predictors of overall mortality. Hypertension, 1-year ΔAF, hs-CRP, and H-FABP were also independent predictors of cardiovascular mortality. One-year ΔAF and plasma H-FABP, used separately and in combination, are strong predictors of overall and cardiovascular mortality in HD patients.

  12. 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.

  13. Predicting the Ah receptor binding affinity of PCDFs using molecular electronegativity interaction vector%分子电性作用矢量预测多氯代苯并呋喃Ah受体结合能力

    Institute of Scientific and Technical Information of China (English)

    冯涛; 周小华; 周兴

    2011-01-01

    Study on the quantitative structure-activity relationship (QSAR) of poly chlorinated dibenzofurans (PCDFs) would be helpful in discussing Ah receptor binding affinity of polychlorinated dibenzofurans (PCDFs). In this paper, a novel molecular electronegativity interaction vector (MEIV), which has been developed according to classification of atomic type, was used to describe the chemical structure of 136 polychlorinated dibenzofurans (PCDFs), a rational quantitative relationship model between the Ah receptor binding affinity of polychlorinated dibenzofurans (PCDFs) and the molecular electronegativity interaction vector (MEIV) was achieved by a multiple linear regression (MLR). The results of significance test were satisfying on the whole (n=26, /J=0.925, SD=0.570, F=15.210). Another more predictive model was constructed with a quite high correlation coefficient (R=0.917) by selecting six parameters form the all elements in the molecular electronegativity interaction vector (MEIV) vectors of the former model through a stepwise multiple regression (SMR). The performance of the six-parameter model was tested through cross-validation by the leave-one-out procedure (LOO) and satisfactory results were obtained(Rcv=0.828), then Ah receptor binding affinity of the rest unknown polychlorinated dibenzofurans (PCDFs) were predicted by the model. It was suggested that molecular electronegativity interaction vector (MEIV) was an excellent vectorial descriptor and possessed good structure selectivity.%首先用分子电性矢量(MEIV)表征多氯代苯并呋喃(PCDFs)136种同系物的结构,再用多元线性回归方法建立多氯代苯并呋喃(PCDFs)Ah受体的结合能力与分子电性矢量之间的定量关系(QSAR)模型,两者的相关性较显著,(n=26,R=0.925,SD=0.570,F=15.210)。此外先用逐步回归方法(SMR)从该模型中选6个参数建立新模型,其相关系数为R=0.917;再用留一法互相检验,其相关系数Re=0.828

  14. Regulation of ryanodine receptor RyR2 by protein-protein interactions: prediction of a PKA binding site on the N-terminal domain of RyR2 and its relation to disease causing mutations [v1; ref status: indexed, http://f1000r.es/4tw

    Directory of Open Access Journals (Sweden)

    Belinda Nazan Walpoth

    2015-01-01

    Full Text Available Protein-protein interactions are the key processes responsible for signaling and function in complex networks. Determining the correct binding partners and predicting the ligand binding sites in the absence of experimental data require predictive models. Hybrid models that combine quantitative atomistic calculations with statistical thermodynamics formulations are valuable tools for bioinformatics predictions. We present a hybrid prediction and analysis model for determining putative binding partners and interpreting the resulting correlations in the yet functionally uncharacterized interactions of the ryanodine RyR2 N-terminal domain. Using extensive docking calculations and libraries of hexameric peptides generated from regulator proteins of the RyR2 channel, we show that the residues 318-323 of protein kinase A, PKA, have a very high affinity for the N-terminal of RyR2. Using a coarse grained Elastic Net Model, we show that the binding site lies at the end of a pathway of evolutionarily conserved residues in RyR2. The two disease causing mutations are also on this path. The program for the prediction of the energetically responsive residues by the Elastic Net Model is freely available on request from the corresponding author.

  15. Diagnostic and Predictive Levels of Calcium-binding Protein A8 and Tumor Necrosis Factor Receptor-associated Factor 6 in Sepsis-associated Encephalopathy: A Prospective Observational Study

    Institute of Scientific and Technical Information of China (English)

    Li-Na Zhang; Xiao-Hong Wang; Long Wu; Li Huang; Chun-Guang Zhao; Qian-Yi Peng; Yu-Hang Ai

    2016-01-01

    Background:Despite its high prevalence,morbidity,and mortality,sepsis-associated encephalopathy (SAE) is still poorly understood.The aim of this prospective and observational study was to investigate the clinical significance of calcium-binding protein A8 (S100A8) in serum and tumor necrosis factor receptor-associated factor 6 (TRAF6) in peripheral blood mononuclear cells (PBMCs) in diagnosing SAE and predicting its prognosis.Methods:Data of septic patients were collected within 24 h after Intensive Care Unit admission from July 2014 to March 2015.Healthy medical personnel served as the control group.SAE was defined as cerebral dysfunction in the presence of sepsis that fulfilled the exclusion criteria.The biochemical indicators,Glasgow Coma Scale,Acute Physiology and Chronic Health Evaluation score Ⅱ,TRAF6 in PBMC,serum S 100A8,S 100β,and neuron-specific enolase were evaluated in SAE patients afresh.TRAF6 and S 100A8 were also measured in the control group.Results:Of the 57 enrolled patients,29 were diagnosed with SAE.The S 100A8 and TRAF6 concentrations in SAE patients were both significantly higher than that in no-encephalopathy (NE) patients,and higher in NE than that in controls (3.74 ± 3.13 vs.1.08 ± 0.75 vs.0.37 ± 0.14 ng/ml,P < 0.01;3.18 ± 1.55 vs.1.02 ± 0.63 vs.0.47 ± 0.10,P < 0.01).S 100A8 levels of 1.93 ng/ml were diagnostic of SAE with 92.90% specificity and 69.00% sensitivity in the receiver operating characteristic (ROC) curve,and the area under the curve was 0.86 (95% confidence interval [CI]:0.76-0.95).TRAF6-relative levels of 1.44 were diagnostic of SAE with 85.70% specificity and 86.20% sensitivity,and the area under the curve was 0.94 (95% CI:0.88-0.99).In addition,S 100A8 levels of 2.41 ng/ml predicted 28-day mortality of SAE with 90.00% specificity and 73.70% sensitivity in the ROC curve,and the area under the curve was 0.88.TRAF6 relative levels of 2.94 predicted 28-day mortality of SAE with 80.00% specificity

  16. Statistics for Transcription Factor Binding Sites

    OpenAIRE

    2008-01-01

    Transcription factors (TFs) play a key role in gene regulation. They interact with specific binding sites or motifs on the DNA sequence and regulate expression of genes downstream of these binding sites. In silico prediction of potential binding of a TF to a binding site is an important task in computational biology. From a statistical point of view, the DNA sequence is a long text consisting of four different letters ('A','C','G', and 'T'). The binding of a TF to the sequence corresponds to ...

  17. The predictive value of serum heparin-binding protein level during the early stage of septic shock%血清肝素结合蛋白在脓毒症休克中的预测作用

    Institute of Scientific and Technical Information of China (English)

    刘杨; 马少林; 王学斌

    2014-01-01

    Objective To study the variation and prognostic value of serum heparin-binding protein (HBP),a potent inducer of vascular leakage in septic shock.Methods A total of 82 sepsis patients admitted to central ICU from August 2011 to July 2012 were enrolled in the prospective study.Eighty-two sepsis patients were divided into septic shock group (group A,n =39) and sepsis without shock group (group B,n =43).At the same time,another 30 shock patients without sepsis were enrolled as group C and 35 patients without sepsis and shock were enrolled as group D.Blood samples obtained within 24 hours after enrollment,and the concentrations of HBP were measured by ELISA and lactate by electrode method.Comparisons of HBP,lactate and APACHE Ⅱ score between groups were carried out by Studers't test,and the early prognostic value in septic shock and mortality was studied by receiver operating characteristic (ROC) curve.Results Patients in group A (t =3.862,P<0.001) and group B(t =5.193,P< 0.001) both had higher HBP level than patients in group D.HBP level in group A patients was also higher than that of group B (t =3.270,P =0.002).Septic shock patients (group A) had higher HBP than patients of group C (t =3.029,P =0.004).The area under curve (AUC) of HBP predicting septic shock patients was 0.834,with sensitivity 0.795,specificity 0.795.The AUC of HBP predicting 28-day mortality of all enrolled patients was was 0.680,and the AUC of predicting 28-day mortality of sepsis patients was 0.784.Conclusions HBP levels were increased in septic patients,especially in septic shock patients.HBP is a better predictor than traditional predictors like lactate and APACHE Ⅱ score for predicting septic shock.HBP is a good and specific evaluation marker for predicting septic shock and the mortality of sepsis patients.The measurement of HBP during the early stage of disease presents valuable information about diagnosis and treatment.%目的 探讨导致血管渗漏的细胞因子肝素结合蛋白(heparin-binding

  18. 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.

  19. 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.

  20. Analyzing binding data.

    Science.gov (United States)

    Motulsky, Harvey J; Neubig, Richard R

    2010-07-01

    Measuring the rate and extent of radioligand binding provides information on the number of binding sites, and their affinity and accessibility of these binding sites for various drugs. This unit explains how to design and analyze such experiments.

  1. 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 (G...

  2. Membrane binding domains

    OpenAIRE

    Hurley, James H.

    2006-01-01

    Eukaryotic signaling and trafficking proteins are rich in modular domains that bind cell membranes. These binding events are tightly regulated in space and time. The structural, biochemical, and biophysical mechanisms for targeting have been worked out for many families of membrane binding domains. This review takes a comparative view of seven major classes of membrane binding domains, the C1, C2, PH, FYVE, PX, ENTH, and BAR domains. These domains use a combination of specific headgroup inter...

  3. Analyzing radioligand binding data.

    Science.gov (United States)

    Motulsky, Harvey; Neubig, Richard

    2002-08-01

    Radioligand binding experiments are easy to perform, and provide useful data in many fields. They can be used to study receptor regulation, discover new drugs by screening for compounds that compete with high affinity for radioligand binding to a particular receptor, investigate receptor localization in different organs or regions using autoradiography, categorize receptor subtypes, and probe mechanisms of receptor signaling, via measurements of agonist binding and its regulation by ions, nucleotides, and other allosteric modulators. This unit reviews the theory of receptor binding and explains how to analyze experimental data. Since binding data are usually best analyzed using nonlinear regression, this unit also explains the principles of curve fitting with nonlinear regression.

  4. Diagnostic and Predictive Levels of Calcium-binding Protein A8 and Tumor Necrosis Factor Receptor-associated Factor 6 in Sepsis-associated Encephalopathy: A Prospective Observational Study

    Directory of Open Access Journals (Sweden)

    Li-Na Zhang

    2016-01-01

    Conclusions: Peripheral blood levels of S100A8 and TRAF6 in SAE patients were elevated and might be related to the severity of SAE and predict the outcome of SAE. The efficacy and specificity of S100A8 for SAE diagnosis were superior, despite its weak sensitivity. S100A8 might be a better biomarker for diagnosis of SAE and predicting prognosis.

  5. Ureaplasma urealyticum binds mannose-binding lectin.

    Science.gov (United States)

    Benstein, Barbara D; Ourth, Donald D; Crouse, Dennis T; Shanklin, D Radford

    2004-10-01

    Mannose-binding C-type lectin (MBL) is an important component of innate immunity in mammals. Mannose-binding lectin (MBL), an acute phase protein, acts as an opsonin for phagocytosis and also activates the mannan-binding lectin complement pathway. It may play a particularly significant role during infancy before adequate specific protection can be provided by the adaptive immune system. Ureaplasma urealyticum has been linked to several diseases including pneumonia and chronic lung disease (CLD) in premature infants. We therefore investigated the ability of U. urealyticum to bind MBL. A guinea pig IgG anti-rabbit-MBL antiserum was produced. An immunoblot (dot-blot) assay done on nitrocellulose membrane determined that the anti-MBL antibody had specificity against both rabbit and human MBL. Pure cultures of U. urealyticum, serotype 3, were used to make slide preparations. The slides containing the organisms were then incubated with nonimmune rabbit serum containing MBL. Ureaplasma was shown to bind rabbit MBL with an immunocytochemical assay using the guinea pig IgG anti-rabbit MBL antiserum. Horseradish peroxidase (HRP)-labeled anti-guinea pig IgG was used to localize the reaction. The anti-MBL antiserum was also used in an immunocytochemical assay to localize U. urealyticum in histological sections of lungs from mice specifically infected with this organism. The same method also indicated binding of MBL by ureaplasma in human lung tissue obtained at autopsy from culture positive infants. Our results demonstrate that ureaplasma has the capacity to bind MBL. The absence of MBL may play a role in the predisposition of diseases related to this organism.

  6. Ligand binding mechanics of maltose binding protein.

    Science.gov (United States)

    Bertz, Morten; Rief, Matthias

    2009-11-13

    In the past decade, single-molecule force spectroscopy has provided new insights into the key interactions stabilizing folded proteins. A few recent studies probing the effects of ligand binding on mechanical protein stability have come to quite different conclusions. While some proteins seem to be stabilized considerably by a bound ligand, others appear to be unaffected. Since force acts as a vector in space, it is conceivable that mechanical stabilization by ligand binding is dependent on the direction of force application. In this study, we vary the direction of the force to investigate the effect of ligand binding on the stability of maltose binding protein (MBP). MBP consists of two lobes connected by a hinge region that move from an open to a closed conformation when the ligand maltose binds. Previous mechanical experiments, where load was applied to the N and C termini, have demonstrated that MBP is built up of four building blocks (unfoldons) that sequentially detach from the folded structure. In this study, we design the pulling direction so that force application moves the two MBP lobes apart along the hinge axis. Mechanical unfolding in this geometry proceeds via an intermediate state whose boundaries coincide with previously reported MBP unfoldons. We find that in contrast to N-C-terminal pulling experiments, the mechanical stability of MBP is increased by ligand binding when load is applied to the two lobes and force breaks the protein-ligand interactions directly. Contour length measurements indicate that MBP is forced into an open conformation before unfolding even if ligand is bound. Using mutagenesis experiments, we demonstrate that the mechanical stabilization effect is due to only a few key interactions of the protein with its ligand. This work illustrates how varying the direction of the applied force allows revealing important details about the ligand binding mechanics of a large protein.

  7. 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

    the positive predictive value and in 10- to 20-year-old children was 52.3% for IGF-I and 56.5% for IGFBP-3. Combination use of IGF-I and IGFBP-3 gave additional diagnostic information. We conclude that a subnormal IGF-I level, and especially a subnormal IGFBP-3 level, are highly predictive of a subnormal GH...... response to a provocative test in prepubertal children in whom GHD is suspected. On the other hand, a normal IGF-I or IGFBP-3 level does not exclude GHD. The predictive value of IGF-I and IGFBP-3 in pubertal children is diminished in comparison with that in prepubertal children. We believe that IGF...

  8. Python bindings for libcloudph++

    OpenAIRE

    Jarecka, Dorota; Arabas, Sylwester; Del Vento, Davide

    2015-01-01

    This technical note introduces the Python bindings for libcloudph++. The libcloudph++ is a C++ library of algorithms for representing atmospheric cloud microphysics in numerical models. The bindings expose the complete functionality of the library to the Python users. The bindings are implemented using the Boost.Python C++ library and use NumPy arrays. This note includes listings with Python scripts exemplifying the use of selected library components. An example solution for using the Python ...

  9. Python bindings for libcloudph++

    CERN Document Server

    Jarecka, Dorota; Del Vento, Davide

    2015-01-01

    This technical note introduces the Python bindings for libcloudph++. The libcloudph++ is a C++ library of algorithms for representing atmospheric cloud microphysics in numerical models. The bindings expose the complete functionality of the library to the Python users. The bindings are implemented using the Boost.Python C++ library and use NumPy arrays. This note includes listings with Python scripts exemplifying the use of selected library components. An example solution for using the Python bindings to access libcloudph++ from Fortran is presented.

  10. DNS & Bind Cookbook

    CERN Document Server

    Liu, Cricket

    2011-01-01

    The DNS & BIND Cookbook presents solutions to the many problems faced by network administrators responsible for a name server. Following O'Reilly's popular problem-and-solution cookbook format, this title is an indispensable companion to DNS & BIND, 4th Edition, the definitive guide to the critical task of name server administration. The cookbook contains dozens of code recipes showing solutions to everyday problems, ranging from simple questions, like, "How do I get BIND?" to more advanced topics like providing name service for IPv6 addresses. It's full of BIND configuration files that yo

  11. 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

    2015-08-03

    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

  12. On Binding Domains

    NARCIS (Netherlands)

    Everaert, M.B.H.

    2005-01-01

    In this paper I want to explore reasons for replacing Binding Theory based on the anaphor-pronoun dichotomy by a Binding Theory allowing more domains restricting/defining anaphoric dependencies. This will, thus, have consequences for the partitioning of anaphoric elements, presupposing more types of

  13. Melanin-binding radiopharmaceuticals

    Energy Technology Data Exchange (ETDEWEB)

    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. (PSB)

  14. DNS BIND Server Configuration

    Directory of Open Access Journals (Sweden)

    Radu MARSANU

    2011-01-01

    Full Text Available After a brief presentation of the DNS and BIND standard for Unix platforms, the paper presents an application which has a principal objective, the configuring of the DNS BIND 9 server. The general objectives of the application are presented, follow by the description of the details of designing the program.

  15. 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.

  16. Thermodynamics of fragment binding.

    Science.gov (United States)

    Ferenczy, György G; Keserű, György M

    2012-04-23

    The ligand binding pockets of proteins have preponderance of hydrophobic amino acids and are typically within the apolar interior of the protein; nevertheless, they are able to bind low complexity, polar, water-soluble fragments. In order to understand this phenomenon, we analyzed high resolution X-ray data of protein-ligand complexes from the Protein Data Bank and found that fragments bind to proteins with two near optimal geometry H-bonds on average. The linear extent of the fragment binding site was found not to be larger than 10 Å, and the H-bonding region was found to be restricted to about 5 Å on average. The number of conserved H-bonds in proteins cocrystallized with multiple different fragments is also near to 2. These fragment binding sites that are able to form limited number of strong H-bonds in a hydrophobic environment are identified as hot spots. An estimate of the free-energy gain of H-bond formation versus apolar desolvation supports that fragment sized compounds need H-bonds to achieve detectable binding. This suggests that fragment binding is mostly enthalpic that is in line with their observed binding thermodynamics documented in Isothermal Titration Calorimetry (ITC) data sets and gives a thermodynamic rationale for fragment based approaches. The binding of larger compounds tends to more rely on apolar desolvation with a corresponding increase of the entropy content of their binding free-energy. These findings explain the reported size-dependence of maximal available affinity and ligand efficiency both behaving differently in the small molecule region featured by strong H-bond formation and in the larger molecule region featured by apolar desolvation.

  17. 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...... insight into the substrate binding by describing residues defining 9 subsites, namely -7 through +2. These structures support that the pseudotetrasaccharide inhibitor acarbose is hydrolyzed by the active enzymes. Moreover, sugar binding was observed to the starch granule-binding site previously determined...... 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...

  18. Cue integration and the perception of action in intentional binding

    DEFF Research Database (Denmark)

    Wolpe, Noham; Haggard, Patrick; Siebner, Hartwig R

    2013-01-01

    'Intentional binding' describes the perceived temporal attraction between a voluntary action and its sensory consequence. Binding has been used in health and disease as an indirect measure of awareness of action or agency, that is, the sense that one controls one's own actions. It has been proposed...... that binding results from cue integration, in which a voluntary action provides information about the timing of its consequences or vice versa. The perception of the timing of either event is then a weighted average, determined according to the reliability of each of these two cues. Here we tested...... the contribution of cue integration to the perception of action and its sensory effect in binding, that is, action and tone binding, by manipulating the sensory reliability of the outcome tone. As predicted, when tone reliability was reduced, action binding was diminished and tone binding was increased. However...

  19. An extended database of keratin binding.

    Science.gov (United States)

    Hansen, Steffi; Selzer, Dominik; Schaefer, Ulrich F; Kasting, Gerald B

    2011-05-01

    Diffusion modeling of dermal absorption relies in large part on high quality input data. Currently, estimates of corneocyte-phase partitioning are based on an analysis of a dataset of limited size and diversity. Therefore, we have updated and broadened the analysis. For this purpose, binding coefficients to different keratins, namely, bovine hoof and horn, human delipidized callus, human delipidized stratum corneum (SC), human nail, human hair, and sheep wool were collected from the literature. In addition, binding coefficients to hoof/horn and delipidized SC were measured for eight hydrophilic compounds including three ionizable compounds that were measured at different pH values. Important results are: (i) only hoof/horn, callus, and delipidized SC are suitable keratins for estimating corneocyte protein binding; (ii) binding coefficients to hoof/horn, callus, and delipidized SC can be predicted from the octanol-water partition coefficients log K(o/w) confirming the analysis of the limited dataset; (iii) binding of ionizable compounds can be predicted by correcting log K(o/w) for pH; (iv) the correlation derived for the extended database is steeper than the relationship derived for the limited dataset. This has consequences for the estimates of SC partition and diffusion coefficients for diffusion modeling of dermal absorption. .

  20. Haptenation: Chemical Reactivity and Protein Binding

    Directory of Open Access Journals (Sweden)

    Itai Chipinda

    2011-01-01

    Full Text Available Low molecular weight chemical (LMW allergens are commonly referred to as haptens. Haptens must complex with proteins to be recognized by the immune system. The majority of occupationally related haptens are reactive, electrophilic chemicals, or are metabolized to reactive metabolites that form covalent bonds with nucleophilic centers on proteins. Nonelectrophilic protein binding may occur through disulfide exchange, coordinate covalent binding onto metal ions on metalloproteins or of metal allergens, themselves, to the major histocompatibility complex. Recent chemical reactivity kinetic studies suggest that the rate of protein binding is a major determinant of allergenic potency; however, electrophilic strength does not seem to predict the ability of a hapten to skew the response between Th1 and Th2. Modern proteomic mass spectrometry methods that allow detailed delineation of potential differences in protein binding sites may be valuable in predicting if a chemical will stimulate an immediate or delayed hypersensitivity. Chemical aspects related to both reactivity and protein-specific binding are discussed.

  1. Electrostatically biased binding of kinesin to microtubules.

    Directory of Open Access Journals (Sweden)

    Barry J Grant

    2011-11-01

    Full Text Available The minimum motor domain of kinesin-1 is a single head. Recent evidence suggests that such minimal motor domains generate force by a biased binding mechanism, in which they preferentially select binding sites on the microtubule that lie ahead in the progress direction of the motor. A specific molecular mechanism for biased binding has, however, so far been lacking. Here we use atomistic Brownian dynamics simulations combined with experimental mutagenesis to show that incoming kinesin heads undergo electrostatically guided diffusion-to-capture by microtubules, and that this produces directionally biased binding. Kinesin-1 heads are initially rotated by the electrostatic field so that their tubulin-binding sites face inwards, and then steered towards a plus-endwards binding site. In tethered kinesin dimers, this bias is amplified. A 3-residue sequence (RAK in kinesin helix alpha-6 is predicted to be important for electrostatic guidance. Real-world mutagenesis of this sequence powerfully influences kinesin-driven microtubule sliding, with one mutant producing a 5-fold acceleration over wild type. We conclude that electrostatic interactions play an important role in the kinesin stepping mechanism, by biasing the diffusional association of kinesin with microtubules.

  2. SHBG (Sex Hormone Binding Globulin)

    Science.gov (United States)

    ... as: Testosterone-estrogen Binding Globulin; TeBG Formal name: Sex Hormone Binding Globulin Related tests: Testosterone , Free Testosterone, ... I should know? How is it used? The sex hormone binding globulin (SHBG) test may be used ...

  3. Thermodynamic prediction of glass formation tendency, cluster-in-jellium model for metallic glasses, ab initio tight-binding calculations, and new density functional theory development for systems with strong electron correlation

    Energy Technology Data Exchange (ETDEWEB)

    Yao, Yongxin [Iowa State Univ., Ames, IA (United States)

    2009-01-01

    also plays an important role, as it may directly track the movement of every atom. Simulation time is a major limit for molecular dynamics, not only because of “slow” computer speed, but also because of the accumulation error in the numerical treatment of the motion equations. There is also a great concern about the reliability of the emperical potentials if using classical molecular dynamics. Ab initio methods based on density functional theory(DFT) do not have this problem, however, it suffers from small simulation cells and is more demanding computationally. When crystal phase is involved, size effect of the simulation cell is more pronounced since long-range elastic energy would be established. Simulation methods which are more efficient in computation but yet have similar reliability as the ab initio methods, like tight-binding method, are highly desirable. While the complexity of metallic glasses comes from the atomistic level, there is also a large field which deals with the complexity from electronic level. The only “ab initio” method applicable to solid state systems is density functional theory with local density approximation( LDA) or generalized gradient approximation(GGA) for the exchange-correlation energy. It is very successful for simple sp element, where it reaches an high accuracy for determining the surface reconstruction. However, there is a large class of materials with strong electron correlation, where DFT based on LDA or GGA fails in a fundamental way. An “ab initio” method which can generally apply to correlated materials, as LDA for simple sp element, is still to be developed. The thesis is prepared to address some of the above problems.

  4. Cooperative binding of copper(I) to the metal binding domains in Menkes disease protein

    DEFF Research Database (Denmark)

    Jensen, P Y; Bonander, N; Møller, L B;

    1999-01-01

    We have optimised the overexpression and purification of the N-terminal end of the Menkes disease protein expressed in Escherichia coli, containing one, two and six metal binding domains (MBD), respectively. The domain(s) have been characterised using circular dichroism (CD) and fluorescence...... spectroscopy, and their copper(I) binding properties have been determined. Structure prediction derived from far-UV CD indicates that the secondary structure is similar in the three proteins and dominated by beta-sheet. The tryptophan fluorescence maximum is blue-shifted in the constructs containing two...... and six MBDs relative to the monomer, suggesting more structurally buried tryptophan(s), compared to the single MBD construct. Copper(I) binding has been studied by equilibrium dialysis under anaerobic conditions. We show that the copper(I) binding to constructs containing two and six domains...

  5. Gamma Oscillations and Visual Binding

    Science.gov (United States)

    Robinson, Peter A.; Kim, Jong Won

    2006-03-01

    At the root of visual perception is the mechanism the brain uses to analyze features in a scene and bind related ones together. Experiments show this process is linked to oscillations of brain activity in the 30-100 Hz gamma band. Oscillations at different sites have correlation functions (CFs) that often peak at zero lag, implying simultaneous firing, even when conduction delays are large. CFs are strongest between cells stimulated by related features. Gamma oscillations are studied here by modeling mm-scale patchy interconnections in the visual cortex. Resulting predictions for gamma responses to stimuli account for numerous experimental findings, including why oscillations and zero-lag synchrony are associated, observed connections with feature preferences, the shape of the zero-lag peak, and variations of CFs with attention. Gamma waves are found to obey the Schroedinger equation, opening the possibility of cortical analogs of quantum phenomena. Gamma instabilities are tied to observations of gamma activity linked to seizures and hallucinations.

  6. 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....

  7. Conformational heterogeneity of the calmodulin binding interface

    Science.gov (United States)

    Shukla, Diwakar; Peck, Ariana; Pande, Vijay S.

    2016-04-01

    Calmodulin (CaM) is a ubiquitous Ca2+ sensor and a crucial signalling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signalling molecules including G-protein-coupled receptors, ion channels and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of CaM binding diversity. Our computational model indicates that in the absence of Ca2+, sub-states in the folded ensemble of CaM's C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca2+-bound regime and, thus, a candidate for pharmacological intervention.

  8. Probabilistic inference of transcription factor binding from multiple data sources.

    Directory of Open Access Journals (Sweden)

    Harri Lähdesmäki

    Full Text Available An important problem in molecular biology is to build a complete understanding of transcriptional regulatory processes in the cell. We have developed a flexible, probabilistic framework to predict TF binding from multiple data sources that differs from the standard hypothesis testing (scanning methods in several ways. Our probabilistic modeling framework estimates the probability of binding and, thus, naturally reflects our degree of belief in binding. Probabilistic modeling also allows for easy and systematic integration of our binding predictions into other probabilistic modeling methods, such as expression-based gene network inference. The method answers the question of whether the whole analyzed promoter has a binding site, but can also be extended to estimate the binding probability at each nucleotide position. Further, we introduce an extension to model combinatorial regulation by several TFs. Most importantly, the proposed methods can make principled probabilistic inference from multiple evidence sources, such as, multiple statistical models (motifs of the TFs, evolutionary conservation, regulatory potential, CpG islands, nucleosome positioning, DNase hypersensitive sites, ChIP-chip binding segments and other (prior sequence-based biological knowledge. We developed both a likelihood and a Bayesian method, where the latter is implemented with a Markov chain Monte Carlo algorithm. Results on a carefully constructed test set from the mouse genome demonstrate that principled data fusion can significantly improve the performance of TF binding prediction methods. We also applied the probabilistic modeling framework to all promoters in the mouse genome and the results indicate a sparse connectivity between transcriptional regulators and their target promoters. To facilitate analysis of other sequences and additional data, we have developed an on-line web tool, ProbTF, which implements our probabilistic TF binding prediction method using multiple

  9. Epitope prediction methods

    DEFF Research Database (Denmark)

    Karosiene, Edita

    Major histocompatibility complex (MHC) molecules play a crucial role in adaptive immunity by sampling peptides from self and non-self proteins to be recognised by the immune system. MHC molecules present peptides on cell surfaces for recognition by CD8+ and CD4+ T lymphocytes that can initiate...... 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...... on machine learning techniques. Several MHC class I binding prediction algorithms have been developed and due to their high accuracy they are used by many immunologists to facilitate the conventional experimental process of epitope discovery. However, the accuracy of these methods depends on data defining...

  10. Terms of Binding

    NARCIS (Netherlands)

    Sevcenco, A.

    2006-01-01

    The present dissertation aimed at achieving two goals. First, it constitutes an attempt to widen the search for phenomena that bear relevance to the idea that binding has a syntactic residue and is not, therefore, an exclusively semantic matter. Second, it tried to provide the technical means to acc

  11. 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

  12. MD-2 binds cholesterol.

    Science.gov (United States)

    Choi, Soo-Ho; Kim, Jungsu; Gonen, Ayelet; Viriyakosol, Suganya; Miller, Yury I

    2016-02-19

    Cholesterol is a structural component of cellular membranes, which is transported from liver to peripheral cells in the form of cholesterol esters (CE), residing in the hydrophobic core of low-density lipoprotein. Oxidized CE (OxCE) is often found in plasma and in atherosclerotic lesions of subjects with cardiovascular disease. Our earlier studies have demonstrated that OxCE activates inflammatory responses in macrophages via toll-like receptor-4 (TLR4). Here we demonstrate that cholesterol binds to myeloid differentiation-2 (MD-2), a TLR4 ancillary molecule, which is a binding receptor for bacterial lipopolysaccharide (LPS) and is indispensable for LPS-induced TLR4 dimerization and signaling. Cholesterol binding to MD-2 was competed by LPS and by OxCE-modified BSA. Furthermore, soluble MD-2 in human plasma and MD-2 in mouse atherosclerotic lesions carried cholesterol, the finding supporting the biological significance of MD-2 cholesterol binding. These results help understand the molecular basis of TLR4 activation by OxCE and mechanisms of chronic inflammation in atherosclerosis.

  13. 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.

  14. 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.

  15. 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

  16. 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 bind and mediate cellular uptake of FBP. Surface plasmon resonance analysis shows binding of bovine and human milk FBP to immobilized megalin, but not to low density lipoprotein receptor related protein. Binding of (125)I-labeled folate binding protein (FBP) to sections of kidney proximal tubule, known...

  17. Glycan masking of Plasmodium vivax Duffy Binding Protein for probing protein binding function and vaccine development.

    Directory of Open Access Journals (Sweden)

    Sowmya Sampath

    Full Text Available Glycan masking is an emerging vaccine design strategy to focus antibody responses to specific epitopes, but it has mostly been evaluated on the already heavily glycosylated HIV gp120 envelope glycoprotein. Here this approach was used to investigate the binding interaction of Plasmodium vivax Duffy Binding Protein (PvDBP and the Duffy Antigen Receptor for Chemokines (DARC and to evaluate if glycan-masked PvDBPII immunogens would focus the antibody response on key interaction surfaces. Four variants of PVDBPII were generated and probed for function and immunogenicity. Whereas two PvDBPII glycosylation variants with increased glycan surface coverage distant from predicted interaction sites had equivalent binding activity to wild-type protein, one of them elicited slightly better DARC-binding-inhibitory activity than wild-type immunogen. Conversely, the addition of an N-glycosylation site adjacent to a predicted PvDBP interaction site both abolished its interaction with DARC and resulted in weaker inhibitory antibody responses. PvDBP is composed of three subdomains and is thought to function as a dimer; a meta-analysis of published PvDBP mutants and the new DBPII glycosylation variants indicates that critical DARC binding residues are concentrated at the dimer interface and along a relatively flat surface spanning portions of two subdomains. Our findings suggest that DARC-binding-inhibitory antibody epitope(s lie close to the predicted DARC interaction site, and that addition of N-glycan sites distant from this site may augment inhibitory antibodies. Thus, glycan resurfacing is an attractive and feasible tool to investigate protein structure-function, and glycan-masked PvDBPII immunogens might contribute to P. vivax vaccine development.

  18. Predicting future of predictive analysis

    OpenAIRE

    Piyush, Duggal

    2014-01-01

    With enormous growth in analytical data and insight about advantage of managing future brings Predictive Analysis in picture. It really has potential to be called one of efficient and competitive technologies that give an edge to business operations. The possibility to predict future market conditions and to know customers’ needs and behavior in advance is the area of interest of every organization. Other areas of interest may be maintenance prediction where we tend to predict when and where ...

  19. DBD2BS: connecting a DNA-binding protein with its binding sites.

    Science.gov (United States)

    Chien, Ting-Ying; Lin, Chih-Kang; Lin, Chih-Wei; Weng, Yi-Zhong; Chen, Chien-Yu; Chang, Darby Tien-Hao

    2012-07-01

    By binding to short and highly conserved DNA sequences in genomes, DNA-binding proteins initiate, enhance or repress biological processes. Accurately identifying such binding sites, often represented by position weight matrices (PWMs), is an important step in understanding the control mechanisms of cells. When given coordinates of a DNA-binding domain (DBD) bound with DNA, a potential function can be used to estimate the change of binding affinity after base substitutions, where the changes can be summarized as a PWM. This technique provides an effective alternative when the chromatin immunoprecipitation data are unavailable for PWM inference. To facilitate the procedure of predicting PWMs based on protein-DNA complexes or even structures of the unbound state, the web server, DBD2BS, is presented in this study. The DBD2BS uses an atom-level knowledge-based potential function to predict PWMs characterizing the sequences to which the query DBD structure can bind. For unbound queries, a list of 1066 DBD-DNA complexes (including 1813 protein chains) is compiled for use as templates for synthesizing bound structures. The DBD2BS provides users with an easy-to-use interface for visualizing the PWMs predicted based on different templates and the spatial relationships of the query protein, the DBDs and the DNAs. The DBD2BS is the first attempt to predict PWMs of DBDs from unbound structures rather than from bound ones. This approach increases the number of existing protein structures that can be exploited when analyzing protein-DNA interactions. In a recent study, the authors showed that the kernel adopted by the DBD2BS can generate PWMs consistent with those obtained from the experimental data. The use of DBD2BS to predict PWMs can be incorporated with sequence-based methods to discover binding sites in genome-wide studies. Available at: http://dbd2bs.csie.ntu.edu.tw/, http://dbd2bs.csbb.ntu.edu.tw/, and http://dbd2bs.ee.ncku.edu.tw.

  20. 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

  1. 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.

  2. PREDICTING RETINOID RECEPTOR BINDING AFFINITY: COREPA-M APPLICATION

    Science.gov (United States)

    Retinoic acid and associated vitamin A derivatives comprise a class of endogenous hormones that activate different retinoic acid receptors RARs). Transcriptional events subsequent to this activation are key to controlling several aspects of vertebrate development. As such, identi...

  3. Analysis of Peptide Ligand Binding to FGFR1

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Simulating annealing algorithm was used in docking computation to predict a selected peptide VYMSPF(P2) binding site on the ectodomain of FGFR1. The peptide is located on the hydrophobic surface of the receptor, which is critical for FGF binding. The synthesized peptide can effectively inhibit the mitogenic activity of aFGF, and has a potential to become a therapeutic agent as an aFGF antagonist.

  4. Relativistic calculation of the triton binding energy and its implications

    CERN Document Server

    Stadler, A; Stadler, Alfred; Gross, Franz

    1996-01-01

    First results for the triton binding energy obtained from the relativistic spectator or Gross equation are reported. The Dirac structure of the nucleons is taken into account. Numerical results are presented for a family of realistic OBE models with off-shell scalar couplings. It is shown that these off-shell couplings improve both the fits to the two-body data and the predictions for the binding energy.

  5. Binding Principles A and B

    Institute of Scientific and Technical Information of China (English)

    陈源

    2014-01-01

    This paper focuses on the discussion of how Binding Principle A and Binding Principe B help with the interpretation of reference in English and Chinese. They are supposedly universal across languages.

  6. Predictive medicine

    NARCIS (Netherlands)

    Boenink, Marianne; Have, ten 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 sta

  7. 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...

  8. 血清视黄醇结合蛋白4对非糖尿病孕妇分娩巨大儿的预测价值%The Predictive Value of Serumal Retinol-Binding Protein 4 for Fetal Macrosomia of Non-Diabetic Pregnant Women

    Institute of Scientific and Technical Information of China (English)

    张保华; 冯晓丹; 沈卫; 余凤萍; 季静; 徐文怡; 王琴; 李岚; 郭洁

    2014-01-01

    Objective:To investigate the predictive value of serumal retinol-binding protein 4(RBP4) level fro fetal macrosomia of non-diabetic pregnant women .Methods :The serumal levels of RBP4 of 500 non-diabetic pregnant women at 12 week ,20 week and 24 week of pregnancy were measured by immune projection turbidimetric method .Fetal macrosomia was defined as birth weight≥4000 g .The cut-off value ,sensitivity and specificity were calculated with receiver operating characteristic (ROC) curve .Results:Of the 500 non-diabetic pregnant women ,30 cases(6% ) got fetal macrosomia .The ROC curve showed that the predictive cut-off values of RBP4 at 12 week ,20 week and 24 week of pregnancy were 61 .0 mg/L ,50 .5 mg/L and 52 .5 mg/L , respectively ;the predictive sensitivity and specificity at 12 week ,20 week and 24 week of pregnancy were 42 .9% and 94 .5% , 70 .0% and 69 .5% ,76 .9% and 73 .2% ,respectively .The predictive cut-off value of RBP4 no later than 24 week of pregnancy was 51 .5 mg/L ;the predictive sensitivity and specificity were 61 .8% and 69 .5% .There was significant difference(P<0 .05) between the serumal level of RBP4 at 24 week of pregnancy in group fetal macrosomia and that in group nonfetal macrosomia . Conclusions :The predictive sensitivity of RBP4 increases in accordance with the increase of serumal level of RBP 4 .The serumal level of RBP4 of non-diabetic pregnant women at 24 week of pregnancy may have higher sensitivity and specificity in the predic-tion of fetal macrosomia .If the serumal level of RBP4 no later than 24 week of pregnancy is beyond 50 mg/L ,then the risk of fetal macrosomia will be higher .%目的:探讨非糖尿病孕妇血清中视黄醇结合蛋白4(retinol-binding protein 4,RBP4)水平对巨大儿的预测价值。方法:采用免疫投射比浊法检测500例非糖尿病孕妇在不同孕周(孕12、20、24周)的血清RBP4水平,以出生体质量≥4000 g者为巨大儿。采

  9. 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.

  10. 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.

  11. 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.

  12. Causal binding of actions to their effects.

    Science.gov (United States)

    Buehner, Marc J; Humphreys, Gruffydd R

    2009-10-01

    According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.

  13. Mycobacterial PE_PGRS Proteins Contain Calcium-Binding Motifs with Parallel β-roll Folds

    Institute of Scientific and Technical Information of China (English)

    Nandita; Bachhawat; Balvinder; Singh

    2007-01-01

    The PE_PGRS family of proteins unique to mycobacteria is demonstrated to con- rain multiple calcium-binding and glycine-rich sequence motifs GGXGXD/NXUX. This sequence repeat constitutes a calcium-binding parallel/3-roll or parallel β-helix structure and is found in RTX toxins secreted by many Gram-negative bacteria. It is predicted that the highly homologous PE_PGRS proteins containing multiple copies of the nona-peptide motif could fold into similar calcium-binding structures. The implication of the predicted calcium-binding property of PE_PGRS proteins in the Ught of macrophage-pathogen interaction and pathogenesis is presented.

  14. Predictive value of heart fatty acid-binding protein to major adverse cardiovascular events in patients with acute myocardial infarction%心型脂肪酸结合蛋白对急性心肌梗死患者主要不良心脏事件的预测价值

    Institute of Scientific and Technical Information of China (English)

    魏庆民; 周彬; 史永堂; 张友良

    2012-01-01

    Objective To observe the predictive value of peak concentration of plasma heart fatty acid-binding protein (H-FABP) to major adverse cardiovascular events ( MACE ) in patients with acute myocardial infarction ( AMI) . Methods The AMI patients ( n=60) were selected within two hours from admission to disease attack from Jan. 2010 to Jim. 2011. The concentration of plasma H-FABP was detected every two hours in the patients from admission to disease attack for 10 hours. According to the average value of H-FABP peak value, all the patients were divided into group A ( with higher H-FABP than average ) and group B ( with lower H-FABP than average ) . The occurrence status of MACE [severe heart failure, malignant arrhythmia, recurrent myocardial infarction, cardiac death and target lesion revascularization ( TLR ) ] was compared between two groups after disease attack for one month and one year. Results After disease attack for one month and one year, the incidence of severe heart failure was higher in group A than that in group B (P<0.05 ) , and the incidence of other events had no statistical difference between two groups. Conclusion The increase of H-FABP peak value has some predictive value to severe heart failure of MACE in AMI patients.%目的 观察血浆心型脂肪酸结合蛋白(heart-type fatty acid-binding protein,H-FABP)的峰值浓度对急性心肌梗死(AMI)患者主要不良心脏事件(MACE)的预测价值.方法 入选2010年1月至2011年6月入院距发病时间2h以内的AMI患者60例,于入院即刻至发病10h期间,每2h测定1次血浆H-FABP的浓度.根据H-FABP峰值水平的平均值将患者划分为H-FABP高值组和低值组,比较两组患者发病后1个月、1年的主要不良心脏事件(严重心力衰竭、恶性心律失常、再发心肌梗死、心源性死亡、靶病变血管重建)的发生率.结果 与H-FABP低值组相比,H-FABP高值组发病后1个月、1年,MACE及严重心力衰竭的发生率更

  15. Value of heart-type fatty acid binding protein to predicting senile unstable angina pectoris%心脏型脂肪酸结合蛋白预测老年不稳定型心绞痛患者预后的价值

    Institute of Scientific and Technical Information of China (English)

    钱海燕; 黄觊; 杨跃进; 杨艳敏; 张朝阳

    2013-01-01

    Objective To explore the value of heart fatty acid binding protein (H-FABP) to predicting the prognosis of senile unstable angina pectoris (UAP).Methods A total of 175 UAP patients aged 60 years or older were divided into positive H-FABP group and negative H-FABP group,and were recorded and analyzed the clinical data and 12-month follow-up survey results.Results The age was older,body mass index was larger,and the proportions of smoking history,family history of coronary heart disease,previous myocardial infarction and coronary artery bypass history,and diabetes mellitus were higher in positive group than those in negative group (P<0.05).The left ventricular ejection fraction was lower in positive group than that in negative group (P<0.05).Coronary angiography results showed that the rates of multi-vessel lesions,diffusive and occlusive lesions,and left main and left anterior descending lesions were higher in positive group than those in negative group (P<0.05).After the follow-up survey for 12 months,the incidence of major adverse cardiovascular events was significantly higher in positive group than that in negative group (P<0.01).Conclusion H FABP is valuable to identify the high risk population and to predict the prognosis of UAP patients aged 60 years or older.%目的 探讨心脏型脂肪酸结合蛋白(heart type fatty acid binding protein,H-FABP)预测老年不稳定型心绞痛患者预后的价值.方法 年龄≥60岁不稳定型心绞痛患者175例,分为H-FABP阳性组48例和阴性组127例,记录并分析2组资料及12个月随访结果.结果 阳性组年龄、体质量指数大于阴性组(P<0.05),吸烟、冠心病家族史、既往心肌梗死病史、冠状动脉介入和旁路移植手术史及合并糖尿病比例明显高于阴性组(P<0.05),左室射血分数低于阴性组(P<0.05);冠状动脉造影显示,阳性组多支、弥漫甚至闭塞病变、累及左主干和前降支比例高于阴性组(P<0.05);随访12个

  16. Substrate Specificity and Ionic Regulation of GlnPQ from Lactococcus lactis. An ATP-Binding Cassette Transporter with Four Extracytoplasmic Substrate-Binding Domains

    NARCIS (Netherlands)

    Schuurman-Wolters, Gea K.; Poolman, Bert

    2005-01-01

    We report on the functional characterization of GlnPQ, an ATP-binding cassette transporter with four extracytoplasmic substrate-binding domains. The first predicted transmembrane helix of GlnP was cleaved off in the mature protein and most likely serves as the signal sequence for the extracytoplasmi

  17. 心肌脂肪酸结合蛋白对急性冠脉综合征患者病情转归的预测价值%The value of heart-type fatty acid-binding protein in predicting disease outcome of patients with acute coronary syndrome

    Institute of Scientific and Technical Information of China (English)

    谢明水; 刘杨; 蒋守涛; 李玲; 晏文强; 张振建

    2013-01-01

    OBJECTIVE To investigate the value of heart-type fatty acid-binding protein in predicting disease outcome of patients with acute coronary syndrome (ACS). METHODS The H-FABP levels of 81 patients with acute coronary syndrome were detected. In 30 cases of acute myocardial infarction (AMI), there were 26 cases of unstable angina pectoris (UAP), 25 cases of stable angina pectoris (SAP). At the same period, 27 healthy volunteers were selected as the control group. The disease outcome of various types of ACS were analyzed and compared. RESULTS The H-FABP level (73.35 ± 56.73ngml) in AMI group was highest, and the H-FABP level (13.50 ± 5.64ng/ml) in UAP group was higher (P 0.05). Compared with all groups, the H-FABP level of AMI group was highest, but the disease outcome and prognosis were the worst (P < 0.05). CONCLUSION The H-FABP level could be used as an effective predictive indicator in disease outcome of patients with acute coronary syndrome.%目的 探讨心肌脂肪酸结合蛋白对急性冠脉综合征患者病情转归的预测价值.方法 81例急性冠脉综合征患者检测血浆心肌脂肪酸结合蛋白表达水平,其中急性心肌梗死(AMI) 30例,不稳定型心绞痛(UAP) 26例,稳定型心绞痛(SAP) 25例,同期选择27例健康体检者为对照组.对各组疾病患者病情转归进行分析比较.结果 AMI组的H-FABP水平(73.35±56.73 ng/ml)最高,UAP组(13.50±5.64 ng/ml)次之(P< 0.01); SAP组(4.65±3.38 ng/ml)与对照组(3.42±1.53 ng/ml)比较差异无统计学意义(P>0.05);各组观察对比,H-FABP水平最高的AMI组病情转归及预后最差(P<0.05).结论 H-FABP可作为一种预测急性冠脉综合征患者病情转归情况的有效指标.

  18. Analytic QCD Binding Potentials

    CERN Document Server

    Fried, H M; Grandou, T; Sheu, Y -M

    2011-01-01

    This paper applies the analytic forms of a recent non-perturbative, manifestly gauge- and Lorentz-invariant description (of the exchange of all possible virtual gluons between quarks ($Q$) and/or anti-quarks ($\\bar{Q}$) in a quenched, eikonal approximation) to extract analytic forms for the binding potentials generating a model $Q$-$\\bar{Q}$ "pion", and a model $QQQ$ "nucleon". Other, more complicated $Q$, $\\bar{Q}$ contributions to such color-singlet states may also be identified analytically. An elementary minimization technique, relevant to the ground states of such bound systems, is adopted to approximate the solutions to a more proper, but far more complicated Schroedinger/Dirac equation; the existence of possible contributions to the pion and nucleon masses due to spin, angular momentum, and "deformation" degrees of freedom is noted but not pursued. Neglecting electromagnetic and weak interactions, this analysis illustrates how the one new parameter making its appearance in this exact, realistic formali...

  19. 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...

  20. 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.

  1. Mechanisms of in vivo binding site selection of the hematopoietic master transcription factor PU.1.

    Science.gov (United States)

    Pham, Thu-Hang; Minderjahn, Julia; Schmidl, Christian; Hoffmeister, Helen; Schmidhofer, Sandra; Chen, Wei; Längst, Gernot; Benner, Christopher; Rehli, Michael

    2013-07-01

    The transcription factor PU.1 is crucial for the development of many hematopoietic lineages and its binding patterns significantly change during differentiation processes. However, the 'rules' for binding or not-binding of potential binding sites are only partially understood. To unveil basic characteristics of PU.1 binding site selection in different cell types, we studied the binding properties of PU.1 during human macrophage differentiation. Using in vivo and in vitro binding assays, as well as computational prediction, we show that PU.1 selects its binding sites primarily based on sequence affinity, which results in the frequent autonomous binding of high affinity sites in DNase I inaccessible regions (25-45% of all occupied sites). Increasing PU.1 concentrations and the availability of cooperative transcription factor interactions during lineage differentiation both decrease affinity thresholds for in vivo binding and fine-tune cell type-specific PU.1 binding, which seems to be largely independent of DNA methylation. Occupied sites were predominantly detected in active chromatin domains, which are characterized by higher densities of PU.1 recognition sites and neighboring motifs for cooperative transcription factors. Our study supports a model of PU.1 binding control that involves motif-binding affinity, PU.1 concentration, cooperativeness with neighboring transcription factor sites and chromatin domain accessibility, which likely applies to all PU.1 expressing cells.

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

    Directory of Open Access Journals (Sweden)

    Hilal Kazan

    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.

  3. 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.

  4. 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.

  5. 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...

  6. Prediction of peptide bonding affinity: kernel methods for nonlinear modeling

    CERN Document Server

    Bergeron, Charles; Sundling, C Matthew; Krein, Michael; Katt, Bill; Sukumar, Nagamani; Breneman, Curt M; Bennett, Kristin P

    2011-01-01

    This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.

  7. Binding Energy and Enzymatic Catalysis.

    Science.gov (United States)

    Hansen, David E.; Raines, Ronald T.

    1990-01-01

    Discussed is the fundamental role that the favorable free energy of binding of the rate-determining transition state plays in catalysis. The principle that all of the catalytic factors discussed are realized by the use of this binding energy is reviewed. (CW)

  8. 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.

  9. Computational Investigation of Glycosylation Effects on a Family 1 Carbohydrate-Binding Module

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, C. B.; Talib, M. F.; McCabe, C.; Bu, L.; Adney, W. S.; Himmel, M. E.; Crowley, M. F.; Beckham, G. T.

    2012-01-27

    Carbohydrate-binding modules (CBMs) are ubiquitous components of glycoside hydrolases, which degrade polysaccharides in nature. CBMs target specific polysaccharides, and CBM binding affinity to cellulose is known to be proportional to cellulase activity, such that increasing binding affinity is an important component of performance improvement. To ascertain the impact of protein and glycan engineering on CBM binding, we use molecular simulation to quantify cellulose binding of a natively glycosylated Family 1 CBM. To validate our approach, we first examine aromatic-carbohydrate interactions on binding, and our predictions are consistent with previous experiments, showing that a tyrosine to tryptophan mutation yields a 2-fold improvement in binding affinity. We then demonstrate that enhanced binding of 3-6-fold over a nonglycosylated CBM is achieved by the addition of a single, native mannose or a mannose dimer, respectively, which has not been considered previously. Furthermore, we show that the addition of a single, artificial glycan on the anterior of the CBM, with the native, posterior glycans also present, can have a dramatic impact on binding affinity in our model, increasing it up to 140-fold relative to the nonglycosylated CBM. These results suggest new directions in protein engineering, in that modifying glycosylation patterns via heterologous expression, manipulation of culture conditions, or introduction of artificial glycosylation sites, can alter CBM binding affinity to carbohydrates and may thus be a general strategy to enhance cellulase performance. Our results also suggest that CBM binding studies should consider the effects of glycosylation on binding and function.

  10. 脂肪细胞型脂肪酸结合蛋白对冠心病危险程度预测价值的研究%Study on Predictive Value of Adipocyte Fatty Acid-binding Protein for Coronary Heart Disease Risk Degree

    Institute of Scientific and Technical Information of China (English)

    王峰; 刘帮助

    2013-01-01

    目的 探讨脂肪细胞型脂肪酸结合蛋白(A-FABP)表达在冠心病危险程度预测中的价值,为其防治提供依据.方法 回顾性分析经过冠状动脉造影确诊为冠心病观察组76例、同期26例阴性对照组患者的血清A-FABP表达水平、平板运动试验Duke评分,揭示A-FABP表达与冠心病危险程度之间的相关性.结果 A-FABP在冠心病观察组表达水平显著高于对照组(P <0.001),在多支病变、双支病变中A-FABP表达水平高于单支病变(P<0.05);A-FABP表达水平与Duke评分、冠状动脉病变支数、总胆固醇呈正相关,与高密度脂蛋白胆固醇水平呈负相关(P<0.05).结论 A-FABP表达与冠心病的发病及病变程度密切相关,可能对冠心病的危险程度具有一定的预测价值.%Objective To explore the predictive value of adipocyte fatty acid-binding protein (A-FABP)expression for coronary heart disease,providing guidance for the prevention and treatment.Methods A-FABP expression level and Duke score in observation group of 76 patients after coronary angiography diagnosed as coronary heart disease and 26 cases of negative controls were retrospectively analyzed to reveal the correlation between the risk degree of coronary heart disease and A-FABP expression.Results A-FABP expression level in patients with coronary heart disease was significantly higher than in the control group(P <0.001).A-FABP expression level in multi-and double-vessel lesions group were significantly higher than in single-vessel lesion and the control group(P < 0.05).A-FABP expression level was positively correlated with Duke score,coronary artery lesions and total cholesterol,and is negatively correlated with high density lipoprotein cholesterol levels(P < 0.05).Conclusion A-FABP expression is closely related with the onset and severity of coronary artery disease,and may have some predictive value for the risk degree of coronary heart disease.

  11. Cooperative binding: a multiple personality.

    Science.gov (United States)

    Martini, Johannes W R; Diambra, Luis; Habeck, Michael

    2016-06-01

    Cooperative binding has been described in many publications and has been related to or defined by several different properties of the binding behavior of the ligand to the target molecule. In addition to the commonly used Hill coefficient, other characteristics such as a sigmoidal shape of the overall titration curve in a linear plot, a change of ligand affinity of the other binding sites when a site of the target molecule becomes occupied, or complex roots of the binding polynomial have been used to define or to quantify cooperative binding. In this work, we analyze how the different properties are related in the most general model for binding curves based on the grand canonical partition function and present several examples which highlight differences between the cooperativity characterizing properties which are discussed. Our results mainly show that among the presented definitions there are not two which fully coincide. Moreover, this work poses the question whether it can make sense to distinguish between positive and negative cooperativity based on the macroscopic binding isotherm only. This article shall emphasize that scientists who investigate cooperative effects in biological systems could help avoiding misunderstandings by stating clearly which kind of cooperativity they discuss.

  12. Mapping of ligand-binding cavities in proteins.

    Science.gov (United States)

    Andersson, C David; Chen, Brian Y; Linusson, Anna

    2010-05-01

    The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs.

  13. 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.

  14. 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

    2016-01-01

    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. PMID:26365245

  15. Molecular Mechanisms of Pharmaceutical Drug Binding into Calsequestrin

    Directory of Open Access Journals (Sweden)

    ChulHee Kang

    2012-11-01

    Full Text Available Calsequestrin (CASQ is a major Ca2+-storage/buffer protein present in the sarcoplasmic reticulum of both skeletal (CASQ1 and cardiac (CASQ2 muscles. CASQ has significant affinity for a number of pharmaceutical drugs with known muscular toxicities. Our approach, with in silico molecular docking, single crystal X-ray diffraction, and isothermal titration calorimetry (ITC, identified three distinct binding pockets on the surface of CASQ2, which overlap with 2-methyl-2,4-pentanediol (MPD binding sites observed in the crystal structure. Those three receptor sites based on canine CASQ1 crystal structure gave a high correlation (R2 = 0.80 to our ITC data. Daunomycin, doxorubicin, thioridazine, and trifluoperazine showed strong affinity to the S1 site, which is a central cavity formed between three domains of CASQ2. Some of the moderate-affinity drugs and some high-affinity drugs like amlodipine and verapamil displayed their binding into S2 sites, which are the thioredoxin-like fold present in each CASQ domain. Docking predictions combined with dissociation constants imply that presence of large aromatic cores and less flexible functional groups determines the strength of binding affinity to CASQ. In addition, the predicted binding pockets for both caffeine and epigallocatechin overlapped with the S1 and S2 sites, suggesting competitive inhibition by these natural compounds as a plausible explanation for their antagonistic effects on cardiotoxic side effects.

  16. NikA binds heme: a new role for an Escherichia coli periplasmic nickel-binding protein.

    Science.gov (United States)

    Shepherd, Mark; Heath, Mathew D; Poole, Robert K

    2007-05-01

    NikA is a periplasmic binding protein involved in nickel uptake in Escherichia coli. NikA was identified as a heme-binding protein in the periplasm of anaerobically grown cells overexpressing CydDC, an ABC transporter that exports reductant to the periplasm. CydDC-overexpressing cells accumulate a heme biosynthesis-derived pigment, P-574. For further biochemical and spectroscopic analysis, unliganded NikA was overexpressed and purified. NikA was found to comigrate with both hemin and protoporphyrin IX during gel filtration. Furthermore, tryptophan fluorescence quenching titrations demonstrated that both hemin and protoporphyrin IX bind to NikA with similar affinity. The binding affinity of NikA for these pigments (Kd approximately 0.5 microM) was unaltered in the presence and absence of saturating concentrations of nickel, suggesting that these tetrapyrroles bind to NikA in a manner independent of nickel. To test the hypothesis that NikA is required for periplasmic heme protein assembly, the effects of a nikA mutation (nikA::Tn5, Km(R) insertion) on accumulation of P-574 by CydDC-overexpressing cells was assessed. This mutation significantly lowered P-574 levels, implying that NikA may be involved in P-574 production. Thus, in the reducing environment of the periplasm, NikA may serve as a heme chaperone as well as a periplasmic nickel-binding protein. The docking of heme onto NikA was modeled using the published crystal structure; many of the predicted complexes exhibit a heme-binding cleft remote from the nickel-binding site, which is consistent with the independent binding of nickel and heme. This work has implications for the incorporation of heme into b- and c-type cytochromes.

  17. Computational redesign of endonuclease DNA binding and cleavage specificity

    Science.gov (United States)

    Ashworth, Justin; Havranek, James J.; Duarte, Carlos M.; Sussman, Django; Monnat, Raymond J.; Stoddard, Barry L.; Baker, David

    2006-06-01

    The reprogramming of DNA-binding specificity is an important challenge for computational protein design that tests current understanding of protein-DNA recognition, and has considerable practical relevance for biotechnology and medicine. Here we describe the computational redesign of the cleavage specificity of the intron-encoded homing endonuclease I-MsoI using a physically realistic atomic-level forcefield. Using an in silico screen, we identified single base-pair substitutions predicted to disrupt binding by the wild-type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild-type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site ~10,000 times more effectively than does the wild-type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease-recognition site complex by X-ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.

  18. Binding in light nuclei: Statistical NN uncertainties vs Computational accuracy

    CERN Document Server

    Perez, R Navarro; Amaro, J E; Arriola, E Ruiz

    2016-01-01

    We analyse the impact of the statistical uncertainties of the the nucleon-nucleon interaction, based on the Granada-2013 np-pp database, on the binding energies of the triton and the alpha particle using a bootstrap method, by solving the Faddeev equations for $^3$H and the Yakubovsky equations for $^4$He respectively. We check that in practice about 30 samples prove enough for a reliable error estimate. An extrapolation of the well fulfilled Tjon-line correlation predicts the experimental binding of the alpha particle within uncertainties.

  19. 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.

  20. 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.

  1. Computational Analysis of the Binding Specificities of PH Domains

    Directory of Open Access Journals (Sweden)

    Zhi Jiang

    2015-01-01

    Full Text Available Pleckstrin homology (PH domains share low sequence identities but extremely conserved structures. They have been found in many proteins for cellular signal-dependent membrane targeting by binding inositol phosphates to perform different physiological functions. In order to understand the sequence-structure relationship and binding specificities of PH domains, quantum mechanical (QM calculations and sequence-based combined with structure-based binding analysis were employed in our research. In the structural aspect, the binding specificities were shown to correlate with the hydropathy characteristics of PH domains and electrostatic properties of the bound inositol phosphates. By comparing these structure properties with sequence-based profiles of physicochemical properties, PH domains can be classified into four functional subgroups according to their binding specificities and affinities to inositol phosphates. The method not only provides a simple and practical paradigm to predict binding specificities for functional genomic research but also gives new insight into the understanding of the basis of diseases with respect to PH domain structures.

  2. Asymmetric cation-binding catalysis

    DEFF Research Database (Denmark)

    Oliveira, Maria Teresa; Lee, Jiwoong

    2017-01-01

    and KCN, are selectively bound to the catalyst, providing exceptionally high enantioselectivities for kinetic resolutions, elimination reactions (fluoride base), and Strecker synthesis (cyanide nucleophile). Asymmetric cation-binding catalysis was recently expanded to silicon-based reagents, enabling...... 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...

  3. The MHC motif viewer: a visualization tool for MHC binding motifs

    DEFF Research Database (Denmark)

    Rapin, Nicolas; Hoof, Ilka; Lund, Ole

    2010-01-01

    of peptides, and knowledge of their binding specificities is important for understanding differences in the immune response between individuals. Algorithms predicting which peptides bind a given MHC molecule have recently been developed with high prediction accuracy. The utility of these algorithms...... is hampered by the lack of tools for browsing and comparing specificity of these molecules. We have developed a Web server, MHC Motif Viewer, which allows the display of the binding motif for MHC class I proteins for human, chimpanzee, rhesus monkey, mouse, and swine, as well as HLA-DR protein sequences...

  4. Probing the human estrogen receptor-α binding requirements for phenolic mono- and di-hydroxyl compounds: a combined synthesis, binding and docking study.

    Science.gov (United States)

    McCullough, Christopher; Neumann, Terrence S; Gone, Jayapal Reddy; He, Zhengjie; Herrild, Christian; Wondergem Nee Lukesh, Julie; Pandey, Rajesh K; Donaldson, William A; Sem, Daniel S

    2014-01-01

    Various estrogen analogs were synthesized and tested for binding to human ERα using a fluorescence polarization displacement assay. Binding affinity and orientation were also predicted using docking calculations. Docking was able to accurately predict relative binding affinity and orientation for estradiol, but only if a tightly bound water molecule bridging Arg394/Glu353 is present. Di-hydroxyl compounds sometimes bind in two orientations, which are flipped in terms of relative positioning of their hydroxyl groups. Di-hydroxyl compounds were predicted to bind with their aliphatic hydroxyl group interacting with His524 in ERα. One nonsteroid-based dihdroxyl compound was 1000-fold specific for ERβ over ERα, and was also 25-fold specific for agonist ERβ versus antagonist activity. Docking predictions suggest this specificity may be due to interaction of the aliphatic hydroxyl with His475 in the agonist form of ERβ, versus with Thr299 in the antagonist form. But, the presence of this aliphatic hydroxyl is not required in all compounds, since mono-hydroxyl (phenolic) compounds bind ERα with high affinity, via hydroxyl hydrogen bonding interactions with the ERα Arg394/Glu353/water triad, and van der Waals interactions with the rest of the molecule.

  5. 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.

  6. Value of urinary liver-type fatty acid binding protein in prediction of renal function progression in patients with chronic glomerulonephritis%尿肝型脂肪酸结合蛋白预测慢性肾小球肾炎进展的价值

    Institute of Scientific and Technical Information of China (English)

    徐维佳; 李佳琳; 王琴; 施蓓莉; 牟姗; 倪兆慧

    2012-01-01

    Objective To evaluate the value of urinary liver-type fatty acid binding protein (L-FABP)as a biomarker in prediction of renal function progression in patients with chronic glomerulonephritis (CGN). Methods A total of 123 patients with newly diagnosed CGN by renal biopsy in Shanghai Renji Hospital between 2004 January and 2005 December were enrolled in the study,Twenty-eight healthy subjects were used as control group.Urine samples were collected before biopsy and treatment,and urinary L-FABP was measured by ELISA.The patients with follow-up every three months for 5 years were divided into progressive group and nonprogressive group.The progression of kidney function impairment was defined as a reduction of GFR ≥ 5 ml·min-1·(1.73 m2)-1·year-1 during follow-up.The risk factors of progressive renal function were evaluated and the Spearman correlation analysis was performed to find out the prognostic indicator of renal function deterioration. Results Urinary L-FABP level of CGN patients was significantly higher than that of healthy control group (P<0.01).Urinary L-FABP in CGN patients was negatively correlated with eGFR (r=-0.565,P<0.01) and positively correhted with proteinuria (r=0.501,P<0.01) and Scr (r=0.601,P<0.01).Kaplan-Meier analysis showed that urinary L-FABP excretion>76.58 μg/g·cr predicted progression of renal function.The AUC of urinary L-FABP for prognosis of CGN progression was 0.95,with 87.5% of sensitivity and 90.5%of specificity at the cutoff value of 119.8 μg/g·cr,which revealed its great value of predicting the prognosis of CGN patients. Conclusion Urinary L-FABP can be a novel biomarker of evaluation for renal injury and early progressive renal function deterioration in patients with CGN.%目的 评估尿肝型脂肪酸结合蛋白(L-FABP)预测慢性肾小球肾炎(CGN)病情进展的临床价值.方法 前瞻性入选2004年1月至2005年12月期间在我院行肾穿刺活检明确病理诊断的原发性CGN患者123

  7. MHC Class II epitope predictive algorithms

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole; Buus, S

    2010-01-01

    reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC-II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan-specific methods have been used to make a graphical...

  8. Identification of ligands that target the HCV-E2 binding site on CD81

    Science.gov (United States)

    Olaby, Reem Al; Azzazy, Hassan M.; Harris, Rodney; Chromy, Brett; Vielmetter, Jost; Balhorn, Rod

    2013-04-01

    Hepatitis C is a global health problem. While many drug companies have active R&D efforts to develop new drugs for treating Hepatitis C virus (HCV), most target the viral enzymes. The HCV glycoprotein E2 has been shown to play an essential role in hepatocyte invasion by binding to CD81 and other cell surface receptors. This paper describes the use of AutoDock to identify ligand binding sites on the large extracellular loop of the open conformation of CD81 and to perform virtual screening runs to identify sets of small molecule ligands predicted to bind to two of these sites. The best sites selected by AutoLigand were located in regions identified by mutational studies to be the site of E2 binding. Thirty-six ligands predicted by AutoDock to bind to these sites were subsequently tested experimentally to determine if they bound to CD81-LEL. Binding assays conducted using surface Plasmon resonance revealed that 26 out of 36 (72 %) of the ligands bound in vitro to the recombinant CD81-LEL protein. Competition experiments performed using dual polarization interferometry showed that one of the ligands predicted to bind to the large cleft between the C and D helices was also effective in blocking E2 binding to CD81-LEL.

  9. Identification of ligands that target the HCV-E2 binding site on CD81.

    Science.gov (United States)

    Olaby, Reem Al; Azzazy, Hassan M; Harris, Rodney; Chromy, Brett; Vielmetter, Jost; Balhorn, Rod

    2013-04-01

    Hepatitis C is a global health problem. While many drug companies have active R&D efforts to develop new drugs for treating Hepatitis C virus (HCV), most target the viral enzymes. The HCV glycoprotein E2 has been shown to play an essential role in hepatocyte invasion by binding to CD81 and other cell surface receptors. This paper describes the use of AutoDock to identify ligand binding sites on the large extracellular loop of the open conformation of CD81 and to perform virtual screening runs to identify sets of small molecule ligands predicted to bind to two of these sites. The best sites selected by AutoLigand were located in regions identified by mutational studies to be the site of E2 binding. Thirty-six ligands predicted by AutoDock to bind to these sites were subsequently tested experimentally to determine if they bound to CD81-LEL. Binding assays conducted using surface Plasmon resonance revealed that 26 out of 36 (72 %) of the ligands bound in vitro to the recombinant CD81-LEL protein. Competition experiments performed using dual polarization interferometry showed that one of the ligands predicted to bind to the large cleft between the C and D helices was also effective in blocking E2 binding to CD81-LEL.

  10. rVISTA for Comparative Sequence-Based Discovery of Functional Transcription Factor Binding Sites

    Energy Technology Data Exchange (ETDEWEB)

    Loots, Gabriela G.; Ovcharenko, Ivan; Pachter, Lior; Dubchak, Inna; Rubin, Edward M.

    2002-03-08

    Identifying transcriptional regulatory elements represents a significant challenge in annotating the genomes of higher vertebrates. We have developed a computational tool, rVISTA, for high-throughput discovery of cis-regulatory elements that combines transcription factor binding site prediction and the analysis of inter-species sequence conservation. Here, we illustrate the ability of rVISTA to identify true transcription factor binding sites through the analysis of AP-1 and NFAT binding sites in the 1 Mb well-annotated cytokine gene cluster1 (Hs5q31; Mm11). The exploitation of orthologous human-mouse data set resulted in the elimination of 95 percent of the 38,000 binding sites predicted upon analysis of the human sequence alone, while it identified 87 percent of the experimentally verified binding sites in this region.

  11. DNA and RNA Quadruplex-Binding Proteins

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    Václav Brázda

    2014-09-01

    Full Text Available Four-stranded DNA structures were structurally characterized in vitro by NMR, X-ray and Circular Dichroism spectroscopy in detail. Among the different types of quadruplexes (i-Motifs, minor groove quadruplexes, G-quadruplexes, etc., the best described are G-quadruplexes which are featured by Hoogsteen base-paring. Sequences with the potential to form quadruplexes are widely present in genome of all organisms. They are found often in repetitive sequences such as telomeric ones, and also in promoter regions and 5' non-coding sequences. Recently, many proteins with binding affinity to G-quadruplexes have been identified. One of the initially portrayed G-rich regions, the human telomeric sequence (TTAGGGn, is recognized by many proteins which can modulate telomerase activity. Sequences with the potential to form G-quadruplexes are often located in promoter regions of various oncogenes. The NHE III1 region of the c-MYC promoter has been shown to interact with nucleolin protein as well as other G-quadruplex-binding proteins. A number of G-rich sequences are also present in promoter region of estrogen receptor alpha. In addition to DNA quadruplexes, RNA quadruplexes, which are critical in translational regulation, have also been predicted and observed. For example, the RNA quadruplex formation in telomere-repeat-containing RNA is involved in interaction with TRF2 (telomere repeat binding factor 2 and plays key role in telomere regulation. All these fundamental examples suggest the importance of quadruplex structures in cell processes and their understanding may provide better insight into aging and disease development.

  12. Characterizing the morphology of protein binding patches.

    Science.gov (United States)

    Malod-Dognin, Noël; Bansal, Achin; Cazals, Frédéric

    2012-12-01

    Let the patch of a partner in a protein complex be the collection of atoms accounting for the interaction. To improve our understanding of the structure-function relationship, we present a patch model decoupling the topological and geometric properties. While the geometry is classically encoded by the atomic positions, the topology is recorded in a graph encoding the relative position of concentric shells partitioning the interface atoms. The topological-geometric duality provides the basis of a generic dynamic programming-based algorithm comparing patches at the shell level, which may favor topological or geometric features. On the biological side, we address four questions, using 249 cocrystallized heterodimers organized in biological families. First, we dissect the morphology of binding patches and show that Nature enjoyed the topological and geometric degrees of freedom independently while retaining a finite set of qualitatively distinct topological signatures. Second, we argue that our shell-based comparison is effective to perform atomic-level comparisons and show that topological similarity is a less stringent than geometric similarity. We also use the topological versus geometric duality to exhibit topo-rigid patches, whose topology (but not geometry) remains stable upon docking. Third, we use our comparison algorithms to infer specificity-related information amidst a database of complexes. Finally, we exhibit a descriptor outperforming its contenders to predict the binding affinities of the affinity benchmark. The softwares developed with this article are availablefrom http://team.inria.fr/abs/vorpatch_compatch/.

  13. Structure-templated predictions of novel protein interactions from sequence information.

    Directory of Open Access Journals (Sweden)

    Doron Betel

    2007-09-01

    Full Text Available The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

  14. Structure-templated predictions of novel protein interactions from sequence information.

    Science.gov (United States)

    Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W V

    2007-09-01

    The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

  15. Unravelling Mg2+-RNA binding with atomistic molecular dynamics.

    Science.gov (United States)

    Cunha, Richard A; Bussi, Giovanni

    2017-02-01

    Interaction with divalent cations is of paramount importance for RNA structural stability and function. We here report a detailed molecular dynamics study of all the possible binding sites for Mg(2+) on a RNA duplex, including both direct (inner sphere) and indirect (outer sphere) binding. In order to tackle sampling issues, we develop a modified version of bias-exchange metadynamics which allows us to simultaneously compute affinities with previously unreported statistical accuracy. Results correctly reproduce trends observed in crystallographic databases. Based on this, we simulate a carefully chosen set of models that allows us to quantify the effects of competition with monovalent cations, RNA flexibility, and RNA hybridization. Our simulations reproduce the decrease and increase of Mg(2+) affinity due to ion competition and hybridization respectively, and predict that RNA flexibility has a site dependent effect. This suggests a non trivial interplay between RNA conformational entropy and divalent cation binding.

  16. Ethylene binding to Au/Cu alloy nanoparticles

    Science.gov (United States)

    Gammage, Michael D.; Stauffer, Shannon; Henkelman, Graeme; Becker, Michael F.; Keto, John W.; Kovar, Desiderio

    2016-11-01

    Weak chemisorption of ethylene has been shown to be an important characteristic in the use of metals for the separation of ethylene from ethane. Previously, density functional theory (DFT) has been used to predict the binding energies of various metals and alloys, with Ag having the lowest chemisorption energy amongst the metals and alloys studied. Here Au/Cu alloys are investigated by a combination of DFT calculations and experimental measurements. It is inferred from experiments that the binding energy between a Au/Cu alloy and ethylene is lower than to either of the pure metals, and DFT calculations confirm that this is the case when Au segregates to the particle surface. Implications of this work suggest that it may be possible to further tune the binding energy with ethylene by compositional and morphological control of films produced from Au-surface segregated alloys.

  17. Cholesterol binding to ion channels

    Directory of Open Access Journals (Sweden)

    Irena eLevitan

    2014-02-01

    Full Text Available Numerous studies demonstrated that membrane cholesterol is a major regulator of ion channel function. The goal of this review is to discuss significant advances that have been recently achieved in elucidating the mechanisms responsible for cholesterol regulation of ion channels. The first major insight that comes from growing number of studies that based on the sterol specificity of cholesterol effects, show that several types of ion channels (nAChR, Kir, BK, TRPV are regulated by specific sterol-protein interactions. This conclusion is supported by demonstrating direct saturable binding of cholesterol to a bacterial Kir channel. The second major advance in the field is the identification of putative cholesterol binding sites in several types of ion channels. These include sites at locations associated with the well-known cholesterol binding motif CRAC and its reversed form CARC in nAChR, BK, and TRPV, as well as novel cholesterol binding regions in Kir channels. Notably, in the majority of these channels, cholesterol is suggested to interact mainly with hydrophobic residues in non-annular regions of the channels being embedded in between transmembrane protein helices. We also discuss how identification of putative cholesterol binding sites is an essential step to understand the mechanistic basis of cholesterol-induced channel regulation. Clearly, however, these are only the first few steps in obtaining a general understanding of cholesterol-ion channels interactions and their roles in cellular and organ functions.

  18. The prion protein binds thiamine.

    Science.gov (United States)

    Perez-Pineiro, Rolando; Bjorndahl, Trent C; Berjanskii, Mark V; Hau, David; Li, Li; Huang, Alan; Lee, Rose; Gibbs, Ebrima; Ladner, Carol; Dong, Ying Wei; Abera, Ashenafi; Cashman, Neil R; Wishart, David S

    2011-11-01

    Although highly conserved throughout evolution, the exact biological function of the prion protein is still unclear. In an effort to identify the potential biological functions of the prion protein we conducted a small-molecule screening assay using the Syrian hamster prion protein [shPrP(90-232)]. The screen was performed using a library of 149 water-soluble metabolites that are known to pass through the blood-brain barrier. Using a combination of 1D NMR, fluorescence quenching and surface plasmon resonance we identified thiamine (vitamin B1) as a specific prion ligand with a binding constant of ~60 μM. Subsequent studies showed that this interaction is evolutionarily conserved, with similar binding constants being seen for mouse, hamster and human prions. Various protein construct lengths, both with and without the unstructured N-terminal region in the presence and absence of copper, were examined. This indicates that the N-terminus has no influence on the protein's ability to interact with thiamine. In addition to thiamine, the more biologically abundant forms of vitamin B1 (thiamine monophosphate and thiamine diphosphate) were also found to bind the prion protein with similar affinity. Heteronuclear NMR experiments were used to determine thiamine's interaction site, which is located between helix 1 and the preceding loop. These data, in conjunction with computer-aided docking and molecular dynamics, were used to model the thiamine-binding pharmacophore and a comparison with other thiamine binding proteins was performed to reveal the common features of interaction.

  19. Cortisol levels, binding, and properties of corticosteroid-binding globulin in the serum of primates.

    Science.gov (United States)

    Klosterman, L L; Murai, J T; Siiteri, P K

    1986-01-01

    New World primates have exceptionally high plasma levels of cortisol and other steroid hormones when compared with humans and other primates. It has been suggested that this difference can be explained by either low affinity or concentration of cellular steroid receptors. We have assessed cortisol availability in serum from several species of New and Old World primates under physiological conditions (whole serum at 37 degrees C). Measurements were made of total and free cortisol, corticosteroid-binding globulin (CBG) binding capacity and affinity for cortisol, distribution of cortisol in serum, and its binding to albumin. In agreement with earlier reports, plasma free cortisol levels in Old World primates, prosimians, and humans range from 10-300 nM. However, very high total plasma cortisol together with low CBG binding capacity and affinity result in free cortisol concentrations of 1-4 microM in some New World primates (squirrel monkey and marmosets) but not in others such as the titi and capuchin. In squirrel monkeys, free cortisol levels are far greater than might be predicted from the affinity of the glucocorticoid receptor estimated in cultured skin fibroblasts. In addition to low affinity, CBG from squirrel monkeys and other New World primates exhibits differences in electrophoretic mobility and sedimentation behavior in sucrose density ultracentrifugation, suggestive of a molecular weight that is approximately twice that of CBG from other species. Together with other data these results indicate that the apparent glucocorticoid resistance found in New World primates is a complex phenomenon that is not easily explained by present concepts of glucocorticoid action.

  20. A probabilistic approach to microRNA-target binding

    Energy Technology Data Exchange (ETDEWEB)

    Ogul, Hasan, E-mail: hogul@baskent.edu.tr [Department of Computer Engineering, Baskent University, Baglica TR-06810, Ankara (Turkey); Umu, Sinan U. [Department of Chemistry, Middle East Technical University, Cankaya TR-06800, Ankara (Turkey); Bioinformatics Program, Informatics Institute, Middle East Technical University, Cankaya TR-06800, Ankara (Turkey); Tuncel, Y. Yener [Bioinformatics Program, Informatics Institute, Middle East Technical University, Cankaya TR-06800, Ankara (Turkey); Akkaya, Mahinur S. [Department of Chemistry, Middle East Technical University, Cankaya TR-06800, Ankara (Turkey)

    2011-09-16

    Highlights: {yields} A new probabilistic model is introduced for microRNA-target binding. {yields} The new model significantly outperforms RNAHybrid and miRTif. {yields} The experiments can unveil the effects of the type and directions of distinct base pairings. -- Abstract: Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target. To this end, we introduce a new model of microRNA-target binding, which transforms an aligned duplex to a new sequence and defines the likelihood of this sequence using a Variable Length Markov Chain. It offers a complementary representation of microRNA-mRNA pairs for microRNA target prediction tools or other probabilistic frameworks of integrative gene regulation analysis. The performance of present model is evaluated by its ability to predict microRNA-target mRNA interaction given a mature microRNA sequence and a putative mRNA binding site. In regard to classification accuracy, it outperforms two recent methods based on thermodynamic stability and sequence complementarity. The experiments can also unveil the effects of base pairing types and non-seed region in duplex formation.

  1. Galectin-3-Binding and Metastasis

    Science.gov (United States)

    Nangia-Makker, Pratima; Balan, Vitaly; Raz, Avraham

    2013-01-01

    i. Summary Galectin-3 is a member of a family of carbohydrate-binding proteins. It is present in the nucleus, the cytoplasm and also extracellular matrix of many normal and neoplastic cell types. Arrays of reports show an upregulation of this protein in transformed and metastatic cell lines (1, 2). Moreover, in many human carcinomas, an increased expression of galectin-3 correlates with progressive tumor stages (3–6). Several lines of analysis have demonstrated that the galectins participate in cell-cell and cell-matrix interactions by recognizing and binding complimentary glycoconjugates and thereby play a crucial role in normal and pathological processes. Elevated expression of the protein is associated with an increased capacity for anchorage-independent growth, homotypic aggregation, and tumor cell lung colonization (7–9). In this chapter we describe the methods of purification of galectin-3 from transformed E. coli and some of the commonly used functional assays for analyzing galectin-3 binding. PMID:22674139

  2. Entropic and enthalpic contributions to annexin V-membrane binding: a comprehensive quantitative model.

    Science.gov (United States)

    Jeppesen, Brian; Smith, Christina; Gibson, Donald F; Tait, Jonathan F

    2008-03-07

    Annexin V binds to membranes with very high affinity, but the factors responsible remain to be quantitatively elucidated. Analysis by isothermal microcalorimetry and calcium titration under conditions of low membrane occupancy showed that there was a strongly positive entropy change upon binding. For vesicles containing 25% phosphatidylserine at 0.15 m ionic strength, the free energy of binding was -53 kcal/mol protein, whereas the enthalpy of binding was -38 kcal/mol. Addition of 4 m urea decreased the free energy of binding by about 30% without denaturing the protein, suggesting that hydrophobic forces make a significant contribution to binding affinity. This was confirmed by mutagenesis studies that showed that binding affinity was modulated by the hydrophobicity of surface residues that are likely to enter the interfacial region upon protein-membrane binding. The change in free energy was quantitatively consistent with predictions from the Wimley-White scale of interfacial hydrophobicity. In contrast, binding affinity was not increased by making the protein surface more positively charged, nor decreased by making it more negatively charged, ruling out general ionic interactions as major contributors to binding affinity. The affinity of annexin V was the same regardless of the head group present on the anionic phospholipids tested (phosphatidylserine, phosphatidylglycerol, phosphatidylmethanol, and cardiolipin), ruling out specific interactions between the protein and non-phosphate moieties of the head group as a significant contributor to binding affinity. Analysis by fluorescence resonance energy transfer showed that multimers did not form on phosphatidylserine membranes at low occupancy, indicating that annexin-annexin interactions did not contribute to binding affinity. In summary, binding of annexin V to membranes is driven by both enthalpic and entropic forces. Dehydration of hydrophobic regions of the protein surface as they enter the interfacial region

  3. 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.

  4. Shared binding sites in Lepidoptera for Bacillus thuringiensis Cry1Ja and Cry1A toxins.

    Science.gov (United States)

    Herrero, S; González-Cabrera, J; Tabashnik, B E; Ferré, J

    2001-12-01

    Bacillus thuringiensis toxins act by binding to specific target sites in the insect midgut epithelial membrane. The best-known mechanism of resistance to B. thuringiensis toxins is reduced binding to target sites. Because alteration of a binding site shared by several toxins may cause resistance to all of them, knowledge of which toxins share binding sites is useful for predicting cross-resistance. Conversely, cross-resistance among toxins suggests that the toxins share a binding site. At least two strains of diamondback moth (Plutella xylostella) with resistance to Cry1A toxins and reduced binding of Cry1A toxins have strong cross-resistance to Cry1Ja. Thus, we hypothesized that Cry1Ja shares binding sites with Cry1A toxins. We tested this hypothesis in six moth and butterfly species, each from a different family: Cacyreus marshalli (Lycaenidae), Lobesia botrana (Tortricidae), Manduca sexta (Sphingidae), Pectinophora gossypiella (Gelechiidae), P. xylostella (Plutellidae), and Spodoptera exigua (Noctuidae). Although the extent of competition varied among species, experiments with biotinylated Cry1Ja and radiolabeled Cry1Ac showed that Cry1Ja and Cry1Ac competed for binding sites in all six species. A recent report also indicates shared binding sites for Cry1Ja and Cry1A toxins in Heliothis virescens (Noctuidae). Thus, shared binding sites for Cry1Ja and Cry1A occur in all lepidopteran species tested so far.

  5. Determination of energies and sites of binding of PFOA and PFOS to human serum albumin.

    Science.gov (United States)

    Salvalaglio, Matteo; Muscionico, Isabella; Cavallotti, Carlo

    2010-11-25

    Structure and energies of the binding sites of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) to human serum albumin (HSA) were determined through molecular modeling. The calculations consisted of a compound approach based on docking, followed by molecular dynamics simulations and by the estimation of the free binding energies adopting WHAM-umbrella sampling and semiempirical methodologies. The binding sites so determined are common either to known HSA fatty acids sites or to other HSA sites known to bind to pharmaceutical compounds such as warfarin, thyroxine, indole, and benzodiazepin. Among the PFOA binding sites, five have interaction energies in excess of -6 kcal/mol, which become nine for PFOS. The calculated binding free energy of PFOA to the Trp 214 binding site is the highest among the PFOA complexes, -8.0 kcal/mol, in good agreement with literature experimental data. The PFOS binding site with the highest energy, -8.8 kcal/mol, is located near the Trp 214 binding site, thus partially affecting its activity. The maximum number of ligands that can be bound to HSA is 9 for PFOA and 11 for PFOS. The calculated data were adopted to predict the level of complexation of HSA as a function of the concentration of PFOA and PFOS found in human blood for different levels of exposition. The analysis of the factors contributing to the complex binding energy permitted to outline a set of guidelines for the rational design of alternative fluorinated surfactants with a lower bioaccumulation potential.

  6. 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.

  7. Synthetic heparin-binding growth factor analogs

    Science.gov (United States)

    Pena, Louis A.; Zamora, Paul; Lin, Xinhua; Glass, John D.

    2007-01-23

    The invention provides synthetic heparin-binding growth factor analogs having at least one peptide chain that binds a heparin-binding growth factor receptor, covalently bound to a hydrophobic linker, which is in turn covalently bound to a non-signaling peptide that includes a heparin-binding domain. The synthetic heparin-binding growth factor analogs are useful as soluble biologics or as surface coatings for medical devices.

  8. 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.

  9. 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

    2016-11-28

    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.

  10. Sequence and structural features of binding site residues in protein-protein complexes: comparison with protein-nucleic acid complexes

    Directory of Open Access Journals (Sweden)

    Selvaraj S

    2011-10-01

    Full Text Available Abstract Background Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology. Methods We have developed an energy based approach for identifying the binding site residues in protein–protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching. Results We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix. Conclusions The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.

  11. 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.

  12. 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.

  13. In Silico Docking, Molecular Dynamics and Binding Energy Insights into the Bolinaquinone-Clathrin Terminal Domain Binding Site

    Directory of Open Access Journals (Sweden)

    Mohammed K. Abdel-Hamid

    2014-05-01

    Full Text Available Clathrin-mediated endocytosis (CME is a process that regulates selective internalization of important cellular cargo using clathrin-coated vesicles. Perturbation of this process has been linked to many diseases including cancer and neurodegenerative conditions. Chemical proteomics identified the marine metabolite, 2-hydroxy-5-methoxy-3-(((1S,4aS,8aS-1,4a,5-trimethyl-1,2,3,4,4a,7,8,8a-octahydronaphthalen-2-ylmethylcyclohexa- 2,5-diene-1,4-dione (bolinaquinone as a clathrin inhibitor. While being an attractive medicinal chemistry target, the lack of data about bolinaquinone’s mode of binding to the clathrin enzyme represents a major limitation for its structural optimization. We have used a molecular modeling approach to rationalize the observed activity of bolinaquinone and to predict its mode of binding with the clathrin terminal domain (CTD. The applied protocol started by global rigid-protein docking followed by flexible docking, molecular dynamics and linear interaction energy calculations. The results revealed the potential of bolinaquinone to interact with various pockets within the CTD, including the clathrin-box binding site. The results also highlight the importance of electrostatic contacts over van der Waals interactions for proper binding between bolinaquinone and its possible binding sites. This study provides a novel model that has the potential to allow rapid elaboration of bolinaquinone analogues as a new class of clathrin inhibitors.

  14. DNA-MATRIX: a tool for constructing transcription factor binding sites Weight matrix

    Directory of Open Access Journals (Sweden)

    Chandra Prakash Singh,

    2009-12-01

    Full Text Available Despite considerable effort to date, DNA transcription factor binding sites prediction in whole genome remains a challenge for the researchers. Currently the genome wide transcription factor binding sites prediction tools required either direct pattern sequence or weight matrix. Although there are known transcription factor binding sites pattern databases and tools for genome level prediction but no tool for weight matrix construction. Considering this, we developed a DNA-MATRIX tool for searching putative transcription factor binding sites in genomic sequences. DNA-MATRIX uses the simple heuristic approach for weight matrix construction, which can be transformed into different formats as per the requirement of researcher’s for further genome wide prediction and therefore provides the possibility to identify the conserved known DNA binding sites in the coregulated genes and also to search for a great variety of different regulatory binding patterns. The user may construct and save specific weight or frequency matrices in different formats derived through user selected set of known motif sequences.

  15. From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

    Science.gov (United States)

    Shazman, Shula; Elber, Gershon; Mandel-Gutfreund, Yael

    2011-09-01

    Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

  16. Protein binding assay for hyaluronate

    Energy Technology Data Exchange (ETDEWEB)

    Lacy, B.E.; Underhill, C.B.

    1986-11-01

    A relatively quick and simple assay for hyaluronate was developed using the specific binding protein, hyaluronectin. The hyaluronectin was obtained by homogenizing the brains of Sprague-Dawley rats, and then centrifuging the homogenate. The resulting supernatant was used as a source of crude hyaluronectin. In the binding assay, the hyaluronectin was mixed with (/sup 3/H)hyaluronate, followed by an equal volume of saturated (NH/sub 4/)/sub 2/SO/sub 4/, which precipitated the hyaluronectin and any (/sup 3/H)hyaluronate associated with it, but left free (/sup 3/H)hyaluronate in solution. The mixture was then centrifuged, and the amount of bound (/sup 3/H)hyaluronate in the precipitate was determined. Using this assay, the authors found that hyaluronectin specifically bound hyaluronate, since other glycosaminoglycans failed to compete for the binding protein. In addition, the interaction between hyaluronectin and hyaluronate was of relatively high affinity, and the size of the hyaluronate did not appear to substantially alter the amount of binding. To determine the amount of hyaluronate in an unknown sample, they used a competition assay in which the binding of a set amount of (/sup 3/H)hyaluronate was blocked by the addition of unlabeled hyaluronate. By comparing the degree of competition of the unknown samples with that of known amounts of hyaluronate, it was possible to determine the amount of hyaluronate in the unknowns. They have found that this method is sensitive to 1 ..mu..g or less of hyaluronate, and is unaffected by the presence of proteins.

  17. Structures and Functions Prediction and Expression Profiles of Calreticulin as Calcium Binding Chaperones in Chicken%鸡钙离子结合分子伴侣Calreticulin的结构与功能预测及组织表达特性

    Institute of Scientific and Technical Information of China (English)

    王丽丽; 李楠; 曹嫦妤; 龚都强; 于东; 王伟; 李金龙

    2014-01-01

    内的终端非还原性α-L-阿拉伯呋喃糖苷残基的水解,作用于α-L-阿拉伯呋喃糖苷、含(1,3)和/或(1,5)糖苷键的阿拉伯聚糖、阿拉伯木聚糖和阿拉伯半乳聚糖,能与糖类分子及Ca2+特异性结合,可监控糖蛋白组装折叠及Ca2+调控,且在消化系统中发挥重要作用。%[Objective] The aim of the current study is to reveal the evolutionary relationships, and investigate the protein structure and functions and the expression profiles of calreticulin (CRT) as a key Ca2+ binding molecular chaperone within the endoplasmic reticulum (ER) of chicken.[Method]The nucleotides and amino acids of CRT in 12 species of vertebrates recorded in Gene bank were analyzed for evolutionary relationships by Laser Gene, and the structures and functions of CRT protein in chicken were predicted by bioinformatics, and the expression profiles of CRT in 30 organizations of chicken was analyzed by real-time PCR.[Result]Results of homology analysis showed that compared with the other 11 species of nucleotide sequences of CRT gene in chicken, gallus gallus and oryctolagus cuniculus had the highest nucleotide sequence homology, which was 78.7%, in addition, gallus gallus and oncorhynchus mykiss had the lowest homology, which was 70.5%. In the homology of amino acid sequences, the relationship between gallus gallus and crotalus adamanteus cadam is the closest by 85.0%, and the furthest relationships with gallus gallus is oncorhynchus mykiss which was 69.0% in amino acid sequence, besides, the homology of gallus gallus with cricetulus griseus, macaca mulatta, homo sapiens, oryctolagus cuniculus, sus scrofa, bos taurus, and xenopus (silurana) tropicalisis relatively close to almost above 80.1%. The protein structure and function prediction revealed that the CRT of chicken was constitute with 404 amino acids, and had a relative molecular mass of 46.8802 kD and a theoretical isoelectric point of 4.41, moreover, the negative charge

  18. Structure, Function, and Evolution of Biogenic Amine-binding Proteins in Soft Ticks

    Energy Technology Data Exchange (ETDEWEB)

    Mans, Ben J.; Ribeiro, Jose M.C.; Andersen, John F. (NIH)

    2008-08-19

    Two highly abundant lipocalins, monomine and monotonin, have been isolated from the salivary gland of the soft tick Argas monolakensis and shown to bind histamine and 5-hydroxytryptamine (5-HT), respectively. The crystal structures of monomine and a paralog of monotonin were determined in the presence of ligands to compare the determinants of ligand binding. Both the structures and binding measurements indicate that the proteins have a single binding site rather than the two sites previously described for the female-specific histamine-binding protein (FS-HBP), the histamine-binding lipocalin of the tick Rhipicephalus appendiculatus. The binding sites of monomine and monotonin are similar to the lower, low affinity site of FS-HBP. The interaction of the protein with the aliphatic amine group of the ligand is very similar for the all of the proteins, whereas specificity is determined by interactions with the aromatic portion of the ligand. Interestingly, protein interaction with the imidazole ring of histamine differs significantly between the low affinity binding site of FS-HBP and monomine, suggesting that histamine binding has evolved independently in the two lineages. From the conserved features of these proteins, a tick lipocalin biogenic amine-binding motif could be derived that was used to predict biogenic amine-binding function in other tick lipocalins. Heterologous expression of genes from salivary gland libraries led to the discovery of biogenic amine-binding proteins in soft (Ornithodoros) and hard (Ixodes) tick genera. The data generated were used to reconstruct the most probable evolutionary pathway for the evolution of biogenic amine-binding in tick lipocalins.

  19. 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

  20. DBD: a transcription factor prediction database.

    Science.gov (United States)

    Kummerfeld, Sarah K; Teichmann, Sarah A

    2006-01-01

    Regulation of gene expression influences almost all biological processes in an organism; sequence-specific DNA-binding transcription factors are critical to this control. For most genomes, the repertoire of transcription factors is only partially known. Hitherto transcription factor identification has been largely based on genome annotation pipelines that use pairwise sequence comparisons, which detect only those factors similar to known genes, or on functional classification schemes that amalgamate many types of proteins into the category of 'transcription factor'. Using a novel transcription factor identification method, the DBD transcription factor database fills this void, providing genome-wide transcription factor predictions for organisms from across the tree of life. The prediction method behind DBD identifies sequence-specific DNA-binding transcription factors through homology using profile hidden Markov models (HMMs) of domains. Thus, it is limited to factors that are homologus to those HMMs. The collection of HMMs is taken from two existing databases (Pfam and SUPERFAMILY), and is limited to models that exclusively detect transcription factors that specifically recognize DNA sequences. It does not include basal transcription factors or chromatin-associated proteins, for instance. Based on comparison with experimentally verified annotation, the prediction procedure is between 95% and 99% accurate. Between one quarter and one-half of our genome-wide predicted transcription factors represent previously uncharacterized proteins. The DBD (www.transcriptionfactor.org) consists of predicted transcription factor repertoires for 150 completely sequenced genomes, their domain assignments and the hand curated list of DNA-binding domain HMMs. Users can browse, search or download the predictions by genome, domain family or sequence identifier, view families of transcription factors based on domain architecture and receive predictions for a protein sequence.

  1. 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 ...

  2. Self-peptides with intermediate capacity to bind and stabilize MHC class I molecules may be immunogenic

    DEFF Research Database (Denmark)

    Andersen, M L M; Ruhwald, Morten; Nissen, M H;

    2003-01-01

    Thirty self-peptides were selected on the basis of their predicted binding to H-2b molecules. The binding of peptides was ascertained experimentally by biochemical (KD measurements) and cellular [major histocompatibility complex class I (MHC-I) stabilization] assays. A weak, but significant, corr...

  3. Structural and histone binding ability characterizations of human PWWP domains.

    Directory of Open Access Journals (Sweden)

    Hong Wu

    Full Text Available BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS: PWWP proteins constitute a new family of methyl lysine histone binders. The PWWP domain consists of three motifs: a canonical β-barrel core, an insertion motif between the second and third β-strands and a C-terminal α-helix bundle. Both the canonical β-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. ENHANCED VERSION: This article can also be viewed as an enhanced version in which the text of the article is integrated with interactive 3D representations and animated transitions. Please note that a web

  4. 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...

  5. Dihedral angle preferences of DNA and RNA binding amino acid residues in proteins.

    Science.gov (United States)

    Ponnuraj, Karthe; Saravanan, Konda Mani

    2017-04-01

    A protein can interact with DNA or RNA molecules to perform various cellular processes. Identifying or analyzing DNA/RNA binding site amino acid residues is important to understand molecular recognition process. It is quite possible to accurately model DNA/RNA binding amino acid residues in experimental protein-DNA/RNA complex by using the electron density map whereas, locating/modeling the binding site amino acid residues in the predicted three dimensional structures of DNA/RNA binding proteins is still a difficult task. Considering the above facts, in the present work, we have carried out a comprehensive analysis of dihedral angle preferences of DNA and RNA binding site amino acid residues by using a classical Ramachandran map. We have computed backbone dihedral angles of non-DNA/RNA binding residues and used as control dataset to make a comparative study. The dihedral angle preference of DNA and RNA binding site residues of twenty amino acid type is presented. Our analysis clearly revealed that the dihedral angles (φ, ψ) of DNA/RNA binding amino acid residues prefer to occupy (-89° to -60°, -59° to -30°) bins. The results presented in this paper will help to model/locate DNA/RNA binding amino acid residues with better accuracy.

  6. ( sup 3 H)cytisine binding to nicotinic cholinergic receptors in brain

    Energy Technology Data Exchange (ETDEWEB)

    Pabreza, L.A.; Dhawan, S.; Kellar, K.J. (Georgetown Univ. School of Medicine, Washington, DC (USA))

    1991-01-01

    Cytisine, a ganglionic agonist, competes with high affinity for brain nicotinic cholinergic receptors labeled by any of several nicotinic {sup 3}H-agonist ligands. Here we have examined the binding of ({sup 3}H)cytisine in rat brain homogenates. ({sup 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 ({sup 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 ({sup 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 ({sup 3}H)cytisine binding sites are those predicted for neuronal nicotinic receptor agonist recognition sites. The high affinity and low nonspecific binding of ({sup 3}H)cytisine should make it a very useful ligand for studying neuronal nicotinic receptors.

  7. 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.

  8. 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...... around the phosphorylated residue are important for the binding affinity of ILKAP. We conclude that solid-phase affinity pull-down of proteins from complex mixtures can be applied in phosphoproteomics and systems biology....

  9. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions

    DEFF Research Database (Denmark)

    Karosiene, Edita; Lundegaard, Claus; Lund, Ole;

    2012-01-01

    A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods...... depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non......-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC–peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple...

  10. 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.

  11. 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.

  12. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design.

    Science.gov (United States)

    Ehrt, Christiane; Brinkjost, Tobias; Koch, Oliver

    2016-05-12

    Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

  13. Bacteriophage endolysin Lyt μ1/6: characterization of the C-terminal binding domain.

    Science.gov (United States)

    Tišáková, Lenka; Vidová, Barbora; Farkašovská, Jarmila; Godány, Andrej

    2014-01-01

    The gene product of orf50 from actinophage μ1/6 of Streptomyces aureofaciens is a putative endolysin, Lyt μ1/6. It has a two-domain modular structure, consisting of an N-terminal catalytic and a C-terminal cell wall binding domain (CBD). Comparative analysis of Streptomyces phage endolysins revealed that they all have a modular structure and contain functional C-terminal domains with conserved amino acids, probably associated with their binding function. A blast analysis of Lyt μ1/6 in conjunction with secondary and tertiary structure prediction disclosed the presence of a PG_binding_1 domain within the CBD. The sequence of the C-terminal domain of lyt μ1/6 and truncated forms of it were cloned and expressed in Escherichia coli. The ability of these CBD variants fused to GFP to bind to the surface of S. aureofaciens NMU was shown by specific binding assays.

  14. Analysis of single particle diffusion with transient binding using particle filtering.

    Science.gov (United States)

    Bernstein, Jason; Fricks, John

    2016-07-21

    Diffusion with transient binding occurs in a variety of biophysical processes, including movement of transmembrane proteins, T cell adhesion, and caging in colloidal fluids. We model diffusion with transient binding as a Brownian particle undergoing Markovian switching between free diffusion when unbound and diffusion in a quadratic potential centered around a binding site when bound. Assuming the binding site is the last position of the particle in the unbound state and Gaussian observational error obscures the true position of the particle, we use particle filtering to predict when the particle is bound and to locate the binding sites. Maximum likelihood estimators of diffusion coefficients, state transition probabilities, and the spring constant in the bound state are computed with a stochastic Expectation-Maximization (EM) algorithm.

  15. Incorporating evolution of transcription factor binding sites into annotated alignments

    Indian Academy of Sciences (India)

    Abha S Bais; Steffen Grossmann; Martin Vingron

    2007-08-01

    Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield ``conserved TFBSs”. Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits) are generated. Moreover, the pair-profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions, as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs, we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification

  16. 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.

  17. Discriminating between HuR and TTP binding sites using the k-spectrum kernel method

    Science.gov (United States)

    Goldberg, Debra S.; Dowell, Robin

    2017-01-01

    Background The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an interesting problem. Machine learning approaches, such as support vector machines (SVMs), may be useful in the identification of discriminative features. However, this method has yet to be applied to studies of RNA binding protein motifs. Results Applying the k-spectrum kernel to a support vector machine (SVM), we first verified the published binding sites of both HuR and TTP. Additional feature engineering highlighted the U-rich binding preference of HuR and AU-rich binding preference for TTP. Domain adaptation along with multi-task learning was used to predict the common binding sites. Conclusion The distinction between HuR and TTP binding appears to be subtle content features. HuR prefers strongly U-rich sequences whereas TTP prefers AU-rich as with increasing A content, the sequences are more likely to be bound only by TTP. Our model is consistent with competitive binding of the two proteins, particularly at intermediate AU-balanced sequences. This suggests that fine changes in the A/U balance within a untranslated region (UTR) can alter the binding and subsequent stability of the message. Both feature engineering and domain adaptation emphasized the extent to which these proteins recognize similar general sequence features. This work suggests that the k-spectrum kernel method could be useful when studying RNA binding proteins and domain adaptation techniques such as feature augmentation could be employed particularly when examining RBPs with similar binding preferences. PMID:28333956

  18. Engineering RNA-binding proteins for biology

    OpenAIRE

    Chen,Yu; Varani, Gabriele

    2013-01-01

    RNA-binding proteins play essential roles in the regulation of gene expression. Many have modular structures and combine relatively few common domains in various arrangements to recognize RNA sequences and/or structures. Recent progress in engineering the specificity of the PUF class RNA-binding proteins has shown that RNA-binding domains may be combined with various effector or functional domains to regulate the metabolism of targeted RNAs. Designer RNA-binding proteins with tailored sequenc...

  19. 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.

  20. Prediction of cytochrome P450 mediated metabolism

    DEFF Research Database (Denmark)

    Olsen, Lars; Oostenbrink, Chris; Jørgensen, Flemming Steen

    2015-01-01

    Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard...... to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based...... methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected...

  1. Binding-site assessment by virtual fragment screening.

    Directory of Open Access Journals (Sweden)

    Niu Huang

    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.

  2. Identification of novel cyclic nucleotide binding proteins in Trypanosoma cruzi.

    Science.gov (United States)

    Jäger, Adriana V; De Gaudenzi, Javier G; Mild, Jesica G; Mc Cormack, Bárbara; Pantano, Sergio; Altschuler, Daniel L; Edreira, Martin M

    2014-12-01

    Cyclic AMP has been implicated as second messenger in a wide range of cellular processes. In the protozoan parasite Trypanosoma cruzi, cAMP is involved in the development of the parasite's life cycle. While cAMP effectors have been widely studied in other eukaryotic cells, little is known about cAMP's mechanism of action in T. cruzi. To date, only a cAMP-dependent protein kinase A (PKA) has been cloned and characterised in this parasite; however experimental evidence indicates the existence of cAMP-dependent, PKA-independent events. In order to identify new cAMP binding proteins as potential cAMP effectors, we carried out in silico studies using the predicted T. cruzi proteome. Using a combination of search methods 27 proteins with putative cNMP binding domains (CBDs) were identified. Phylogenetic analysis of the CBDs presented a homogeneous distribution, with sequences segregated into two main branches: one containing kinases-like proteins and the other gathering hypothetical proteins with different function or no other known. Comparative modelling of the strongest candidates provides support for the hypothesis that these proteins may give rise to structurally viable cyclic nucleotide binding domains. Pull-down and nucleotide displacement assays strongly suggest that TcCLB.508523.80 could bind cAMP and eventually be a new putative PKA-independent cAMP effector in T. cruzi.

  3. 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

  4. A Nucleolar PUF RNA-binding Protein with Specificity for a Unique RNA Sequence.

    Science.gov (United States)

    Zhang, Chi; Muench, Douglas G

    2015-12-11

    PUF proteins are a conserved group of sequence specific RNA-binding proteins that bind to RNA in a modular fashion. The RNA-binding domain of PUF proteins typically consists of eight clustered Puf repeats. Plant genomes code for large families of PUF proteins that show significant variability in their predicted Puf repeat number, organization, and amino acid sequence. Here we sought to determine whether the observed variability in the RNA-binding domains of four plant PUFs results in a preference for nonclassical PUF RNA target sequences. We report the identification of a novel RNA binding sequence for a nucleolar Arabidopsis PUF protein that contains an atypical RNA-binding domain. The Arabidopsis PUM23 (APUM23) binding sequence was 10 nucleotides in length, contained a centrally located UUGA core element, and had a preferred cytosine at nucleotide position 8. These RNA sequence characteristics differ from those of other PUF proteins, because all natural PUFs studied to date bind to RNAs that contain a conserved UGU sequence at their 5' end and lack specificity for cytosine. Gel mobility shift assays validated the identity of the APUM23 binding sequence and supported the location of 3 of the 10 predicted Puf repeats in APUM23, including the cytosine-binding repeat. The preferred 10-nucleotide sequence bound by APUM23 is present within the 18S rRNA sequence, supporting the known role of APUM23 in 18S rRNA maturation. This work also reveals that APUM23, an ortholog of yeast Nop9, could provide an advanced structural backbone for Puf repeat engineering and target-specific regulation of cellular RNAs.

  5. Feature-Based Binding and Phase Theory

    Science.gov (United States)

    Antonenko, Andrei

    2012-01-01

    Current theories of binding cannot provide a uniform account for many facts associated with the distribution of anaphors, such as long-distance binding effects and the subject-orientation of monomorphemic anaphors. Further, traditional binding theory is incompatible with minimalist assumptions. In this dissertation I propose an analysis of…

  6. SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

    Science.gov (United States)

    Brannan, Kristopher W; Jin, Wenhao; Huelga, Stephanie C; Banks, Charles A S; Gilmore, Joshua M; Florens, Laurence; Washburn, Michael P; Van Nostrand, Eric L; Pratt, Gabriel A; Schwinn, Marie K; Daniels, Danette L; Yeo, Gene W

    2016-10-20

    RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

  7. Making detailed predictions makes (some) predictions worse

    Science.gov (United States)

    Kelly, Theresa F.

    In this paper, we investigate whether making detailed predictions about an event makes other predictions worse. Across 19 experiments, 10,895 participants, and 415,960 predictions about 724 professional sports games, we find that people who made detailed predictions about sporting events (e.g., how many hits each baseball team would get) made worse predictions about more general outcomes (e.g., which team would win). We rule out that this effect is caused by inattention or fatigue, thinking too hard, or a differential reliance on holistic information about the teams. Instead, we find that thinking about game-relevant details before predicting winning teams causes people to give less weight to predictive information, presumably because predicting details makes information that is relatively useless for predicting the winning team more readily accessible in memory and therefore incorporated into forecasts. Furthermore, we show that this differential use of information can be used to predict what kinds of games will and will not be susceptible to the negative effect of making detailed predictions.

  8. Linear Interaction Energy (LIE) Models for Ligand Binding in Implicit Solvent:  Theory and Application to the Binding of NNRTIs to HIV-1 Reverse Transcriptase.

    Science.gov (United States)

    Su, Yang; Gallicchio, Emilio; Das, Kalyan; Arnold, Eddy; Levy, Ronald M

    2007-01-01

    Expressions for Linear Interaction Energy (LIE) estimators for the binding of ligands to a protein receptor in implicit solvent are derived based on linear response theory and the cumulant expansion expression for the free energy. Using physical arguments, values of the LIE linear response proportionality coefficients are predicted for the explicit and implicit solvent electrostatic and van der Waals terms. Motivated by the fact that the receptor and solution media may respond differently to the introduction of the ligand, a novel form of the LIE regression equation is proposed to model independently the processes of insertion of the ligand in the receptor and in solution. We apply these models to the problem of estimating the binding free energy of two non-nucleoside classes of inhibitors of HIV-1 RT (HEPT and TIBO analogues). We develop novel regression models with greater predictive ability than more standard LIE formulations. The values of the regression coefficients generally conform to linear response predictions, and we use this fact to develop a LIE regression equation with only one adjustable parameter (excluding the intercept parameter) which is superior to the other models we tested and to previous results in terms of predictive accuracy for the HEPT and TIBO compounds individually. The new models indicate that, due to the different effects of induced steric strain of the receptor, an increase of ligand size alone opposes binding for ligands of the HEPT class, whereas it favors binding for ligands of the TIBO class.

  9. Trends for isolated amino acids and dipeptides: Conformation, divalent ion binding, and remarkable similarity of binding to calcium and lead

    Science.gov (United States)

    Ropo, M.; Blum, V.; Baldauf, C.

    2016-11-01

    We derive structural and binding energy trends for twenty amino acids, their dipeptides, and their interactions with the divalent cations Ca2+, Ba2+, Sr2+, Cd2+, Pb2+, and Hg2+. The underlying data set consists of more than 45,000 first-principles predicted conformers with relative energies up to ~4 eV (~400 kJ/mol). We show that only very few distinct backbone structures of isolated amino acids and their dipeptides emerge as lowest-energy conformers. The isolated amino acids predominantly adopt structures that involve an acidic proton shared between the carboxy and amino function. Dipeptides adopt one of two intramolecular-hydrogen bonded conformations C5 or . Upon complexation with a divalent cation, the accessible conformational space shrinks and intramolecular hydrogen bonding is prevented due to strong electrostatic interaction of backbone and side chain functional groups with cations. Clear correlations emerge from the binding energies of the six divalent ions with amino acids and dipeptides. Cd2+ and Hg2+ show the largest binding energies–a potential correlation with their known high acute toxicities. Ca2+ and Pb2+ reveal almost identical binding energies across the entire series of amino acids and dipeptides. This observation validates past indications that ion-mimicry of calcium and lead should play an important role in a toxicological context.

  10. Synthetic heparin-binding factor analogs

    Science.gov (United States)

    Pena, Louis A.; Zamora, Paul O.; Lin, Xinhua; Glass, John D.

    2010-04-20

    The invention provides synthetic heparin-binding growth factor analogs having at least one peptide chain, and preferably two peptide chains branched from a dipeptide branch moiety composed of two trifunctional amino acid residues, which peptide chain or chains bind a heparin-binding growth factor receptor and are covalently bound to a non-signaling peptide that includes a heparin-binding domain, preferably by a linker, which may be a hydrophobic linker. The synthetic heparin-binding growth factor analogs are useful as pharmaceutical agents, soluble biologics or as surface coatings for medical devices.

  11. Defining Starch Binding by Glucan Phosphatases

    DEFF Research Database (Denmark)

    Auger, Kyle; Raththagala, Madushi; Wilkens, Casper

    2015-01-01

    phosphatases. The main objective of this study was to quantify the binding affinity of different enzymes that are involved in this cyclic process. We established a protocol to quickly, reproducibly, and quantitatively measure the binding of the enzymes to glucans utilizing Affinity Gel Electrophoresis (AGE...... glucan phosphatases showed similar affinities for the short oligosaccharide β-cyclodextrin. We performed structure-guided mutagenesis to define the mechanism of these differences. We found that the carbohydrate binding module (CBM) domain provided a stronger binding affinity compared to surface binding...

  12. Synthesis and DNA-binding properties of novel DNA cyclo-intercalators containing purine-glucuronic acid hybrids.

    Science.gov (United States)

    Zhang, Renshuai; Chen, Shaopeng; Wang, Xueting; Yu, Rilei; Li, Mingjing; Ren, Sumei; Jiang, Tao

    2016-06-24

    Novel DNA cyclo-intercalators, which incorporated two intercalator subunits linked by two bridges, were synthesized. Binding of the compounds to calf-thymus DNA was studied by fluorescence spectroscopy, and docking simulations were used to predict the binding modes of these cyclic compounds. The spectral data demonstrated that all of these compounds can interact with CT-DNA. The sugar moiety played an important role in the process of binding between the intercalators containing glucuronic acid and DNA. The length and flexibility of the connecting bridges affected the binding affinity of the resultant cyclo-intercalators. Docking simulations showed that compounds 7 and 8 interact with DNA as mono-intercalators.

  13. Identification of a functional hepatocyte nuclear factor 4 binding site in the neutral ceramidase promoter

    DEFF Research Database (Denmark)

    Maltesen, Henrik R; Troelsen, Jesper T; Olsen, Jørgen

    2010-01-01

    in ceramide digestion. It was the purpose of the present work to experimentally verify the functional importance of a HNF-4a binding site predicted by bioinformatic analysis to be present in the Asah2 promoter. Using supershift analysis, HNF-4a overexpression, and HNF-4a knockdown experiments it was confirmed...... that the predicted HNF-4a binding site identified in the Asah2 promoter is functional. The results support the hypothesis that HNF-4a might be important for intestinal glycolipid metabolism....

  14. Binding in light nuclei: Statistical NN uncertainties vs Computational accuracy

    Science.gov (United States)

    Navarro Pérez, R.; Nogga, A.; Amaro, J. E.; Ruiz Arriola, E.

    2016-08-01

    We analyse the impact of the statistical uncertainties of the the nucleon-nucleon interaction, based on the Granada-2013 np-pp database, on the binding energies of the triton and the alpha particle using a bootstrap method, by solving the Faddeev equations for 3H and the Yakubovsky equations for 4He respectively. We check that in practice about 30 samples prove enough for a reliable error estimate. An extrapolation of the well fulfilled Tjon-line correlation predicts the experimental binding of the alpha particle within uncertainties. Presented by RNP at Workshop for young scientists with research interests focused on physics at FAIR 14-19 February 2016 Garmisch-Partenkirchen (Germany).

  15. DBD2BS: connecting a DNA-binding protein with its binding sites

    OpenAIRE

    2012-01-01

    By binding to short and highly conserved DNA sequences in genomes, DNA-binding proteins initiate, enhance or repress biological processes. Accurately identifying such binding sites, often represented by position weight matrices (PWMs), is an important step in understanding the control mechanisms of cells. When given coordinates of a DNA-binding domain (DBD) bound with DNA, a potential function can be used to estimate the change of binding affinity after base substitutions, where the changes c...

  16. 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.

  17. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.

    Science.gov (United States)

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Wong, Hau-San

    2016-01-01

    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 = 8∼10). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build TFBS (also known as DNA 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 if choosing 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.

  18. A Caenorhabditis elegans PUF protein family with distinct RNA binding specificity.

    Science.gov (United States)

    Stumpf, Craig R; Kimble, Judith; Wickens, Marvin

    2008-08-01

    PUF proteins comprise a highly conserved family of sequence-specific RNA binding proteins that regulate target mRNAs via binding directly to their 3'UTRs. The Caenorhabditis elegans genome encodes several PUF proteins, which cluster into four groups based on sequence similarity; all share amino acids that interact with the RNA in the cocrystal of human Pumilio with RNA. Members of the FBF and the PUF-8/9 groups bind different but related RNA sequences. We focus here on the binding specificity of representatives of a third cluster, comprising PUF-5, -6, and -7. We performed in vivo selection experiments using the yeast three-hybrid system to identify RNA sequences that bind PUF-5 and PUF-6, and we confirmed binding to optimal sites in vitro. The consensus sequences derived from the screens are similar for PUF-5 and PUF-6 but differ from those of the FBF or PUF-8/-9 groups. Similarly, neither PUF-5 nor PUF-6 bind the recognition sites preferred by the other clusters. Mutagenesis studies confirmed the unique RNA specificity of PUF-5/-6. Using the PUF-5 consensus derived from our experiments, we searched a database of C. elegans 3'UTRs to identify potential targets of PUF-5, several of which indeed bind PUF-5. Therefore the consensus has predictive value and provides a route to finding genuine targets of these proteins.

  19. Thermodynamics of cationic lipid binding to DNA and DNA condensation: roles of electrostatics and hydrophobicity.

    Science.gov (United States)

    Matulis, Daumantas; Rouzina, Ioulia; Bloomfield, Victor A

    2002-06-26

    Alkylammonium binding to DNA was studied by isothermal titration calorimetry. Experimental data, obtained as functions of alkyl chain length, salt concentration, DNA concentration, and temperature, provided a detailed thermodynamic description of lipid-DNA binding reactions leading to DNA condensation. Lipid binding, counterion displacement, and DNA condensation were highly cooperative processes, driven by a large increase in entropy and opposed by a relatively small endothermic enthalpy at room temperature. Large negative heat capacity change indicated a contribution from hydrophobic interactions between aliphatic tails. An approximation of lipid-DNA binding as dominated by two factors-ionic and hydrophobic interactions-yielded a model that was consistent with experimental data. Chemical group contributions to the energetics of binding were determined and could be used to predict energetics of other lipid binding to DNA. Electrostatic and hydrophobic contributions to Gibbs free energy, enthalpy, entropy, and heat capacity could be distinguished by applying additivity principles. Binding of lipids with two, three, and four aliphatic tails was investigated and compared to single-tailed lipid binding. Structurally, the model suggests that lipid cationic headgroups and aliphatic tails distribute evenly and lay down on DNA surface without the formation of micelles.

  20. CLiBE: a database of computed ligand binding energy for ligand-receptor complexes.

    Science.gov (United States)

    Chen, X; Ji, Z L; Zhi, D G; Chen, Y Z

    2002-11-01

    Consideration of binding competitiveness of a drug candidate against natural ligands and other drugs that bind to the same receptor site may facilitate the rational development of a candidate into a potent drug. A strategy that can be applied to computer-aided drug design is to evaluate ligand-receptor interaction energy or other scoring functions of a designed drug with that of the relevant ligands known to bind to the same binding site. As a tool to facilitate such a strategy, a database of ligand-receptor interaction energy is developed from known ligand-receptor 3D structural entries in the Protein Databank (PDB). The Energy is computed based on a molecular mechanics force field that has been used in the prediction of therapeutic and toxicity targets of drugs. This database also contains information about ligand function and other properties and it can be accessed at http://xin.cz3.nus.edu.sg/group/CLiBE.asp. The computed energy components may facilitate the probing of the mode of action and other profiles of binding. A number of computed energies of some PDB ligand-receptor complexes in this database are studied and compared to experimental binding affinity. A certain degree of correlation between the computed energy and experimental binding affinity is found, which suggests that the computed energy may be useful in facilitating a qualitative analysis of drug binding competitiveness.

  1. 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.

  2. Training-free atomistic prediction of nucleosome occupancy.

    Science.gov (United States)

    Minary, Peter; Levitt, Michael

    2014-04-29

    Nucleosomes alter gene expression by preventing transcription factors from occupying binding sites along DNA. DNA methylation can affect nucleosome positioning and so alter gene expression epigenetically (without changing DNA sequence). Conventional methods to predict nucleosome occupancy are trained on observed DNA sequence patterns or known DNA oligonucleotide structures. They are statistical and lack the physics needed to predict subtle epigenetic changes due to DNA methylation. The training-free method presented here uses physical principles and state-of-the-art all-atom force fields to predict both nucleosome occupancy along genomic sequences as well as binding to known positioning sequences. Our method calculates the energy of both nucleosomal and linear DNA of the given sequence. Based on the DNA deformation energy, we accurately predict the in vitro occupancy profile observed experimentally for a 20,000-bp genomic region as well as the experimental locations of nucleosomes along 13 well-established positioning sequence elements. DNA with all C bases methylated at the 5 position shows less variation of nucleosome binding: Strong binding is weakened and weak binding is strengthened compared with normal DNA. Methylation also alters the preference of nucleosomes for some positioning sequences but not others.

  3. Infinite sets and double binds.

    Science.gov (United States)

    Arden, M

    1984-01-01

    There have been many attempts to bring psychoanalytical theory up to date. This paper approaches the problem by discussing the work of Gregory Bateson and Ignacio Matte-Blanco, with particular reference to the use made by these authors of Russell's theory of logical types. Bateson's theory of the double bind and Matte-Blanco's bilogic are both based on concepts of logical typing. It is argued that the two theories can be linked by the idea that neurotic symptoms are based on category errors in thinking. Clinical material is presented from the analysis of a middle-aged woman. The intention is to demonstrate that the process of making interpretations can be thought of as revealing errors in thinking. Changes in the patient's inner world are then seen to be the result of clarifying childhood experiences based on category errors. Matte-Blanco's theory of bilogic and infinite experiences is a re-evaluation of the place of the primary process in mental life. It is suggested that a combination of bilogic and double bind theory provides a possibility of reformulating psychoanalytical theory.

  4. Cross-arm binding efficiency of an EGFR x c-Met bispecific antibody.

    Science.gov (United States)

    Zheng, Songmao; Moores, Sheri; Jarantow, Stephen; Pardinas, Jose; Chiu, Mark; Zhou, Honghui; Wang, Weirong

    2016-01-01

    Multispecific proteins, such as bispecific antibodies (BsAbs), that bind to two different ligands are becoming increasingly important therapeutic agents. Such BsAbs can exhibit markedly increased target binding and target residence time when both pharmacophores bind simultaneously to their targets. The cross-arm binding efficiency (χ) describes an increase in apparent affinity when a BsAb binds to the second target or receptor (R2) following its binding to the first target or receptor (R1) on the same cell. χ is an intrinsic characteristic of a BsAb mostly related to the binding epitopes on R1 and R2. χ can have significant impacts on the binding to R2 for BsAbs targeting two receptors on the same cell. JNJ-61186372, a BsAb that targets epidermal growth factor receptor (EGFR) and c-Met, was used as the model compound for establishing a method to characterize χ. The χ for JNJ-61186372 was successfully determined via fitting of in vitro cell binding data to a ligand binding model that incorporated χ. The model-derived χ value was used to predict the binding of JNJ-61186372 to individual EGFR and c-Met receptors on tumor cell lines, and the results agreed well with the observed IC50 for EGFR and c-Met phosphorylation inhibition by JNJ-61186372. Consistent with the model, JNJ-61186372 was shown to be more effective than the combination therapy of anti-EGFR and anti-c-Met monovalent antibodies at the same dose level in a mouse xenograft model. Our results showed that χ is an important characteristic of BsAbs, and should be considered for rationale design of BsAbs targeting two membrane bound targets on the same cell.

  5. Effect of positional dependence and alignment strategy on modeling transcription factor binding sites

    Directory of Open Access Journals (Sweden)

    Quader Saad

    2012-07-01

    Full Text Available Abstract Background Many consensus-based and Position Weight Matrix-based methods for recognizing transcription factor binding sites (TFBS are not well suited to the variability in the lengths of binding sites. Besides, many methods discard known binding sites while building the model. Moreover, the impact of Information Content (IC and the positional dependence of nucleotides within an aligned set of TFBS has not been well researched for modeling variable-length binding sites. In this paper, we propose ML-Consensus (Mixed-Length Consensus: a consensus model for variable-length TFBS which does not exclude any reported binding sites. Methods We consider Pairwise Score (PS as a measure of positional dependence of nucleotides within an alignment of TFBS. We investigate how the prediction accuracy of ML-Consensus is affected by the incorporation of IC and PS with a particular binding site alignment strategy. We perform cross-validations for datasets of six species from the TRANSFAC public database, and analyze the results using ROC curves and the Wilcoxon matched-pair signed-ranks test. Results We observe that the incorporation of IC and PS in ML-Consensus results in statistically significant improvement in the prediction accuracy of the model. Moreover, the existence of a core region among the known binding sites (of any length is witnessed by the pairwise coexistence of nucleotides within the core length. Conclusions These observations suggest the possibility of an efficient multiple sequence alignment algorithm for aligning TFBS, accommodating known binding sites of any length, for optimal (or near-optimal TFBS prediction. However, designing such an algorithm is a matter of further investigation.

  6. Trends for isolated amino acids and dipeptides: Conformation, divalent ion binding, and remarkable similarity of binding to calcium and lead

    CERN Document Server

    Ropo, Matti; Baldauf, Carsten

    2016-01-01

    We derive structural and binding energy trends for twenty amino acids, their dipeptides, and their interactions with the divalent cations Ca$^{2+}$, Ba$^{2+}$, Sr$^{2+}$, Cd$^{2+}$, Pb$^{2+}$, and Hg$^{2+}$. The underlying data set consists of 45,892 first-principles predicted conformers with relative energies up to about 4 eV (about 400kJ/mol). We show that only very few distinct backbone structures of isolated amino acids and their dipeptides emerge as lowest-energy conformers. The isolated amino acids predominantly adopt structures that involve an acidic proton shared between the carboxy and amino function. Dipeptides adopt one of two intramolecular-hydrogen bonded conformations C$_5$ or equatorial C$_7$. Upon complexation with a divalent cation, the accessible conformational space shrinks and intramolecular hydrogen bonding is prevented due to strong electrostatic interaction of backbone and side chain functional groups with cations. Clear correlations emerge from the binding energies of the six divalent ...

  7. 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.

  8. Text mining improves prediction of protein functional sites.

    Science.gov (United States)

    Verspoor, Karin M; Cohn, Judith D; Ravikumar, Komandur E; Wall, Michael E

    2012-01-01

    We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

  9. Text mining improves prediction of protein functional sites.

    Directory of Open Access Journals (Sweden)

    Karin M Verspoor

    Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

  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. 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.

  12. Robust Array-Based Coregulator Binding Assay Predicting ERa-Agonist Potency and Generating Binding Profiles Reflecting Ligand Structure

    NARCIS (Netherlands)

    Aarts, J.M.M.J.G.; Wang, S.; Houtman, R.; Beuningen, van R.M.G.J.; Westerink, W.M.A.; Waart, van de B.J.; Rietjens, I.M.C.M.; Bovee, T.F.H.

    2013-01-01

    Testing chemicals for their endocrine-disrupting potential, including interference with estrogen receptor (ER) signaling, is an important aspect of chemical safety testing. Because of the practical drawbacks of animal testing, the development of in vitro alternatives for the uterotrophic assay and o

  13. A Conserved Steroid Binding Site in Cytochrome c Oxidase

    Energy Technology Data Exchange (ETDEWEB)

    Qin, Ling; Mills, Denise A.; Buhrow, Leann; Hiser, Carrie; Ferguson-Miller, Shelagh (Michigan)

    2010-09-02

    Micromolar concentrations of the bile salt deoxycholate are shown to rescue the activity of an inactive mutant, E101A, in the K proton pathway of Rhodobacter sphaeroides cytochrome c oxidase. A crystal structure of the wild-type enzyme reveals, as predicted, deoxycholate bound with its carboxyl group at the entrance of the K path. Since cholate is a known potent inhibitor of bovine oxidase and is seen in a similar position in the bovine structure, the crystallographically defined, conserved steroid binding site could reveal a regulatory site for steroids or structurally related molecules that act on the essential K proton path.

  14. Relativistic tight-binding model: Application to Pt surfaces

    Science.gov (United States)

    Tchernatinsky, A.; Halley, J. W.

    2011-05-01

    We report a parametrization of a previous self-consistent tight-binding model, suitable for metals with a high atomic number in which nonscalar-relativistic effects are significant in the electron physics of condensed phases. The method is applied to platinum. The model is fitted to density functional theory band structures and cohesive energies and spectroscopic data on platinum atoms in five oxidation states, and is then shown without further parametrization to correctly reproduce several low index surface structures. We also predict reconstructions of some vicinal surfaces.

  15. 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....

  16. DNA binding hydroxyl radical probes

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Vicky J.; Konigsfeld, Katie M.; Aguilera, Joe A. [Department of Radiology, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0610 (United States); Milligan, Jamie R., E-mail: jmilligan@ucsd.edu [Department of Radiology, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0610 (United States)

    2012-01-15

    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. - Highlights: > Examined four aromatic groups as a means to detect hydroxyl radicals by fluorescence. > Coumarin system suffers from the fewest disadvantages. > Characterized its reactivity when linked to a hexa-arginine peptide.

  17. Why tight-binding theory?

    Science.gov (United States)

    Harrison, Walter A.

    2002-12-01

    In the context of computational physics other methods are more accurate, but tight-binding theory allows very direct physical interpretation and is simple enough to allow much more realistic treatments beyond the local density approximation. We address several important questions of this last category: How does the gap enhancement from Coulomb correlations vary from material to material? Should the enhanced gap be used for calculating the dielectric constant? For calculating the effective mass in k-dot-p theory? How valid is the scissors approximation? How does one line up bands at an interface? How should we match the envelope function at interfaces in effective-mass theory? Why can the resulting quantum-well states seem to violate the uncertainty principle? How should f-shell electrons be treated when they are intermediate between band-like and core-like? The answers to all of these questions are given and discussed.

  18. 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

  19. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    Directory of Open Access Journals (Sweden)

    Xin Deng

    2015-07-01

    Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  20. 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.

  1. Structural basis for the evolutionary inactivation of Ca[superscript 2+] binding to synaptotagmin 4

    Energy Technology Data Exchange (ETDEWEB)

    Dai, Han; Shin, Ok-Ho; Machius, Mischa; Tomchick, Diana R.; Südhof, Thomas C.; Rizo, Josep (U. of Texas-SMED)

    2010-11-16

    The neuronal protein synaptotagmin 1 functions as a Ca{sup 2+} sensor in exocytosis via two Ca{sup 2+}-binding C{sub 2} domains. The very similar synaptotagmin 4, which includes all the predicted Ca{sup 2+}-binding residues in the C{sub 2}B domain but not in the C{sub 2}A domain, is also thought to function as a neuronal Ca{sup 2+} sensor. Here we show that, unexpectedly, both C{sub 2} domains of fly synaptotagmin 4 exhibit Ca{sup 2+}-dependent phospholipid binding, whereas neither C{sub 2} domain of rat synaptotagmin 4 binds Ca{sup 2+} or phospholipids efficiently. Crystallography reveals that changes in the orientations of critical Ca{sup 2+} ligands, and perhaps their flexibility, render the rat synaptotagmin 4 C{sub 2}B domain unable to form full Ca{sup 2+}-binding sites. These results indicate that synaptotagmin 4 is a Ca{sup 2+} sensor in the fly but not in the rat, that the Ca{sup 2+}-binding properties of C{sub 2} domains cannot be reliably predicted from sequence analyses, and that proteins clearly identified as orthologs may nevertheless have markedly different functional properties.

  2. 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.

  3. Putative hAPN receptor binding sites in SARS_CoV spike protein

    Institute of Scientific and Technical Information of China (English)

    YUXiao-Jing; LUOCheng; LinJian-Cheng; HAOPei; HEYou-Yu; GUOZong-Ming; QINLei; SUJiong; LIUBo-Shu; HUANGYin; NANPeng; LIChuan-Song; XIONGBin; LUOXiao-Min; ZHAOGuo-Ping; PEIGang; CHENKai-Xian; SHENXu; SHENJian-Hua; ZOUJian-Ping; HEWei-Zhong; SHITie-Liu; ZHONGYang; JIANGHua-Liang; LIYi-Xue

    2003-01-01

    AIM:To obtain the information of ligand-receptor binding between thd S protein of SARS_CoV and CD13, identify the possible interacting domains or motifs related to binding sites, and provide clues for studying the functions of SARS proteins and designing anti-SARS drugs and vaccines. METHODS: On the basis of comparative genomics, the homology search, phylogenetic analyses, and multi-sequence alignment were used to predict CD13 related interacting domains and binding sites sites in the S protein of SARS_CoV. Molecular modeling and docking simulation methods were employed to address the interaction feature between CD13 and S protein of SARS_CoV in validating the bioinformatics predictions. RESULTS:Possible binding sites in the SARS_CoV S protein to CD13 have been mapped out by using bioinformatics analysis tools. The binding for one protein-protein interaction pair (D757-R761 motif of the SARS_CoV S protein to P585-A653 domain of CD13) has been simulated by molecular modeling and docking simulation methods. CONCLUSION:CD13 may be a possible receptor of the SARS_CoV S protein which may be associated with the SARS infection. This study also provides a possible strategy for mapping the possible binding receptors of the proteins in a genome.

  4. 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...... and composition. A large set of available synthetic peptides (n=127) was tested for binding to calreticulin and the results analysed by multivariate data analysis. The parameter that correlated best with binding was hydrophobicity while beta-turn potential disfavoured binding. Only hydrophobic peptides longer...... a peptide-binding specificity for hydrophobic sequences and delineate the fine specificity of calreticulin for hydrophobic amino acid residues....

  5. The helical structure of DNA facilitates binding

    Science.gov (United States)

    Berg, Otto G.; Mahmutovic, Anel; Marklund, Emil; Elf, Johan

    2016-09-01

    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.

  6. Scrutinizing MHC-I binding peptides and their limits of variation.

    Directory of Open Access Journals (Sweden)

    Christian P Koch

    Full Text Available Designed peptides that bind to major histocompatibility protein I (MHC-I allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K(b is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K(b in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012.

  7. Great interactions: How binding incorrect partners can teach us about protein recognition and function.

    Science.gov (United States)

    Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra; Sacquin-Mora, Sophie

    2016-10-01

    Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

  8. A computational model for feature binding

    Institute of Scientific and Technical Information of China (English)

    SHI ZhiWei; SHI ZhongZhi; LIU Xi; SHI ZhiPing

    2008-01-01

    The "Binding Problem" is an important problem across many disciplines, including psychology, neuroscience, computational modeling, and even philosophy. In this work, we proposed a novel computational model, Bayesian Linking Field Model, for feature binding in visual perception, by combining the idea of noisy neuron model, Bayesian method, Linking Field Network and competitive mechanism.Simulation Experiments demonstrated that our model perfectly fulfilled the task of feature binding in visual perception and provided us some enlightening idea for future research.

  9. A computational model for feature binding

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The "Binding Problem" is an important problem across many disciplines, including psychology, neuroscience, computational modeling, and even philosophy. In this work, we proposed a novel computational model, Bayesian Linking Field Model, for feature binding in visual perception, by combining the idea of noisy neuron model, Bayesian method, Linking Field Network and competitive mechanism. Simulation Experiments demonstrated that our model perfectly fulfilled the task of feature binding in visual perception and provided us some enlightening idea for future research.

  10. A cellular protein specifically binds to the 3'-terminal sequences of hepatitis C virus intermediate negative-strand RNA

    Institute of Scientific and Technical Information of China (English)

    王巍; 邓庆丽; 黄开红; 段朝晖; 邵静; 黄志清; 黄志明

    2003-01-01

    ObjectiveTo study the mechanism of the cellular proteins involved in the process of replication of hepatitis C virus (HCV) negative-strand RNA.MethodsUltraviolet (UV) cross-linking was used to identify the cellular proteins that would bind to the 3'-end of HCV negative-strand RNA. Competition experimentwas used to confirm the specificity of this binding, in which excess nonhomologous protein and RNA transcripts were used as competitors. The required binding sequence was determined by mapping, then the binding site was predicted through secondary structure analysis.ResultsA cellular protein of 45 kD (p45) was found to bind specifically to the 3'-endof HCV negative-strand RNA by UV cross-linking. nhomologous proteins and RNA transcripts could not compete out this binding, whereas the unlabeled 3'-endof HCV negative-strand RNA could. Mapping of the protein-binding site suggested that the 3'-end 131-278nt of HCV negative-strand RNA was the possible protein-binding region. Analysis of RNA secondary structure presumed that the potential binding site was located at 194-GAAAGAAC-201. ConclusionThe cellular protein p45 could specifically bind to the secondary structure of the 3'-end of HCV intermediate negative-strand RNA, and may play an important role in HCV RNA replication.

  11. Calcium Binding to Amino Acids and Small Glycine Peptides in Aqueous Solution: Toward Peptide Design for Better Calcium Bioavailability.

    Science.gov (United States)

    Tang, Ning; Skibsted, Leif H

    2016-06-01

    Deprotonation of amino acids as occurs during transfer from stomach to intestines during food digestion was found by comparison of complex formation constants as determined electrochemically for increasing pH to increase calcium binding (i) by a factor of around 6 for the neutral amino acids, (ii) by a factor of around 4 for anions of the acidic amino acids aspartic and glutamic acid, and (iii) by a factor of around 5.5 for basic amino acids. Optimized structures of the 1:1 complexes and ΔHbinding for calcium binding as calculated by density functional theory (DFT) confirmed in all complexes a stronger calcium binding and shorter calcium-oxygen bond length in the deprotonated form. In addition, the stronger calcium binding was also accompanied by a binding site shift from carboxylate binding to chelation by α-amino group and carboxylate oxygen for leucine, aspartate, glutamate, alanine, and asparagine. For binary amino acid mixtures, the calcium-binding constant was close to the predicted geometric mean of the individual amino acid binding constants indicating separate binding of calcium to two amino acids when present together in solution. At high pH, corresponding to conditions for calcium absorption, the binding affinity increased in the order Lys amino acid mixtures.

  12. Predictions of the bond length and vibrational frequency of Ge/sub 2/

    Energy Technology Data Exchange (ETDEWEB)

    Northrup, J.E.; Cohen, M.L.

    1983-12-02

    A pseudopotential local spin density calculation of the bond length, vibrational frequency, and binding energy for the ..sigma../sub g//sup -/ state of the germanium dimer is presented. Predictions for the equilibrium bond length and vibrational frequency are given. An overestimate of the binding energy is obtained; this is consistent with other local density calculations for sp bonded diatomic molecules.

  13. Predictions of the bond length and vibrational frequency of Ge 2

    Science.gov (United States)

    Northrup, John E.; Cohen, Marvin L.

    1983-12-01

    We present a pseudopotential local spin density calculation of the bond length, vibrational frequency, and binding energy for the 3Σ g- state of the germanium dimer. Predictions for the equilibrium bond length and vibrational frequency are given. An overestimate of the binding energy is obtained; this is consistent with other local spin density calculations for sp bonded diatomic molecules.

  14. 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.

  15. 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...

  16. Advances on Plant Pathogenic Mycotoxin Binding Proteins

    Institute of Scientific and Technical Information of China (English)

    WANG Chao-hua; DONG Jin-gao

    2002-01-01

    Toxin-binding protein is one of the key subjects in plant pathogenic mycotoxin research. In this paper, new advances in toxin-binding proteins of 10 kinds of plant pathogenic mycotoxins belonging to Helminthosporium ,Alternaria ,Fusicoccum ,Verticillium were reviewed, especially the techniques and methods of toxin-binding proteins of HS-toxin, HV-toxin, HMT-toxin, HC-toxin. It was proposed that the isotope-labeling technique and immunological chemistry technique should be combined together in research of toxin-binding protein, which will be significant to study the molecular recognition mechanism between host and pathogenic fungus.

  17. Antibody specific epitope prediction-emergence of a new paradigm.

    Science.gov (United States)

    Sela-Culang, Inbal; Ofran, Yanay; Peters, Bjoern

    2015-04-01

    The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data.

  18. Dynamics of biomolecules, ligand binding & biological functions

    Science.gov (United States)

    Yi, Myunggi

    Proteins are flexible and dynamic. One static structure alone does not often completely explain biological functions of the protein, and some proteins do not even have high resolution structures. In order to provide better understanding to the biological functions of nicotinic acetylcholine receptor, Diphtheria toxin repressor and M2 proton channel, the dynamics of these proteins are investigated using molecular modeling and molecular dynamics (MD) simulations. With absence of high resolution structure of alpha7 receptor, the homology models of apo and cobra toxin bound forms have been built. From the MD simulations of these model structures, we observed one subunit of apo simulation moved away from other four subunits. With local movement of flexible loop regions, the whole subunit tilted clockwise. These conformational changes occurred spontaneously, and were strongly correlated with the conformational change when the channel is activated by agonists. Unlike other computational studies, we directly compared our model of open conformation with the experimental data. However, the subunits of toxin bound form were stable, and conformational change is restricted by the bound cobra toxin. These results provide activation and inhibition mechanisms of alpha7 receptors and a possible explanation for intermediate conductance of the channel. Intramolecular complex of SH3-like domain with a proline-rich (Pr) peptide segment in Diphtheria toxin repressor (DtxR) is stabilized in inactive state. Upon activation of DtxR by transition metal binding, this intramolecular complex should be dissociated. The dynamics of this intramolecular complex is investigated using MD simulations and NMR spectroscopy. We observed spontaneous opening and closing motions of the Pr segment binding pockets in both Pr-SH3 and SH3 simulations. The MD simulation results and NMR relaxation data suggest that the Pr segment exhibits a binding ↔ unbinding equilibrium. Despite a wealth of experimental

  19. Computational exploration of a protein receptor binding space with student proposed peptide ligands.

    Science.gov (United States)

    King, Matthew D; Phillips, Paul; Turner, Matthew W; Katz, Michael; Lew, Sarah; Bradburn, Sarah; Andersen, Tim; McDougal, Owen M

    2016-01-01

    Computational molecular docking is a fast and effective in silico method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The DockoMatic tutorial described herein provides a framework by which instructors can guide students through a drug screening exercise. Using receptor models derived from readily available protein crystal structures, docking programs have the ability to predict ligand binding properties, such as preferential binding orientations and binding affinities. The use of computational studies can significantly enhance complimentary wet chemical experimentation by providing insight into the important molecular interactions within the system of interest, as well as guide the design of new candidate ligands based on observed binding motifs and energetics. In this laboratory tutorial, the graphical user interface, DockoMatic, facilitates docking job submissions to the docking engine, AutoDock 4.2. The purpose of this exercise is to successfully dock a 17-amino acid peptide, α-conotoxin TxIA, to the acetylcholine binding protein from Aplysia californica-AChBP to determine the most stable binding configuration. Each student will then propose two specific amino acid substitutions of α-conotoxin TxIA to enhance peptide binding affinity, create the mutant in DockoMatic, and perform docking calculations to compare their results with the class. Students will also compare intermolecular forces, binding energy, and geometric orientation of their prepared analog to their initial α-conotoxin TxIA docking results.

  20. Basis for half-site ligand binding in yeast NAD(+)-specific isocitrate dehydrogenase.

    Science.gov (United States)

    Lin, An-Ping; McAlister-Henn, Lee

    2011-09-27

    Yeast NAD(+)-specific isocitrate dehydrogenase is an allosterically regulated octameric enzyme composed of four heterodimers of a catalytic IDH2 subunit and a regulatory IDH1 subunit. Despite structural predictions that the enzyme would contain eight isocitrate binding sites, four NAD(+) binding sites, and four AMP binding sites, only half of the sites for each ligand can be measured in binding assays. On the basis of a potential interaction between side chains of Cys-150 residues in IDH2 subunits in each tetramer of the enzyme, ligand binding assays of wild-type (IDH1/IDH2) and IDH1/IDH2(C150S) octameric enzymes were conducted in the presence of dithiothreitol. These assays demonstrated the presence of eight isocitrate and four AMP binding sites for the wild-type enzyme in the presence of dithiothreitol and for the IDH1/IDH2(C150S) enzyme in the absence or presence of this reagent, suggesting that interactions between sulfhydryl side chains of IDH2 Cys-150 residues limit access to these sites. However, only two NAD(+) sites could be measured for either enzyme. A tetrameric form of IDH (an IDH1(G15D)/IDH2 mutant enzyme) demonstrated half-site binding for isocitrate (two sites) in the absence of dithiothreitol and full-site binding (four sites) in the presence of dithiothreitol. Only one NAD(+) site could be measured for the tetramer under both conditions. In the context of the structure of the enzyme, these results suggest that an observed asymmetry between heterotetramers in the holoenzyme contributes to interactions between IDH2 Cys-150 residues and to half-site binding of isocitrate, but that a form of negative cooperativity may limit access to apparently equivalent NAD(+) binding sites.

  1. 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

  2. Computational Exploration of a Protein Receptor Binding Space with Student Proposed Peptide Ligands

    Science.gov (United States)

    King, Matthew D.; Phillips, Paul; Turner, Matthew W.; Katz, Michael; Lew, Sarah; Bradburn, Sarah; Andersen, Tim; Mcdougal, Owen M.

    2017-01-01

    Computational molecular docking is a fast and effective in silico method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The DockoMatic tutorial described herein provides a framework by which instructors can guide students through a drug screening exercise. Using receptor models derived from readily available protein crystal structures, docking programs have the ability to predict ligand binding properties, such as preferential binding orientations and binding affinities. The use of computational studies can significantly enhance complimentary wet chemical experimentation by providing insight into the important molecular interactions within the system of interest, as well as guide the design of new candidate ligands based on observed binding motifs and energetics. In this laboratory tutorial, the graphical user interface, DockoMatic, facilitates docking job submissions to the docking engine, AutoDock 4.2. The purpose of this exercise is to successfully dock a 17-amino acid peptide, α-conotoxin TxIA, to the acetylcholine binding protein from Aplysia californica-AChBP to determine the most stable binding configuration. Each student will then propose two specific amino acid substitutions of α-conotoxin TxIA to enhance peptide binding affinity, create the mutant in DockoMatic, and perform docking calculations to compare their results with the class. Students will also compare intermolecular forces, binding energy, and geometric orientation of their prepared analog to their initial α-conotoxin TxIA docking results. PMID:26537635

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 混合微粒群神经网络系统的构建及其在HLA-A*0201限制性T细胞表位活性预测中的应用%Design and construction of hybrid particle swarm optimizer-artificial neural network and its application in predicting the binding affinity of T-cell epitope to HLA-A*0201

    Institute of Scientific and Technical Information of China (English)

    任彦荣

    2011-01-01

    A novel modeling method that we named hybrid particle swarm optimizer-artificial neural network (HPSO-ANN) is developed by introducing "reproduction", "hybrid", "mutation" operator and "Metropolis" sampling into exploration of particle swarm optimizer and then applies to optimize the weighted values of feed-forward multilayer perceptron. By performing this newly proposed method to quantitatively predict the binding affinity of 152 CTL epitopes to their common receptor of HLA-A*0201 protein, it is suggested that the developed method enormously increases abilities of global searching for such algorithms in their former periods and/or earlier stages and local convergence in their latter periods and/or final stages except for little time-consuming of CPU. By comparing with QSAR modeling results obtained from reports in references, this proposed method is effective in solving practical problems, especially in cases of optimizations with non-linear, high-dimensional, etc.%尝试将“复制”、“杂交”、“变异”算子和“Metropolis”采样策略引入到微粒群算法(PSO)搜索进程,并将其用于前馈型多层神经网络(FMANN)连接权值优化当中,形成了1种新的非线性统计建模方法:混合微粒群神经网络系统(hybrid particle Swarmoptimizer-artificial neural network,HPSO-ANN)。通过仿真对比及对152个HLA-A*0201限制性T细胞表位活性预测表明:HPSO-ANN仅在少量增加CPU耗时的同时大大提高了算法前期全局搜索能力及后期局部收敛性,特别是对于非线性、高维数等复杂问题该法往往能够取得优于传统QSAR建模方法的实际效果。

  8. 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

    2015-08-13

    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 nonoptimal 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.

  9. THE USE OF HAMMETT CONSTANTS TO UNDERSTAND THE NON-COVALENT BINDING OF AROMATICS

    Directory of Open Access Journals (Sweden)

    Michael Lewis

    2012-04-01

    Full Text Available Non-covalent interactions of aromatics are important in a wide range of chemical and biological applications. The past two decades have seen numerous reports of arene-arene binding being understood in terms Hammett substituent constants, and similar analyses have recently been extended to cation-arene and anion-arene binding. It is not immediately clear why electrostatic Hammett parameters should work so well in predicting the binding for all three interactions, given that different intermolecular forces dominate each interaction. This review explores such anomalies, and summarizes how Hammett substituent constants have been employed to understand the non-covalent binding in arene-arene, cation-arene and anion-arene interactions.

  10. Fragmentation cross sections and binding energies of neutron-rich nuclei

    Science.gov (United States)

    Tsang, M. B.; Lynch, W. G.; Friedman, W. A.; Mocko, M.; Sun, Z. Y.; Aoi, N.; Cook, J. M.; Delaunay, F.; Famiano, M. A.; Hui, H.; Imai, N.; Iwasaki, H.; Motobayashi, T.; Niikura, M.; Onishi, T.; Rogers, A. M.; Sakurai, H.; Suzuki, H.; Takeshita, E.; Takeuchi, S.; Wallace, M. S.

    2007-10-01

    An exponential dependence of the fragmentation cross section on the average binding energy is observed and reproduced with a statistical model. The observed functional dependence is robust and allows the extraction of binding energies from measured cross sections. From the systematics of Cu isotope cross sections, the binding energies of Cu76,77,78,79 have been extracted. They are 636.94±0.4,647.1±0.4,651.6±0.4, and 657.8±0.5 MeV, respectively. Specifically, the uncertainty of the binding energy of Cu75 is reduced from 980 keV, as listed in the 2003 mass table of Audi, Wapstra, and Thibault to 400 keV. The predicted cross sections of two near drip-line nuclei, Na39 and Mg40 from the fragmentation of Ca48 are discussed.

  11. Function of the PEX19-binding site of human adrenoleukodystrophy protein as targeting motif in man and yeast. PMP targeting is evolutionarily conserved.

    Science.gov (United States)

    Halbach, André; Lorenzen, Stephan; Landgraf, Christiane; Volkmer-Engert, Rudolf; Erdmann, Ralf; Rottensteiner, Hanspeter

    2005-06-01

    We predicted in human peroxisomal membrane proteins (PMPs) the binding sites for PEX19, a key player in the topogenesis of PMPs, by virtue of an algorithm developed for yeast PMPs. The best scoring PEX19-binding site was found in the adrenoleukodystrophy protein (ALDP). The identified site was indeed bound by human PEX19 and was also recognized by the orthologous yeast PEX19 protein. Likewise, both human and yeast PEX19 bound with comparable affinities to the PEX19-binding site of the yeast PMP Pex13p. Interestingly, the identified PEX19-binding site of ALDP coincided with its previously determined targeting motif. We corroborated the requirement of the ALDP PEX19-binding site for peroxisomal targeting in human fibroblasts and showed that the minimal ALDP fragment targets correctly also in yeast, again in a PEX19-binding site-dependent manner. Furthermore, the human PEX19-binding site of ALDP proved interchangeable with that of yeast Pex13p in an in vivo targeting assay. Finally, we showed in vitro that most of the predicted binding sequences of human PMPs represent true binding sites for human PEX19, indicating that human PMPs harbor common PEX19-binding sites that do resemble those of yeast. Our data clearly revealed a role for PEX19-binding sites as PMP-targeting motifs across species, thereby demonstrating the evolutionary conservation of PMP signal sequences from yeast to man.

  12. Observation of Protein Structural Vibrational Mode Sensitivity to Ligand Binding

    Science.gov (United States)

    Niessen, Katherine; Xu, Mengyang; Snell, Edward; Markelz, Andrea

    2014-03-01

    We report the first measurements of the dependence of large-scale protein intramolecular vibrational modes on ligand binding. These collective vibrational modes in the terahertz (THz) frequency range (5-100 cm-1) are of great interest due to their predicted relation to protein function. Our technique, Crystals Anisotropy Terahertz Microscopy (CATM), allows for room temperature, table-top measurements of the optically active intramolecular modes. CATM measurements have revealed surprisingly narrowband features. CATM measurements are performed on single crystals of chicken egg-white lysozyme (CEWL) as well as CEWL bound to tri-N-acetylglucosamine (CEWL-3NAG) inhibitor. We find narrow band resonances that dramatically shift with binding. Quasiharmonic calculations are performed on CEWL and CEWL-3NAG proteins with CHARMM using normal mode analysis. The expected CATM response of the crystals is then calculated by summing over all protein orientations within the unit cell. We will compare the CATM measurements with the calculated results and discuss the changes which arise with protein-ligand binding. This work is supported by NSF grant MRI 2 grant DBI2959989.

  13. 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 w