The structure and binding mode of citrate in the stabilization of gold nanoparticles

KAUST Repository

Al-Johani, Hind

2017-03-27

Elucidating the binding mode of carboxylate-containing ligands to gold nanoparticles (AuNPs) is crucial to understand their stabilizing role. A detailed picture of the three-dimensional structure and coordination modes of citrate, acetate, succinate and glutarate to AuNPs is obtained by 13C and 23Na solid-state NMR in combination with computational modelling and electron microscopy. The binding between the carboxylates and the AuNP surface is found to occur in three different modes. These three modes are simultaneously present at low citrate to gold ratios, while a monocarboxylate monodentate (1κO1) mode is favoured at high citrate:gold ratios. The surface AuNP atoms are found to be predominantly in the zero oxidation state after citrate coordination, although trace amounts of Auδ+ are observed. 23Na NMR experiments show that Na+ ions are present near the gold surface, indicating that carboxylate binding occurs as a 2e− L-type interaction for each oxygen atom involved. This approach has broad potential to probe the binding of a variety of ligands to metal nanoparticles.

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

Science.gov (United States)

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

2017-09-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2011-06-15

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

The Binding Mode of the Sonic Hedgehog Inhibitor Robotnikinin, a Combined Docking and QM/MM MD Study

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Manuel Hitzenberger

2017-10-01

Full Text Available Erroneous activation of the Hedgehog pathway has been linked to a great amount of cancerous diseases and therefore a large number of studies aiming at its inhibition have been carried out. One leverage point for novel therapeutic strategies targeting the proteins involved, is the prevention of complex formation between the extracellular signaling protein Sonic Hedgehog and the transmembrane protein Patched 1. In 2009 robotnikinin, a small molecule capable of binding to and inhibiting the activity of Sonic Hedgehog has been identified, however in the absence of X-ray structures of the Sonic Hedgehog-robotnikinin complex, the binding mode of this inhibitor remains unknown. In order to aid with the identification of novel Sonic Hedgehog inhibitors, the presented investigation elucidates the binding mode of robotnikinin by performing an extensive docking study, including subsequent molecular mechanical as well as quantum mechanical/molecular mechanical molecular dynamics simulations. The attained configurations enabled the identification of a number of key protein-ligand interactions, aiding complex formation and providing stabilizing contributions to the binding of the ligand. The predicted structure of the Sonic Hedgehog-robotnikinin complex is provided via a PDB file as Supplementary Material and can be used for further reference.

Binding modes and functional surface of anti-mammalian scorpion α-toxins to sodium channels.

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Chen, Rong; Chung, Shin-Ho

2012-10-02

Scorpion α-toxins bind to the voltage-sensing domains of voltage-gated sodium (Na(V)) channels and interfere with the inactivation mechanisms. The functional surface of α-toxins has been shown to contain an NC-domain consisting of the five-residue turn (positions 8-12) and the C-terminus (positions 56-64) and a core-domain centered on the residue 18. The NC- and core-domains are interconnected by the linker-domain (positions 8-18). Here with atomistic molecular dynamics simulations, we examine the binding modes between two α-toxins, the anti-mammalian AahII and the anti-insect LqhαIT, and the voltage-sensing domain of rat Na(V)1.2, a subtype of Na(V) channels expressed in nerve cells. Both toxins are docked to the extracellular side of the voltage-sensing domain of Na(V)1.2 using molecular dynamics simulations, with the linker-domain assumed to wedge into the binding pocket. Several salt bridges and hydrophobic clusters are observed to form between the NC- and core-domains of the toxins and Na(V)1.2 and stabilize the toxin-channel complexes. The binding modes predicted are consistent with available mutagenesis data and can readily explain the relative affinities of AahII and LqhαIT for Na(V)1.2. The dissociation constants for the two toxin-channel complexes are derived, which compare favorably with experiment. Our models demonstrate that the functional surface of anti-mammalian scorpion α-toxins is centered on the linker-domain, similar to that of β-toxins.

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

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Waqasuddin Khan

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

Binding of ethidium to the nucleosome core particle. 2. Internal and external binding modes

International Nuclear Information System (INIS)

McMurray, C.T.; Small, E.W.; van Holde, K.E.

1991-01-01

The authors have previously reported that the binding of ethidium bromide to the nucleosome core particle results in a stepwise dissociation of the structure which involves the initial release of one copy each of H2A and H2B. In this report, they have examined the absorbance and fluorescence properties of intercalated and outside bound forms of ethidium bromide. From these properties, they have measured the extent of external, electrostatic binding of the dye versus internal, intercalation binding to the core particle, free from contribution by linker DNA. They have established that dissociation is induced by the intercalation mode of binding to DNA within the core particle DNA, and not by binding to the histones or by nonintercalative binding to DNA. The covalent binding of [ 3 H]-8-azidoethidium to the core particle clearly shows that < 1.0 adduct is formed per histone octamer over a wide range of input ratios. Simultaneously, analyses of steady-state fluorescence enhancement and fluorescence lifetime data from bound ethidium complexes demonstrate extensive intercalation binding. Combined analyses from steady-state fluorescence intensity with equilibrium dialysis or fluorescence lifetime data revealed that dissociation began when ∼14 ethidium molecules are bound by intercalation to each core particle and < 1.0 nonintercalated ion pair was formed per core particle

The binding cavity of mouse major urinary protein is optimised for a variety of ligand binding modes

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Pertinhez, Thelma A.; Ferrari, Elena; Casali, Emanuela [Department of Experimental Medicine, University of Parma, Via Volturno, 39, 43100 Parma (Italy); Patel, Jital A. [Department of Chemistry, University of Oxford, Inorganic Chemistry Laboratory, South Parks Road, Oxford OX1 3QR (United Kingdom); Spisni, Alberto, E-mail: alberto.spisni@unipr.it [Department of Experimental Medicine, University of Parma, Via Volturno, 39, 43100 Parma (Italy); Smith, Lorna J., E-mail: lorna.smith@chem.ox.ac.uk [Department of Chemistry, University of Oxford, Inorganic Chemistry Laboratory, South Parks Road, Oxford OX1 3QR (United Kingdom)

2009-12-25

{sup 15}N and {sup 1}HN chemical shift data and {sup 15}N relaxation studies have been used to characterise the binding of N-phenyl-naphthylamine (NPN) to mouse major urinary protein (MUP). NPN binds in the {beta}-barrel cavity of MUP, hydrogen bonding to Tyr120 and making extensive non-bonded contacts with hydrophobic side chains. In contrast to the natural pheromone 2-sec-butyl-4,5-dihydrothiazole, NPN binding gives no change to the overall mobility of the protein backbone of MUP. Comparison with 11 different ligands that bind to MUP shows a range of binding modes involving 16 different residues in the {beta}-barrel cavity. These finding justify why MUP is able to adapt to allow for many successful binding partners.

Distinct ubiquitin binding modes exhibited by SH3 domains: molecular determinants and functional implications.

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Jose L Ortega Roldan

Full Text Available SH3 domains constitute a new type of ubiquitin-binding domains. We previously showed that the third SH3 domain (SH3-C of CD2AP binds ubiquitin in an alternative orientation. We have determined the structure of the complex between first CD2AP SH3 domain and ubiquitin and performed a structural and mutational analysis to decipher the determinants of the SH3-C binding mode to ubiquitin. We found that the Phe-to-Tyr mutation in CD2AP and in the homologous CIN85 SH3-C domain does not abrogate ubiquitin binding, in contrast to previous hypothesis and our findings for the first two CD2AP SH3 domains. The similar alternative binding mode of the SH3-C domains of these related adaptor proteins is characterised by a higher affinity to C-terminal extended ubiquitin molecules. We conclude that CD2AP/CIN85 SH3-C domain interaction with ubiquitin constitutes a new ubiquitin-binding mode involved in a different cellular function and thus changes the previously established mechanism of EGF-dependent CD2AP/CIN85 mono-ubiquitination.

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

Science.gov (United States)

Si, Jingna; Zhao, Rui; Wu, Rongling

2015-03-06

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

A computational analysis of the binding mode of closantel as inhibitor of the Onchocerca volvulus chitinase: insights on macrofilaricidal drug design

Science.gov (United States)

Segura-Cabrera, Aldo; Bocanegra-García, Virgilio; Lizarazo-Ortega, Cristian; Guo, Xianwu; Correa-Basurto, José; Rodríguez-Pérez, Mario A.

2011-12-01

Onchocerciasis is a leading cause of blindness with at least 37 million people infected and more than 120 million people at risk of contracting the disease; most (99%) of this population, threatened by infection, live in Africa. The drug of choice for mass treatment is the microfilaricidal Mectizan® (ivermectin); it does not kill the adult stages of the parasite at the standard dose which is a single annual dose aimed at disease control. However, multiple treatments a year with ivermectin have effects on adult worms. The discovery of new therapeutic targets and drugs directed towards the killing of the adult parasites are thus urgently needed. The chitinase of filarial nematodes is a new drug target due to its essential function in the metabolism and molting of the parasite. Closantel is a potent and specific inhibitor of chitinase of Onchocerca volvulus (OvCHT1) and other filarial chitinases. However, the binding mode and specificity of closantel towards OvCHT1 remain unknown. In the absence of a crystallographic structure of OvCHT1, we developed a homology model of OvCHT1 using the currently available X-ray structures of human chitinases as templates. Energy minimization and molecular dynamics (MD) simulation of the model led to a high quality of 3D structure of OvCHIT1. A flexible docking study using closantel as the ligand on the binding site of OvCHIT1 and human chitinases was performed and demonstrated the differences in the closantel binding mode between OvCHIT1 and human chitinase. Furthermore, molecular dynamics simulations and free-energy calculation were employed to determine and compare the detailed binding mode of closantel with OvCHT1 and the structure of human chitinase. This comparative study allowed identification of structural features and properties responsible for differences in the computationally predicted closantel binding modes. The homology model and the closantel binding mode reported herein might help guide the rational development of

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

Science.gov (United States)

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

2018-01-01

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

An Overview of the Prediction of Protein DNA-Binding Sites

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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.

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

Science.gov (United States)

Ma, Xin; Guo, Jing; Sun, Xiao

2016-01-01

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

Transcription factor binding sites prediction based on modified nucleosomes.

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Mohammad Talebzadeh

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

Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of d-amino acid oxidase inhibitors

Science.gov (United States)

Orgován, Zoltán; Ferenczy, György G.; Steinbrecher, Thomas; Szilágyi, Bence; Bajusz, Dávid; Keserű, György M.

2018-02-01

Optimization of fragment size d-amino acid oxidase (DAAO) inhibitors was investigated using a combination of computational and experimental methods. Retrospective free energy perturbation (FEP) calculations were performed for benzo[d]isoxazole derivatives, a series of known inhibitors with two potential binding modes derived from X-ray structures of other DAAO inhibitors. The good agreement between experimental and computed binding free energies in only one of the hypothesized binding modes strongly support this bioactive conformation. Then, a series of 1-H-indazol-3-ol derivatives formerly not described as DAAO inhibitors was investigated. Binding geometries could be reliably identified by structural similarity to benzo[d]isoxazole and other well characterized series and FEP calculations were performed for several tautomers of the deprotonated and protonated compounds since all these forms are potentially present owing to the experimental pKa values of representative compounds in the series. Deprotonated compounds are proposed to be the most important bound species owing to the significantly better agreement between their calculated and measured affinities compared to the protonated forms. FEP calculations were also used for the prediction of the affinities of compounds not previously tested as DAAO inhibitors and for a comparative structure-activity relationship study of the benzo[d]isoxazole and indazole series. Selected indazole derivatives were synthesized and their measured binding affinity towards DAAO was in good agreement with FEP predictions.

Machine Learning Reveals a Non-Canonical Mode of Peptide Binding to MHC class II Molecules

DEFF Research Database (Denmark)

Andreatta, Massimo; Jurtz, Vanessa Isabell; Kaever, Thomas

2017-01-01

binding motif with a non-canonical binding core of length different from nine. This previously undescribed mode of peptide binding to MHCII molecules gives a more complete picture of peptide presentation by MHCII and allows us to model more accurately this event. This article is protected by copyright...

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

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Hu Jianjun

2011-05-01

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

Solar g-modes? Comparison of detected asymptotic g-mode frequencies with solar model predictions

Science.gov (United States)

Wood, Suzannah Rebecca; Guzik, Joyce Ann; Mussack, Katie; Bradley, Paul A.

2018-06-01

After many years of searching for solar gravity modes, Fossat et al. (2017) reported detection of the nearly equally spaced high-order g-modes periods using a 15-year time series of GOLF data from the SOHO spacecraft. Here we report progress towards and challenges associated with calculating and comparing g-mode period predictions for several previously published standard solar models using various abundance mixtures and opacities, as well as the predictions for some non-standard models incorporating early mass loss, and compare with the periods reported by Fossat et al (2017). Additionally, we have a side-by-side comparison of results of different stellar pulsation codes for calculating g-mode predictions. These comparisons will allow for testing of nonstandard physics input that affect the core, including an early more massive Sun and dynamic electron screening.

The Drosophila hnRNP F/H Homolog Glorund Uses Two Distinct RNA-Binding Modes to Diversify Target Recognition

Energy Technology Data Exchange (ETDEWEB)

Tamayo, Joel V.; Teramoto, Takamasa; Chatterjee, Seema; Hall, Traci M. Tanaka; Gavis, Elizabeth R. (Princeton); (NIH)

2017-04-01

The Drosophila hnRNP F/H homolog, Glorund (Glo), regulates nanos mRNA translation by interacting with a structured UA-rich motif in the nanos 3' untranslated region. Glo regulates additional RNAs, however, and mammalian homologs bind G-tract sequences to regulate alternative splicing, suggesting that Glo also recognizes G-tract RNA. To gain insight into how Glo recognizes both structured UA-rich and G-tract RNAs, we used mutational analysis guided by crystal structures of Glo’s RNA-binding domains and identified two discrete RNA-binding surfaces that allow Glo to recognize both RNA motifs. By engineering Glo variants that favor a single RNA-binding mode, we show that a subset of Glo’s functions in vivo is mediated solely by the G-tract binding mode, whereas regulation of nanos requires both recognition modes. Our findings suggest a molecular mechanism for the evolution of dual RNA motif recognition in Glo that may be applied to understanding the functional diversity of other RNA-binding proteins.

Prediction of failure modes for concrete nuclear-containment buildings

International Nuclear Information System (INIS)

Butler, T.A.

1982-01-01

The failure modes and associated failure pressures for two common generic types of PWR containments are predicted. One building type is a lightly reinforced, posttensioned structure represented by the Zion nuclear reactor containment. The other is the normally reinforced Indian Point containment. Two-dimensional models of the buildings developed using the finite element method are used to predict the failure modes and failure pressures. Predicted failure modes for both containments involve loss of structural integrity at the intersection of the cylindrical sidewall with the base slab

Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms

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Sangmin Seo

2018-01-01

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

The Drosophila hnRNP F/H Homolog Glorund Uses Two Distinct RNA-Binding Modes to Diversify Target Recognition.

Science.gov (United States)

Tamayo, Joel V; Teramoto, Takamasa; Chatterjee, Seema; Hall, Traci M Tanaka; Gavis, Elizabeth R

2017-04-04

The Drosophila hnRNP F/H homolog, Glorund (Glo), regulates nanos mRNA translation by interacting with a structured UA-rich motif in the nanos 3' untranslated region. Glo regulates additional RNAs, however, and mammalian homologs bind G-tract sequences to regulate alternative splicing, suggesting that Glo also recognizes G-tract RNA. To gain insight into how Glo recognizes both structured UA-rich and G-tract RNAs, we used mutational analysis guided by crystal structures of Glo's RNA-binding domains and identified two discrete RNA-binding surfaces that allow Glo to recognize both RNA motifs. By engineering Glo variants that favor a single RNA-binding mode, we show that a subset of Glo's functions in vivo is mediated solely by the G-tract binding mode, whereas regulation of nanos requires both recognition modes. Our findings suggest a molecular mechanism for the evolution of dual RNA motif recognition in Glo that may be applied to understanding the functional diversity of other RNA-binding proteins. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

The Drosophila hnRNP F/H Homolog Glorund Uses Two Distinct RNA-Binding Modes to Diversify Target Recognition

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Joel V. Tamayo

2017-04-01

Full Text Available The Drosophila hnRNP F/H homolog, Glorund (Glo, regulates nanos mRNA translation by interacting with a structured UA-rich motif in the nanos 3′ untranslated region. Glo regulates additional RNAs, however, and mammalian homologs bind G-tract sequences to regulate alternative splicing, suggesting that Glo also recognizes G-tract RNA. To gain insight into how Glo recognizes both structured UA-rich and G-tract RNAs, we used mutational analysis guided by crystal structures of Glo’s RNA-binding domains and identified two discrete RNA-binding surfaces that allow Glo to recognize both RNA motifs. By engineering Glo variants that favor a single RNA-binding mode, we show that a subset of Glo’s functions in vivo is mediated solely by the G-tract binding mode, whereas regulation of nanos requires both recognition modes. Our findings suggest a molecular mechanism for the evolution of dual RNA motif recognition in Glo that may be applied to understanding the functional diversity of other RNA-binding proteins.

Automated benchmarking of peptide-MHC class I binding predictions

Science.gov (United States)

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

2015-01-01

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

Knowledge-based Fragment Binding Prediction

Science.gov (United States)

Tang, Grace W.; Altman, Russ B.

2014-01-01

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

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

Science.gov (United States)

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

2017-11-03

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

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

Science.gov (United States)

2014-01-01

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

Predictable and unpredictable modes of seasonal mean precipitation over Northeast China

Science.gov (United States)

Ying, Kairan; Frederiksen, Carsten S.; Zhao, Tianbao; Zheng, Xiaogu; Xiong, Zhe; Yi, Xue; Li, Chunxiang

2018-04-01

This study investigates the patterns of interannual variability that arise from the potentially predictable (slow) and unpredictable (intraseasonal) components of seasonal mean precipitation over Northeast (NE) China, using observations from a network of 162 meteorological stations for the period 1961-2014. A variance decomposition method is applied to identify the sources of predictability, as well as the sources of prediction uncertainty, for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS) and October-November-December (OND). The averaged potential predictability (ratio of slow to total variance) of NE China precipitation has the highest value of 0.32 during JAS and lowest value of 0.1 in AMJ. Possible sources of seasonal prediction for the leading predictable precipitation EOF modes come from the SST anomalies in the Japan Sea, as well as the North Atlantic during JFM, the Indian Ocean SST in AMJ, and the eastern tropical Pacific SST in JAS and OND. The prolonged linear trend, which is seen in the principal component time series of the leading predictable mode in JFM and OND, may also serve as a source of predictability. The Polar-Eurasia and Northern Annular Mode atmospheric teleconnection patterns are closely connected with the leading and the second predictable mode of JAS, respectively. The Hadley cell circulation is closely related to the leading predictable mode of OND. The leading/second unpredictable precipitation modes for all these four seasons show a similar monopole/dipole structure, and can be largely attributed to the intraseasonal variabilities of the atmosphere.

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. Copyright © 2011 Wiley Periodicals, Inc.

CW EPR parameters reveal cytochrome P450 ligand binding modes.

Science.gov (United States)

Lockart, Molly M; Rodriguez, Carlo A; Atkins, William M; Bowman, Michael K

2018-06-01

Cytochrome P450 (CYP) monoxygenses utilize heme cofactors to catalyze oxidation reactions. They play a critical role in metabolism of many classes of drugs, are an attractive target for drug development, and mediate several prominent drug interactions. Many substrates and inhibitors alter the spin state of the ferric heme by displacing the heme's axial water ligand in the resting enzyme to yield a five-coordinate iron complex, or they replace the axial water to yield a nitrogen-ligated six-coordinate iron complex, which are traditionally assigned by UV-vis spectroscopy. However, crystal structures and recent pulsed electron paramagnetic resonance (EPR) studies find a few cases where molecules hydrogen bond to the axial water. The water-bridged drug-H 2 O-heme has UV-vis spectra similar to nitrogen-ligated, six-coordinate complexes, but are closer to "reverse type I" complexes described in older liteature. Here, pulsed and continuous wave (CW) EPR demonstrate that water-bridged complexes are remarkably common among a range of nitrogenous drugs or drug fragments that bind to CYP3A4 or CYP2C9. Principal component analysis reveals a distinct clustering of CW EPR spectral parameters for water-bridged complexes. CW EPR reveals heterogeneous mixtures of ligated states, including multiple directly-coordinated complexes and water-bridged complexes. These results suggest that water-bridged complexes are under-represented in CYP structural databases and can have energies similar to other ligation modes. The data indicates that water-bridged binding modes can be identified and distinguished from directly-coordinated binding by CW EPR. Copyright © 2018 Elsevier Inc. All rights reserved.

Binding Mode and Structure-Activity Relationships of ITE as an Aryl Hydrocarbon Receptor (AhR) Agonist.

Science.gov (United States)

Dolciami, Daniela; Gargaro, Marco; Cerra, Bruno; Scalisi, Giulia; Bagnoli, Luana; Servillo, Giuseppe; Fazia, Maria Agnese Della; Puccetti, Paolo; Quintana, Francisco J; Fallarino, Francesca; Macchiarulo, Antonio

2018-02-06

Discovered as a modulator of the toxic response to environmental pollutants, aryl hydrocarbon receptor (AhR) has recently gained attention for its involvement in various physiological and pathological pathways. AhR is a ligand-dependent transcription factor activated by a large array of chemical compounds, which include metabolites of l-tryptophan (l-Trp) catabolism as endogenous ligands of the receptor. Among these, 2-(1'H-indole-3'-carbonyl)thiazole-4-carboxylic acid methyl ester (ITE) has attracted interest in the scientific community, being endowed with nontoxic, immunomodulatory, and anticancer AhR-mediated functions. So far, no information about the binding mode and interactions of ITE with AhR is available. In this study, we used docking and molecular dynamics to propose a putative binding mode of ITE into the ligand binding pocket of AhR. Mutagenesis studies were then instrumental in validating the proposed binding mode, identifying His 285 and Tyr 316 as important key residues for ligand-dependent receptor activation. Finally, a set of ITE analogues was synthesized and tested to further probe molecular interactions of ITE to AhR and characterize the relevance of specific functional groups in the chemical structure for receptor activity. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A systems biology approach to transcription factor binding site prediction.

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Xiang Zhou

2010-03-01

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

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

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C. Ruben Vosmeer

2014-01-01

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

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

KAUST Repository

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

2016-01-01

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

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

Science.gov (United States)

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

2016-01-15

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

New insight into the binding modes of TNP-AMP to human liver fructose-1,6-bisphosphatase.

Science.gov (United States)

Han, Xinya; Huang, Yunyuan; Zhang, Rui; Xiao, San; Zhu, Shuaihuan; Qin, Nian; Hong, Zongqin; Wei, Lin; Feng, Jiangtao; Ren, Yanliang; Feng, Lingling; Wan, Jian

2016-08-05

Human liver fructose-1,6-bisphosphatase (FBPase) contains two binding sites, a substrate fructose-1,6-bisphosphate (FBP) active site and an adenosine monophosphate (AMP) allosteric site. The FBP active site works by stabilizing the FBPase, and the allosteric site impairs the activity of FBPase through its binding of a nonsubstrate molecule. The fluorescent AMP analogue, 2',3'-O-(2,4,6-trinitrophenyl)adenosine 5'-monophosphate (TNP-AMP) has been used as a fluorescent probe as it is able to competitively inhibit AMP binding to the AMP allosteric site and, therefore, could be used for exploring the binding modes of inhibitors targeted on the allosteric site. In this study, we have re-examined the binding modes of TNP-AMP to FBPase. However, our present enzyme kinetic assays show that AMP and FBP both can reduce the fluorescence from the bound TNP-AMP through competition for FBPase, suggesting that TNP-AMP binds not only to the AMP allosteric site but also to the FBP active site. Mutagenesis assays of K274L (located in the FBP active site) show that the residue K274 is very important for TNP-AMP to bind to the active site of FBPase. The results further prove that TNP-AMP is able to bind individually to the both sites. Our present study provides a new insight into the binding mechanism of TNP-AMP to the FBPase. The TNP-AMP fluorescent probe can be used to exam the binding site of an inhibitor (the active site or the allosteric site) using FBPase saturated by AMP and FBP, respectively, or the K247L mutant FBPase. Copyright © 2016 Elsevier B.V. All rights reserved.

Expression, purification and DNA-binding activities of two putative ModE proteins of Herbaspirillum seropedicae (Burkholderiales, Oxalobacteraceae

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André L.F. Souza

2008-01-01

Full Text Available In prokaryotes molybdenum is taken up by a high-affinity ABC-type transporter system encoded by the modABC genes. The endophyte β-Proteobacterium Herbaspirillum seropedicae has two modABC gene clusters and two genes encoding putative Mo-dependent regulator proteins (ModE1 and ModE2. Analysis of the amino acid sequence of the ModE1 protein of H. seropedicae revealed the presence of an N-terminal domain containing a DNA-binding helix-turn-helix motif (HTH and a C-terminal domain with a molybdate-binding motif. The second putative regulator protein, ModE2, contains only the helix-turn-helix motif, similar to that observed in some sequenced genomes. We cloned the modE1 (810 bp and modE2 (372 bp genes and expressed them in Escherichia coli as His-tagged fusion proteins, which we subsequently purified. The over-expressed recombinant His-ModE1 was insoluble and was purified after solubilization with urea and then on-column refolded during affinity chromatography. The His-ModE2 was expressed as a soluble protein and purified by affinity chromatography. These purified proteins were analyzed by DNA band-shift assays using the modA2 promoter region as probe. Our results indicate that His-ModE1 and His-ModE2 are able to bind to the modA2 promoter region, suggesting that both proteins may play a role in the regulation of molybdenum uptake and metabolism in H. seropedicae.

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

Science.gov (United States)

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

2016-03-15

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

Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak

International Nuclear Information System (INIS)

Ding Yonghua; Jin Xuesong; Chen Zhenzhen; Zhuang Ge

2013-01-01

Prediction of disruptions caused by locked modes using the Back-Propagation (BP) neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode coils signals as input data, and outputs a signal including information of prediction of locked mode. The rate of successful prediction of locked modes is more than 90%. For intrinsic locked mode disruptions, the network can give a prewarning signal about 1 ms ahead of the locking-time. For the disruption caused by resonant magnetic perturbation (RMPs) locked modes, the network can give a prewarning signal about 10 ms ahead of the locking-time

Accurate and sensitive quantification of protein-DNA binding affinity.

Science.gov (United States)

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

2018-04-17

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

Binding modes and pathway of RHPS4 to human telomeric G-quadruplex and duplex DNA probed by all-atom molecular dynamics simulations with explicit solvent.

Science.gov (United States)

Mulholland, Kelly; Siddiquei, Farzana; Wu, Chun

2017-07-19

RHPS4, a potent binder to human telomeric DNA G-quadruplex, shows high efficacy in tumor cell growth inhibition. However, it's preferential binding to DNA G-quadruplex over DNA duplex (about 10 fold) remains to be improved toward its clinical application. A high resolution structure of the single-stranded telomeric DNA G-quadruplexes, or B-DNA duplex, in complex with RHPS4 is not available yet, and the binding nature of this ligand to these DNA forms remains to be elusive. In this study, we carried out 40 μs molecular dynamics binding simulations with a free ligand to decipher the binding pathway of RHPS4 to a DNA duplex and three G-quadruplex folders (parallel, antiparallel and hybrid) of the human telomeric DNA sequence. The most stable binding mode identified for the duplex, parallel, antiparallel and hybrid G-quadruplexes is an intercalation, bottom stacking, top intercalation and bottom intercalation mode, respectively. The intercalation mode with similar binding strength to both the duplex and the G-quadruplexes, explains the lack of binding selectivity of RHPS4 to the G-quadruplex form. Therefore, a ligand modification that destabilizes the duplex intercalation mode but stabilizes the G-quadruplex intercalation mode will improve the binding selectivity toward G-quadruplex. The intercalation mode of RHPS4 to both the duplex and the antiparallel and the hybrid G-quadruplex follows a base flipping-insertion mechanism rather than an open-insertion mechanism. The groove binding, the side binding and the intercalation with flipping out of base were observed to be intermediate states before the full intercalation state with paired bases.

Improved Wind Speed Prediction Using Empirical Mode Decomposition

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ZHANG, Y.

2018-05-01

Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

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

Science.gov (United States)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Rianon Zaman

2017-01-01

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

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

Science.gov (United States)

Han, Youngmahn; Kim, Dongsup

2017-12-28

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

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

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

KAUST Repository

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

2015-01-01

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

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

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Lee Sael

2010-12-01

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

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

Science.gov (United States)

Sael, Lee; Kihara, Daisuke

2010-01-01

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

Surface-enhanced Raman Scattering Study of the Binding Modes of a Dibenzotetraaza[14]annulene Derivative with DNA/RNA Polynucleotides

OpenAIRE

Miljanić, Snežana; Dijanošić, Adriana; Kalac, Matea; Radić Stojković, Marijana; Piantanida, Ivo; Pawlica, Dariusz; Eilmes, Julita

2012-01-01

Binding modes of a dibenzotetraaza14annulene (DBTAA) derivative with synthetic nucleic acids were studied using surface-enhanced Raman spectroscopy (SERS). Changes in SERS intensity and appearance of new bands in spectra were attributed to different complexes formed between the DBTAA molecules and DNA/RNA polynucleotides. A decrease in intensity pointed to intercalation as the dominant binding mode of the annulene derivative with poly dGdC-poly dGdC and poly rA-poly rU, whereas new bands in...

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

DEFF Research Database (Denmark)

Hoof, Ilka; Peters, B; Sidney, J

2009-01-01

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

Fluoroquinolone-gyrase-DNA complexes: two modes of drug binding.

Science.gov (United States)

Mustaev, Arkady; Malik, Muhammad; Zhao, Xilin; Kurepina, Natalia; Luan, Gan; Oppegard, Lisa M; Hiasa, Hiroshi; Marks, Kevin R; Kerns, Robert J; Berger, James M; Drlica, Karl

2014-05-02

DNA gyrase and topoisomerase IV control bacterial DNA topology by breaking DNA, passing duplex DNA through the break, and then resealing the break. This process is subject to reversible corruption by fluoroquinolones, antibacterials that form drug-enzyme-DNA complexes in which the DNA is broken. The complexes, called cleaved complexes because of the presence of DNA breaks, have been crystallized and found to have the fluoroquinolone C-7 ring system facing the GyrB/ParE subunits. As expected from x-ray crystallography, a thiol-reactive, C-7-modified chloroacetyl derivative of ciprofloxacin (Cip-AcCl) formed cross-linked cleaved complexes with mutant GyrB-Cys(466) gyrase as evidenced by resistance to reversal by both EDTA and thermal treatments. Surprisingly, cross-linking was also readily seen with complexes formed by mutant GyrA-G81C gyrase, thereby revealing a novel drug-gyrase interaction not observed in crystal structures. The cross-link between fluoroquinolone and GyrA-G81C gyrase correlated with exceptional bacteriostatic activity for Cip-AcCl with a quinolone-resistant GyrA-G81C variant of Escherichia coli and its Mycobacterium smegmatis equivalent (GyrA-G89C). Cip-AcCl-mediated, irreversible inhibition of DNA replication provided further evidence for a GyrA-drug cross-link. Collectively these data establish the existence of interactions between the fluoroquinolone C-7 ring and both GyrA and GyrB. Because the GyrA-Gly(81) and GyrB-Glu(466) residues are far apart (17 Å) in the crystal structure of cleaved complexes, two modes of quinolone binding must exist. The presence of two binding modes raises the possibility that multiple quinolone-enzyme-DNA complexes can form, a discovery that opens new avenues for exploring and exploiting relationships between drug structure and activity with type II DNA topoisomerases.

Oligosaccharide binding to barley alpha-amylase 1

DEFF Research Database (Denmark)

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

2005-01-01

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

Cytotoxic protein from the mushroom Coprinus comatus possesses a unique mode for glycan binding and specificity.

Science.gov (United States)

Zhang, Peilan; Li, Kunhua; Yang, Guang; Xia, Changqing; Polston, Jane E; Li, Gengnan; Li, Shiwu; Lin, Zhao; Yang, Li-Jun; Bruner, Steven D; Ding, Yousong

2017-08-22

Glycans possess significant chemical diversity; glycan binding proteins (GBPs) recognize specific glycans to translate their structures to functions in various physiological and pathological processes. Therefore, the discovery and characterization of novel GBPs and characterization of glycan-GBP interactions are significant to provide potential targets for therapeutic intervention of many diseases. Here, we report the biochemical, functional, and structural characterization of a 130-amino-acid protein, Y3, from the mushroom Coprinus comatus Biochemical studies of recombinant Y3 from a yeast expression system demonstrated the protein is a unique GBP. Additionally, we show that Y3 exhibits selective and potent cytotoxicity toward human T-cell leukemia Jurkat cells compared with a panel of cancer cell lines via inducing caspase-dependent apoptosis. Screening of a glycan array demonstrated GalNAcβ1-4(Fucα1-3)GlcNAc (LDNF) as a specific Y3-binding ligand. To provide a structural basis for function, the crystal structure was solved to a resolution of 1.2 Å, revealing a single-domain αβα-sandwich motif. Two monomers were dimerized to form a large 10-stranded, antiparallel β-sheet flanked by α-helices on each side, representing a unique oligomerization mode among GBPs. A large glycan binding pocket extends into the dimeric interface, and docking of LDNF identified key residues for glycan interactions. Disruption of residues predicted to be involved in LDNF/Y3 interactions resulted in the significant loss of binding to Jurkat T-cells and severely impaired their cytotoxicity. Collectively, these results demonstrate Y3 to be a GBP with selective cytotoxicity toward human T-cell leukemia cells and indicate its potential use in cancer diagnosis and treatment.

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.

Estrogen Receptor Folding Modulates cSrc Kinase SH2 Interaction via a Helical Binding Mode.

Science.gov (United States)

Nieto, Lidia; Tharun, Inga M; Balk, Mark; Wienk, Hans; Boelens, Rolf; Ottmann, Christian; Milroy, Lech-Gustav; Brunsveld, Luc

2015-11-20

The estrogen receptors (ERs) feature, next to their transcriptional role, important nongenomic signaling actions, with emerging clinical relevance. The Src Homology 2 (SH2) domain mediated interaction between cSrc kinase and ER plays a key role in this; however the molecular determinants of this interaction have not been elucidated. Here, we used phosphorylated ER peptide and semisynthetic protein constructs in a combined biochemical and structural study to, for the first time, provide a quantitative and structural characterization of the cSrc SH2-ER interaction. Fluorescence polarization experiments delineated the SH2 binding motif in the ER sequence. Chemical shift perturbation analysis by nuclear magnetic resonance (NMR) together with molecular dynamics (MD) simulations allowed us to put forward a 3D model of the ER-SH2 interaction. The structural basis of this protein-protein interaction has been compared with that of the high affinity SH2 binding sequence GpYEEI. The ER features a different binding mode from that of the "two-pronged plug two-hole socket" model in the so-called specificity determining region. This alternative binding mode is modulated via the folding of ER helix 12, a structural element directly C-terminal of the key phosphorylated tyrosine. The present findings provide novel molecular entries for understanding nongenomic ER signaling and targeting the corresponding disease states.

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....

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

Science.gov (United States)

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

2016-03-25

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

Crystal structure of glucose isomerase in complex with xylitol inhibitor in one metal binding mode.

Science.gov (United States)

Bae, Ji-Eun; Kim, In Jung; Nam, Ki Hyun

2017-11-04

Glucose isomerase (GI) is an intramolecular oxidoreductase that interconverts aldoses and ketoses. These characteristics are widely used in the food, detergent, and pharmaceutical industries. In order to obtain an efficient GI, identification of novel GI genes and substrate binding/inhibition have been studied. Xylitol is a well-known inhibitor of GI. In Streptomyces rubiginosus, two crystal structures have been reported for GI in complex with xylitol inhibitor. However, a structural comparison showed that xylitol can have variable conformation at the substrate binding site, e.g., a nonspecific binding mode. In this study, we report the crystal structure of S. rubiginosus GI in a complex with xylitol and glycerol. Our crystal structure showed one metal binding mode in GI, which we presumed to represent the inactive form of the GI. The metal ion was found only at the M1 site, which was involved in substrate binding, and was not present at the M2 site, which was involved in catalytic function. The O 2 and O 4 atoms of xylitol molecules contributed to the stable octahedral coordination of the metal in M1. Although there was no metal at the M2 site, no large conformational change was observed for the conserved residues coordinating M2. Our structural analysis showed that the metal at the M2 site was not important when a xylitol inhibitor was bound to the M1 site in GI. Thus, these findings provided important information for elucidation or engineering of GI functions. Copyright © 2017 Elsevier Inc. All rights reserved.

Computational Studies of Difference in Binding Modes of Peptide and Non-Peptide Inhibitors to MDM2/MDMX Based on Molecular Dynamics Simulations

Directory of Open Access Journals (Sweden)

Yuxin Zhang

2012-02-01

Full Text Available Inhibition of p53-MDM2/MDMX interaction is considered to be a promising strategy for anticancer drug design to activate wild-type p53 in tumors. We carry out molecular dynamics (MD simulations to study the binding mechanisms of peptide and non-peptide inhibitors to MDM2/MDMX. The rank of binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA method agrees with one of the experimental values. The results suggest that van der Waals energy drives two kinds of inhibitors to MDM2/MDMX. We also find that the peptide inhibitors can produce more interaction contacts with MDM2/MDMX than the non-peptide inhibitors. Binding mode predictions based on the inhibitor-residue interactions show that the π–π, CH–π and CH–CH interactions dominated by shape complimentarity, govern the binding of the inhibitors in the hydrophobic cleft of MDM2/MDMX. Our studies confirm the residue Tyr99 in MDMX can generate a steric clash with the inhibitors due to energy and structure. This finding may theoretically provide help to develop potent dual-specific or MDMX inhibitors.

Peptide binding predictions for HLA DR, DP and DQ molecules

DEFF Research Database (Denmark)

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

2010-01-01

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

Investigation of the binding mode of a novel cruzain inhibitor by docking, molecular dynamics, ab initio and MM/PBSA calculations

Science.gov (United States)

Martins, Luan Carvalho; Torres, Pedro Henrique Monteiro; de Oliveira, Renata Barbosa; Pascutti, Pedro Geraldo; Cino, Elio A.; Ferreira, Rafaela Salgado

2018-05-01

Chagas disease remains a major health problem in South America, and throughout the world. The two drugs clinically available for its treatment have limited efficacy and cause serious adverse effects. Cruzain is an established therapeutic target of Trypanosoma cruzi, the protozoan that causes Chagas disease. Our group recently identified a competitive cruzain inhibitor (compound 1) with an IC50 = 15 µM that is also more synthetically accessible than the previously reported lead, compound 2. Prior studies, however, did not propose a binding mode for compound 1, hindering understanding of the structure-activity relationship and optimization. Here, the cruzain binding mode of compound 1 was investigated using docking, molecular dynamics (MD) simulations with ab initio derived parameters, ab initio calculations, and MM/PBSA. Two ligand protonation states and four binding poses were evaluated. A careful ligand parameterization method was employed to derive more physically meaningful parameters than those obtained by automated tools. The poses of unprotonated 1 were unstable in MD, showing large conformational changes and diffusing away from the binding site, whereas the protonated form showed higher stability and interaction with negatively charged residues Asp161 and Cys25. MM/PBSA also suggested that these two residues contribute favorably to binding of compound 1. By combining results from MD, ab initio calculations, and MM/PBSA, a binding mode of 1 is proposed. The results also provide insights for further optimization of 1, an interesting lead compound for the development of new cruzain inhibitors.

Resolving dual binding conformations of cellulosome cohesin-dockerin complexes using single-molecule force spectroscopy.

Science.gov (United States)

Jobst, Markus A; Milles, Lukas F; Schoeler, Constantin; Ott, Wolfgang; Fried, Daniel B; Bayer, Edward A; Gaub, Hermann E; Nash, Michael A

2015-10-31

Receptor-ligand pairs are ordinarily thought to interact through a lock and key mechanism, where a unique molecular conformation is formed upon binding. Contrary to this paradigm, cellulosomal cohesin-dockerin (Coh-Doc) pairs are believed to interact through redundant dual binding modes consisting of two distinct conformations. Here, we combined site-directed mutagenesis and single-molecule force spectroscopy (SMFS) to study the unbinding of Coh:Doc complexes under force. We designed Doc mutations to knock out each binding mode, and compared their single-molecule unfolding patterns as they were dissociated from Coh using an atomic force microscope (AFM) cantilever. Although average bulk measurements were unable to resolve the differences in Doc binding modes due to the similarity of the interactions, with a single-molecule method we were able to discriminate the two modes based on distinct differences in their mechanical properties. We conclude that under native conditions wild-type Doc from Clostridium thermocellum exocellulase Cel48S populates both binding modes with similar probabilities. Given the vast number of Doc domains with predicted dual binding modes across multiple bacterial species, our approach opens up new possibilities for understanding assembly and catalytic properties of a broad range of multi-enzyme complexes.

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

Science.gov (United States)

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

2017-07-27

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

AgI -Induced Switching of DNA Binding Modes via Formation of a Supramolecular Metallacycle.

Science.gov (United States)

Basak, Shibaji; Léon, J Christian; Ferranco, Annaleizle; Sharma, Renu; Hebenbrock, Marian; Lough, Alan; Müller, Jens; Kraatz, Heinz-Bernhard

2018-03-12

The histidine derivative L1 of the DNA intercalator naphthalenediimide (NDI) forms a triangular Ag I complex (C2). The interactions of L1 and of C2 with DNA were studied by circular dichroism (CD) and UV/Vis spectroscopy and by viscosity studies. Different binding modes were observed for L1 and for C2, as the Ag I complex C2 is too large in size to act as an intercalator. If Ag I is added to the NDI molecule that is already intercalated into a duplex, higher order complexes are formed within the DNA duplex and cause disruptions in the helical duplex structure, which leads to a significant decrease in the characteristic CD features of B-DNA. Thus, via addition of a metal we show how a classic and well-known organic intercalator unit can be turned into a partial metallo insertor. We also show how electrochemical impedance spectroscopy (EIS) can be used to probe DNA binding modes on DNA films that are immobilized on gold surfaces. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cytotoxic protein from the mushroom Coprinus comatus possesses a unique mode for glycan binding and specificity

Science.gov (United States)

Zhang, Peilan; Yang, Guang; Xia, Changqing; Polston, Jane E.; Li, Gengnan; Li, Shiwu; Lin, Zhao; Yang, Li-jun; Bruner, Steven D.

2017-01-01

Glycans possess significant chemical diversity; glycan binding proteins (GBPs) recognize specific glycans to translate their structures to functions in various physiological and pathological processes. Therefore, the discovery and characterization of novel GBPs and characterization of glycan–GBP interactions are significant to provide potential targets for therapeutic intervention of many diseases. Here, we report the biochemical, functional, and structural characterization of a 130-amino-acid protein, Y3, from the mushroom Coprinus comatus. Biochemical studies of recombinant Y3 from a yeast expression system demonstrated the protein is a unique GBP. Additionally, we show that Y3 exhibits selective and potent cytotoxicity toward human T-cell leukemia Jurkat cells compared with a panel of cancer cell lines via inducing caspase-dependent apoptosis. Screening of a glycan array demonstrated GalNAcβ1–4(Fucα1–3)GlcNAc (LDNF) as a specific Y3-binding ligand. To provide a structural basis for function, the crystal structure was solved to a resolution of 1.2 Å, revealing a single-domain αβα-sandwich motif. Two monomers were dimerized to form a large 10-stranded, antiparallel β-sheet flanked by α-helices on each side, representing a unique oligomerization mode among GBPs. A large glycan binding pocket extends into the dimeric interface, and docking of LDNF identified key residues for glycan interactions. Disruption of residues predicted to be involved in LDNF/Y3 interactions resulted in the significant loss of binding to Jurkat T-cells and severely impaired their cytotoxicity. Collectively, these results demonstrate Y3 to be a GBP with selective cytotoxicity toward human T-cell leukemia cells and indicate its potential use in cancer diagnosis and treatment. PMID:28784797

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/.

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

DEFF Research Database (Denmark)

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

1999-01-01

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

Half-life predictions for decay modes of superheavy nuclei

International Nuclear Information System (INIS)

Duarte, S.B.; Tavares, O.A.P.; Goncalves, M.; Rodriguez, O.; Guzman, F.; Barbosa, T.N.; Garcia, F.; Dimarco, A.

2004-09-01

We applied the Effective Liquid Drop Model (ELDM) to predict the alpha-decay, cluster emission and cold fission half-life-values of nuclei in the region of Superheavy Elements (SHE). The present calculations have been made in the region of the ZN-plane defined by 155 <=N <=220 and 110<=Z<=135. Shell effects are included via the Q-value of the corresponding decay case. We report the results of a systematic calculation of the half-life for the three nuclear decay modes in a region of the ZN-plane where superheavy elements are expected to be found. Results have shown that, among the decay modes investigated here, the alpha decay is the dominant one. i.e, the decay mode of smallest half-lives. Half-life predictions for alpha decay, cluster emission and cold fission for the isotopic family of the most recent SHE detected of Z=115 and for the isotopic family of the already consolidated SHE of Z=111 are presented. (author)

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

Science.gov (United States)

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

2015-08-01

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

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

Directory of Open Access Journals (Sweden)

Zing Tsung-Yeh Tsai

2015-08-01

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

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

Science.gov (United States)

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

2016-09-01

binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Binding of matrix metalloproteinase inhibitors to extracellular matrix: 3D-QSAR analysis.

Science.gov (United States)

Zhang, Yufen; Lukacova, Viera; Bartus, Vladimir; Nie, Xiaoping; Sun, Guorong; Manivannan, Ethirajan; Ghorpade, Sandeep R; Jin, Xiaomin; Manyem, Shankar; Sibi, Mukund P; Cook, Gregory R; Balaz, Stefan

2008-10-01

Binding to the extracellular matrix, one of the most abundant human protein complexes, significantly affects drug disposition. Specifically, the interactions with extracellular matrix determine the free concentrations of small molecules acting in tissues, including signaling peptides, inhibitors of tissue remodeling enzymes such as matrix metalloproteinases, and other drug candidates. The nature of extracellular matrix binding was elucidated for 63 matrix metalloproteinase inhibitors, for which the association constants to an extracellular matrix mimic were reported here. The data did not correlate with lipophilicity as a common determinant of structure-nonspecific, orientation-averaged binding. A hypothetical structure of the binding site of the solidified extracellular matrix surrogate was analyzed using the Comparative Molecular Field Analysis, which needed to be applied in our multi-mode variant. This fact indicates that the compounds bind to extracellular matrix in multiple modes, which cannot be considered as completely orientation-averaged and exhibit structural dependence. The novel comparative molecular field analysis models, exhibiting satisfactory descriptive and predictive abilities, are suitable for prediction of the extracellular matrix binding for the untested chemicals, which are within applicability domains. The results contribute to a better prediction of the pharmacokinetic parameters such as the distribution volume and the tissue-blood partition coefficients, in addition to a more imminent benefit for the development of more effective matrix metalloproteinase inhibitors.

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

Science.gov (United States)

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

2016-01-01

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

From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

Directory of Open Access Journals (Sweden)

Mu Gao

2009-03-01

Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

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

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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

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

Structural analysis of substrate recognition by glucose isomerase in Mn2+ binding mode at M2 site in S. rubiginosus.

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Bae, Ji-Eun; Hwang, Kwang Yeon; Nam, Ki Hyun

2018-06-16

Glucose isomerase (GI) catalyzes the reversible enzymatic isomerization of d-glucose and d-xylose to d-fructose and d-xylulose, respectively. This is one of the most important enzymes in the production of high-fructose corn syrup (HFCS) and biofuel. We recently determined the crystal structure of GI from S. rubiginosus (SruGI) complexed with a xylitol inhibitor in one metal binding mode. Although we assessed inhibitor binding at the M1 site, the metal binding at the M2 site and the substrate recognition mechanism for SruGI remains the unclear. Here, we report the crystal structure of the two metal binding modes of SruGI and its complex with glucose. This study provides a snapshot of metal binding at the SruGI M2 site in the presence of Mn 2+ , but not in the presence of Mg 2+ . Metal binding at the M2 site elicits a configuration change at the M1 site. Glucose molecule can only bind to the M1 site in presence of Mn 2+ at the M2 site. Glucose and Mn 2+ at the M2 site were bridged by water molecules using a hydrogen bonding network. The metal binding geometry of the M2 site indicates a distorted octahedral coordination with an angle of 55-110°, whereas the M1 site has a relatively stable octahedral coordination with an angle of 85-95°. We suggest a two-step sequential process for SruGI substrate recognition, in Mn 2+ binding mode, at the M2 site. Our results provide a better understanding of the molecular role of the M2 site in GI substrate recognition. Copyright © 2018. Published by Elsevier Inc.

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

KAUST Repository

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

2018-01-01

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

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

KAUST Repository

Khamis, Abdullah M.

2018-03-20

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

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

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

2017-07-01

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

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

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

2006-06-09

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

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

DEFF Research Database (Denmark)

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

2011-01-01

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

MAPPIN: a method for annotating, predicting pathogenicity and mode of inheritance for nonsynonymous variants.

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Gosalia, Nehal; Economides, Aris N; Dewey, Frederick E; Balasubramanian, Suganthi

2017-10-13

Nonsynonymous single nucleotide variants (nsSNVs) constitute about 50% of known disease-causing mutations and understanding their functional impact is an area of active research. Existing algorithms predict pathogenicity of nsSNVs; however, they are unable to differentiate heterozygous, dominant disease-causing variants from heterozygous carrier variants that lead to disease only in the homozygous state. Here, we present MAPPIN (Method for Annotating, Predicting Pathogenicity, and mode of Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorithm to distinguish between nsSNVs with dominant, recessive, and benign effects. We apply MAPPIN to a set of Mendelian disease-causing mutations and accurately predict pathogenicity for all mutations. Furthermore, MAPPIN predicts mode of inheritance correctly for 70.3% of nsSNVs. MAPPIN also correctly predicts pathogenicity for 87.3% of mutations from the Deciphering Developmental Disorders Study with a 78.5% accuracy for mode of inheritance. When tested on a larger collection of mutations from the Human Gene Mutation Database, MAPPIN is able to significantly discriminate between mutations in known dominant and recessive genes. Finally, we demonstrate that MAPPIN outperforms CADD and Eigen in predicting disease inheritance modes for all validation datasets. To our knowledge, MAPPIN is the first nsSNV pathogenicity prediction algorithm that provides mode of inheritance predictions, adding another layer of information for variant prioritization. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Two modes of interaction of the single-stranded DNA-binding protein of bacteriophage T7 with the DNA polymerase-thioredoxin complex

KAUST Repository

Ghosh, Sharmistha; Hamdan, Samir; Richardson, Charles C.

2010-01-01

The DNA polymerase encoded by bacteriophage T7 has low processivity. Escherichia coli thioredoxin binds to a segment of 76 residues in the thumb subdomain of the polymerase and increases the processivity. The binding of thioredoxin leads to the formation of two basic loops, loops A and B, located within the thioredoxin-binding domain (TBD). Both loops interact with the acidic C terminus of the T7 helicase. A relatively weak electrostatic mode involves the C-terminal tail of the helicase and the TBD, whereas a high affinity interaction that does not involve the C-terminal tail occurs when the polymerase is in a polymerization mode. T7 gene 2.5 single-stranded DNA-binding protein (gp2.5) also has an acidic C-terminal tail. gp2.5 also has two modes of interaction with the polymerase, but both involve the C-terminal tail of gp2.5. An electrostatic interaction requires the basic residues in loops A and B, and gp2.5 binds to both loops with similar affinity as measured by surface plasmon resonance. When the polymerase is in a polymerization mode, the C terminus of gene 2.5 protein interacts with the polymerase in regions outside the TBD.gp2.5 increases the processivity of the polymerase-helicase complex during leading strand synthesis. When loop B of the TBD is altered, abortive DNA products are observed during leading strand synthesis. Loop B appears to play an important role in communication with the helicase and gp2.5, whereas loop A plays a stabilizing role in these interactions. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.

Two modes of interaction of the single-stranded DNA-binding protein of bacteriophage T7 with the DNA polymerase-thioredoxin complex

KAUST Repository

Ghosh, Sharmistha

2010-04-06

The DNA polymerase encoded by bacteriophage T7 has low processivity. Escherichia coli thioredoxin binds to a segment of 76 residues in the thumb subdomain of the polymerase and increases the processivity. The binding of thioredoxin leads to the formation of two basic loops, loops A and B, located within the thioredoxin-binding domain (TBD). Both loops interact with the acidic C terminus of the T7 helicase. A relatively weak electrostatic mode involves the C-terminal tail of the helicase and the TBD, whereas a high affinity interaction that does not involve the C-terminal tail occurs when the polymerase is in a polymerization mode. T7 gene 2.5 single-stranded DNA-binding protein (gp2.5) also has an acidic C-terminal tail. gp2.5 also has two modes of interaction with the polymerase, but both involve the C-terminal tail of gp2.5. An electrostatic interaction requires the basic residues in loops A and B, and gp2.5 binds to both loops with similar affinity as measured by surface plasmon resonance. When the polymerase is in a polymerization mode, the C terminus of gene 2.5 protein interacts with the polymerase in regions outside the TBD.gp2.5 increases the processivity of the polymerase-helicase complex during leading strand synthesis. When loop B of the TBD is altered, abortive DNA products are observed during leading strand synthesis. Loop B appears to play an important role in communication with the helicase and gp2.5, whereas loop A plays a stabilizing role in these interactions. © 2010 by The American Society for Biochemistry and Molecular Biology, Inc.

Tris-amidoximate uranyl complexes via η2 binding mode coordinated in aqueous solution shown by X-ray absorption spectroscopy and density functional theory methods.

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Zhang, Linjuan; Qie, Meiying; Su, Jing; Zhang, Shuo; Zhou, Jing; Li, Jiong; Wang, Yu; Yang, Shitong; Wang, Shuao; Li, Jingye; Wu, Guozhong; Wang, Jian Qiang

2018-03-01

The present study sheds some light on the long-standing debate concerning the coordination properties between uranyl ions and the amidoxime ligand, which is a key ingredient for achieving efficient extraction of uranium. Using X-ray absorption fine structure combined with theoretical simulation methods, the binding mode and bonding nature of a uranyl-amidoxime complex in aqueous solution were determined for the first time. The results show that in a highly concentrated amidoxime solution the preferred binding mode between UO 2 2+ and the amidoxime ligand is η 2 coordination with tris-amidoximate species. In such a uranyl-amidoximate complex with η 2 binding motif, strong covalent interaction and orbital hybridization between U 5f/6d and (N, O) 2p should be responsible for the excellent binding ability of the amidoximate ligand to uranyl. The study was performed directly in aqueous solution to avoid the possible binding mode differences caused by crystallization of a single-crystal sample. This work also is an example of the simultaneous study of local structure and electronic structure in solution systems using combined diagnostic tools.

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

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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.

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

DEFF Research Database (Denmark)

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-01-01

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

Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

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

2015-11-01

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

Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes

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Kvaternik, Raymond G.

2013-01-01

Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.

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

Science.gov (United States)

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-07-04

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

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

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

Science.gov (United States)

Zhu, Xiaolei; Xiong, Yi; Kihara, Daisuke

2015-03-01

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

Fatigue crack growth and life prediction under mixed-mode loading

Science.gov (United States)

Sajith, S.; Murthy, K. S. R. K.; Robi, P. S.

2018-04-01

Fatigue crack growth life as a function of crack length is essential for the prevention of catastrophic failures from damage tolerance perspective. In damage tolerance design approach, principles of fracture mechanics are usually applied to predict the fatigue life of structural components. Numerical prediction of crack growth versus number of cycles is essential in damage tolerance design. For cracks under mixed mode I/II loading, modified Paris law (d/a d N =C (ΔKe q ) m ) along with different equivalent stress intensity factor (ΔKeq) model is used for fatigue crack growth rate prediction. There are a large number of ΔKeq models available for the mixed mode I/II loading, the selection of proper ΔKeq model has significant impact on fatigue life prediction. In the present investigation, the performance of ΔKeq models in fatigue life prediction is compared with respect to the experimental findings as there are no guidelines/suggestions available on the selection of these models for accurate and/or conservative predictions of fatigue life. Within the limitations of availability of experimental data and currently available numerical simulation techniques, the results of present study attempt to outline models that would provide accurate and conservative life predictions. Such a study aid the numerical analysts or engineers in the proper selection of the model for numerical simulation of the fatigue life. Moreover, the present investigation also suggests a procedure to enhance the accuracy of life prediction using Paris law.

Probable mode prediction for H.264 advanced video coding P slices using removable SKIP mode distortion estimation

Science.gov (United States)

You, Jongmin; Jeong, Jechang

2010-02-01

The H.264/AVC (advanced video coding) is used in a wide variety of applications including digital broadcasting and mobile applications, because of its high compression efficiency. The variable block mode scheme in H.264/AVC contributes much to its high compression efficiency but causes a selection problem. In general, rate-distortion optimization (RDO) is the optimal mode selection strategy, but it is computationally intensive. For this reason, the H.264/AVC encoder requires a fast mode selection algorithm for use in applications that require low-power and real-time processing. A probable mode prediction algorithm for the H.264/AVC encoder is proposed. To reduce the computational complexity of RDO, the proposed method selects probable modes among all allowed block modes using removable SKIP mode distortion estimation. Removable SKIP mode distortion is used to estimate whether or not a further divided block mode is appropriate for a macroblock. It is calculated using a no-motion reference block with a few computations. Then the proposed method reduces complexity by performing the RDO process only for probable modes. Experimental results show that the proposed algorithm can reduce encoding time by an average of 55.22% without significant visual quality degradation and increased bit rate.

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.

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.

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...

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.

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.

Nonlinear Predictive Sliding Mode Control for Active Suspension System

Directory of Open Access Journals (Sweden)

Dazhuang Wang

2018-01-01

Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

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/.

Models for Predicting Boundary Conditions in L-Mode Tokamak Plasma

International Nuclear Information System (INIS)

Siriwitpreecha, A.; Onjun, T.; Suwanna, S.; Poolyarat, N.; Picha, R.

2009-07-01

Full text: The models for predicting temperature and density of ions and electrons at boundary conditions in L-mode tokamak plasma are developed using an empirical approach and optimized against the experimental data obtained from the latest public version of the International Pedestal Database (version 3.2). It is assumed that the temperature and density at boundary of L-mode plasma are functions of engineering parameters such as plasma current, toroidal magnetic field, total heating power, line averaged density, hydrogenic particle mass (A H ), major radius, minor radius, and elongation at the separatrix. Multiple regression analysis is carried out for these parameters with 86 data points in L-mode from Aug (61) and JT60U (25). The RMSE of temperature and density at boundary of L-mode plasma are found to be 24.41% and 18.81%, respectively. These boundary models are implemented in BALDUR code, which will be used to simulate the L-mode plasma in the tokamak

Memory Binding Test Predicts Incident Amnestic Mild Cognitive Impairment.

Science.gov (United States)

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

2016-07-14

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

Analysis of experimental positron-molecule binding energies

International Nuclear Information System (INIS)

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

2010-01-01

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

Structure of Bacillus subtilis γ-glutamyltranspeptidase in complex with acivicin: diversity of the binding mode of a classical and electrophilic active-site-directed glutamate analogue

Energy Technology Data Exchange (ETDEWEB)

Ida, Tomoyo [Osaka University, Toyonaka, Osaka 560-0043 (Japan); Suzuki, Hideyuki [Kyoto Institute of Technology, Goshokaido-cho, Matsugasaki, Sakyo-ku, Kyoto 606-8585 (Japan); Fukuyama, Keiichi [Osaka University, Toyonaka, Osaka 560-0043 (Japan); Hiratake, Jun [Kyoto University, Uji, Kyoto 611-0011 (Japan); Wada, Kei, E-mail: keiwada@med.miyazaki-u.ac.jp [University of Miyazaki, Miyazaki 889-1692 (Japan); Osaka University, Toyonaka, Osaka 560-0043 (Japan)

2014-02-01

The binding modes of acivicin, a classical and an electrophilic active-site-directed glutamate analogue, to bacterial γ-glutamyltranspeptidases were found to be diverse. γ-Glutamyltranspeptidase (GGT) is an enzyme that plays a central role in glutathione metabolism, and acivicin is a classical inhibitor of GGT. Here, the structure of acivicin bound to Bacillus subtilis GGT determined by X-ray crystallography to 1.8 Å resolution is presented, in which it binds to the active site in a similar manner to that in Helicobacter pylori GGT, but in a different binding mode to that in Escherichia coli GGT. In B. subtilis GGT, acivicin is bound covalently through its C3 atom with sp{sup 2} hybridization to Thr403 O{sup γ}, the catalytic nucleophile of the enzyme. The results show that acivicin-binding sites are common, but the binding manners and orientations of its five-membered dihydroisoxazole ring are diverse in the binding pockets of GGTs.

Structure of Bacillus subtilis γ-glutamyltranspeptidase in complex with acivicin: diversity of the binding mode of a classical and electrophilic active-site-directed glutamate analogue

International Nuclear Information System (INIS)

Ida, Tomoyo; Suzuki, Hideyuki; Fukuyama, Keiichi; Hiratake, Jun; Wada, Kei

2014-01-01

The binding modes of acivicin, a classical and an electrophilic active-site-directed glutamate analogue, to bacterial γ-glutamyltranspeptidases were found to be diverse. γ-Glutamyltranspeptidase (GGT) is an enzyme that plays a central role in glutathione metabolism, and acivicin is a classical inhibitor of GGT. Here, the structure of acivicin bound to Bacillus subtilis GGT determined by X-ray crystallography to 1.8 Å resolution is presented, in which it binds to the active site in a similar manner to that in Helicobacter pylori GGT, but in a different binding mode to that in Escherichia coli GGT. In B. subtilis GGT, acivicin is bound covalently through its C3 atom with sp 2 hybridization to Thr403 O γ , the catalytic nucleophile of the enzyme. The results show that acivicin-binding sites are common, but the binding manners and orientations of its five-membered dihydroisoxazole ring are diverse in the binding pockets of GGTs

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.

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

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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.

LIGAND-BINDING SITES ON THE MYCOBACTERIUM TUBERCULOSIS UREASE

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Lisnyak Yu. V.

2017-10-01

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

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

Science.gov (United States)

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

2017-05-11

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

A novel cofactor-binding mode in bacterial IMP dehydrogenases explains inhibitor selectivity.

Science.gov (United States)

Makowska-Grzyska, Magdalena; Kim, Youngchang; Maltseva, Natalia; Osipiuk, Jerzy; Gu, Minyi; Zhang, Minjia; Mandapati, Kavitha; Gollapalli, Deviprasad R; Gorla, Suresh Kumar; Hedstrom, Lizbeth; Joachimiak, Andrzej

2015-02-27

The steadily rising frequency of emerging diseases and antibiotic resistance creates an urgent need for new drugs and targets. Inosine 5'-monophosphate dehydrogenase (IMP dehydrogenase or IMPDH) is a promising target for the development of new antimicrobial agents. IMPDH catalyzes the oxidation of IMP to XMP with the concomitant reduction of NAD(+), which is the pivotal step in the biosynthesis of guanine nucleotides. Potent inhibitors of bacterial IMPDHs have been identified that bind in a structurally distinct pocket that is absent in eukaryotic IMPDHs. The physiological role of this pocket was not understood. Here, we report the structures of complexes with different classes of inhibitors of Bacillus anthracis, Campylobacter jejuni, and Clostridium perfringens IMPDHs. These structures in combination with inhibition studies provide important insights into the interactions that modulate selectivity and potency. We also present two structures of the Vibrio cholerae IMPDH in complex with IMP/NAD(+) and XMP/NAD(+). In both structures, the cofactor assumes a dramatically different conformation than reported previously for eukaryotic IMPDHs and other dehydrogenases, with the major change observed for the position of the NAD(+) adenosine moiety. More importantly, this new NAD(+)-binding site involves the same pocket that is utilized by the inhibitors. Thus, the bacterial IMPDH-specific NAD(+)-binding mode helps to rationalize the conformation adopted by several classes of prokaryotic IMPDH inhibitors. These findings offer a potential strategy for further ligand optimization. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

A Novel Cofactor-binding Mode in Bacterial IMP Dehydrogenases Explains Inhibitor Selectivity*

Science.gov (United States)

Makowska-Grzyska, Magdalena; Kim, Youngchang; Maltseva, Natalia; Osipiuk, Jerzy; Gu, Minyi; Zhang, Minjia; Mandapati, Kavitha; Gollapalli, Deviprasad R.; Gorla, Suresh Kumar; Hedstrom, Lizbeth; Joachimiak, Andrzej

2015-01-01

The steadily rising frequency of emerging diseases and antibiotic resistance creates an urgent need for new drugs and targets. Inosine 5′-monophosphate dehydrogenase (IMP dehydrogenase or IMPDH) is a promising target for the development of new antimicrobial agents. IMPDH catalyzes the oxidation of IMP to XMP with the concomitant reduction of NAD+, which is the pivotal step in the biosynthesis of guanine nucleotides. Potent inhibitors of bacterial IMPDHs have been identified that bind in a structurally distinct pocket that is absent in eukaryotic IMPDHs. The physiological role of this pocket was not understood. Here, we report the structures of complexes with different classes of inhibitors of Bacillus anthracis, Campylobacter jejuni, and Clostridium perfringens IMPDHs. These structures in combination with inhibition studies provide important insights into the interactions that modulate selectivity and potency. We also present two structures of the Vibrio cholerae IMPDH in complex with IMP/NAD+ and XMP/NAD+. In both structures, the cofactor assumes a dramatically different conformation than reported previously for eukaryotic IMPDHs and other dehydrogenases, with the major change observed for the position of the NAD+ adenosine moiety. More importantly, this new NAD+-binding site involves the same pocket that is utilized by the inhibitors. Thus, the bacterial IMPDH-specific NAD+-binding mode helps to rationalize the conformation adopted by several classes of prokaryotic IMPDH inhibitors. These findings offer a potential strategy for further ligand optimization. PMID:25572472

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

Directory of Open Access Journals (Sweden)

Amy L Bauer

2010-11-01

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

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…

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

Science.gov (United States)

Lee, Hui Sun; Im, Wonpil

2017-01-01

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

Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

A. M. Carmona

2015-04-01

Full Text Available The hydro-climatology of Colombia exhibits strong natural variability at a broad range of time scales including: inter-decadal, decadal, inter-annual, annual, intra-annual, intra-seasonal, and diurnal. Diverse applied sectors rely on quantitative predictions of river discharges for operational purposes including hydropower generation, agriculture, human health, fluvial navigation, territorial planning and management, risk preparedness and mitigation, among others. Various methodologies have been used to predict monthly mean river discharges that are based on "Predictive Analytics", an area of statistical analysis that studies the extraction of information from historical data to infer future trends and patterns. Our study couples the Empirical Mode Decomposition (EMD with traditional methods, e.g. Autoregressive Model of Order 1 (AR1 and Neural Networks (NN, to predict mean monthly river discharges in Colombia, South America. The EMD allows us to decompose the historical time series of river discharges into a finite number of intrinsic mode functions (IMF that capture the different oscillatory modes of different frequencies associated with the inherent time scales coexisting simultaneously in the signal (Huang et al. 1998, Huang and Wu 2008, Rao and Hsu, 2008. Our predictive method states that it is easier and simpler to predict each IMF at a time and then add them up together to obtain the predicted river discharge for a certain month, than predicting the full signal. This method is applied to 10 series of monthly mean river discharges in Colombia, using calibration periods of more than 25 years, and validation periods of about 12 years. Predictions are performed for time horizons spanning from 1 to 12 months. Our results show that predictions obtained through the traditional methods improve when the EMD is used as a previous step, since errors decrease by up to 13% when the AR1 model is used, and by up to 18% when using Neural Networks is

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

Conditional mode regression: Application to functional time series prediction

OpenAIRE

Dabo-Niang, Sophie; Laksaci, Ali

2008-01-01

We consider $\\alpha$-mixing observations and deal with the estimation of the conditional mode of a scalar response variable $Y$ given a random variable $X$ taking values in a semi-metric space. We provide a convergence rate in $L^p$ norm of the estimator. A useful and typical application to functional times series prediction is given.

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

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2016-10-01

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

Glycosaminoglycans are interactants of Langerin: comparison with gp120 highlights an unexpected calcium-independent binding mode.

Science.gov (United States)

Chabrol, Eric; Nurisso, Alessandra; Daina, Antoine; Vassal-Stermann, Emilie; Thepaut, Michel; Girard, Eric; Vivès, Romain R; Fieschi, Franck

2012-01-01

Langerin is a C-type lectin specifically expressed in Langerhans cells. As recently shown for HIV, Langerin is thought to capture pathogens and mediate their internalisation into Birbeck Granules for elimination. However, the precise functions of Langerin remain elusive, mostly because of the lack of information on its binding properties and physiological ligands. Based on recent reports that Langerin binds to sulfated sugars, we conducted here a comparative analysis of Langerin interaction with mannose-rich HIV glycoprotein gp120 and glycosaminoglycan (GAGs), a family of sulfated polysaccharides expressed at the surface of most mammalian cells. Our results first revealed that Langerin bound to these different glycans through very distinct mechanisms and led to the identification of a novel, GAG-specific binding mode within Langerin. In contrast to the canonical lectin domain, this new binding site showed no Ca(2+)-dependency, and could only be detected in entire, trimeric extracellular domains of Langerin. Interestingly binding to GAGs, did not simply rely on a net charge effect, but rather on more discrete saccharide features, such as 6-O-sulfation, or iduronic acid content. Using molecular modelling simulations, we proposed a model of Langerin/heparin complex, which located the GAG binding site at the interface of two of the three Carbohydrate-recognition domains of the protein, at the edge of the a-helix coiled-coil. To our knowledge, the binding properties that we have highlighted here for Langerin, have never been reported for C-type lectins before. These findings provide new insights towards the understanding of Langerin biological functions.

Glycosaminoglycans are interactants of Langerin: comparison with gp120 highlights an unexpected calcium-independent binding mode.

Directory of Open Access Journals (Sweden)

Eric Chabrol

Full Text Available Langerin is a C-type lectin specifically expressed in Langerhans cells. As recently shown for HIV, Langerin is thought to capture pathogens and mediate their internalisation into Birbeck Granules for elimination. However, the precise functions of Langerin remain elusive, mostly because of the lack of information on its binding properties and physiological ligands. Based on recent reports that Langerin binds to sulfated sugars, we conducted here a comparative analysis of Langerin interaction with mannose-rich HIV glycoprotein gp120 and glycosaminoglycan (GAGs, a family of sulfated polysaccharides expressed at the surface of most mammalian cells. Our results first revealed that Langerin bound to these different glycans through very distinct mechanisms and led to the identification of a novel, GAG-specific binding mode within Langerin. In contrast to the canonical lectin domain, this new binding site showed no Ca(2+-dependency, and could only be detected in entire, trimeric extracellular domains of Langerin. Interestingly binding to GAGs, did not simply rely on a net charge effect, but rather on more discrete saccharide features, such as 6-O-sulfation, or iduronic acid content. Using molecular modelling simulations, we proposed a model of Langerin/heparin complex, which located the GAG binding site at the interface of two of the three Carbohydrate-recognition domains of the protein, at the edge of the a-helix coiled-coil. To our knowledge, the binding properties that we have highlighted here for Langerin, have never been reported for C-type lectins before. These findings provide new insights towards the understanding of Langerin biological functions.

An analysis of the binding of repressor protein ModE to modABCD (molybdate transport) operator/promoter DNA of Escherichia coli.

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Grunden, A M; Self, W T; Villain, M; Blalock, J E; Shanmugam, K T

1999-08-20

Expression of the modABCD operon in Escherichia coli, which codes for a molybdate-specific transporter, is repressed by ModE in vivo in a molybdate-dependent fashion. In vitro DNase I-footprinting experiments identified three distinct regions of protection by ModE-molybdate on the modA operator/promoter DNA, GTTATATT (-15 to -8; region 1), GCCTACAT (-4 to +4; region 2), and GTTACAT (+8 to +14; region 3). Within the three regions of the protected DNA, a pentamer sequence, TAYAT (Y = C or T), can be identified. DNA-electrophoretic mobility experiments showed that the protected regions 1 and 2 are essential for binding of ModE-molybdate to DNA, whereas the protected region 3 increases the affinity of the DNA to the repressor. The stoichiometry of this interaction was found to be two ModE-molybdate per modA operator DNA. ModE-molybdate at 5 nM completely protected the modABCD operator/promoter DNA from DNase I-catalyzed hydrolysis, whereas ModE alone failed to protect the DNA even at 100 nM. The apparent K(d) for the interaction between the modA operator DNA and ModE-molybdate was 0.3 nM, and the K(d) increased to 8 nM in the absence of molybdate. Among the various oxyanions tested, only tungstate replaced molybdate in the repression of modA by ModE, but the affinity of ModE-tungstate for modABCD operator DNA was 6 times lower than with ModE-molybdate. A mutant ModE(T125I) protein, which repressed modA-lac even in the absence of molybdate, protected the same region of modA operator DNA in the absence of molybdate. The apparent K(d) for the interaction between modA operator DNA and ModE(T125I) was 3 nM in the presence of molybdate and 4 nM without molybdate. The binding of molybdate to ModE resulted in a decrease in fluorescence emission, indicating a conformational change of the protein upon molybdate binding. The fluorescence emission spectra of mutant ModE proteins, ModE(T125I) and ModE(Q216*), were unaffected by molybdate. The molybdate-independent mutant ModE

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

DEFF Research Database (Denmark)

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

2003-01-01

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

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

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

2015-06-12

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

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

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

2018-01-06

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

Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques

Science.gov (United States)

2018-04-30

Resources: N/A TOTAL: $18,687 2 TECHNICAL STATUS REPORT Abstract The program goal is analysis of sea ice dynamical behavior using Koopman Mode Decompo...Title: Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques Subject: Monthly Progress Report Period of...Attn: Code 5596 4555 Overlook Avenue, SW Washington, D.C. 20375-5320 E-mail: reports@library.nrl.navy.mil Defense Technical Information Center

Atomistic fingerprint of hyaluronan-CD44 binding

DEFF Research Database (Denmark)

Vuorio, Joni; Vattulainen, Ilpo; Martinez-Seara, Hector

2017-01-01

that hyaluronan can bind CD44 with three topographically different binding modes that in unison define an interaction fingerprint, thus providing a plausible explanation for the disagreement between the earlier studies. Our results confirm that the known crystallographic mode is the strongest of the three binding...

Probe the Binding Mode of Aristololactam-β-D-glucoside to Phenylalanine Transfer RNA in Silico

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Xiao, Xingqing; Zhao, Binwu; Yang, Li

2016-01-01

Understanding the interactions of drug molecules with biomacromolecules at a micro-scale level is essential to design potent drugs for the treatments of human genome diseases. To unravel the mechanism of binding of aristololactam-β-D-glucoside (ADG) and phenylalanine transfer RNA (t...... on the tRNAPhe, and atomistic MD simulations were conducted to examine the thermal stability of five predicted binding poses for the complex of ADG and the tRNAPhe. The binding free energies of the five complexes were then calculated using the molecular mechanics/generalized born surface area approach...

Predictive modelling of edge transport phenomena in ELMy H-mode tokamak fusion plasmas

International Nuclear Information System (INIS)

Loennroth, J.-S.

2009-01-01

This thesis discusses a range of work dealing with edge plasma transport in magnetically confined fusion plasmas by means of predictive transport modelling, a technique in which qualitative predictions and explanations are sought by running transport codes equipped with models for plasma transport and other relevant phenomena. The focus is on high confinement mode (H-mode) tokamak plasmas, which feature improved performance thanks to the formation of an edge transport barrier. H-mode plasmas are generally characterized by the occurrence of edge localized modes (ELMs), periodic eruptions of particles and energy, which limit confinement and may turn out to be seriously damaging in future tokamaks. The thesis introduces schemes and models for qualitative study of the ELM phenomenon in predictive transport modelling. It aims to shed new light on the dynamics of ELMs using these models. It tries to explain various experimental observations related to the performance and ELM-behaviour of H-mode plasmas. Finally, it also tries to establish more generally the potential effects of ripple-induced thermal ion losses on H-mode plasma performance and ELMs. It is demonstrated that the proposed ELM modelling schemes can qualitatively reproduce the experimental dynamics of a number of ELM regimes. Using a theory-motivated ELM model based on a linear instability model, the dynamics of combined ballooning-peeling mode ELMs is studied. It is shown that the ELMs are most often triggered by a ballooning mode instability, which renders the plasma peeling mode unstable, causing the ELM to continue in a peeling mode phase. Understanding the dynamics of ELMs will be a key issue when it comes to controlling and mitigating the ELMs in future large tokamaks. By means of integrated modelling, it is shown that an experimentally observed increase in the ELM frequency and deterioration of plasma confinement triggered by external neutral gas puffing might be due to a transition from the second to

Determination of the binding mode for the cyclopentapeptide CXCR4 antagonist FC131 using a dual approach of ligand modifications and receptor mutagenesis

DEFF Research Database (Denmark)

Thiele, Stefanie; Mungalpara, J; Steen, A

2014-01-01

have previously been suggested based on molecular docking guided by structure-activity relationship (SAR) data; however, none of these have been verified by in vitro experiments. EXPERIMENTAL APPROACH: Heterologous (125) I-12G5-competition binding and functional assays (inhibition of CXCL12-mediated...... activation) of FC131 and three analogues were performed on wild-type CXCR4 and 25 receptor mutants. Computational modelling was used to rationalize the experimental data. KEY RESULTS: The Arg(2) and 2-Nal(3) side chains of FC131 interact with residues in TM-3 (His(113) , Asp(171) ) and TM-5 (hydrophobic......-bond in CXCR4 crystal structures and mutation of either residue to Ala abolishes CXCR4 activity. CONCLUSIONS AND IMPLICATIONS: Ligand modification, receptor mutagenesis and computational modelling approaches were used to identify the binding mode of FC131 in CXCR4, which was in agreement with binding modes...

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

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Chih-Hao Lu

2015-01-01

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

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

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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.

A Unified Model of the GABA(A) Receptor Comprising Agonist and Benzodiazepine Binding Sites

DEFF Research Database (Denmark)

Kongsbak, Kristine Grønning; Bergmann, Rikke; Sørensen, Pernille Louise

2013-01-01

We present a full-length a1b2c2 GABA receptor model optimized for agonists and benzodiazepine (BZD) allosteric modulators. We propose binding hypotheses for the agonists GABA, muscimol and THIP and for the allosteric modulator diazepam (DZP). The receptor model is primarily based on the glutamate......-gated chloride channel (GluCl) from C. elegans and includes additional structural information from the prokaryotic ligand-gated ion channel ELIC in a few regions. Available mutational data of the binding sites are well explained by the model and the proposed ligand binding poses. We suggest a GABA binding mode...... of the agonists in the orthosteric site. The carbonyl group of DZP is predicted to interact with two threonines a1T206 and c2T142, similar to the acidic moiety of GABA. The chlorine atom of DZP is placed near the important a1H101 and the N-methyl group near a1Y159, a1T206, and a1Y209. We present a binding mode...

Precise Ab-initio prediction of terahertz vibrational modes in crystalline systems

DEFF Research Database (Denmark)

Jepsen, Peter Uhd; Clark, Stewart J.

2007-01-01

We use a combination of experimental THz time-domain spectroscopy and ab-initio density functional perturbative theory to accurately predict the terahertz vibrational spectrum of molecules in the crystalline phase. Our calculations show that distinct vibrational modes found in solid-state materials...

Bridging Binding Modes of Phosphine-Stabilized Nitrous Oxide to Zn(C6F5)2

NARCIS (Netherlands)

Neu, Rebecca C.; Otten, Edwin; Stephan, Douglas W.

2009-01-01

Reaction of [tBu3PN2O(B(C6H4F)3)] with 1, 1.5, or 2 equivalents of Zn(C6F5)2 affords the species [{tBu3PN2OZn(C6F5)2}2], [{tBu3PN2OZn(C6F5)2}2Zn(C6F5)2], and [tBu3PN2O{Zn(C6F5)2}2] displaying unique binding modes of Zn to the phosphine-stabilized N2O fragment.

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

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Zhang Ya-Nan

2012-05-01

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

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

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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.

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

Science.gov (United States)

Konc, Janez; Janežič, Dušanka

2014-07-01

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

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

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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.

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

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

2017-12-09

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

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.

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

2015-12-10

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

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

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Gregory A Ross

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

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

Science.gov (United States)

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

2015-01-01

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

Predictive simulations of radio frequency heated plasmas of Tore Supra using the Multi-Mode model

International Nuclear Information System (INIS)

Voitsekhovitch, Irina; Bateman, Glenn; Kritz, Arnold H.; Pankin, Alexei

2002-01-01

Multichannel integrated predictive simulations using the Multi-Mode transport model are carried out for radio frequency heated Tore Supra tokamak discharges in which helium is the primary ion component. Lower hybrid heated discharges in which the total current is driven noninductively [X. Litaudon et al., Plasma Phys. Controlled Fusion 43, 677 (2001)] and a discharge with ion cyclotron radio frequency heating of the hydrogen minority ions [G. T. Hoang et al., Nucl. Fusion 38, 117 (1998)] are simulated. The simulations of these discharges represent the first test of the Multi-Mode model in helium plasmas with dominant electron heating. Also for the first time, the particle transport in Tore Supra discharges is computed and the density profiles are predicted self-consistently with other transport channels. It is found in these simulations that the anomalous transport driven by trapped electron mode turbulence is dominant compared to the transport driven by the ion temperature gradient turbulence. The feature of the Multi-Mode model to calculate the impurity transport self-consistently with other transport channels is used in this study to predict the influence of carbon impurity influx on the discharge evolution

Interaction of the amyloid precursor protein-like protein 1 (APLP1) E2 domain with heparan sulfate involves two distinct binding modes

Energy Technology Data Exchange (ETDEWEB)

Dahms, Sven O., E-mail: sdahms@fli-leibniz.de [Leibniz Institute for Age Research (FLI), Beutenbergstrasse 11, 07745 Jena (Germany); Mayer, Magnus C. [Freie Universität Berlin, Thielallee 63, 14195 Berlin (Germany); Miltenyi Biotec GmbH, Robert-Koch-Strasse 1, 17166 Teterow (Germany); Roeser, Dirk [Leibniz Institute for Age Research (FLI), Beutenbergstrasse 11, 07745 Jena (Germany); Multhaup, Gerd [McGill University Montreal, Montreal, Quebec H3G 1Y6 (Canada); Than, Manuel E., E-mail: sdahms@fli-leibniz.de [Leibniz Institute for Age Research (FLI), Beutenbergstrasse 11, 07745 Jena (Germany)

2015-03-01

Two X-ray structures of APLP1 E2 with and without a heparin dodecasaccharide are presented, revealing two distinct binding modes of the protein to heparan sulfate. The data provide a mechanistic explanation of how APP-like proteins bind to heparan sulfates and how they specifically recognize nonreducing structures of heparan sulfates. Beyond the pathology of Alzheimer’s disease, the members of the amyloid precursor protein (APP) family are essential for neuronal development and cell homeostasis in mammals. APP and its paralogues APP-like protein 1 (APLP1) and APP-like protein 2 (APLP2) contain the highly conserved heparan sulfate (HS) binding domain E2, which effects various (patho)physiological functions. Here, two crystal structures of the E2 domain of APLP1 are presented in the apo form and in complex with a heparin dodecasaccharide at 2.5 Å resolution. The apo structure of APLP1 E2 revealed an unfolded and hence flexible N-terminal helix αA. The (APLP1 E2){sub 2}–(heparin){sub 2} complex structure revealed two distinct binding modes, with APLP1 E2 explicitly recognizing the heparin terminus but also interacting with a continuous heparin chain. The latter only requires a certain register of the sugar moieties that fits to a positively charged surface patch and contributes to the general heparin-binding capability of APP-family proteins. Terminal binding of APLP1 E2 to heparin specifically involves a structure of the nonreducing end that is very similar to heparanase-processed HS chains. These data reveal a conserved mechanism for the binding of APP-family proteins to HS and imply a specific regulatory role of HS modifications in the biology of APP and APP-like proteins.

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

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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

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

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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.

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

Science.gov (United States)

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

2015-01-01

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

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

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Luigi Capoferri

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

Prediction of multipactor in the iris region of rf deflecting mode cavities

Directory of Open Access Journals (Sweden)

G. Burt

2011-12-01

Full Text Available Multipactor is a major cause of field limitation in many superconducting rf cavities. Multipacting is a particular issue for deflecting mode cavities as the typical behavior is not well studied, understood, or parametrized. In this paper an approximate analytical model for the prediction of multipactor in the iris region of deflecting mode cavities is developed. This new but simple model yields a clear explanation on the broad range of rf field levels over which the multipactor can occur. The principle multipactors under investigation here are two-point multipactors associated with cyclotron motion in the cavity’s rf magnetic field. The predictions from the model are compared to numerical simulations and good agreement is obtained. The results are also compared to experimental results previously reported by KEK and are also found in good agreement.

Predicting plasticity with soft vibrational modes: from dislocations to glasses.

Science.gov (United States)

Rottler, Jörg; Schoenholz, Samuel S; Liu, Andrea J

2014-04-01

We show that quasilocalized low-frequency modes in the vibrational spectrum can be used to construct soft spots, or regions vulnerable to rearrangement, which serve as a universal tool for the identification of flow defects in solids. We show that soft spots not only encode spatial information, via their location, but also directional information, via directors for particles within each soft spot. Single crystals with isolated dislocations exhibit low-frequency phonon modes that localize at the core, and their polarization pattern predicts the motion of atoms during elementary dislocation glide in two and three dimensions in exquisite detail. Even in polycrystals and disordered solids, we find that the directors associated with particles in soft spots are highly correlated with the direction of particle displacements in rearrangements.

Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

KAUST Repository

Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed

2016-01-01

In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

Fault Tolerant Flight Control Using Sliding Modes and Subspace Identification-Based Predictive Control

KAUST Repository

Siddiqui, Bilal A.

2016-07-26

In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.

Retinoid-binding proteins: similar protein architectures bind similar ligands via completely different ways.

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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.

Fast dynamics perturbation analysis for prediction of protein functional sites

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Cohn Judith D

2008-01-01

Full Text Available Abstract Background We present a fast version of the dynamics perturbation analysis (DPA algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy Dx. Such regions are associated with functional sites. Results The Fast DPA algorithm, which accelerates DPA calculations, is motivated by an empirical observation that Dx in a normal-modes model is highly correlated with an entropic term that only depends on the eigenvalues of the normal modes. The eigenvalues are accurately estimated using first-order perturbation theory, resulting in a N-fold reduction in the overall computational requirements of the algorithm, where N is the number of residues in the protein. The performance of the original and Fast DPA algorithms was compared using protein structures from a standard small-molecule docking test set. For nominal implementations of each algorithm, top-ranked Fast DPA predictions overlapped the true binding site 94% of the time, compared to 87% of the time for original DPA. In addition, per-protein recall statistics (fraction of binding-site residues that are among predicted residues were slightly better for Fast DPA. On the other hand, per-protein precision statistics (fraction of predicted residues that are among binding-site residues were slightly better using original DPA. Overall, the performance of Fast DPA in predicting ligand-binding-site residues was comparable to that of the original DPA algorithm. Conclusion Compared to the original DPA algorithm, the decreased run time with comparable performance makes Fast DPA well-suited for implementation on a web server and for high-throughput analysis.

Degradation mode analysis: An approach to establish effective predictive maintenance tasks

International Nuclear Information System (INIS)

Sonnett, D.E.; Douglass, P.T.; Barnard, D.D.

1991-01-01

A significant number of nuclear generating stations have been employing Reliability Centered Maintenance methodology to arrive at applicable and effective maintenance tasks for their plant equipment. The resultant endpoint of most programs has been an increased emphasis on predictive maintenance as the task of choice for monitoring and trending plant equipment condition to address failure mechanisms of the analyses. Many of these plants have spent several years conducting reliability centered analysis before they seriously begin implementing predictive program improvements. In this paper we present another methodology, entitled Degradation Mode Analysis, which provides a more direct method to quickly and economically achieve the major benefit of reliability centered analysis, namely predictive maintenance. (author)

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

International Nuclear Information System (INIS)

Sun Zhong-Hua; Jiang Fan

2010-01-01

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

The DNA-recognition mode shared by archaeal feast/famine-regulatory proteins revealed by the DNA-binding specificities of TvFL3, FL10, FL11 and Ss-LrpB

Science.gov (United States)

Yokoyama, Katsushi; Nogami, Hideki; Kabasawa, Mamiko; Ebihara, Sonomi; Shimowasa, Ai; Hashimoto, Keiko; Kawashima, Tsuyoshi; Ishijima, Sanae A.; Suzuki, Masashi

2009-01-01

The DNA-binding mode of archaeal feast/famine-regulatory proteins (FFRPs), i.e. paralogs of the Esherichia coli leucine-responsive regulatory protein (Lrp), was studied. Using the method of systematic evolution of ligands by exponential enrichment (SELEX), optimal DNA duplexes for interacting with TvFL3, FL10, FL11 and Ss-LrpB were identified as TACGA[AAT/ATT]TCGTA, GTTCGA[AAT/ATT]TCGAAC, CCGAAA[AAT/ATT]TTTCGG and TTGCAA[AAT/ATT]TTGCAA, respectively, all fitting into the form abcdeWWWedcba. Here W is A or T, and e.g. a and a are bases complementary to each other. Apparent equilibrium binding constants of the FFRPs and various DNA duplexes were determined, thereby confirming the DNA-binding specificities of the FFRPs. It is likely that these FFRPs recognize DNA in essentially the same way, since their DNA-binding specificities were all explained by the same pattern of relationship between amino-acid positions and base positions to form chemical interactions. As predicted from this relationship, when Gly36 of TvFL3 was replaced by Thr, the b base in the optimal DNA duplex changed from A to T, and, when Thr36 of FL10 was replaced by Ser, the b base changed from T to G/A. DNA-binding characteristics of other archaeal FFRPs, Ptr1, Ptr2, Ss-Lrp and LysM, are also consistent with the relationship. PMID:19468044

Evolutionary Limitation and Opportunities for Developing tRNA Synthetase Inhibitors with 5-Binding-Mode Classification

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Pengfei Fang

2015-12-01

Full Text Available Aminoacyl-tRNA synthetases (aaRSs are enzymes that catalyze the transfer of amino acids to their cognate tRNAs as building blocks for translation. Each of the aaRS families plays a pivotal role in protein biosynthesis and is indispensable for cell growth and survival. In addition, aaRSs in higher species have evolved important non-translational functions. These translational and non-translational functions of aaRS are attractive for developing antibacterial, antifungal, and antiparasitic agents and for treating other human diseases. The interplay between amino acids, tRNA, ATP, EF-Tu and non-canonical binding partners, had shaped each family with distinct pattern of key sites for regulation, with characters varying among species across the path of evolution. These sporadic variations in the aaRSs offer great opportunity to target these essential enzymes for therapy. Up to this day, growing numbers of aaRS inhibitors have been discovered and developed. Here, we summarize the latest developments and structural studies of aaRS inhibitors, and classify them with distinct binding modes into five categories.

Tube Model Predictive Control with an Auxiliary Sliding Mode Controller

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Miodrag Spasic

2016-07-01

Full Text Available This paper studies Tube Model Predictive Control (MPC with a Sliding Mode Controller (SMC as an auxiliary controller. It is shown how to calculate the tube widths under SMC control, and thus how much the constraints of the nominal MPC have to be tightened in order to achieve robust stability and constraint fulfillment. The analysis avoids the assumption of infinitely fast switching in the SMC controller.

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

Science.gov (United States)

2011-01-01

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

An adaptive mode-driven spatiotemporal motion vector prediction for wavelet video coding

Science.gov (United States)

Zhao, Fan; Liu, Guizhong; Qi, Yong

2010-07-01

The three-dimensional subband/wavelet codecs use 5/3 filters rather than Haar filters for the motion compensation temporal filtering (MCTF) to improve the coding gain. In order to curb the increased motion vector rate, an adaptive motion mode driven spatiotemporal motion vector prediction (AMDST-MVP) scheme is proposed. First, by making use of the direction histograms of four motion vector fields resulting from the initial spatial motion vector prediction (SMVP), the motion mode of the current GOP is determined according to whether the fast or complex motion exists in the current GOP. Then the GOP-level MVP scheme is thereby determined by either the S-MVP or the AMDST-MVP, namely, AMDST-MVP is the combination of S-MVP and temporal-MVP (T-MVP). If the latter is adopted, the motion vector difference (MVD) between the neighboring MV fields and the S-MVP resulting MV of the current block is employed to decide whether or not the MV of co-located block in the previous frame is used for prediction the current block. Experimental results show that AMDST-MVP not only can improve the coding efficiency but also reduce the number of computation complexity.

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

NARCIS (Netherlands)

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

2016-01-01

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

Prediction of RNA-Binding Proteins by Voting Systems

Directory of Open Access Journals (Sweden)

C. R. Peng

2011-01-01

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

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...

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...

Ligand deconstruction: Why some fragment binding positions are conserved and others are not

Science.gov (United States)

Kozakov, Dima; Hall, David R.; Jehle, Stefan; Luo, Lingqi; Ochiana, Stefan O.; Jones, Elizabeth V.; Pollastri, Michael; Allen, Karen N.; Whitty, Adrian; Vajda, Sandor

2015-01-01

Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots—regions of the protein where interactions with a ligand contribute substantial binding free energy—the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand. PMID:25918377

Ligand deconstruction: Why some fragment binding positions are conserved and others are not.

Science.gov (United States)

Kozakov, Dima; Hall, David R; Jehle, Stefan; Jehle, Sefan; Luo, Lingqi; Ochiana, Stefan O; Jones, Elizabeth V; Pollastri, Michael; Allen, Karen N; Whitty, Adrian; Vajda, Sandor

2015-05-19

Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots--regions of the protein where interactions with a ligand contribute substantial binding free energy--the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand.

Binding of indomethacin methyl ester to cyclooxygenase-2. A computational study.

Science.gov (United States)

Sárosi, Menyhárt-Botond

2018-06-05

Inhibitors selective towards the second isoform of prostaglandin synthase (cyclooxygenase, COX-2) are promising nonsteroidal anti-inflammatory drugs and antitumor medications. Methylation of the carboxylate group in the relatively nonselective COX inhibitor indomethacin confers significant COX-2 selectivity. Several other modifications converting indomethacin into a COX-2 selective inhibitor have been reported. Earlier experimental and computational studies on neutral indomethacin derivatives suggest that the methyl ester derivative likely binds to COX-2 with a similar binding mode as that observed for the parent indomethacin. However, docking studies followed by molecular dynamics simulations revealed two possible binding modes in COX-2 for indomethacin methyl ester, which differs from the experimental binding mode found for indomethacin. Both alternative binding modes might explain the observed COX-2 selectivity of indomethacin methyl ester. Graphical abstract Binding of indomethacin methyl ester to cyclooxygenase-2.

Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

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Md. Rabiul Islam

2012-01-01

Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

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

Science.gov (United States)

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

2012-05-01

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

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/.

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

Science.gov (United States)

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

2012-11-01

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

Predicting the Diversity of Foreign Entry Modes

DEFF Research Database (Denmark)

Hashai, Niron; Geisler Asmussen, Christian; Benito, Gabriel

2007-01-01

diversity across value chain activities and host markets. Analyzing a sample of Israeli based firms we show that larger firms exhibit a higher degree of entry mode diversity both across value chain activities and across host markets. Higher levels of knowledge intensity are also associated with more......This paper expands entry mode literature by referring to multiple modes exerted in different value chain activities within and across host markets, rather than to a single entry mode at the host market level. Scale of operations and knowledge intensity are argued to affect firms' entry mode...... diversity in firms' entry modes across both dimensions....

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

Science.gov (United States)

Liu, Hui; Hou, Tingjun

2016-07-15

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

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

Directory of Open Access Journals (Sweden)

Sarai Akinori

2011-02-01

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

firestar--advances in the prediction of functionally important residues.

Science.gov (United States)

Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L

2011-07-01

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php.

Model predictive control of an air suspension system with damping multi-mode switching damper based on hybrid model

Science.gov (United States)

Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long

2017-09-01

This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.

Mutation analysis and molecular modeling for the investigation of ligand-binding modes of GPR84.

Science.gov (United States)

Nikaido, Yoshiaki; Koyama, Yuuta; Yoshikawa, Yasushi; Furuya, Toshio; Takeda, Shigeki

2015-05-01

GPR84 is a G protein-coupled receptor for medium-chain fatty acids. Capric acid and 3,3'-diindolylmethane are specific agonists for GPR84. We built a homology model of a GPR84-capric acid complex to investigate the ligand-binding mode using the crystal structure of human active-state β2-adrenergic receptor. We performed site-directed mutagenesis to subject ligand-binding sites to our model using GPR84-Giα fusion proteins and a [(35)S]GTPγS-binding assay. We compared the activity of the wild type and mutated forms of GPR84 by [(35)S]GTPγS binding to capric acid and diindolylmethane. The mutations L100D `Ballesteros-Weinstein numbering: 3.32), F101Y (3.33) and N104Q (3.36) in the transmembrane helix III and N357D (7.39) in the transmembrane helix VII resulted in reduced capric acid activity but maintained the diindolylmethane responses. Y186F (5.46) and Y186H (5.46) mutations had no characteristic effect on capric acid but with diindolylmethane they significantly affected the G protein activation efficiency. The L100D (3.32) mutant responded to decylamine, a fatty amine, instead of a natural agonist, the fatty acid capric acid, suggesting that we have identified a mutated G protein-coupled receptor-artificial ligand pairing. Our molecular model provides an explanation for these results and interactions between GPR84 and capric acid. Further, from the results of a double stimulation assay, we concluded that diindolylmethane was a positive allosteric modulator for GPR84. © The Authors 2014. Published by Oxford University Press on behalf of the Japanese Biochemical Society. All rights reserved.

Mode of bindings of zinc oxide nanoparticles to myoglobin and horseradish peroxidase: A spectroscopic investigations

Science.gov (United States)

Mandal, Gopa; Bhattacharya, Sudeshna; Ganguly, Tapan

2011-07-01

The interactions between two heme proteins myoglobin (HMb) and horseradish peroxidase (HRP) with zinc oxide (ZnO) nanoparticles are investigated by using UV-vis absorption, steady state fluorescence, synchronous fluorescence, time-resolved fluorescence, FT-IR, atomic force microscopy (AFM) and circular dichroism (CD) techniques under physiological condition of pH˜7.4. The presence of mainly static mode in fluorescence quenching mechanism of HMb and HRP by ZnO nanoparticle indicates the possibility of formation of ground state complex. The processes of bindings of ZnO nanoparticles with the two proteins are spontaneous molecular interaction procedures. In both cases hydrogen bonding plays a major role. The circular dichroism (CD) spectra reveal that a helicity of the proteins is reduced by increasing ZnO nanoparticle concentration although the α-helical structures of HMb and HRP retain their identity. On binding to the ZnO nanoparticles the secondary structure of HRP molecules (or HMb molecules) remains unchanged while there is a substantial change in the environment of the tyrosin active site in case of HRP molecules and tryptophan active site in case of HMb molecules. Tapping mode atomic force microscopy (AFM) was applied for the investigation the structure of HRP adsorbed in the environment of nanoparticles on the silicon and on the bare silicon. HRP molecules adsorb and aggregate on the mica with ZnO nanoparticle. The aggregation indicates an attractive interaction among the adsorbed molecules. The molecules are randomly distributed on the bare silicon wafer. The adsorption of HRP in the environment of ZnO nanoparticle changes drastically the domains due to a strong interaction between HRP and ZnO nanoparticles. Similar situation is observed in case of HMb molecules. These findings demonstrate the efficacy of biomedical applications of ZnO nanoparticles as well as in elucidating their mechanisms of action as drugs in both human and plant systems.

DRD2 genotype-based variation of default mode network activity and of its relationship with striatal DAT binding.

Science.gov (United States)

Sambataro, Fabio; Fazio, Leonardo; Taurisano, Paolo; Gelao, Barbara; Porcelli, Annamaria; Mancini, Marina; Sinibaldi, Lorenzo; Ursini, Gianluca; Masellis, Rita; Caforio, Grazia; Di Giorgio, Annabella; Niccoli-Asabella, Artor; Popolizio, Teresa; Blasi, Giuseppe; Bertolino, Alessandro

2013-01-01

The default mode network (DMN) comprises a set of brain regions with "increased" activity during rest relative to cognitive processing. Activity in the DMN is associated with functional connections with the striatum and dopamine (DA) levels in this brain region. A functional single-nucleotide polymorphism within the dopamine D2 receptor gene (DRD2, rs1076560 G > T) shifts splicing of the 2 D2 isoforms, D2 short and D2 long, and has been associated with striatal DA signaling as well as with cognitive processing. However, the effects of this polymorphism on DMN have not been explored. The aim of this study was to evaluate the effects of rs1076560 on DMN and striatal connectivity and on their relationship with striatal DA signaling. Twenty-eight subjects genotyped for rs1076560 underwent functional magnetic resonance imaging during a working memory task and 123 55 I-Fluoropropyl-2-beta-carbomethoxy-3-beta(4-iodophenyl) nortropan Single Photon Emission Computed Tomography ([(123)I]-FP-CIT SPECT) imaging (a measure of dopamine transporter [DAT] binding). Spatial group-independent component (IC) analysis was used to identify DMN and striatal ICs. Within the anterior DMN IC, GG subjects had relatively greater connectivity in medial prefrontal cortex (MPFC), which was directly correlated with striatal DAT binding. Within the posterior DMN IC, GG subjects had reduced connectivity in posterior cingulate relative to T carriers. Additionally, rs1076560 genotype predicted connectivity differences within a striatal network, and these changes were correlated with connectivity in MPFC and posterior cingulate within the DMN. These results suggest that genetically determined D2 receptor signaling is associated with DMN connectivity and that these changes are correlated with striatal function and presynaptic DA signaling.

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.

Characterization of the differences in the cyclopiazonic acid binding mode to mammalian and P. Falciparum Ca2+ pumps: a computational study.

KAUST Repository

Di Marino, Daniele

2015-03-01

Despite the investments in malaria research, an effective vaccine has not yet been developed and the causative parasites are becoming increasingly resistant to most of the available drugs. PfATP6, the sarco/endoplasmic reticulum Ca2+ pump (SERCA) of P. falciparum, has been recently genetically validated as a potential antimalarial target and cyclopiazonic acid (CPA) has been found to be a potent inhibitor of SERCAs in several organisms, including P. falciparum. In position 263, PfATP6 displays a leucine residue, whilst the corresponding position in the mammalian SERCA is occupied by a glutamic acid. The PfATP6 L263E mutation has been studied in relation to the artemisinin inhibitory effect on P. falciparum and recent studies have provided evidence that the parasite with this mutation is more susceptible to CPA. Here, we characterized, for the first time, the interaction of CPA with PfATP6 and its mammalian counterpart to understand similarities and differences in the mode of binding of the inhibitor to the two Ca2+ pumps. We found that, even though CPA does not directly interact with the residue in position 263, the presence of a hydrophobic residue in this position in PfATP6 rather than a negatively charged one, as in the mammalian SERCA, entails a conformational arrangement of the binding pocket which, in turn, determines a relaxation of CPA leading to a different binding mode of the compound. Our findings highlight differences between the plasmodial and human SERCA CPA-binding pockets that may be exploited to design CPA derivatives more selective toward PfATP6.

Characterization of the differences in the cyclopiazonic acid binding mode to mammalian and P. Falciparum Ca2+ pumps: a computational study.

KAUST Repository

Di Marino, Daniele; D'Annessa, Ilda; Coletta, Andrea; Via, Allegra; Tramontano, Anna

2015-01-01

Despite the investments in malaria research, an effective vaccine has not yet been developed and the causative parasites are becoming increasingly resistant to most of the available drugs. PfATP6, the sarco/endoplasmic reticulum Ca2+ pump (SERCA) of P. falciparum, has been recently genetically validated as a potential antimalarial target and cyclopiazonic acid (CPA) has been found to be a potent inhibitor of SERCAs in several organisms, including P. falciparum. In position 263, PfATP6 displays a leucine residue, whilst the corresponding position in the mammalian SERCA is occupied by a glutamic acid. The PfATP6 L263E mutation has been studied in relation to the artemisinin inhibitory effect on P. falciparum and recent studies have provided evidence that the parasite with this mutation is more susceptible to CPA. Here, we characterized, for the first time, the interaction of CPA with PfATP6 and its mammalian counterpart to understand similarities and differences in the mode of binding of the inhibitor to the two Ca2+ pumps. We found that, even though CPA does not directly interact with the residue in position 263, the presence of a hydrophobic residue in this position in PfATP6 rather than a negatively charged one, as in the mammalian SERCA, entails a conformational arrangement of the binding pocket which, in turn, determines a relaxation of CPA leading to a different binding mode of the compound. Our findings highlight differences between the plasmodial and human SERCA CPA-binding pockets that may be exploited to design CPA derivatives more selective toward PfATP6.

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

Science.gov (United States)

Miao, Zhichao; Westhof, Eric

2016-07-08

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

A unique binding mode enables MCM2 to chaperone histones H3-H4 at replication forks

DEFF Research Database (Denmark)

Huang, Hongda; Strømme, Caroline B; Saredi, Giulia

2015-01-01

During DNA replication, chromatin is reassembled by recycling of modified old histones and deposition of new ones. How histone dynamics integrates with DNA replication to maintain genome and epigenome information remains unclear. Here, we reveal how human MCM2, part of the replicative helicase......, chaperones histones H3-H4. Our first structure shows an H3-H4 tetramer bound by two MCM2 histone-binding domains (HBDs), which hijack interaction sites used by nucleosomal DNA. Our second structure reveals MCM2 and ASF1 cochaperoning an H3-H4 dimer. Mutational analyses show that the MCM2 HBD is required...... for MCM2-7 histone-chaperone function and normal cell proliferation. Further, we show that MCM2 can chaperone both new and old canonical histones H3-H4 as well as H3.3 and CENPA variants. The unique histone-binding mode of MCM2 thus endows the replicative helicase with ideal properties for recycling...

A unique binding mode enables MCM2 to chaperone histones H3-H4 at replication forks.

Science.gov (United States)

Huang, Hongda; Strømme, Caroline B; Saredi, Giulia; Hödl, Martina; Strandsby, Anne; González-Aguilera, Cristina; Chen, Shoudeng; Groth, Anja; Patel, Dinshaw J

2015-08-01

During DNA replication, chromatin is reassembled by recycling of modified old histones and deposition of new ones. How histone dynamics integrates with DNA replication to maintain genome and epigenome information remains unclear. Here, we reveal how human MCM2, part of the replicative helicase, chaperones histones H3-H4. Our first structure shows an H3-H4 tetramer bound by two MCM2 histone-binding domains (HBDs), which hijack interaction sites used by nucleosomal DNA. Our second structure reveals MCM2 and ASF1 cochaperoning an H3-H4 dimer. Mutational analyses show that the MCM2 HBD is required for MCM2-7 histone-chaperone function and normal cell proliferation. Further, we show that MCM2 can chaperone both new and old canonical histones H3-H4 as well as H3.3 and CENPA variants. The unique histone-binding mode of MCM2 thus endows the replicative helicase with ideal properties for recycling histones genome wide during DNA replication.

Structure of calmodulin complexed with an olfactory CNG channel fragment and role of the central linker: Residual dipolar couplings to evaluate calmodulin binding modes outside the kinase family

International Nuclear Information System (INIS)

Contessa, Gian Marco; Orsale, Maria; Melino, Sonia; Torre, Vincent; Paci, Maurizio; Desideri, Alessandro; Cicero, Daniel O.

2005-01-01

The NMR high-resolution structure of calmodulin complexed with a fragment of the olfactory cyclic-nucleotide gated channel is described. This structure shows features that are unique for this complex, including an active role of the linker connecting the N- and C-lobes of calmodulin upon binding of the peptide. Such linker is not only involved in the formation of an hydrophobic pocket to accommodate a bulky peptide residue, but it also provides a positively charged region complementary to a negative charge of the target. This complex of calmodulin with a target not belonging to the kinase family was used to test the residual dipolar coupling (RDC) approach for the determination of calmodulin binding modes to peptides. Although the complex here characterized belongs to the (1--14) family, high Q values were obtained with all the 1:1 complexes for which crystalline structures are available. Reduction of the RDC data set used for the correlation analysis to structured regions of the complex allowed a clear identification of the binding mode. Excluded regions comprise calcium binding loops and loops connecting the EF-hand motifs

Prediction of Accurate Mixed Mode Fatigue Crack Growth Curves using the Paris' Law

Science.gov (United States)

Sajith, S.; Krishna Murthy, K. S. R.; Robi, P. S.

2017-12-01

Accurate information regarding crack growth times and structural strength as a function of the crack size is mandatory in damage tolerance analysis. Various equivalent stress intensity factor (SIF) models are available for prediction of mixed mode fatigue life using the Paris' law. In the present investigation these models have been compared to assess their efficacy in prediction of the life close to the experimental findings as there are no guidelines/suggestions available on selection of these models for accurate and/or conservative predictions of fatigue life. Within the limitations of availability of experimental data and currently available numerical simulation techniques, the results of present study attempts to outline models that would provide accurate and conservative life predictions.

Coordination properties of warfarin towards Pr(III) predicted from DFT and FT-IR studies

International Nuclear Information System (INIS)

Mihaylov, Tz.; Trendafilova, N.; Georgieva, I.; Kostova, I.

2010-01-01

Graphical abstract: The coordination behavior of warfarin towards Pr(III) in Pr(L) 3 .5H 2 O complex (L - warfarin) is investigated through molecular modeling at B3LYP/6-31G(d,p) level and consequent exhaustive comparative vibrational analysis of the ligand and the complex. The calculations predicted that the ligand binds to the metal through the deprotonated enol group and the keto C=O group in pseudo-octahedral polyhedron. The simulated vibrational spectrum of the model complex proposed is in excellent agreement with the experimental one. - Abstract: The coordination behavior of warfarin towards Pr(III) in Pr(L) 3 .5H 2 O complex (L - warfarin) is investigated through molecular modeling at B3LYP/6-31G(d,p) level and consequent exhaustive comparative vibrational analysis of the ligand and the complex. The calculated NPA charges, Fukui functions and MEP values of the anionic ligand in solution pointed out that the oxygen atoms of the deprotonated hydroxyl and the coumarin carbonyl groups are the most probable reactive sites upon coordination. The metal-ligand binding mode of warfarin is predicted through molecular modeling and energy estimation of different Pr(III)-warfarin structures. In the most stable model structure, the ligand-metal binding is realized through the oxygen of the deprotonated OH group and the oxygen of the keto C=O group in pseudo-octahedral polyhedron. The suggested metal-ligand binding mode is confirmed by comparative vibrational analysis of the free ligand and various model structures with different metal-ligand binding modes.

Coordination properties of warfarin towards Pr(III) predicted from DFT and FT-IR studies

Energy Technology Data Exchange (ETDEWEB)

Mihaylov, Tz., E-mail: tzmihay@svr.igic.bas.bg [Institute of General and Inorganic Chemistry, Bulgarian Academy of Sciences, Sofia (Bulgaria); Trendafilova, N.; Georgieva, I. [Institute of General and Inorganic Chemistry, Bulgarian Academy of Sciences, Sofia (Bulgaria); Kostova, I., E-mail: irenakostova@yahoo.com [Department of Chemistry, Faculty of Pharmacy, Medical University, Sofia (Bulgaria)

2010-08-23

Graphical abstract: The coordination behavior of warfarin towards Pr(III) in Pr(L){sub 3}.5H{sub 2}O complex (L - warfarin) is investigated through molecular modeling at B3LYP/6-31G(d,p) level and consequent exhaustive comparative vibrational analysis of the ligand and the complex. The calculations predicted that the ligand binds to the metal through the deprotonated enol group and the keto C=O group in pseudo-octahedral polyhedron. The simulated vibrational spectrum of the model complex proposed is in excellent agreement with the experimental one. - Abstract: The coordination behavior of warfarin towards Pr(III) in Pr(L){sub 3}.5H{sub 2}O complex (L - warfarin) is investigated through molecular modeling at B3LYP/6-31G(d,p) level and consequent exhaustive comparative vibrational analysis of the ligand and the complex. The calculated NPA charges, Fukui functions and MEP values of the anionic ligand in solution pointed out that the oxygen atoms of the deprotonated hydroxyl and the coumarin carbonyl groups are the most probable reactive sites upon coordination. The metal-ligand binding mode of warfarin is predicted through molecular modeling and energy estimation of different Pr(III)-warfarin structures. In the most stable model structure, the ligand-metal binding is realized through the oxygen of the deprotonated OH group and the oxygen of the keto C=O group in pseudo-octahedral polyhedron. The suggested metal-ligand binding mode is confirmed by comparative vibrational analysis of the free ligand and various model structures with different metal-ligand binding modes.

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.

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

Science.gov (United States)

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

2018-02-15

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

Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

Science.gov (United States)

Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

2015-10-01

A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM.

Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

International Nuclear Information System (INIS)

Setiadi, A C; Brunsell, P R; Frassinetti, L

2015-01-01

A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM. (paper)

Discovery of a Potent Class of PI3Kα Inhibitors with Unique Binding Mode via Encoded Library Technology (ELT).

Science.gov (United States)

Yang, Hongfang; Medeiros, Patricia F; Raha, Kaushik; Elkins, Patricia; Lind, Kenneth E; Lehr, Ruth; Adams, Nicholas D; Burgess, Joelle L; Schmidt, Stanley J; Knight, Steven D; Auger, Kurt R; Schaber, Michael D; Franklin, G Joseph; Ding, Yun; DeLorey, Jennifer L; Centrella, Paolo A; Mataruse, Sibongile; Skinner, Steven R; Clark, Matthew A; Cuozzo, John W; Evindar, Ghotas

2015-05-14

In the search of PI3K p110α wild type and H1047R mutant selective small molecule leads, an encoded library technology (ELT) campaign against the desired target proteins was performed which led to the discovery of a selective chemotype for PI3K isoforms from a three-cycle DNA encoded library. An X-ray crystal structure of a representative inhibitor from this chemotype demonstrated a unique binding mode in the p110α protein.

Deep learning and model predictive control for self-tuning mode-locked lasers

Science.gov (United States)

Baumeister, Thomas; Brunton, Steven L.; Nathan Kutz, J.

2018-03-01

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\\em deep learning} (DL) architecture with {\\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

Integrated predictive modeling of high-mode tokamak plasmas using a combination of core and pedestal models

International Nuclear Information System (INIS)

Bateman, Glenn; Bandres, Miguel A.; Onjun, Thawatchai; Kritz, Arnold H.; Pankin, Alexei

2003-01-01

A new integrated modeling protocol is developed using a model for the temperature and density pedestal at the edge of high-mode (H-mode) plasmas [Onjun et al., Phys. Plasmas 9, 5018 (2002)] together with the Multi-Mode core transport model (MMM95) [Bateman et al., Phys. Plasmas 5, 1793 (1998)] in the BALDUR integrated modeling code to predict the temperature and density profiles of 33 H-mode discharges. The pedestal model is used to provide the boundary conditions in the simulations, once the heating power rises above the H-mode power threshold. Simulations are carried out for 20 discharges in the Joint European Torus and 13 discharges in the DIII-D tokamak. These discharges include systematic scans in normalized gyroradius, plasma pressure, collisionality, isotope mass, elongation, heating power, and plasma density. The average rms deviation between experimental data and the predicted profiles of temperature and density, normalized by central values, is found to be about 10%. It is found that the simulations tend to overpredict the temperature profiles in discharges with low heating power per plasma particle and to underpredict the temperature profiles in discharges with high heating power per particle. Variations of the pedestal model are used to test the sensitivity of the simulation results

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...

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

Science.gov (United States)

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

2014-08-27

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

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

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Ravindranath, Pradeep Anand; Sanner, Michel F.

2016-01-01

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

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

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

2003-08-01

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

Search for β2 adrenergic receptor ligands by virtual screening via grid computing and investigation of binding modes by docking and molecular dynamics simulations.

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Qifeng Bai

Full Text Available We designed a program called MolGridCal that can be used to screen small molecule database in grid computing on basis of JPPF grid environment. Based on MolGridCal program, we proposed an integrated strategy for virtual screening and binding mode investigation by combining molecular docking, molecular dynamics (MD simulations and free energy calculations. To test the effectiveness of MolGridCal, we screened potential ligands for β2 adrenergic receptor (β2AR from a database containing 50,000 small molecules. MolGridCal can not only send tasks to the grid server automatically, but also can distribute tasks using the screensaver function. As for the results of virtual screening, the known agonist BI-167107 of β2AR is ranked among the top 2% of the screened candidates, indicating MolGridCal program can give reasonable results. To further study the binding mode and refine the results of MolGridCal, more accurate docking and scoring methods are used to estimate the binding affinity for the top three molecules (agonist BI-167107, neutral antagonist alprenolol and inverse agonist ICI 118,551. The results indicate agonist BI-167107 has the best binding affinity. MD simulation and free energy calculation are employed to investigate the dynamic interaction mechanism between the ligands and β2AR. The results show that the agonist BI-167107 also has the lowest binding free energy. This study can provide a new way to perform virtual screening effectively through integrating molecular docking based on grid computing, MD simulations and free energy calculations. The source codes of MolGridCal are freely available at http://molgridcal.codeplex.com.

Structural Dynamics Investigation of Human Family 1 & 2 Cystatin-Cathepsin L1 Interaction: A Comparison of Binding Modes.

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Suman Kumar Nandy

Full Text Available Cystatin superfamily is a large group of evolutionarily related proteins involved in numerous physiological activities through their inhibitory activity towards cysteine proteases. Despite sharing the same cystatin fold, and inhibiting cysteine proteases through the same tripartite edge involving highly conserved N-terminal region, L1 and L2 loop; cystatins differ widely in their inhibitory affinity towards C1 family of cysteine proteases and molecular details of these interactions are still elusive. In this study, inhibitory interactions of human family 1 & 2 cystatins with cathepsin L1 are predicted and their stability and viability are verified through protein docking & comparative molecular dynamics. An overall stabilization effect is observed in all cystatins on complex formation. Complexes are mostly dominated by van der Waals interaction but the relative participation of the conserved regions varied extensively. While van der Waals contacts prevail in L1 and L2 loop, N-terminal segment chiefly acts as electrostatic interaction site. In fact the comparative dynamics study points towards the instrumental role of L1 loop in directing the total interaction profile of the complex either towards electrostatic or van der Waals contacts. The key amino acid residues surfaced via interaction energy, hydrogen bonding and solvent accessible surface area analysis for each cystatin-cathepsin L1 complex influence the mode of binding and thus control the diverse inhibitory affinity of cystatins towards cysteine proteases.

A human transcription factor in search mode.

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Hauser, Kevin; Essuman, Bernard; He, Yiqing; Coutsias, Evangelos; Garcia-Diaz, Miguel; Simmerling, Carlos

2016-01-08

Transcription factors (TF) can change shape to bind and recognize DNA, shifting the energy landscape from a weak binding, rapid search mode to a higher affinity recognition mode. However, the mechanism(s) driving this conformational change remains unresolved and in most cases high-resolution structures of the non-specific complexes are unavailable. Here, we investigate the conformational switch of the human mitochondrial transcription termination factor MTERF1, which has a modular, superhelical topology complementary to DNA. Our goal was to characterize the details of the non-specific search mode to complement the crystal structure of the specific binding complex, providing a basis for understanding the recognition mechanism. In the specific complex, MTERF1 binds a significantly distorted and unwound DNA structure, exhibiting a protein conformation incompatible with binding to B-form DNA. In contrast, our simulations of apo MTERF1 revealed significant flexibility, sampling structures with superhelical pitch and radius complementary to the major groove of B-DNA. Docking these structures to B-DNA followed by unrestrained MD simulations led to a stable complex in which MTERF1 was observed to undergo spontaneous diffusion on the DNA. Overall, the data support an MTERF1-DNA binding and recognition mechanism driven by intrinsic dynamics of the MTERF1 superhelical topology. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

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Vincenti Matthew P

2005-11-01

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

Agrobacterium uses a unique ligand-binding mode for trapping opines and acquiring a competitive advantage in the niche construction on plant host.

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Lang, Julien; Vigouroux, Armelle; Planamente, Sara; El Sahili, Abbas; Blin, Pauline; Aumont-Nicaise, Magali; Dessaux, Yves; Moréra, Solange; Faure, Denis

2014-10-01

By modifying the nuclear genome of its host, the plant pathogen Agrobacterium tumefaciens induces the development of plant tumours in which it proliferates. The transformed plant tissues accumulate uncommon low molecular weight compounds called opines that are growth substrates for A. tumefaciens. In the pathogen-induced niche (the plant tumour), a selective advantage conferred by opine assimilation has been hypothesized, but not experimentally demonstrated. Here, using genetics and structural biology, we deciphered how the pathogen is able to bind opines and use them to efficiently compete in the plant tumour. We report high resolution X-ray structures of the periplasmic binding protein (PBP) NocT unliganded and liganded with the opine nopaline (a condensation product of arginine and α-ketoglurate) and its lactam derivative pyronopaline. NocT exhibited an affinity for pyronopaline (K(D) of 0.6 µM) greater than that for nopaline (KD of 3.7 µM). Although the binding-mode of the arginine part of nopaline/pyronopaline in NocT resembled that of arginine in other PBPs, affinity measurement by two different techniques showed that NocT did not bind arginine. In contrast, NocT presented specific residues such as M117 to stabilize the bound opines. NocT relatives that exhibit the nopaline/pyronopaline-binding mode were only found in genomes of the genus Agrobacterium. Transcriptomics and reverse genetics revealed that A. tumefaciens uses the same pathway for assimilating nopaline and pyronopaline. Fitness measurements showed that NocT is required for a competitive colonization of the plant tumour by A. tumefaciens. Moreover, even though the Ti-plasmid conjugal transfer was not regulated by nopaline, the competitive advantage gained by the nopaline-assimilating Ti-plasmid donors led to a preferential horizontal propagation of this Ti-plasmid amongst the agrobacteria colonizing the plant-tumour niche. This work provided structural and genetic evidences to support the niche

Agrobacterium uses a unique ligand-binding mode for trapping opines and acquiring a competitive advantage in the niche construction on plant host.

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Julien Lang

2014-10-01

Full Text Available By modifying the nuclear genome of its host, the plant pathogen Agrobacterium tumefaciens induces the development of plant tumours in which it proliferates. The transformed plant tissues accumulate uncommon low molecular weight compounds called opines that are growth substrates for A. tumefaciens. In the pathogen-induced niche (the plant tumour, a selective advantage conferred by opine assimilation has been hypothesized, but not experimentally demonstrated. Here, using genetics and structural biology, we deciphered how the pathogen is able to bind opines and use them to efficiently compete in the plant tumour. We report high resolution X-ray structures of the periplasmic binding protein (PBP NocT unliganded and liganded with the opine nopaline (a condensation product of arginine and α-ketoglurate and its lactam derivative pyronopaline. NocT exhibited an affinity for pyronopaline (K(D of 0.6 µM greater than that for nopaline (KD of 3.7 µM. Although the binding-mode of the arginine part of nopaline/pyronopaline in NocT resembled that of arginine in other PBPs, affinity measurement by two different techniques showed that NocT did not bind arginine. In contrast, NocT presented specific residues such as M117 to stabilize the bound opines. NocT relatives that exhibit the nopaline/pyronopaline-binding mode were only found in genomes of the genus Agrobacterium. Transcriptomics and reverse genetics revealed that A. tumefaciens uses the same pathway for assimilating nopaline and pyronopaline. Fitness measurements showed that NocT is required for a competitive colonization of the plant tumour by A. tumefaciens. Moreover, even though the Ti-plasmid conjugal transfer was not regulated by nopaline, the competitive advantage gained by the nopaline-assimilating Ti-plasmid donors led to a preferential horizontal propagation of this Ti-plasmid amongst the agrobacteria colonizing the plant-tumour niche. This work provided structural and genetic evidences to

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

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

2014-02-21

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

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

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

2017-01-01

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

Reliability analysis and prediction of mixed mode load using Markov Chain Model

International Nuclear Information System (INIS)

Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.

2014-01-01

The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading

Predictive Sliding Mode Control for Attitude Tracking of Hypersonic Vehicles Using Fuzzy Disturbance Observer

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Xianlei Cheng

2015-01-01

Full Text Available We propose a predictive sliding mode control (PSMC scheme for attitude control of hypersonic vehicle (HV with system uncertainties and external disturbances based on an improved fuzzy disturbance observer (IFDO. First, for a class of uncertain affine nonlinear systems with system uncertainties and external disturbances, we propose a predictive sliding mode control based on fuzzy disturbance observer (FDO-PSMC, which is used to estimate the composite disturbances containing system uncertainties and external disturbances. Afterward, to enhance the composite disturbances rejection performance, an improved FDO-PSMC (IFDO-PSMC is proposed by incorporating a hyperbolic tangent function with FDO to compensate for the approximate error of FDO. Finally, considering the actuator dynamics, the proposed IFDO-PSMC is applied to attitude control system design for HV to track the guidance commands with high precision and strong robustness. Simulation results demonstrate the effectiveness and robustness of the proposed attitude control scheme.

Temperature-dependent binding of cyclosporine to an erythrocyte protein

International Nuclear Information System (INIS)

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

1987-01-01

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

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.

Binding Mode of Insulin Receptor and Agonist Peptide

Institute of Scientific and Technical Information of China (English)

无

2006-01-01

Insulin is a protein hormone secreted by pancreatic β cells. One of its main functions is to keep the balance of glucose inside the body by regulating the absorption and metabolism of glucose in the periphery tissue, as well as the production and storage of hepatic glycogen. The insulin receptor is a transmembrane glycoprotein in which two α subunits with a molecular weight of 135 kD and twoβ subunits with a molecular weight of 95 kD are joined by a disulfide bond to form a β-α-α-β structure. The extracellular α subunit, especially, its three domains near the N-terminal are partially responsible for signal transduction or ligand-binding, as indicated by the experiments. The extracellular α subunits are involved in binding the ligands. The experimental results indicate that the three domains of the N-terminal of the α subunits are the main determinative parts of the insulin receptor to bind the insulin or mimetic peptide.We employed the extracellular domain (PDBID: 1IGR) of the insulin-like growth factor-1 receptor (IGF-1 R ) as the template to simulate and optimize the spatial structures of the three domains in the extracellular domain of the insulin receptor, which includes 468 residues. The work was accomplished by making use of the homology program in the Insight Ⅱ package on an Origin3800 server. The docking calculations of the insulin receptor obtained by homology with hexapeptides were carried out by means of the program Affinity. The analysis indicated that there were hydrogen bonding, and electrostatic and hydrophobic effects in the docking complex of the insulin receptor with hexapeptides.Moreover, we described the spatial orientation of a mimetic peptide with agonist activity in the docking complex. We obtained a rough model of binding of DLAPSQ or STIVYS with the insulin receptor, which provides the powerful theoretical support for designing the minimal insulin mimetic peptide with agonist activity, making it possible to develop oral small

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

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

2017-11-01

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

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

OpenAIRE

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

2017-01-01

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

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

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Setny, Piotr

2015-12-08

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

Protein docking prediction using predicted protein-protein interface

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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.

Protein docking prediction using predicted protein-protein interface.

Science.gov (United States)

Li, Bin; Kihara, Daisuke

2012-01-10

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

The H-mode operational window as determined from the ITER H-mode database

International Nuclear Information System (INIS)

Ryter, F.; Kardaun, O.J.W.F.; Stroth, U.

1994-01-01

The H-mode is a promising regime for fusion reactors and it is essential to be able to predict its operational window in future devices. The 'H-Mode Database Working Group' started in 1992 to gather, analyze and compare H-mode threshold data from several divertor tokamaks so that predictions could be made. The database and first results were presented and the threshold database has been improved and extended since. The work has two objectives: 1) to predict the minimum heating power necessary to reach the H-mode in future devices, 2) to contribute to physics studies of the L-H transition. (author) 11 refs., 2 figs

Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.

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Peng Ren

Full Text Available Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD to obtain their Intrinsic Mode Functions (IMF. Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.

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

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

2017-12-12

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

Synthesis, structure, DNA/BSA binding and antibacterial studies of NNO tridentate Schiff base metal complexes

Science.gov (United States)

Sakthi, Marimuthu; Ramu, Andy

2017-12-01

A new salicylaldehyde derived 2,4-diiodo-6-((2-phenylaminoethylimino)methyl)phenol Schiff base(L) and its transition metal complexes of the type MLCl where, M = Cu(II), Ni(II), Co(II), Mn(II) and Zn(II) have been synthesized. The coordination mode of Schiff base holding NNO donor atoms with metal ions was well investigated by elemental analysis, ESI-mass as well as IR, UV-vis, CV and NMR spectral studies. The binding efficiency and mode of these complexes with biological macromolecules viz., herring sperm DNA (HS- DNA) and bovine serum albumin (BSA) have been explored through various spectroscopic techniques. The characteristic changes in absorption, emission and, circular dichroism spectra of the complexes with DNA indicate the noticeable interaction between them. From the all spectral information complexes could interact with DNA via non-intercalation mode of binding. The hyperchromisim in absorption band and hypochromisim in emission intensity of BSA with different complex concentrations shown significant information, and the binding affinity value has been predicted from Stern-Volmer plots. Further, all the complexes could cleave the circular plasmid pUC19 DNA efficiently by using an activator H2O2. The ligand and all metal(II) complexes showed good antibacterial activities. The molecular docking studies of the complexes with DNA were performed in order to make a comparison and conclusion with spectral technic results.

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.

Molecular Dynamics Simulations to Investigate the Binding Mode of the Natural Product Liphagal with Phosphoinositide 3-Kinase α

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Yanjuan Gao

2016-06-01

Full Text Available Phosphatidylinositol 3-kinase α (PI3Kα is an attractive target for anticancer drug design. Liphagal, isolated from the marine sponge Aka coralliphaga, possesses the special “liphagane” meroterpenoid carbon skeleton and has been demonstrated as a PI3Kα inhibitor. Molecular docking and molecular dynamics simulations were performed to explore the dynamic behaviors of PI3Kα binding with liphagal, and free energy calculations and energy decomposition analysis were carried out by use of molecular mechanics/Poisson-Boltzmann (generalized Born surface area (MM/PB(GBSA methods. The results reveal that the heteroatom rich aromatic D-ring of liphagal extends towards the polar region of the binding site, and the D-ring 15-hydroxyl and 16-hydroxyl form three hydrogen bonds with Asp810 and Tyr836. The cyclohexyl A-ring projects up into the upper pocket of the lipophilic region, and the hydrophobic/van der Waals interactions with the residues Met772, Trp780, Ile800, Ile848, Val850, Met922, Phe930, Ile932 could be the key interactions for the affinity of liphagal to PI3Kα. Thus, a new strategy for the rational design of more potent analogs of liphagal against PI3Kα is provided. Our proposed PI3Kα/liphagal binding mode would be beneficial for the discovery of new active analogs of liphagal against PI3Kα.

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

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Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

2012-04-05

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications

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Kobayashi Hiroki

2012-04-01

Full Text Available Abstract Background Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. Results We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system. As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. Conclusions This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

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

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Wangchao Lou

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

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

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

2015-08-15

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

Interaction of the N-(3-Methylpyridin-2-ylamide Derivatives of Flurbiprofen and Ibuprofen with FAAH: Enantiomeric Selectivity and Binding Mode.

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Jessica Karlsson

Full Text Available Combined fatty acid amide hydrolase (FAAH and cyclooxygenase (COX inhibition is a promising approach for pain-relief. The Flu-AM1 and Ibu-AM5 derivatives of flurbiprofen and ibuprofen retain similar COX-inhibitory properties and are more potent inhibitors of FAAH than the parent compounds. However, little is known as to the nature of their interaction with FAAH, or to the importance of their chirality. This has been explored here.FAAH inhibitory activity was measured in rat brain homogenates and in lysates expressing either wild-type or FAAH(T488A-mutated enzyme. Molecular modelling was undertaken using both docking and molecular dynamics. The (R- and (S-enantiomers of Flu-AM1 inhibited rat FAAH with similar potencies (IC50 values of 0.74 and 0.99 μM, respectively, whereas the (S-enantiomer of Ibu-AM5 (IC50 0.59 μM was more potent than the (R-enantiomer (IC50 5.7 μM. Multiple inhibition experiments indicated that both (R-Flu-AM1 and (S-Ibu-AM5 inhibited FAAH in a manner mutually exclusive to carprofen. Computational studies indicated that the binding site for the Flu-AM1 and Ibu-AM5 enantiomers was located between the acyl chain binding channel and the membrane access channel, in a site overlapping the carprofen binding site, and showed a binding mode in line with that proposed for carprofen and other non-covalent ligands. The potency of (R-Flu-AM1 was lower towards lysates expressing FAAH mutated at the proposed carprofen binding area than in lysates expressing wild-type FAAH.The study provides kinetic and structural evidence that the enantiomers of Flu-AM1 and Ibu-AM5 bind in the substrate channel of FAAH. This information will be useful in aiding the design of novel dual-action FAAH: COX inhibitors.

An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency.

Science.gov (United States)

Guo, Wei-Li; Huang, De-Shuang

2017-08-22

Transcription factors (TFs) are DNA-binding proteins that have a central role in regulating gene expression. Identification of DNA-binding sites of TFs is a key task in understanding transcriptional regulation, cellular processes and disease. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) enables genome-wide identification of in vivo TF binding sites. However, it is still difficult to map every TF in every cell line owing to cost and biological material availability, which poses an enormous obstacle for integrated analysis of gene regulation. To address this problem, we propose a novel computational approach, TFBSImpute, for predicting additional TF binding profiles by leveraging information from available ChIP-seq TF binding data. TFBSImpute fuses the dataset to a 3-mode tensor and imputes missing TF binding signals via simultaneous completion of multiple TF binding matrices with positional consistency. We show that signals predicted by our method achieve overall similarity with experimental data and that TFBSImpute significantly outperforms baseline approaches, by assessing the performance of imputation methods against observed ChIP-seq TF binding profiles. Besides, motif analysis shows that TFBSImpute preforms better in capturing binding motifs enriched in observed data compared with baselines, indicating that the higher performance of TFBSImpute is not simply due to averaging related samples. We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease.

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

An Exploration of the Calcium-Binding Mode of Egg White Peptide, Asp-His-Thr-Lys-Glu, and In Vitro Calcium Absorption Studies of Peptide-Calcium Complex.

Science.gov (United States)

Sun, Na; Jin, Ziqi; Li, Dongmei; Yin, Hongjie; Lin, Songyi

2017-11-08

The binding mode between the pentapeptide (DHTKE) from egg white hydrolysates and calcium ions was elucidated upon its structural and thermodynamics characteristics. The present study demonstrated that the DHTKE peptide could spontaneously bind calcium with a 1:1 stoichiometry, and that the calcium-binding site corresponded to the carboxyl oxygen, amino nitrogen, and imidazole nitrogen atoms of the DHTKE peptide. Moreover, the effect of the DHTKE-calcium complex on improving the calcium absorption was investigated in vitro using Caco-2 cells. Results showed that the DHTKE-calcium complex could facilitate the calcium influx into the cytosol and further improve calcium absorption across Caco-2 cell monolayers by more than 7 times when compared to calcium-free control. This study facilitates the understanding about the binding mechanism between peptides and calcium ions as well as suggests a potential application of egg white peptides as nutraceuticals to improve calcium absorption.

Structure-guided approach identifies a novel class of HIV-1 ribonuclease H inhibitors: binding mode insights through magnesium complexation and site-directed mutagenesis studies

DEFF Research Database (Denmark)

Poongavanam, Vasanthanathan; Corona, Angela; Steinmann, Casper

2018-01-01

is a long and expensive process that can be speeded up by in silico methods. In the present study, a structure-guided screening is coupled with a similarity-based search on the Specs database to identify a new class of HIV-1 RNase H inhibitors. Out of the 45 compounds selected for experimental testing, 15...... inhibited the RNase H function below 100 μM with three hits exhibiting IC50 values active compound, AA, inhibits HIV-1 RNase H with an IC50 of 5.1 μM and exhibits a Mg-independent mode of inhibition. Site-directed mutagenesis studies provide valuable insight into the binding mode of newly...

Absolute analytical prediction of photonic crystal guided mode resonance wavelengths

International Nuclear Information System (INIS)

Hermannsson, Pétur Gordon; Vannahme, Christoph; Smith, Cameron L. C.; Kristensen, Anders

2014-01-01

A class of photonic crystal resonant reflectors known as guided mode resonant filters are optical structures that are widely used in the field of refractive index sensing, particularly in biosensing. For the purposes of understanding and design, their behavior has traditionally been modeled numerically with methods such as rigorous coupled wave analysis. Here it is demonstrated how the absolute resonance wavelengths of such structures can be predicted by analytically modeling them as slab waveguides in which the propagation constant is determined by a phase matching condition. The model is experimentally verified to be capable of predicting the absolute resonance wavelengths to an accuracy of within 0.75 nm, as well as resonance wavelength shifts due to changes in cladding index within an accuracy of 0.45 nm across the visible wavelength regime in the case where material dispersion is taken into account. Furthermore, it is demonstrated that the model is valid beyond the limit of low grating modulation, for periodically discontinuous waveguide layers, high refractive index contrasts, and highly dispersive media.

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/.

Comparisons of theoretically predicted transport from ion temperature gradient instabilities to L-mode tokamak experiments

International Nuclear Information System (INIS)

Kotschenreuther, M.; Wong, H.V.; Lyster, P.L.; Berk, H.L.; Denton, R.; Miner, W.H.; Valanju, P.

1991-12-01

The theoretical transport from kinetic micro-instabilities driven by ion temperature gradients is a sheared slab is compared to experimentally inferred transport in L-mode tokamaks. Low noise gyrokinetic simulation techniques are used to obtain the ion thermal transport coefficient X. This X is much smaller than in experiments, and so cannot explain L-mode confinement. Previous predictions based on fluid models gave much greater X than experiments. Linear and nonlinear comparisons with the fluid model show that it greatly overestimates transport for experimental parameters. In addition, disagreements among previous analytic and simulation calculations of X in the fluid model are reconciled

Determination of Vanadium Binding Mode on Seawater-Contacted Polyamidoxime Adsorbents

Energy Technology Data Exchange (ETDEWEB)

Zhang, Zhicheng [Lawrence Berkeley National Laboratory (LBNL); Rao, Linfeng [Lawrence Berkeley National Laboratory (LBNL); Abney, Carter W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Bryantsev, Vyacheslav [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ivanov, Aleksandr [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-09-01

Adsorbents developed for the recovery of uranium from seawater display poor selectivity over other transition metals present in the ocean, with vanadium particularly problematic. To improve selectivity, an indispensable step is the positive identification of metal binding environments following actual seawater deployment. In this work we apply x-ray absorption fine structure (XAFS) spectroscopy to directly investigate the vanadium binding environment on seawater-deployed polyamidoxime adsorbents. Comparison of the x-ray absorption near edge spectra (XANES) reveal marked similarities to recently a reported non-oxido vanadium (V) structure formed upon binding with cyclic imidedioxime, a byproduct of generating amidoxime functionalities. Density functional theory (DFT) calculations provided a series of putative vanadium binding environments for both vanadium (IV) and vanadium (V) oxidation states, and with both amidoxime and cyclic imidedioxime. Fits of the extended XAFS (EXAFS) data confirmed vanadium (V) is bound exclusively by the cyclic imidedioxime moiety in a 1:2 metal:ligand fashion, though a modest structural distortion is also observed compared to crystal structure data and computationally optimized geometries which is attributed to morphology effects from the polymer graft chain and the absence of crystal packing interactions. These results demonstrate that improved selectivity for uranium over vanadium can be achieved by suppressing the formation of cyclic imidedioxime during preparation of polyamidoxime adsorbents for seawater uranium recovery.

Predictions and observations of global beta-induced Alfven-acoustic modes in JET and NSTX

Energy Technology Data Exchange (ETDEWEB)

Gorelenkov, N N [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Berk, H L [Institute for Fusion Studies, University of Texas, Austin, TX 78712 (United States); Crocker, N A [Institute of Plasma and Fusion Research, University of California, Los Angeles, CA 90095-1354 (United States); Fredrickson, E D [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Kaye, S [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Kubota, S [Institute of Plasma and Fusion Research, University of California, Los Angeles, CA 90095-1354 (United States); Park, H [Princeton Plasma Physics Laboratory, Princeton University, Princeton, NJ 08543 (United States); Peebles, W [Institute of Plasma and Fusion Research, University of California, Los Angeles, CA 90095-1354 (United States); Sabbagh, S A [Department of Applied Physics, Columbia University, New York, NY 10027-6902 (United States); Sharapov, S E [Euroatom/UKAEA Fusion Association, Culham Science Centre, Abingdon, Oxfordshire OX14 3DB (United Kingdom); Stutmat, D [Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218 (United States); Tritz, K [Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218 (United States); Levinton, F M [Nova Photonics, One Oak Place, Princeton, NJ 08540 (United States); Yuh, H [Nova Photonics, One Oak Place, Princeton, NJ 08540 (United States)

2007-12-15

In this paper we report on observations and interpretations of a new class of global MHD eigenmode solutions arising in gaps in the low frequency Alfven-acoustic continuum below the geodesic acoustic mode frequency. These modes have been just reported (Gorelenkov et al 2007 Phys. Lett. 370 70-7) where preliminary comparisons indicate qualitative agreement between theory and experiment. Here we show a more quantitative comparison emphasizing recent NSTX experiments on the observations of the global eigenmodes, referred to as beta-induced Alfven-acoustic eigenmodes (BAAEs), which exist near the extrema of the Alfven-acoustic continuum. In accordance to the linear dispersion relations, the frequency of these modes may shift as the safety factor, q, profile relaxes. We show that BAAEs can be responsible for observations in JET plasmas at relatively low beta <2% as well as in NSTX plasmas at relatively high beta >20%. In NSTX plasma observed magnetic activity has the same properties as predicted by theory for the mode structure and the frequency. Found numerically in NOVA simulations BAAEs are used to explain the observed properties of relatively low frequency experimental signals seen in NSTX and JET tokamaks.

Determination of the binding mode for anti-inflammatory natural product xanthohumol with myeloid differentiation protein 2

Directory of Open Access Journals (Sweden)

Fu W

2016-01-01

Full Text Available Weitao Fu,1,* Lingfeng Chen,1,* Zhe Wang,1 Chengwei Zhao,1 Gaozhi Chen,1 Xing Liu,1 Yuanrong Dai,2 Yuepiao Cai,1 Chenglong Li,1,3 Jianmin Zhou,1 Guang Liang1 1Chemical Biology Research Center, School of Pharmaceutical Sciences, 2Department of Respiratory Medicine, the Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China; 3Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, Ohio State University, Columbus, OH, USA *These authors contributed equally to this work Abstract: It is recognized that myeloid differentiation protein 2 (MD-2, a coreceptor of toll-like receptor 4 (TLR4 for innate immunity, plays an essential role in activation of the lipopolysaccharide signaling pathway. MD-2 is known as a neoteric and suitable therapeutical target. Therefore, there is great interest in the development of a potent MD-2 inhibitor for anti-inflammatory therapeutics. Several studies have reported that xanthohumol (XN, an anti-inflammatory natural product from hops and beer, can block the TLR4 signaling by binding to MD-2 directly. However, the interaction between MD-2 and XN remains unknown. Herein, our work aims at characterizing interactions between MD-2 and XN. Using a combination of experimental and theoretical modeling analysis, we found that XN can embed into the hydrophobic pocket of MD-2 and form two stable hydrogen bonds with residues ARG-90 and TYR-102 of MD-2. Moreover, we confirmed that ARG-90 and TYR-102 were two necessary residues during the recognition process of XN binding to MD-2. Results from this study identified the atomic interactions between the MD-2 and XN, which will contribute to future structural design of novel MD-2-targeting molecules for the treatment of inflammatory diseases. Keywords: myeloid differentiation 2, xanthohumol, binding mode, inflammation, molecular dynamics simulation 

Ligand photo-isomerization triggers conformational changes in iGluR2 ligand binding domain.

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Tino Wolter

Full Text Available Neurological glutamate receptors bind a variety of artificial ligands, both agonistic and antagonistic, in addition to glutamate. Studying their small molecule binding properties increases our understanding of the central nervous system and a variety of associated pathologies. The large, oligomeric multidomain membrane protein contains a large and flexible ligand binding domains which undergoes large conformational changes upon binding different ligands. A recent application of glutamate receptors is their activation or inhibition via photo-switchable ligands, making them key systems in the emerging field of optochemical genetics. In this work, we present a theoretical study on the binding mode and complex stability of a novel photo-switchable ligand, ATA-3, which reversibly binds to glutamate receptors ligand binding domains (LBDs. We propose two possible binding modes for this ligand based on flexible ligand docking calculations and show one of them to be analogues to the binding mode of a similar ligand, 2-BnTetAMPA. In long MD simulations, it was observed that transitions between both binding poses involve breaking and reforming the T686-E402 protein hydrogen bond. Simulating the ligand photo-isomerization process shows that the two possible configurations of the ligand azo-group have markedly different complex stabilities and equilibrium binding modes. A strong but slow protein response is observed after ligand configuration changes. This provides a microscopic foundation for the observed difference in ligand activity upon light-switching.

Multiple ligand-binding modes in bacterial R67 dihydrofolate reductase

Science.gov (United States)

Alonso, Hernán; Gillies, Malcolm B.; Cummins, Peter L.; Bliznyuk, Andrey A.; Gready, Jill E.

2005-03-01

R67 dihydrofolate reductase (DHFR), a bacterial plasmid-encoded enzyme associated with resistance to the drug trimethoprim, shows neither sequence nor structural homology with the chromosomal DHFR. It presents a highly symmetrical toroidal structure, where four identical monomers contribute to the unique central active-site pore. Two reactants (dihydrofolate, DHF), two cofactors (NADPH) or one of each (R67•DHF•NADPH) can be found simultaneously within the active site, the last one being the reactive ternary complex. As the positioning of the ligands has proven elusive to empirical determination, we addressed the problem from a theoretical perspective. Several potential structures of the ternary complex were generated using the docking programs AutoDock and FlexX. The variability among the final poses, many of which conformed to experimental data, prompted us to perform a comparative scoring analysis and molecular dynamics simulations to assess the stability of the complexes. Analysis of ligand-ligand and ligand-protein interactions along the 4 ns trajectories of eight different structures allowed us to identify important inter-ligand contacts and key protein residues. Our results, combined with published empirical data, clearly suggest that multipe binding modes of the ligands are possible within R67 DHFR. While the pterin ring of DHF and the nicotinamide ring of NADPH assume a stacked endo-conformation at the centre of the pore, probably assisted by V66, Q67 and I68, the tails of the molecules extend towards opposite ends of the cavity, adopting multiple configurations in a solvent rich-environment where hydrogen-bond interactions with K32 and Y69 may play important roles.

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

Science.gov (United States)

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

2010-12-01

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

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

Science.gov (United States)

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

2010-01-01

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

Geological predictions for the long-term isolation of radioactive waste based on extrapolating uniform mode and rate of crustal movements

International Nuclear Information System (INIS)

Umeda, Koji; Tanikawa, Shin-ichi; Yasue, Ken-ichi

2013-01-01

Long-term predictions of geological and tectonic disturbances are key issues for the safety assessment of radioactive waste disposal, especially on the Japanese Islands. Geological predictions of disturbances should be performed by extrapolating uniform mode and rate of crustal movements under the current framework. Multiple lines of geological evidence in Japan strongly suggest that the present mode of tectonics began during the late Pliocene to early Quaternary, and was fully developed by the middle Pleistocene. The uplift rates of mountains in Japan are determined to have been approximately constant until the middle Pleistocene based on simulations of temporal changes in mean altitude developed under concurrent tectonics and denudation processes. The onset of the neotectonic mode of deformation was probably triggered by the initiation of the eastward movement of the Amur Plate and the collision of the Izu block with central Honshu. The uncertainty of predictions beyond steady-state crustal deformation would, in general, increase for long-term predictions using the extrapolation procedure. Consequently, future geological and tectonic disturbances in Japan can be estimated with relatively high reliability for the next 100,000 years. (author)

The Anabaena sensory rhodopsin transducer defines a novel superfamily of prokaryotic small-molecule binding domains

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De Souza Robson F

2009-08-01

Full Text Available Abstract The Anabaena sensory rhodopsin transducer (ASRT is a small protein that has been claimed to function as a signaling molecule downstream of the cyanobacterial sensory rhodopsin. However, orthologs of ASRT have been detected in several bacteria that lack rhodopsin, raising questions about the generality of this function. Using sequence profile searches we show that ASRT defines a novel superfamily of β-sandwich fold domains. Through contextual inference based on domain architectures and predicted operons and structural analysis we present strong evidence that these domains bind small molecules, most probably sugars. We propose that the intracellular versions like ASRT probably participate as sensors that regulate a diverse range of sugar metabolism operons or even the light sensory behavior in Anabaena by binding sugars or related metabolites. We also show that one of the extracellular versions define a predicted sugar-binding structure in a novel cell-surface lipoprotein found across actinobacteria, including several pathogens such as Tropheryma, Actinomyces and Thermobifida. The analysis of this superfamily also provides new data to investigate the evolution of carbohydrate binding modes in β-sandwich domains with very different topologies. Reviewers: This article was reviewed by M. Madan Babu and Mark A. Ragan.

Validation of the Predicted Circumferential and Radial Mode Sound Power Levels in the Inlet and Exhaust Ducts of a Fan Ingesting Distorted Inflow

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Koch, L. Danielle

2012-01-01

Fan inflow distortion tone noise has been studied computationally and experimentally. Data from two experiments in the NASA Glenn Advanced Noise Control Fan rig have been used to validate acoustic predictions. The inflow to the fan was distorted by cylindrical rods inserted radially into the inlet duct one rotor chord length upstream of the fan. The rods were arranged in both symmetric and asymmetric circumferential patterns. In-duct and farfield sound pressure level measurements were recorded. It was discovered that for positive circumferential modes, measured circumferential mode sound power levels in the exhaust duct were greater than those in the inlet duct and for negative circumferential modes, measured total circumferential mode sound power levels in the exhaust were less than those in the inlet. Predicted trends in overall sound power level were proven to be useful in identifying circumferentially asymmetric distortion patterns that reduce overall inlet distortion tone noise, as compared to symmetric arrangements of rods. Detailed comparisons between the measured and predicted radial mode sound power in the inlet and exhaust duct indicate limitations of the theory.

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

DEFF Research Database (Denmark)

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

2017-01-01

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

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

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Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

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

A comprehensive approach to ascertain the binding mode of curcumin with DNA

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Haris, P.; Mary, Varughese; Aparna, P.; Dileep, K. V.; Sudarsanakumar, C.

2017-03-01

Curcumin is a natural phytochemical from the rhizoma of Curcuma longa, the popular Indian spice that exhibits a wide range of pharmacological properties like antioxidant, anticancer, anti-inflammatory, antitumor, and antiviral activities. In the published literatures we can see different studies and arguments on the interaction of curcumin with DNA. The intercalative binding, groove binding and no binding of curcumin with DNA were reported. In this context, we conducted a detailed study to understand the mechanism of recognition of dimethylsulfoxide-solubilized curcumin by DNA. The interaction of curcumin with calf thymus DNA (ctDNA) was confirmed by agarose gel electrophoresis. The nature of binding and energetics of interaction were studied by Isothermal Titration Calorimetry (ITC), Differential Scanning Calorimetry (DSC), UV-visible, fluorescence and melting temperature (Tm) analysis. The experimental data were compared with molecular modeling studies. Our investigation confirmed that dimethylsulfoxide-solubilized curcumin binds in the minor groove of the ctDNA without causing significant structural alteration to the DNA.

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

Directory of Open Access Journals (Sweden)

Carson M Andorf

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

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

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

2004-11-21

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

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

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Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

2018-01-01

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

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

Science.gov (United States)

Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

2018-01-01

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

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

Science.gov (United States)

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

2017-10-01

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

Reliability prediction of engineering systems with competing failure modes due to component degradation

International Nuclear Information System (INIS)

Son, Young Kap

2011-01-01

Reliability of an engineering system depends on two reliability metrics: the mechanical reliability, considering component failures, that a functional system topology is maintained and the performance reliability of adequate system performance in each functional configuration. Component degradation explains not only the component aging processes leading to failure in function, but also system performance change over time. Multiple competing failure modes for systems with degrading components in terms of system functionality and system performance are considered in this paper with the assumption that system functionality is not independent of system performance. To reduce errors in system reliability prediction, this paper tries to extend system performance reliability prediction methods in open literature through combining system mechanical reliability from component reliabilities and system performance reliability. The extended reliability prediction method provides a useful way to compare designs as well as to determine effective maintenance policy for efficient reliability growth. Application of the method to an electro-mechanical system, as an illustrative example, is explained in detail, and the prediction results are discussed. Both mechanical reliability and performance reliability are compared to total system reliability in terms of reliability prediction errors

Rupture prediction for induction bends under opening mode bending with emphasis on strain localization

International Nuclear Information System (INIS)

Mitsuya, Masaki; Sakanoue, Takashi

2015-01-01

This study focuses on the opening mode of induction bends; this mode represents the deformation outside a bend. Bending experiments on induction bends are shown and the manner of failure of these bends was investigated. Ruptures occur at the intrados of the bends, which undergo tensile stress, and accompany the local reduction of wall thickness, i.e., necking that indicates strain localization. By implementing finite element analysis (FEA), it was shown that the rupture is dominated not by the fracture criterion of material but by the initiation of strain localization that is a deformation characteristic of the material. These ruptures are due to the rapid increase of local strain after the initiation of strain localization and suddenly reach the fracture criterion. For the evaluation of the deformability of the bends, a method based on FEA that can predict the displacement at the rupture is proposed. We show that the yield surface shape and the true stress–strain relationship after uniform elongation have to be defined on the basis of the actual properties of the bend material. The von Mises yield criterion, which is commonly used in cases of elastic–plastic FEA, could not predict the rupture and overestimated the deformability. In contrast, a yield surface obtained by performing tensile tests on a biaxial specimen could predict the rupture. The prediction of the rupture was accomplished by an inverse calibration method that determined the true stress-strain relationship after uniform elongation. As an alternative to the inverse calibration, a simple extrapolation method of the true stress-strain relationship after uniform elongation which can predict the rupture is proposed. - Highlights: • A method based on FEA that can predict the displacement at the rupture is proposed. • The yield surface shape and the true stress–strain have to be defined precisely. • The von Mises yield criterion overestimated the deformability. • The ruptures are due to the

The Escherichia coli modE gene: effect of modE mutations on molybdate dependent modA expression.

Science.gov (United States)

McNicholas, P M; Chiang, R C; Gunsalus, R P

1996-11-15

The Escherichia coli modABCD operon, which encodes a high-affinity molybdate uptake system, is transcriptionally regulated in response to molybdate availability by ModE. Here we describe a highly effective enrichment protocol, applicable to any gene with a repressor role, and establish its application in the isolation of transposon mutations in modE. In addition we show that disruption of the ModE C-terminus abolishes derepression in the absence of molybdate, implying this region of ModE controls the repressor activity. Finally, a mutational analysis of a proposed molybdate binding motif indicates that this motif does not function in regulating the repressor activity of ModE.

Towards accurate free energy calculations in ligand protein-binding studies.

Science.gov (United States)

Steinbrecher, Thomas; Labahn, Andreas

2010-01-01

Cells contain a multitude of different chemical reaction paths running simultaneously and quite independently next to each other. This amazing feat is enabled by molecular recognition, the ability of biomolecules to form stable and specific complexes with each other and with their substrates. A better understanding of this process, i.e. of the kinetics, structures and thermodynamic properties of biomolecule binding, would be invaluable in the study of biological systems. In addition, as the mode of action of many pharmaceuticals is based upon their inhibition or activation of biomolecule targets, predictive models of small molecule receptor binding are very helpful tools in rational drug design. Since the goal here is normally to design a new compound with a high inhibition strength, one of the most important thermodynamic properties is the binding free energy DeltaG(0). The prediction of binding constants has always been one of the major goals in the field of computational chemistry, because the ability to reliably assess a hypothetical compound's binding properties without having to synthesize it first would save a tremendous amount of work. The different approaches to this question range from fast and simple empirical descriptor methods to elaborate simulation protocols aimed at putting the computation of free energies onto a solid foundation of statistical thermodynamics. While the later methods are still not suited for the screenings of thousands of compounds that are routinely performed in computational drug design studies, they are increasingly put to use for the detailed study of protein ligand interactions. This review will focus on molecular mechanics force field based free energy calculations and their application to the study of protein ligand interactions. After a brief overview of other popular methods for the calculation of free energies, we will describe recent advances in methodology and a variety of exemplary studies of molecular dynamics

Binding of the respiratory chain inhibitor ametoctradin to the mitochondrial bc1 complex.

Science.gov (United States)

Fehr, Marcus; Wolf, Antje; Stammler, Gerd

2016-03-01

Ametoctradin is an agricultural fungicide that inhibits the mitochondrial bc1 complex of oomycetes. The bc1 complex has two quinone binding sites that can be addressed by inhibitors. Depending on their binding sites and binding modes, the inhibitors show different degrees of cross-resistance that need to be considered when designing spray programmes for agricultural fungicides. The binding site of ametoctradin was unknown. Cross-resistance analyses, the reduction of isolated Pythium sp. bc1 complex in the presence of different inhibitors and molecular modelling studies were used to analyse the binding site and binding mode of ametoctradin. All three approaches provide data supporting the argument that ametoctradin binds to the Pythium bc1 complex similarly to stigmatellin. The binding mode of ametoctradin differs from other agricultural fungicides such as cyazofamid and the strobilurins. This explains the lack of cross-resistance with strobilurins and related inhibitors, where resistance is mainly caused by G143A amino acid exchange. Accordingly, mixtures or alternating applications of these fungicides and ametoctradin can help to minimise the risk of the emergence of new resistant isolates. © 2015 Society of Chemical Industry.

Mutational analysis of the extracellular disulphide bridges of the atypical chemokine receptor ACKR3/CXCR7 uncovers multiple binding and activation modes for its chemokine and endogenous non-chemokine agonists.

Science.gov (United States)

Szpakowska, Martyna; Meyrath, Max; Reynders, Nathan; Counson, Manuel; Hanson, Julien; Steyaert, Jan; Chevigné, Andy

2018-07-01

The atypical chemokine receptor ACKR3/CXCR7 plays crucial roles in numerous physiological processes but also in viral infection and cancer. ACKR3 shows strong propensity for activation and, unlike classical chemokine receptors, can respond to chemokines from both the CXC and CC families as well as to the endogenous peptides BAM22 and adrenomedullin. Moreover, despite belonging to the G protein coupled receptor family, its function appears to be mainly dependent on β-arrestin. ACKR3 has also been shown to continuously cycle between the plasma membrane and the endosomal compartments, suggesting a possible role as a scavenging receptor. So far, the molecular basis accounting for these atypical binding and signalling properties remains elusive. Noteworthy, ACKR3 extracellular domains bear three disulphide bridges. Two of them lie on top of the two main binding subpockets and are conserved among chemokine receptors, and one, specific to ACKR3, forms an intra-N terminus four-residue-loop of so far unknown function. Here, by mutational and functional studies, we examined the impact of the different disulphide bridges for ACKR3 folding, ligand binding and activation. We showed that, in contrast to most classical chemokine receptors, none of the extracellular disulphide bridges was essential for ACKR3 function. However, the disruption of the unique ACKR3 N-terminal loop drastically reduced the binding of CC chemokines whereas it only had a mild impact on CXC chemokine binding. Mutagenesis also uncovered that chemokine and endogenous non-chemokine ligands interact and activate ACKR3 according to distinct binding modes characterized by different transmembrane domain subpocket occupancy and N-terminal loop contribution, with BAM22 mimicking the binding mode of CC chemokine N terminus. Copyright © 2018 Elsevier Inc. All rights reserved.

DNA binding studies of tartrazine food additive.

Science.gov (United States)

Kashanian, Soheila; Zeidali, Sahar Heidary

2011-07-01

The interaction of native calf thymus DNA with tartrazine in 10 mM Tris-HCl aqueous solution at neutral pH 7.4 was investigated. Tartrazine is a nitrous derivative and may cause allergic reactions, with a potential of toxicological risk. Also, tartrazine induces oxidative stress and DNA damage. Its DNA binding properties were studied by UV-vis and circular dichroism spectra, competitive binding with Hoechst 33258, and viscosity measurements. Tartrazine molecules bind to DNA via groove mode as illustrated by hyperchromism in the UV absorption band of tartrazine, decrease in Hoechst-DNA solution fluorescence, unchanged viscosity of DNA, and conformational changes such as conversion from B-like to C-like in the circular dichroism spectra of DNA. The binding constants (K(b)) of DNA with tartrazine were calculated at different temperatures. Enthalpy and entropy changes were calculated to be +37 and +213 kJ mol(-1), respectively, according to the Van't Hoff equation, which indicated that the reaction is predominantly entropically driven. Also, tartrazine does not cleave plasmid DNA. Tartrazine interacts with calf thymus DNA via a groove interaction mode with an intrinsic binding constant of 3.75 × 10(4) M(-1).

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

Science.gov (United States)

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

2018-02-20

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

Stereochemical determinants of C-terminal specificity in PDZ peptide-binding domains: a novel contribution of the carboxylate-binding loop.

Science.gov (United States)

Amacher, Jeanine F; Cushing, Patrick R; Bahl, Christopher D; Beck, Tobias; Madden, Dean R

2013-02-15

PDZ (PSD-95/Dlg/ZO-1) binding domains often serve as cellular traffic engineers, controlling the localization and activity of a wide variety of binding partners. As a result, they play important roles in both physiological and pathological processes. However, PDZ binding specificities overlap, allowing multiple PDZ proteins to mediate distinct effects on shared binding partners. For example, several PDZ domains bind the cystic fibrosis (CF) transmembrane conductance regulator (CFTR), an epithelial ion channel mutated in CF. Among these binding partners, the CFTR-associated ligand (CAL) facilitates post-maturational degradation of the channel and is thus a potential therapeutic target. Using iterative optimization, we previously developed a selective CAL inhibitor peptide (iCAL36). Here, we investigate the stereochemical basis of iCAL36 specificity. The crystal structure of iCAL36 in complex with the CAL PDZ domain reveals stereochemical interactions distributed along the peptide-binding cleft, despite the apparent degeneracy of the CAL binding motif. A critical selectivity determinant that distinguishes CAL from other CFTR-binding PDZ domains is the accommodation of an isoleucine residue at the C-terminal position (P(0)), a characteristic shared with the Tax-interacting protein-1. Comparison of the structures of these two PDZ domains in complex with ligands containing P(0) Leu or Ile residues reveals two distinct modes of accommodation for β-branched C-terminal side chains. Access to each mode is controlled by distinct residues in the carboxylate-binding loop. These studies provide new insights into the primary sequence determinants of binding motifs, which in turn control the scope and evolution of PDZ interactomes.

Exploiting protein flexibility to predict the location of allosteric sites

Directory of Open Access Journals (Sweden)

Panjkovich Alejandro

2012-10-01

Full Text Available Abstract Background Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. Results By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity, by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing

Structural characterization of the binding interactions of various endogenous estrogen metabolites with human estrogen receptor α and β subtypes: a molecular modeling study.

Directory of Open Access Journals (Sweden)

Pan Wang

Full Text Available In the present study, we used the molecular docking approach to study the binding interactions of various derivatives of 17β-estradiol (E2 with human estrogen receptor (ER α and β. First, we determined the suitability of the molecular docking method to correctly predict the binding modes and interactions of two representative agonists (E2 and diethylstilbesterol in the ligand binding domain (LBD of human ERα. We showed that the docked structures of E2 and diethylstilbesterol in the ERα LBD were almost exactly the same as the known crystal structures of ERα in complex with these two estrogens. Using the same docking approach, we then characterized the binding interactions of 27 structurally similar E2 derivatives with the LBDs of human ERα and ERβ. While the binding modes of these E2 derivatives are very similar to that of E2, there are distinct subtle differences, and these small differences contribute importantly to their differential binding affinities for ERs. In the case of A-ring estrogen derivatives, there is a strong inverse relationship between the length of the hydrogen bonds formed with ERs and their binding affinity. We found that a better correlation between the computed binding energy values and the experimentally determined logRBA values could be achieved for various A-ring derivatives by re-adjusting the relative weights of the van der Waals interaction energy and the Coulomb interaction energy in computing the overall binding energy values.

Long chain fatty acids alter the interactive binding of ligands to the two principal drug binding sites of human serum albumin.

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Keishi Yamasaki

Full Text Available A wide variety of drugs bind to human serum albumin (HSA at its two principal sites, namely site I and site II. A number of reports indicate that drug binding to these two binding sites are not completely independent, and that interactions between ligands of these two discrete sites can play a role. In this study, the effect of the binding of long-chain fatty acids on the interactive binding between dansyl-L-asparagine (DNSA; site I ligand and ibuprofen (site II ligand at pH6.5 was examined. Binding experiments showed that the binding of sodium oleate (Ole to HSA induces conformational changes in the molecule, which, in turn, changes the individual binding of DNSA and ibuprofen, as well as the mode of interaction between these two ligands from a 'competitive-like' allosteric interaction in the case of the defatted HSA conformer to a 'nearly independent' binding in the case of non-defatted HSA conformer. Circular dichroism measurements indicated that ibuprofen and Ole are likely to modify the spatial orientation of DNSA at its binding site. Docking simulations suggest that the long-distance electric repulsion between DNSA and ibuprofen on defatted HSA contributes to a 'competitive-like' allosteric interaction, whereas extending the distance between ligands and/or increasing the flexibility or size of the DNSA binding site in fatted HSA evokes a change in the interaction mode to 'nearly independent' binding. The present findings provide further insights into the structural dynamics of HSA upon the binding of fatty acids, and its effects on drug binding and drug-drug interactions that occur on HSA.

Fear on the move: predator hunting mode predicts variation in prey mortality and plasticity in prey spatial response.

Science.gov (United States)

Miller, Jennifer R B; Ament, Judith M; Schmitz, Oswald J

2014-01-01

Ecologists have long searched for a framework of a priori species traits to help predict predator-prey interactions in food webs. Empirical evidence has shown that predator hunting mode and predator and prey habitat domain are useful traits for explaining predator-prey interactions. Yet, individual experiments have yet to replicate predator hunting mode, calling into question whether predator impacts can be attributed to hunting mode or merely species identity. We tested the effects of spider predators with sit-and-wait, sit-and-pursue and active hunting modes on grasshopper habitat domain, activity and mortality in a grassland system. We replicated hunting mode by testing two spider predator species of each hunting mode on the same grasshopper prey species. We observed grasshoppers with and without each spider species in behavioural cages and measured their mortality rates, movements and habitat domains. We likewise measured the movements and habitat domains of spiders to characterize hunting modes. We found that predator hunting mode explained grasshopper mortality and spider and grasshopper movement activity and habitat domain size. Sit-and-wait spider predators covered small distances over a narrow domain space and killed fewer grasshoppers than sit-and-pursue and active predators, which ranged farther distances across broader domains and killed more grasshoppers, respectively. Prey adjusted their activity levels and horizontal habitat domains in response to predator presence and hunting mode: sedentary sit-and-wait predators with narrow domains caused grasshoppers to reduce activity in the same-sized domain space; more mobile sit-and-pursue predators with broader domains caused prey to reduce their activity within a contracted horizontal (but not vertical) domain space; and highly mobile active spiders led grasshoppers to increase their activity across the same domain area. All predators impacted prey activity, and sit-and-pursue predators generated strong

Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work.

Science.gov (United States)

Dalton, Alice M; Jones, Andrew P; Panter, Jenna R; Ogilvie, David

2013-01-01

Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics. Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS) software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression. Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20-1.85]; cycling OR 0.43 [95% CI 0.23-0.83]), low street connectivity (OR 0.22, [0.07-0.67]; OR 0.48 [0.26-0.90]) and free car parking at work (OR 0.24 [0.10-0.59]; OR 0.55 [0.32-0.95]). Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools) close to work (OR 0.36 [0.21-0.62]) and a railway station further from home (OR 0.53 [0.30-0.93]). Covariates strongly predicted travel mode (pseudo r-squared 0.74). Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode choice

Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work.

Directory of Open Access Journals (Sweden)

Alice M Dalton

Full Text Available Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20-1.85]; cycling OR 0.43 [95% CI 0.23-0.83], low street connectivity (OR 0.22, [0.07-0.67]; OR 0.48 [0.26-0.90] and free car parking at work (OR 0.24 [0.10-0.59]; OR 0.55 [0.32-0.95]. Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools close to work (OR 0.36 [0.21-0.62] and a railway station further from home (OR 0.53 [0.30-0.93]. Covariates strongly predicted travel mode (pseudo r-squared 0.74.Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode

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.

A highly tilted binding mode by a self-reactive T cell receptor results in altered engagement of peptide and MHC

Energy Technology Data Exchange (ETDEWEB)

Sethi, D.K.; Heroux, A.; Schubert, D. A.; Anders, A.-K.; Bonsor, D. A.; Thomas, C. P.; Sundberg, E. J.; Pyrdol, J.; Wucherpfennig, K. W.

2011-01-17

Self-reactive T cells that escape elimination in the thymus can cause autoimmune pathology, and it is therefore important to understand the structural mechanisms of self-antigen recognition. We report the crystal structure of a T cell receptor (TCR) from a patient with relapsing-remitting multiple sclerosis that engages its self-peptide-major histocompatibility complex (pMHC) ligand in an unusual manner. The TCR is bound in a highly tilted orientation that prevents interaction of the TCR-{alpha} chain with the MHC class II {beta} chain helix. In this structure, only a single germline-encoded TCR loop engages the MHC protein, whereas in most other TCR-pMHC structures all four germline-encoded TCR loops bind to the MHC helices. The tilted binding mode also prevents peptide contacts by the short complementarity-determining region (CDR) 3{beta} loop, and interactions that contribute to peptide side chain specificity are focused on the CDR3{alpha} loop. This structure is the first example in which only a single germline-encoded TCR loop contacts the MHC helices. Furthermore, the reduced interaction surface with the peptide may facilitate TCR cross-reactivity. The structural alterations in the trimolecular complex are distinct from previously characterized self-reactive TCRs, indicating that there are multiple unusual ways for self-reactive TCRs to bind their pMHC ligand.

A Highly Tilted Binding Mode by a Self-Reactive T Cell Receptor Results in Altered Engagement of Peptide and MHC

Energy Technology Data Exchange (ETDEWEB)

D Sethi; D Schubert; A Anders; A Heroux; D Bonsor; C Thomas; E Sundberg; J Pyrdol; K Wucherpfennig

2011-12-31

Self-reactive T cells that escape elimination in the thymus can cause autoimmune pathology, and it is therefore important to understand the structural mechanisms of self-antigen recognition. We report the crystal structure of a T cell receptor (TCR) from a patient with relapsing-remitting multiple sclerosis that engages its self-peptide-major histocompatibility complex (pMHC) ligand in an unusual manner. The TCR is bound in a highly tilted orientation that prevents interaction of the TCR-{alpha} chain with the MHC class II {beta} chain helix. In this structure, only a single germline-encoded TCR loop engages the MHC protein, whereas in most other TCR-pMHC structures all four germline-encoded TCR loops bind to the MHC helices. The tilted binding mode also prevents peptide contacts by the short complementarity-determining region (CDR) 3{beta} loop, and interactions that contribute to peptide side chain specificity are focused on the CDR3{alpha} loop. This structure is the first example in which only a single germline-encoded TCR loop contacts the MHC helices. Furthermore, the reduced interaction surface with the peptide may facilitate TCR cross-reactivity. The structural alterations in the trimolecular complex are distinct from previously characterized self-reactive TCRs, indicating that there are multiple unusual ways for self-reactive TCRs to bind their pMHC ligand.

Predictions of the near edge transport shortfall in DIII-D L-mode plasmas using the trapped gyro-Landau-fluid model

Energy Technology Data Exchange (ETDEWEB)

Kinsey, J. E. [CompX, P.O. Box 2672, Del Mar, California 92014 (United States); Staebler, G. M.; Candy, J.; Petty, C. C.; Waltz, R. E. [General Atomics, P.O. Box 85608, San Diego, California 92186-5608 (United States); Rhodes, T. L. [Physics Department and PSTI, University of California, Los Angeles, California 90095 (United States)

2015-01-15

Previous studies of DIII-D L-mode plasmas have shown that a transport shortfall exists in that our current models of turbulent transport can significantly underestimate the energy transport in the near edge region. In this paper, the Trapped Gyro-Landau-Fluid (TGLF) drift wave transport model is used to simulate the near edge transport in a DIII-D L-mode experiment designed to explore the impact of varying the safety factor on the shortfall. We find that the shortfall systematically increases with increasing safety factor and is more pronounced for the electrons than for the ions. Within the shortfall dataset, a single high current case has been found where no transport shortfall is predicted. Reduced neutral beam injection power has been identified as the key parameter separating this discharge from other discharges exhibiting a shortfall. Further analysis shows that the energy transport in the L-mode near edge region is not stiff according to TGLF. Unlike the H-mode core region, the predicted temperature profiles are relatively more responsive to changes in auxiliary heating power. In testing the fidelity of TGLF for the near edge region, we find that a recalibration of the collision model is warranted. A recalibration improves agreement between TGLF and nonlinear gyrokinetic simulations performed using the GYRO code with electron-ion collisions. The recalibration only slightly impacts the predicted shortfall.

How Native and Alien Metal Cations Bind ATP: Implications for Lithium as a Therapeutic Agent

Science.gov (United States)

Dudev, Todor; Grauffel, Cédric; Lim, Carmay

2017-02-01

Adenosine triphosphate (ATP), the major energy currency of the cell, exists in solution mostly as ATP-Mg. Recent experiments suggest that Mg2+ interacts with the highly charged ATP triphosphate group and Li+ can co-bind with the native Mg2+ to form ATP-Mg-Li and modulate the neuronal purine receptor response. However, it is unclear how the negatively charged ATP triphosphate group binds Mg2+ and Li+ (i.e. which phosphate group(s) bind Mg2+/Li+) and how the ATP solution conformation depends on the type of metal cation and the metal-binding mode. Here, we reveal the preferred ATP-binding mode of Mg2+/Li+ alone and combined: Mg2+ prefers to bind ATP tridentately to each of the three phosphate groups, but Li+ prefers to bind bidentately to the terminal two phosphates. We show that the solution ATP conformation depends on the cation and its binding site/mode, but it does not change significantly when Li+ binds to Mg2+-loaded ATP. Hence, ATP-Mg-Li, like Mg2+-ATP, can fit in the ATP-binding site of the host enzyme/receptor, activating specific signaling pathways.

Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

International Nuclear Information System (INIS)

Zhang, Yachao; Liu, Kaipei; Qin, Liang; An, Xueli

2016-01-01

Highlights: • Variational mode decomposition is adopted to process original wind power series. • A novel combined model based on machine learning methods is established. • An improved differential evolution algorithm is proposed for weight adjustment. • Probabilistic interval prediction is performed by quantile regression averaging. - Abstract: Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid

Predictive transport modelling of type I ELMy H-mode dynamics using a theory-motivated combined ballooning-peeling model

International Nuclear Information System (INIS)

Loennroth, J-S; Parail, V; Dnestrovskij, A; Figarella, C; Garbet, X; Wilson, H

2004-01-01

This paper discusses predictive transport simulations of the type I ELMy high confinement mode (H-mode) with a theory-motivated edge localized mode (ELM) model based on linear ballooning and peeling mode stability theory. In the model, a total mode amplitude is calculated as a sum of the individual mode amplitudes given by two separate linear differential equations for the ballooning and peeling mode amplitudes. The ballooning and peeling mode growth rates are represented by mutually analogous terms, which differ from zero upon the violation of a critical pressure gradient and an analytical peeling mode stability criterion, respectively. The damping of the modes due to non-ideal magnetohydrodynamic effects is controlled by a term driving the mode amplitude towards the level of background fluctuations. Coupled to simulations with the JETTO transport code, the model qualitatively reproduces the experimental dynamics of type I ELMy H-mode, including an ELM frequency that increases with the external heating power. The dynamics of individual ELM cycles is studied. Each ELM is usually triggered by a ballooning mode instability. The ballooning phase of the ELM reduces the pressure gradient enough to make the plasma peeling unstable, whereby the ELM continues driven by the peeling mode instability, until the edge current density has been depleted to a stable level. Simulations with current ramp-up and ramp-down are studied as examples of situations in which pure peeling and pure ballooning mode ELMs, respectively, can be obtained. The sensitivity with respect to the ballooning and peeling mode growth rates is investigated. Some consideration is also given to an alternative formulation of the model as well as to a pure peeling model

Phosphate-binding protein from Polaromonas JS666: purification, characterization, crystallization and sulfur SAD phasing

Energy Technology Data Exchange (ETDEWEB)

Pegos, Vanessa R.; Hey, Louis; LaMirande, Jacob; Pfeffer, Rachel; Lipsh, Rosalie; Amitay, Moshe; Gonzalez, Daniel; Elias, Mikael (JCT-Israel); (UMM); (CNRS-UMR)

2017-05-25

Phosphate-binding proteins (PBPs) are key proteins that belong to the bacterial ABC-type phosphate transporters. PBPs are periplasmic (or membrane-anchored) proteins that capture phosphate anions from the environment and release them to the transmembrane transporter. Recent work has suggested that PBPs have evolved for high affinity as well as high selectivity. In particular, a short, unique hydrogen bond between the phosphate anion and an aspartate residue has been shown to be critical for selectivity, yet is not strictly conserved in PBPs. Here, the PBP fromPolaromonasJS666 is focused on. Interestingly, this PBP is predicted to harbor different phosphate-binding residues to currently known PBPs. Here, it is shown that the PBP fromPolaromonasJS666 is capable of binding phosphate, with a maximal binding activity at pH 8. Its structure is expected to reveal its binding-cleft configuration as well as its phosphate-binding mode. Here, the expression, purification, characterization, crystallization and X-ray diffraction data collection to 1.35 Å resolution of the PBP fromPolaromonasJS666 are reported.

'In-Crystallo' Capture of a Michaelis Complex And Product Binding Modes of a Bacterial Phosphotriesterase

Energy Technology Data Exchange (ETDEWEB)

Jackson, C.J.; Foo, J.-L.; Kim, H.-K.; Carr, P.D.; Liu, J.-W.; Salem, G.; Ollis, D.L.

2009-05-18

The mechanism by which the binuclear metallophosphotriesterases (PTEs, E.C. 3.1.8.1) catalyse substrate hydrolysis has been extensively studied. The {mu}-hydroxo bridge between the metal ions has been proposed to be the initiating nucleophile in the hydrolytic reaction. In contrast, analysis of some biomimetic systems has indicated that {mu}-hydroxo bridges are often not themselves nucleophiles, but act as general bases for freely exchangeable nucleophilic water molecules. Herein, we present crystallographic analyses of a bacterial PTE from Agrobacterium radiobacter, OpdA, capturing the enzyme-substrate complex during hydrolysis. This model of the Michaelis complex suggests the alignment of the substrate will favor attack from a solvent molecule terminally coordinated to the {alpha}-metal ion. The bridging of both metal ions by the product, without disruption of the {mu}-hydroxo bridge, is also consistent with nucleophilic attack occurring from the terminal position. When phosphodiesters are soaked into crystals of OpdA, they coordinate bidentately to the {beta}-metal ion, displacing the {mu}-hydroxo bridge. Thus, alternative product-binding modes exist for the PTEs, and it is the bridging mode that appears to result from phosphotriester hydrolysis. Kinetic analysis of the PTE and promiscuous phosphodiesterase activities confirms that the presence of a {mu}-hydroxo bridge during phosphotriester hydrolysis is correlated with a lower pK{sub a} for the nucleophile, consistent with a general base function during catalysis.

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

Science.gov (United States)

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

2014-07-01

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

Molecular aspects of herbicide binding in chloroplasts = [Molekulaire aspekten van herbicide binding in chloroplasten

NARCIS (Netherlands)

Naber, D.

1989-01-01

Many weed-controlling agents act by inhibiting the process of photosynthesis. Their mode of action is a displacement of the secondary quinone electron acceptor of photosystem II from its proteinaceous binding environment. This results in a blocking of the electron transport. Consequently

Neuronal Calcium Sensor-1 Binds the D2 Dopamine Receptor and G-protein-coupled Receptor Kinase 1 (GRK1) Peptides Using Different Modes of Interactions.

Science.gov (United States)

Pandalaneni, Sravan; Karuppiah, Vijaykumar; Saleem, Muhammad; Haynes, Lee P; Burgoyne, Robert D; Mayans, Olga; Derrick, Jeremy P; Lian, Lu-Yun

2015-07-24

Neuronal calcium sensor-1 (NCS-1) is the primordial member of the neuronal calcium sensor family of EF-hand Ca(2+)-binding proteins. It interacts with both the G-protein-coupled receptor (GPCR) dopamine D2 receptor (D2R), regulating its internalization and surface expression, and the cognate kinases GRK1 and GRK2. Determination of the crystal structures of Ca(2+)/NCS-1 alone and in complex with peptides derived from D2R and GRK1 reveals that the differential recognition is facilitated by the conformational flexibility of the C-lobe-binding site. We find that two copies of the D2R peptide bind within the hydrophobic crevice on Ca(2+)/NCS-1, but only one copy of the GRK1 peptide binds. The different binding modes are made possible by the C-lobe-binding site of NCS-1, which adopts alternative conformations in each complex. C-terminal residues Ser-178-Val-190 act in concert with the flexible EF3/EF4 loop region to effectively form different peptide-binding sites. In the Ca(2+)/NCS-1·D2R peptide complex, the C-terminal region adopts a 310 helix-turn-310 helix, whereas in the GRK1 peptide complex it forms an α-helix. Removal of Ser-178-Val-190 generated a C-terminal truncation mutant that formed a dimer, indicating that the NCS-1 C-terminal region prevents NCS-1 oligomerization. We propose that the flexible nature of the C-terminal region is essential to allow it to modulate its protein-binding sites and adapt its conformation to accommodate both ligands. This appears to be driven by the variability of the conformation of the C-lobe-binding site, which has ramifications for the target specificity and diversity of NCS-1. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

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

Science.gov (United States)

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

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

Poloidal rotation and the evolution of H-mode and VH-mode profiles

International Nuclear Information System (INIS)

Hinton, F.L.; Staebler, G.M.; Kim, Y.B.

1993-12-01

The physics which determines poloidal rotation, and its role in the development of profiles during H- and VH-modes, is discussed. A simple phenomenological transport model, which incorporates the rvec E x rvec B flow shear suppression of turbulence, is shown to predict profile evolution similar to that observed experimentally during H-mode and VH-mode

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....

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

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

Science.gov (United States)

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

2017-11-01

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

Stability of longitudinal modes in a bunched beam with mode coupling

International Nuclear Information System (INIS)

Satoh, K.

1981-06-01

In this paper we study a longitudinal coherent bunch instability in which the growth time is comparable to or less than the period of synchrotron oscillations. Both longitudinal and transverse bunch instabilities have been studied. In most treatments, however, the coherent force is assumed to be small and is treated as a perturbation compared with the synchrotron force. This makes the problem simpler because an individual synchrotron mode is decoupled. As bunch current increases, the coherent force is no longer small and the mode frequency shift becomes significant compared with the synchrotron frequency. Therefore in this case it is necessary to include coupling of the synchrotron modes. Recently a fast blow-up instability which comes from mode coupling was studied. Their method is to derive a dispersion relation for a bunched beam using the Vlasov equation and to analyze it as in a coasting beam. They showed that if mode coupling is included the Vlasov equation predicts a fast microwave instability with a stability condition similar to that for a coasting beam. In this paper we will partly follow their method and present a formalism which includes coupling between higher-order radial modes as well as coupling between synchrotron modes. The formalism is considered to be generalization of the Sacherer formalism without mode coupling. This theory predicts that instability is induced not only by coupling between different synchrotron modes, but also by coupling between positive and negative modes, since negative synchrotron modes are included in the theory in a natural manner. This formalism is to be used for a Gaussian bunch and a parabolic bunch, and is also useful for transverse problems

Mode I and mixed mode crack-tip fields in strain gradient plasticity

DEFF Research Database (Denmark)

Goutianos, Stergios

2011-01-01

Strain gradients develop near the crack-tip of Mode I or mixed mode cracks. A finite strain version of the phenomenological strain gradient plasticity theory of Fleck–Hutchinson (2001) is used here to quantify the effect of the material length scales on the crack-tip stress field for a sharp...... stationary crack under Mode I and mixed mode loading. It is found that for material length scales much smaller than the scale of the deformation gradients, the predictions converge to conventional elastic–plastic solutions. For length scales sufficiently large, the predictions converge to elastic solutions....... Thus, the range of length scales over which a strain gradient plasticity model is necessary is identified. The role of each of the three material length scales, incorporated in the multiple length scale theory, in altering the near-tip stress field is systematically studied in order to quantify...

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

DEFF Research Database (Denmark)

Nielsen, Morten; Andreatta, Massimo

2016-01-01

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

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

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

Science.gov (United States)

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

2013-02-07

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

On Kinetic Slow Modes, Fluid Slow Modes, and Pressure-balanced Structures in the Solar Wind

Energy Technology Data Exchange (ETDEWEB)

Verscharen, Daniel [Space Science Center and Department of Physics, University of New Hampshire, Durham, NH 03824 (United States); Chen, Christopher H. K. [Department of Physics, Imperial College London, London SW7 2AZ (United Kingdom); Wicks, Robert T., E-mail: daniel.verscharen@unh.edu, E-mail: christopher.chen@imperial.ac.uk, E-mail: r.wicks@ucl.ac.uk [Mullard Space Science Laboratory, University College London, London WC1E 6BT (United Kingdom)

2017-05-10

Observations in the solar wind suggest that the compressive component of inertial-range solar-wind turbulence is dominated by slow modes. The low collisionality of the solar wind allows for nonthermal features to survive, which suggests the requirement of a kinetic plasma description. The least-damped kinetic slow mode is associated with the ion-acoustic (IA) wave and a nonpropagating (NP) mode. We derive analytical expressions for the IA-wave dispersion relation in an anisotropic plasma in the framework of gyrokinetics and then compare them to fully kinetic numerical calculations, results from two-fluid theory, and magnetohydrodynamics (MHD). This comparison shows major discrepancies in the predicted wave phase speeds from MHD and kinetic theory at moderate to high β . MHD and kinetic theory also dictate that all plasma normal modes exhibit a unique signature in terms of their polarization. We quantify the relative amplitude of fluctuations in the three lowest particle velocity moments associated with IA and NP modes in the gyrokinetic limit and compare these predictions with MHD results and in situ observations of the solar-wind turbulence. The agreement between the observations of the wave polarization and our MHD predictions is better than the kinetic predictions, which suggests that the plasma behaves more like a fluid in the solar wind than expected.

On Kinetic Slow Modes, Fluid Slow Modes, and Pressure-balanced Structures in the Solar Wind

International Nuclear Information System (INIS)

Verscharen, Daniel; Chen, Christopher H. K.; Wicks, Robert T.

2017-01-01

Observations in the solar wind suggest that the compressive component of inertial-range solar-wind turbulence is dominated by slow modes. The low collisionality of the solar wind allows for nonthermal features to survive, which suggests the requirement of a kinetic plasma description. The least-damped kinetic slow mode is associated with the ion-acoustic (IA) wave and a nonpropagating (NP) mode. We derive analytical expressions for the IA-wave dispersion relation in an anisotropic plasma in the framework of gyrokinetics and then compare them to fully kinetic numerical calculations, results from two-fluid theory, and magnetohydrodynamics (MHD). This comparison shows major discrepancies in the predicted wave phase speeds from MHD and kinetic theory at moderate to high β . MHD and kinetic theory also dictate that all plasma normal modes exhibit a unique signature in terms of their polarization. We quantify the relative amplitude of fluctuations in the three lowest particle velocity moments associated with IA and NP modes in the gyrokinetic limit and compare these predictions with MHD results and in situ observations of the solar-wind turbulence. The agreement between the observations of the wave polarization and our MHD predictions is better than the kinetic predictions, which suggests that the plasma behaves more like a fluid in the solar wind than expected.

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

Science.gov (United States)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Nicolas Panel

2017-09-01

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

4-Aminoquinoline-pyrimidine hybrids: synthesis, antimalarial activity, heme binding and docking studies.

Science.gov (United States)

Kumar, Deepak; Khan, Shabana I; Tekwani, Babu L; Ponnan, Prija; Rawat, Diwan S

2015-01-07

A series of novel 4-aminoquinoline-pyrimidine hybrids has been synthesized and evaluated for their antimalarial activity. Several compounds showed promising in vitro antimalarial activity against both CQ-sensitive and CQ-resistant strains with high selectivity index. All the compounds were found to be non-toxic to the mammalian cell lines. Selected compound 7g exhibited significant suppression of parasitemia in the in vivo assay. The heme binding studies were conducted to determine the mode of action of these hybrid molecules. These compounds form a stable 1:1 complex with hematin suggesting that heme may be one of the possible targets of these hybrids. The interaction of these conjugate hybrids was also investigated by the molecular docking studies in the binding site of PfDHFR. The pharmacokinetic property analysis of best active compounds was also studied using ADMET prediction. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Dendrimers bind antioxidant polyphenols and cisplatin drug.

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Amine Abderrezak

Full Text Available Synthetic polymers of a specific shape and size play major role in drug delivery systems. Dendrimers are unique synthetic macromolecules of nanometer dimensions with a highly branched structure and globular shape with potential applications in gene and drug delivery. We examine the interaction of several dendrimers of different compositions mPEG-PAMAM (G3, mPEG-PAMAM (G4 and PAMAM (G4 with hydrophilic and hydrophobic drugs cisplatin, resveratrol, genistein and curcumin at physiological conditions. FTIR and UV-visible spectroscopic methods as well as molecular modeling were used to analyse drug binding mode, the binding constant and the effects of drug complexation on dendrimer stability and conformation. Structural analysis showed that cisplatin binds dendrimers in hydrophilic mode via Pt cation and polymer terminal NH(2 groups, while curcumin, genistein and resveratrol are located mainly in the cavities binding through both hydrophobic and hydrophilic contacts. The overall binding constants of durg-dendrimers are ranging from 10(2 M(-1 to 10(3 M(-1. The affinity of dendrimer binding was PAMAM-G4>mPEG-PAMAM-G4>mPEG-PAMAM-G3, while the order of drug-polymer stability was curcumin>cisplatin>genistein>resveratrol. Molecular modeling showed larger stability for genisten-PAMAM-G4 (ΔG = -4.75 kcal/mol than curcumin-PAMAM-G4 ((ΔG = -4.53 kcal/mol and resveratrol-PAMAM-G4 ((ΔG = -4.39 kcal/mol. Dendrimers might act as carriers to transport hydrophobic and hydrophilic drugs.

SwarmDock and the Use of Normal Modes in Protein-Protein Docking

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Paul A. Bates

2010-09-01

Full Text Available Here is presented an investigation of the use of normal modes in protein-protein docking, both in theory and in practice. Upper limits of the ability of normal modes to capture the unbound to bound conformational change are calculated on a large test set, with particular focus on the binding interface, the subset of residues from which the binding energy is calculated. Further, the SwarmDock algorithm is presented, to demonstrate that the modelling of conformational change as a linear combination of normal modes is an effective method of modelling flexibility in protein-protein docking.

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.

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

Science.gov (United States)

Zhan, Yiling; Guo, Shuyuan

2015-01-01

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

Color vision predicts processing modes of goal activation during action cascading.

Science.gov (United States)

Jongkees, Bryant J; Steenbergen, Laura; Colzato, Lorenza S

2017-09-01

One of the most important functions of cognitive control is action cascading: the ability to cope with multiple response options when confronted with various task goals. A recent study implicates a key role for dopamine (DA) in this process, suggesting higher D1 efficiency shifts the action cascading strategy toward a more serial processing mode, whereas higher D2 efficiency promotes a shift in the opposite direction by inducing a more parallel processing mode (Stock, Arning, Epplen, & Beste, 2014). Given that DA is found in high concentration in the retina and modulation of retinal DA release displays characteristics of D2-receptors (Peters, Schweibold, Przuntek, & Müller, 2000), color vision discrimination might serve as an index of D2 efficiency. We used color discrimination, assessed with the Lanthony Desaturated Panel D-15 test, to predict individual differences (N = 85) in a stop-change paradigm that provides a well-established measure of action cascading. In this task it is possible to calculate an individual slope value for each participant that estimates the degree of overlap in task goal activation. When the stopping process of a previous task goal has not finished at the time the change process toward a new task goal is initiated (parallel processing), the slope value becomes steeper. In case of less overlap (more serial processing), the slope value becomes flatter. As expected, participants showing better color vision were more prone to activate goals in a parallel manner as indicated by a steeper slope. Our findings suggest that color vision might represent a predictor of D2 efficiency and the predisposed processing mode of goal activation during action cascading. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of DNA-binding specificity in zinc finger proteins

Indian Academy of Sciences (India)

2012-06-25

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

Ligand binding modes from low resolution GPCR models and mutagenesis: chicken bitter taste receptor as a test-case.

Science.gov (United States)

Di Pizio, Antonella; Kruetzfeldt, Louisa-Marie; Cheled-Shoval, Shira; Meyerhof, Wolfgang; Behrens, Maik; Niv, Masha Y

2017-08-15

Bitter taste is one of the basic taste modalities, warning against consuming potential poisons. Bitter compounds activate members of the bitter taste receptor (Tas2r) subfamily of G protein-coupled receptors (GPCRs). The number of functional Tas2rs is species-dependent. Chickens represent an intriguing minimalistic model, because they detect the bitter taste of structurally different molecules with merely three bitter taste receptor subtypes. We investigated the binding modes of several known agonists of a representative chicken bitter taste receptor, ggTas2r1. Because of low sequence similarity between ggTas2r1 and crystallized GPCRs (~10% identity, ~30% similarity at most), the combination of computational approaches with site-directed mutagenesis was used to characterize the agonist-bound conformation of ggTas2r1 binding site between TMs 3, 5, 6 and 7. We found that the ligand interactions with N93 in TM3 and/or N247 in TM5, combined with hydrophobic contacts, are typically involved in agonist recognition. Next, the ggTas2r1 structural model was successfully used to identify three quinine analogues (epiquinidine, ethylhydrocupreine, quinidine) as new ggTas2r1 agonists. The integrated approach validated here may be applicable to additional cases where the sequence identity of the GPCR of interest and the existing experimental structures is low.

Vitual screening and binding mode elucidation of curcumin analogues on Cyclooxygenase-2 using AYO_COX2_V1.1 protocol

Science.gov (United States)

Mulatsari, E.; Mumpuni, E.; Herfian, A.

2017-05-01

Curcumin is yellow colored phenolic compounds contained in Curcuma longa. Curcumin is known to have biological activities as anti-inflammatory, antiviral, antioxidant, and anti-infective agent [1]. Synthesis of curcumin analogue compounds has been done and some of them had biological activity like curcumin. In this research, the virtual screening of curcumin analogue compounds has been conducted. The purpose of this research was to determine the activity of these compounds as selective Cyclooxygenase-2inhibitors in in-silico. Binding mode elucidation was made by active and inactive representative compounds to see the interaction of the amino acids in the binding site of the compounds. This research used AYO_COX2_V.1.1, a structure-based virtual screening protocol (SBVS) that has been validated by Mumpuni E et al, 2014 [2]. AYO_COX2_V.1.1 protocol using a variety of integrated applications such as SPORES, PLANTS, BKchem, OpenBabel and PyMOL. The results of virtual screening conducted on 49 curcumin analogue compounds obtained 8 compounds with 4 active amino acid residues (GLY340, ILE503, PHE343, and PHE367) that were considered active as COX-2 inhibitor.

Fluorescence quenching and the "ring-mode" to "red-mode" transition in alkali inductively coupled plasmas

Science.gov (United States)

Huang, M.; Bazurto, R.; Camparo, J.

2018-01-01

The ring-mode to red-mode transition in alkali metal inductively coupled plasmas (ICPs) (i.e., rf-discharge lamps) is perhaps the most important physical phenomenon affecting these devices as optical pumping light sources for atomic clocks and magnetometers. It sets the limit on useful ICP operating temperature, thereby setting a limit on ICP light output for atomic-clock/magnetometer signal generation, and it is a temperature region of ICP operation associated with discharge instability. Previous work has suggested that the mechanism driving the ring-mode to red-mode transition is associated with radiation trapping, but definitive experimental evidence validating that hypothesis has been lacking. Based on that hypothesis, one would predict that the introduction of an alkali-fluorescence quenching gas (i.e., N2) into the ICP would increase the ring-mode to red-mode transition temperature. Here, we test that prediction, finding direct evidence supporting the radiation-trapping hypothesis.

Substituent and noncovalent interaction effects in the reactivity of purine derivatives with tetracarboxylato-dirhodium(II) units. Rationalization of a rare binding mode via N3.

Science.gov (United States)

Amo-Ochoa, Pilar; Castillo, Oscar; Harrington, Ross W; Zamora, Félix; Houlton, Andrew

2013-02-18

Reactions between [Rh(2)(CH(3)COO)(4)] with 2,6-diaminopurine (HDap) or 6-chloro-2-aminopurine (HClap) and [Rh(2)((CH(3))(3)CCOO)(4)] with HClap produce, three new dirhodium(II) carboxylate complexes of the general form, [Rh(2)(RCOO)(4)(Purine)(2)] (R = CH(3), (CH(3))(3)C). Single crystal X-ray diffraction studies confirm that in all cases the purine coordinates to the axial position of the dirhodium(II)tetracarboxylate unit. However, while the complex obtained with HDap features the typical purine binding mode via N(7), complexes containing HClap show unusual N3 coordination. This is an extremely rare instance of an unrestricted purine binding via N3. Some rationalization of these data is offered based on a series of DFT calculations.

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

OpenAIRE

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

2009-01-01

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

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.

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

Directory of Open Access Journals (Sweden)

Tingjun Hou

2006-01-01

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

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

DEFF Research Database (Denmark)

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

2016-01-01

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

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.

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

Science.gov (United States)

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

2017-01-06

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

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

DEFF Research Database (Denmark)

Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael

2015-01-01

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

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

Science.gov (United States)

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Fuzzy Sliding Mode Observer with Grey Prediction for the Estimation of the State-of-Charge of a Lithium-Ion Battery

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Daehyun Kim

2015-11-01

Full Text Available We propose a state-of-charge (SOC estimation method for Li-ion batteries that combines a fuzzy sliding mode observer (FSMO with grey prediction. Unlike the existing methods based on a conventional first-order sliding mode observer (SMO and an adaptive gain SMO, the proposed method eliminates chattering in SOC estimation. In this method, which uses a fuzzy inference system, the gains of the SMO are adjusted according to the predicted future error and present estimation error of the terminal voltage. To forecast the future error value, a one-step-ahead terminal voltage prediction is obtained using a grey predictor. The proposed estimation method is validated through two types of discharge tests (a pulse discharge test and a random discharge test. The SOC estimation results are compared to the results of the conventional first-order SMO-based and the adaptive gain SMO-based methods. The experimental results show that the proposed method not only reduces chattering, but also improves estimation accuracy.

The Relative Importance of Family History, Gender, Mode of Onset, and Age at Onsetin Predicting Clinical Features of First-Episode Psychotic Disorders.

Science.gov (United States)

Compton, Michael T; Berez, Chantal; Walker, Elaine F

Family history of psychosis, gender, mode of onset, and age at onset are considered prognostic factors important to clinicians evaluating first-episode psychosis; yet, clinicians have little guidance as to how these four factors differentially predict early-course substance abuse, symptomatology, and functioning. We conducted a "head-to-head comparison" of these four factors regarding their associations with key clinical features at initial hospitalization. We also assessed potential interactions between gender and family history with regard to age at onset of psychosis and symptom severity. Consecutively admitted first-episode patients (n=334) were evaluated in two studies that rigorously assessed a number of early-course variables. Associations among variables of interest were examined using Pearson correlations, χ 2 tests, Student's t-tests, and 2×2 factorial analyses of variance. Substance (nicotine, alcohol, and cannabis) abuse and positive symptom severity were predicted only by male gender. Negative symptom severity and global functioning impairments were predicted by earlier age at onset of psychosis. General psychopathology symptom severity was predicted by both mode of onset and age at onset. Interaction effects were not observed with regard to gender and family history in predicting age at onset or symptom severity. The four prognostic features have differential associations with substance abuse, domains of symptom severity, and global functioning. Gender and age at onset of psychosis appear to be more predictive of clinical features at the time of initial evaluation (and thus presumably longer term outcomes) than the presence of a family history of psychosis and a more gradual mode of onset.

The EPED pedestal model and edge localized mode-suppressed regimes: Studies of quiescent H-mode and development of a model for edge localized mode suppression via resonant magnetic perturbations

Energy Technology Data Exchange (ETDEWEB)

Snyder, P. B.; Osborne, T. H.; Burrell, K. H.; Groebner, R. J.; Leonard, A. W.; Wade, M. R. [General Atomics, P.O. Box 85608, San Diego, California 92186-5608 (United States); Nazikian, R. [Princeton Plasma Physics Laboratory, Princeton, New Jersey (United States); Orlov, D. M. [University of California-San Diego, San Diego, California 92093 (United States); Schmitz, O. [Institut fuer Plasmaphysik, Forschungszentrum Juelich GmbH, Association FZJ-EURATOM, Juelich (Germany); Wilson, H. R. [York Plasma Institute, Department of Physics, University of York, Heslington, York YO10 5DD (United Kingdom)

2012-05-15

The EPED model predicts the H-mode pedestal height and width based upon two fundamental and calculable constraints: (1) onset of non-local peeling-ballooning modes at low to intermediate mode number, (2) onset of nearly local kinetic ballooning modes at high mode number. We present detailed tests of the EPED model in discharges with edge localized modes (ELMs), employing new high resolution measurements, and finding good quantitative agreement across a range of parameters. The EPED model is then applied for the first time to quiescent H-mode (QH), finding a similar level of agreement between predicted and observed pedestal height and width, and suggesting that the model can be used to predict the critical density for QH-mode operation. Finally, the model is applied toward understanding the suppression of ELMs with 3D resonant magnetic perturbations (RMP). Combining EPED with plasma response physics, a new working model for RMP ELM suppression is developed. We propose that ELMs are suppressed when a 'wall' associated with the RMP blocks the inward penetration of the edge transport barrier. A calculation of the required location of this 'wall' with EPED is consistent with observed profile changes during RMP ELM suppression and offers an explanation for the observed dependence on safety factor (q{sub 95}).

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

Science.gov (United States)

Fukunishi, Yoshifumi

2010-01-01

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

Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution.

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Xin He

2009-03-01

Full Text Available Cross-species comparison has emerged as a powerful paradigm for predicting cis-regulatory modules (CRMs and understanding their evolution. The comparison requires reliable sequence alignment, which remains a challenging task for less conserved noncoding sequences. Furthermore, the existing models of DNA sequence evolution generally do not explicitly treat the special properties of CRM sequences. To address these limitations, we propose a model of CRM evolution that captures different modes of evolution of functional transcription factor binding sites (TFBSs and the background sequences. A particularly novel aspect of our work is a probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution. We present a computational framework that uses this model to solve the problems of CRM alignment and prediction. Our alignment method is similar to existing methods of statistical alignment but uses the conserved binding sites to improve alignment. Our CRM prediction method deals with the inherent uncertainties of binding site annotations and sequence alignment in a probabilistic framework. In simulated as well as real data, we demonstrate that our program is able to improve both alignment and prediction of CRM sequences over several state-of-the-art methods. Finally, we used alignments produced by our program to study binding site conservation in genome-wide binding data of key transcription factors in the Drosophila blastoderm, with two intriguing results: (i the factor-bound sequences are under strong evolutionary constraints even if their neighboring genes are not expressed in the blastoderm and (ii binding sites in distal bound sequences (relative to transcription start sites tend to be more conserved than those in proximal regions. Our approach is implemented as software, EMMA (Evolutionary Model-based cis-regulatory Module Analysis, ready to be applied in a broad biological context.

Binding modes of dihydroquinoxalinones in a homology model of bradykinin receptor 1.

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Ha, Sookhee N; Hey, Pat J; Ransom, Rick W; Harrell, C Meacham; Murphy, Kathryn L; Chang, Ray; Chen, Tsing-Bau; Su, Dai-Shi; Markowitz, M Kristine; Bock, Mark G; Freidinger, Roger M; Hess, Fred J

2005-05-27

We report the first homology model of human bradykinin receptor B1 generated from the crystal structure of bovine rhodopsin as a template. Using an automated docking procedure, two B1 receptor antagonists of the dihydroquinoxalinone structural class were docked into the receptor model. Site-directed mutagenesis data of the amino acid residues in TM1, TM3, TM6, and TM7 were incorporated to place the compounds in the binding site of the homology model of the human B1 bradykinin receptor. The best pose in agreement with the mutation data was selected for detailed study of the receptor-antagonist interaction. To test the model, the calculated antagonist-receptor binding energy was correlated with the experimentally measured binding affinity (K(i)) for nine dihydroquinoxalinone analogs. The model was used to gain insight into the molecular mechanism for receptor function and to optimize the dihydroquinoxalinone analogs.

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

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

2013-01-01

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

Fatty acid modulated human serum albumin binding of the β-carboline alkaloids norharmane and harmane.

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Domonkos, Celesztina; Fitos, Ilona; Visy, Júlia; Zsila, Ferenc

2013-12-02

Harmane and norharmane are representative members of the large group of natural β-carboline alkaloids featured with diverse pharmacological activities. In blood, these agents are transported by human serum albumin (HSA) which has a profound impact on the pharmacokinetic and pharmacodynamic properties of many therapeutic drugs and xenobiotics. By combination of various spectroscopic methods, the present contribution is aimed to elucidate how nonesterified fatty acids (FAs), the primary endogenous ligands of HSA, affect the binding properties of harmane and norharmane. Analysis of induced circular dichroism (CD) and fluorescence spectroscopic data indicates the inclusion of the neutral form of both molecules into the binding pocket of subdomain IIIA, which hosts two FA binding sites, too. The induced CD and UV absorption spectra of harmane and norharmane exhibit peculiar changes upon addition of FAs, suggesting the formation of ternary complexes in which the lipid ligands significantly alter the binding mode of the alkaloids via cooperative allosteric mechanism. To our knowledge, it is the first instance of the demonstration of drug-FA cobinding at site IIIA. In line with these results, molecular docking calculations showed two distinct binding positions of norharmane within subdomain IIIA. The profound increase in the affinity constants of β-carbolines estimated in the presence of FAs predicts that the unbound, pharmacologically active serum fraction of these compounds strongly depends on the actual lipid binding profile of HSA.

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

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

2014-09-01

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

Dispersion of coupled mode-gap cavities

NARCIS (Netherlands)

Lian, Jin; Sokolov, Sergei; Yuce, E.; Combrie, S.; de Rossi, A.; Mosk, Allard

2015-01-01

The dispersion of a coupled resonator optical waveguide made of photonic crystal mode-gap cavities is pronouncedly asymmetric. This asymmetry cannot be explained by the standard tight binding model. We show that the fundamental cause of the asymmetric dispersion is the inherent dispersive cavity

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

Science.gov (United States)

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

2016-07-30

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

Structure-based Understanding of Binding Affinity and Mode of Estrogen Receptor α Agonists and Antagonists.

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The flexible hydrophobic ligand binding pocket (LBP) of estrogen receptor α (ERα) allows the binding of a wide variety of endocrine disruptors. Upon ligand binding, the LBP reshapes around the contours of the ligand and stabilizes the complex by complementary hydrophobic interact...

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

Science.gov (United States)

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

2014-09-01

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

Unveiling a novel transient druggable pocket in BACE-1 through molecular simulations: Conformational analysis and binding mode of multisite inhibitors

Science.gov (United States)

Di Pietro, Ornella; Laughton, Charles A.

2017-01-01

The critical role of BACE-1 in the formation of neurotoxic ß-amyloid peptides in the brain makes it an attractive target for an efficacious treatment of Alzheimer’s disease. However, the development of clinically useful BACE-1 inhibitors has proven to be extremely challenging. In this study we examine the binding mode of a novel potent inhibitor (compound 1, with IC50 80 nM) designed by synergistic combination of two fragments—huprine and rhein—that individually are endowed with very low activity against BACE-1. Examination of crystal structures reveals no appropriate binding site large enough to accommodate 1. Therefore we have examined the conformational flexibility of BACE-1 through extended molecular dynamics simulations, paying attention to the highly flexible region shaped by loops 8–14, 154–169 and 307–318. The analysis of the protein dynamics, together with studies of pocket druggability, has allowed us to detect the transient formation of a secondary binding site, which contains Arg307 as a key residue for the interaction with small molecules, at the edge of the catalytic cleft. The formation of this druggable “floppy” pocket would enable the binding of multisite inhibitors targeting both catalytic and secondary sites. Molecular dynamics simulations of BACE-1 bound to huprine-rhein hybrid compounds support the feasibility of this hypothesis. The results provide a basis to explain the high inhibitory potency of the two enantiomeric forms of 1, together with the large dependence on the length of the oligomethylenic linker. Furthermore, the multisite hypothesis has allowed us to rationalize the inhibitory potency of a series of tacrine-chromene hybrid compounds, specifically regarding the apparent lack of sensitivity of the inhibition constant to the chemical modifications introduced in the chromene unit. Overall, these findings pave the way for the exploration of novel functionalities in the design of optimized BACE-1 multisite inhibitors

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.

Context influences on TALE–DNA binding revealed by quantitative profiling

Science.gov (United States)

Rogers, Julia M.; Barrera, Luis A.; Reyon, Deepak; Sander, Jeffry D.; Kellis, Manolis; Joung, J Keith; Bulyk, Martha L.

2015-01-01

Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE–DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000–20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE–DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design. PMID:26067805

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

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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

Computational identification of antigen-binding antibody fragments.

Science.gov (United States)

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

2013-03-01

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

A Novel Procedure for Prediction of Mixed Mode I/II in Fracture Toughness of Laminate Composites

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M. Mahmood Shokrieh

2014-06-01

Full Text Available Delamination is one of the important modes of failure in laminated composite materials. In this respect, the mixed mode I/II fracture is the most major mode of delamination incidence in laminated composite. In the present research, a relation between the fracture toughness of double cantilever beam (DCB and asymmetric double cantilever beam (ADCB specimens is presented. The DCB and ADCB samples are used for measuring the mode I and mixed mode I/II fracture toughness (G of laminated composite materials, respectively. By considering the diversity of the stacking sequence of lay-ups, the test performance on all different types of lay-ups in order to measure the fracture toughness of laminated composites is a tedious, costly and time consuming task. The purpose of deriving this relation is to estimate the value of the strain energy release rate of laminated composite ADCB specimens by testing a unidirectional DCB. To develop this relationship, the geometry of DCB and ADCB specimens are considered to obtain fracture toughness of multi-directional laminate composites of ADCB samples with arbitrary ply sequence which may be used for design purposes. The procedure presented here reduces the calculation costs of the finite element modeling and its corresponding test significantly. The results obtained by this method are compared with those of experimental and numerical methods. It is shown that the fracture toughness of multi-directional lay-ups can be predicted by measuring the unidirectional ply with an error less than 10% demonstrating the accuracy of the procedure developed in the present research.

Decreased seasonal mesor of platelet 3H-imipramine binding in depression

International Nuclear Information System (INIS)

DeMet, E.M.; Reist, C.; Bell, K.M.; Gerner, R.H.; Chicz-DeMet, A.; Warren, S.; Wu, J.

1991-01-01

Seasonal cycles of platelet 3 H-imipramine binding were compared in 49 endogenous unipolar depressed patients and 20 normal volunteers. A significant sinusoidal component was detected in the Bmax of binding in both patients and controls with similar amplitudes and seasonal peaks. However, the yearly average (mesor) of the patient group was significantly lower (20.0%) than that of the normal controls. The results support earlier claims of a diminished platelet binding in endogenous depression and indicate that this decrease was still evident in the presence of a 48.2% (controls) to 65.8% (patients) seasonal variation. Control Bmax values were normally distributed about a best-fit mean (cosinor fit). In contrast, patient values appeared to be bimodally distributed with one mode that was similar to controls and one mode that was substantially lower. In general, psychiatric symptoms failed to distinguish between patients with high and low platelet binding and no correlation was found between Bmax and severity of illness (HAM-D)

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

Science.gov (United States)

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

2018-01-01

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

All-atom normal-mode analysis reveals an RNA-induced allostery in a bacteriophage coat protein.

Science.gov (United States)

Dykeman, Eric C; Twarock, Reidun

2010-03-01

Assembly of the T=3 bacteriophage MS2 is initiated by the binding of a 19 nucleotide RNA stem loop from within the phage genome to a symmetric coat protein dimer. This binding event effects a folding of the FG loop in one of the protein subunits of the dimer and results in the formation of an asymmetric dimer. Since both the symmetric and asymmetric forms of the dimer are needed for the assembly of the protein container, this allosteric switch plays an important role in the life cycle of the phage. We provide here details of an all-atom normal-mode analysis of this allosteric effect. The results suggest that asymmetric contacts between the A -duplex RNA phosphodiester backbone of the stem loop with the EF loop in one coat protein subunit results in an increased dynamic behavior of its FG loop. The four lowest-frequency modes, which encompass motions predominantly on the FG loops, account for over 90% of the increased dynamic behavior due to a localization of the vibrational pattern on a single FG loop. Finally, we show that an analysis of the allosteric effect using an elastic network model fails to predict this localization effect, highlighting the importance of using an all-atom full force field method for this problem.

Binding mode dependent signaling for the detection of Cu2 +: An experimental and theoretical approach with practical applications

Science.gov (United States)

Ghosh, Soumen; Khan, Mehebub Ali; Ganguly, Aniruddha; Masum, Abdulla Al; Alam, Md. Akhtarul; Guchhait, Nikhil

2018-02-01

Two amido-schiff bases (3-Hydroxy-naphthalene-2-carboxylic acid pyren-1-ylmethylene-hydrazide and Naphthalene-2-carboxylic acid pyren-1-ylmethylene-hydrazide) have been synthesized having a common structural unit and only differs by a -OH group in the naphthalene ring. Both of them can detect Cu2 + ion selectively in semi-aqueous medium in distinctly different output modes (one detects Cu2 + by naked-eye color change where as the other detects Cu2 + by fluorescence enhancement). The difference in the binding of Cu 2 + with the compounds is the reason for this observation. The detection limit is found to be micromolar region for compound which contains -OH group whereas the compound without -OH group detects copper in nano-molar region. DFT calculations have been performed in order to demonstrate the structure of the compounds and their copper complexes. Practical utility has been explored by successful paper strip response of both the compounds. The biological applications have been evaluated in RAW 264.7.

Probing ligand binding modes of Mycobacterium tuberculosis MurC ligase by molecular modeling, dynamics simulation and docking.

Science.gov (United States)

Anuradha, C M; Mulakayala, Chaitanya; Babajan, Banaganapalli; Naveen, M; Rajasekhar, Chikati; Kumar, Chitta Suresh

2010-01-01

Multi drug resistance capacity for Mycobacterium tuberculosis (MDR-Mtb) demands the profound need for developing new anti-tuberculosis drugs. The present work is on Mtb-MurC ligase, which is an enzyme involved in biosynthesis of peptidoglycan, a component of Mtb cell wall. In this paper the 3-D structure of Mtb-MurC has been constructed using the templates 1GQQ and 1P31. Structural refinement and energy minimization of the predicted Mtb-MurC ligase model has been carried out by molecular dynamics. The streochemical check failures in the energy minimized model have been evaluated through Procheck, Whatif ProSA, and Verify 3D. Further torsion angles for the side chains of amino acid residues of the developed model were determined using Predictor. Docking analysis of Mtb-MurC model with ligands and natural substrates enabled us to identify specific residues viz. Gly125, Lys126, Arg331, and Arg332, within the Mtb-MurC binding pocket to play an important role in ligand and substrate binding affinity and selectivity. The availability of Mtb-MurC ligase built model, together with insights gained from docking analysis will promote the rational design of potent and selective Mtb-MurC ligase inhibitors as antituberculosis therapeutics.

Identification of the quinolinedione inhibitor binding site in Cdc25 phosphatase B through docking and molecular dynamics simulations

Science.gov (United States)

Ge, Yushu; van der Kamp, Marc; Malaisree, Maturos; Liu, Dan; Liu, Yi; Mulholland, Adrian J.

2017-11-01

Cdc25 phosphatase B, a potential target for cancer therapy, is inhibited by a series of quinones. The binding site and mode of quinone inhibitors to Cdc25B remains unclear, whereas this information is important for structure-based drug design. We investigated the potential binding site of NSC663284 [DA3003-1 or 6-chloro-7-(2-morpholin-4-yl-ethylamino)-quinoline-5, 8-dione] through docking and molecular dynamics simulations. Of the two main binding sites suggested by docking, the molecular dynamics simulations only support one site for stable binding of the inhibitor. Binding sites in and near the Cdc25B catalytic site that have been suggested previously do not lead to stable binding in 50 ns molecular dynamics (MD) simulations. In contrast, a shallow pocket between the C-terminal helix and the catalytic site provides a favourable binding site that shows high stability. Two similar binding modes featuring protein-inhibitor interactions involving Tyr428, Arg482, Thr547 and Ser549 are identified by clustering analysis of all stable MD trajectories. The relatively flexible C-terminal region of Cdc25B contributes to inhibitor binding. The binding mode of NSC663284, identified through MD simulation, likely prevents the binding of protein substrates to Cdc25B. The present results provide useful information for the design of quinone inhibitors and their mechanism of inhibition.

Identification of the quinolinedione inhibitor binding site in Cdc25 phosphatase B through docking and molecular dynamics simulations.

Science.gov (United States)

Ge, Yushu; van der Kamp, Marc; Malaisree, Maturos; Liu, Dan; Liu, Yi; Mulholland, Adrian J

2017-11-01

Cdc25 phosphatase B, a potential target for cancer therapy, is inhibited by a series of quinones. The binding site and mode of quinone inhibitors to Cdc25B remains unclear, whereas this information is important for structure-based drug design. We investigated the potential binding site of NSC663284 [DA3003-1 or 6-chloro-7-(2-morpholin-4-yl-ethylamino)-quinoline-5, 8-dione] through docking and molecular dynamics simulations. Of the two main binding sites suggested by docking, the molecular dynamics simulations only support one site for stable binding of the inhibitor. Binding sites in and near the Cdc25B catalytic site that have been suggested previously do not lead to stable binding in 50 ns molecular dynamics (MD) simulations. In contrast, a shallow pocket between the C-terminal helix and the catalytic site provides a favourable binding site that shows high stability. Two similar binding modes featuring protein-inhibitor interactions involving Tyr428, Arg482, Thr547 and Ser549 are identified by clustering analysis of all stable MD trajectories. The relatively flexible C-terminal region of Cdc25B contributes to inhibitor binding. The binding mode of NSC663284, identified through MD simulation, likely prevents the binding of protein substrates to Cdc25B. The present results provide useful information for the design of quinone inhibitors and their mechanism of inhibition.

Conservation of transcription factor binding events predicts gene expression across species

OpenAIRE

Hemberg, Martin; Kreiman, Gabriel

2011-01-01

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

Pad-mode-induced instantaneous mode instability for simple models of brake systems

Science.gov (United States)

Oberst, S.; Lai, J. C. S.

2015-10-01

Automotive disc brake squeal is fugitive, transient and remains difficult to predict. In particular, instantaneous mode squeal observed experimentally does not seem to be associated with mode coupling and its mechanism is not clear. The effects of contact pressures, friction coefficients as well as material properties (pressure and temperature dependency and anisotropy) for brake squeal propensity have not been systematically explored. By analysing a finite element model of an isotropic pad sliding on a plate similar to that of a previously reported experimental study, pad modes have been identified and found to be stable using conventional complex eigenvalue analysis. However, by subjecting the model to contact pressure harmonic excitation for a range of pressures and friction coefficients, a forced response analysis reveals that the dissipated energy for pad modes is negative and becomes more negative with increasing contact pressures and friction coefficients, indicating the potential for instabilities. The frequency of the pad mode in the sliding direction is within the range of squeal frequencies observed experimentally. Nonlinear time series analysis of the vibration velocity also confirms the evolution of instabilities induced by pad modes as the friction coefficient increases. By extending this analysis to a more realistic but simple brake model in the form of a pad-on-disc system, in-plane pad-modes, which a complex eigenvalue analysis predicts to be stable, have also been identified by negative dissipated energy for both isotropic and anisotropic pad material properties. The influence of contact pressures on potential instabilities has been found to be more dominant than changes in material properties owing to changes in pressure or temperature. Results here suggest that instantaneous mode squeal is likely caused by in-plane pad-mode instabilities.

Homology Modelling of the GABA Transporter and Analysis of Tiagabine Binding

DEFF Research Database (Denmark)

Skovstrup, S.; Taboureau, Olivier; Bräuner-Osborne, H.

2010-01-01

by Phe 294) to the extracellular vestibule, where the side chain is stabilised by aliphatic residues. The tiagabine binding mode, reaching from the substrate binding site to the extracellular vestibule, forces the side chain of Phe 294 to adopt a distinct conformation from that found in the occluded...

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

Science.gov (United States)

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

2017-05-15

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

MANAGING TIGHT BINDING RECEPTORS FOR NEW SPEARATIONS TECHNOLOGIES

Energy Technology Data Exchange (ETDEWEB)

DARYLE H BUSCH RICHARD S GIVENS

2004-12-10

even more interesting. They convert from rings to structures that wrap around a metal ion to form a cage. These ligands are called cryptands. Switch release is accomplished by photolytic cleavage of a bond to convert a cyclic ligand into a linear ligand or to break similar bonds in a cryptate. Our studies have demonstrated switch binding and switch release with cryptates of calcium. These remarkable cyclic ligands and cage-like ligands are indeed tight-binding and may, in principle, be incorporated in various separations methodologies, including the soil poultice. The soil poultice mimics the way in which microbes secrete extremely powerful ligands into the soil in order to harvest iron. The cellular membrane of the microbe recognizes the iron/ligand complex and admits it into the cell. The soil poultice uses molecularly imprinted polymers (MIPs) to play the role of the cellular membrane. Imprinting involves creation of the polymer in the presence of the metal/ligand complex. In principle, a well design ligand/MIP combination can be highly selective toward almost any targeted metal ion. The principles for that design are the focus of these investigations. An imprinting molecule can interact with the polymer through any, some, or all of the so-called supramolecular modes; e.g., hydrogen bonding, electrostatic charge, minor ligand bonding, Pi-Pi stacking, and hydrophobic and van der Waals interactions. Historically these modes of binding have given MIPs only small re-binding capacities and very limited selectivities. This program has shown that each mode of interaction can be made more powerful than previously suspected and that combinations of different supramolecular interaction modes can produce remarkable synergisms. The results of this systematic study provide a firm foundation for tailoring molecular imprinted polymers for reclamation of specific metal ion, including those important to the DOE EM mission.

Imparting albumin-binding affinity to a human protein by mimicking the contact surface of a bacterial binding protein.

Science.gov (United States)

Oshiro, Satoshi; Honda, Shinya

2014-04-18

Attachment of a bacterial albumin-binding protein module is an attractive strategy for extending the plasma residence time of protein therapeutics. However, a protein fused with such a bacterial module could induce unfavorable immune reactions. To address this, we designed an alternative binding protein by imparting albumin-binding affinity to a human protein using molecular surface grafting. The result was a series of human-derived 6 helix-bundle proteins, one of which specifically binds to human serum albumin (HSA) with adequate affinity (KD = 100 nM). The proteins were designed by transferring key binding residues of a bacterial albumin-binding module, Finegoldia magna protein G-related albumin-binding domain (GA) module, onto the human protein scaffold. Despite 13-15 mutations, the designed proteins maintain the original secondary structure by virtue of careful grafting based on structural informatics. Competitive binding assays and thermodynamic analyses of the best binders show that the binding mode resembles that of the GA module, suggesting that the contacting surface of the GA module is mimicked well on the designed protein. These results indicate that the designed protein may act as an alternative low-risk binding module to HSA. Furthermore, molecular surface grafting in combination with structural informatics is an effective approach for avoiding deleterious mutations on a target protein and for imparting the binding function of one protein onto another.

Nature of Interlayer Binding and Stacking of sp–sp 2 Hybridized Carbon Layers: A Quantum Monte Carlo Study

International Nuclear Information System (INIS)

Shin, Hyeondeok; Lee, Hoonkyung; Heinonen, Olle; Benali, Anouar; Kwon, Yongkyung

2017-01-01

α-graphyne is a two-dimensional sheet of sp-sp2 hybridized carbon atoms in a honeycomb lattice. While the geometrical structure is similar to that of graphene, the hybridized triple bonds give rise to electronic structure that is different from that of graphene. Similar to graphene, α-graphyne can be stacked in bilayers with two stable configurations, but the different stackings have very different electronic structures: one is predicted to have gapless parabolic bands and the other a tunable bandgap which is attractive for applications. In order to realize applications, it is crucial to understand which stacking is more stable. This is difficult to model, as the stability is a result of weak interlayer van der Waals interactions which are not well captured by density functional theory (DFT). We have used quantum Monte Carlo simulations that accurately include van der Waals interactions to calculate the interlayer binding energy of bilayer graphyne and to determine its most stable stacking mode. Our results show that inter-layer bindings of sp- and sp2-bonded carbon networks are significantly underestimated in a Kohn-Sham DFT approach, even with an exchange-correlation potential corrected to include, in some approximation, van der Waals interactions. Finally, our quantum Monte Carlo calculations reveal that the interlayer binding energy difference between the two stacking modes is only 0.9(4) eV/atom. From this we conclude that the two stable stacking modes of bilayer α-graphyne are almost degenerate with each other, and both will occur with about the same probability at room temperature unless there is a synthesis path that prefers one stacking over the other.

Pedestal structure and stability in H-mode and I-mode: a comparative study on Alcator C-Mod

International Nuclear Information System (INIS)

Hughes, J.W.; Walk, J.R.; Davis, E.M.; LaBombard, B.; Baek, S.G.; Churchill, R.M.; Greenwald, M.; Hubbard, A.E.; Lipschultz, B.; Marmar, E.S.; Reinke, M.L.; Rice, J.E.; Theiler, C.; Terry, J.; White, A.E.; Whyte, D.G.; Snyder, P.B.; Groebner, R.J.; Osborne, T.; Diallo, A.

2013-01-01

New experimental data from the Alcator C-Mod tokamak are used to benchmark predictive modelling of the edge pedestal in various high-confinement regimes, contributing to greater confidence in projection of pedestal height and width in ITER and reactors. ELMy H-modes operate near stability limits for ideal peeling–ballooning modes, as shown by calculations with the ELITE code. Experimental pedestal width in ELMy H-mode scales as the square root of β pol at the pedestal top, i.e. the dependence expected from theory if kinetic ballooning modes (KBMs) were responsible for limiting the pedestal width. A search for KBMs in experiment has revealed a short-wavelength electromagnetic fluctuation in the pedestal that is a candidate driver for inter-edge localized mode (ELM) pedestal regulation. A predictive pedestal model (EPED) has been tested on an extended set of ELMy H-modes from C-Mod, reproducing pedestal height and width reasonably well across the data set, and extending the tested range of EPED to the highest absolute pressures available on any existing tokamak and to within a factor of three of the pedestal pressure targeted for ITER. In addition, C-Mod offers access to two regimes, enhanced D-alpha (EDA) H-mode and I-mode, that have high pedestals, but in which large ELM activity is naturally suppressed and, instead, particle and impurity transport are regulated continuously. Pedestals of EDA H-mode and I-mode discharges are found to be ideal magnetohydrodynamic (MHD) stable with ELITE, consistent with the general absence of ELM activity. Invocation of alternative physics mechanisms may be required to make EPED-like predictions of pedestals in these kinds of intrinsically ELM-suppressed regimes, which would be very beneficial to operation in burning plasma devices. (paper)

Predicting the creep life and failure mode of low-alloy steel weldments

Energy Technology Data Exchange (ETDEWEB)

Brear, J M; Middleton, C J; Aplin, P F [ERA Technology Ltd., Leatherhead (United Kingdom)

1999-12-31

This presentation reviews and consolidates experience gained through a number of research projects and practical plant assessments in predicting both the life and the likely failure mode and location in low alloy steel weldments. The approach adopted begins with the recognition that the relative strength difference between the microstructural regions is a key factor controlling both life and failure location. Practical methods based on hardness measurement and adaptable to differing weld geometries are presented and evidence for correlations between hardness ratio, damage accumulation and strain development is discussed. Predictor diagrams relating weld life and failure location to the service conditions and the hardness of the individual microstructural constituents are suggested and comments are given on the implications for identifying the circumstances in which Type IV cracking is to be expected. (orig.) 6 refs.

Predicting the creep life and failure mode of low-alloy steel weldments

Energy Technology Data Exchange (ETDEWEB)

Brear, J.M.; Middleton, C.J.; Aplin, P.F. [ERA Technology Ltd., Leatherhead (United Kingdom)

1998-12-31

This presentation reviews and consolidates experience gained through a number of research projects and practical plant assessments in predicting both the life and the likely failure mode and location in low alloy steel weldments. The approach adopted begins with the recognition that the relative strength difference between the microstructural regions is a key factor controlling both life and failure location. Practical methods based on hardness measurement and adaptable to differing weld geometries are presented and evidence for correlations between hardness ratio, damage accumulation and strain development is discussed. Predictor diagrams relating weld life and failure location to the service conditions and the hardness of the individual microstructural constituents are suggested and comments are given on the implications for identifying the circumstances in which Type IV cracking is to be expected. (orig.) 6 refs.

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

Science.gov (United States)

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-01-01

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

Constant-frequency, clamped-mode resonant converters

Science.gov (United States)

Tsai, Fu-Sheng; Materu, Peter; Lee, Fred C.

1987-01-01

Two novel clamped-mode resonant converters are proposed which operate at a constant frequency while retaining many desired features of conventional series- and parallel-resonant converters. State-plane analysis techniques are used to identify all possible operating modes and define their mode boundaries. Control-to-output characteristics are derived that specify the regions for natural and forced commutation. The predicted operating modes are verified using a prototype circuit.

Comparison of high-mode predictive simulations using Mixed Bohm/gyro-Bohm and Multi-Mode (MMM95) transport models

International Nuclear Information System (INIS)

Hannum, David; Bateman, Glenn; Kinsey, Jon; Kritz, Arnold H.; Onjun, Thawatchai; Pankin, Alexei

2001-01-01

Two different transport models -- the Mixed Bohm/gyro-Bohm [Joint European Torus (JET)] model [Erba , Plasma Phys. Controlled Fusion 39, 261 (1997)] and the Multi-Mode model (MMM95) [Bateman , Phys. Plasmas 5, 1793 (1998)] -- are used in predictive transport simulations of 22 high-mode discharges. Fourteen discharges that include systematic scans in normalized gyroradius (ρ * ), plasma pressure (β), collisionality, and isotope mass in the JET tokamak [Rebut , Nucl. Fusion 25, 1011 (1985)] and eight discharges that include scans in ρ * , elongation (κ), power, and density in the DIII-D tokamak [J. L. Luxon and L. G. Davis, Fusion Technol. 8, 441 (1985)] are considered. When simulation temperature and density profiles are compared with processed experimental data from the International Profile Database, it is found that the results with either the JET or MMM95 transport model match experimental data about equally well. With either model, the average normalized rms deviation is approximately 10%. In the simulations carried out using the JET model, the component of the model with Bohm scaling (which is proportional to gyroradius) dominates over much of the plasma. In contrast, the MMM95 model has purely gyro-Bohm scaling (proportional to gyroradius squared). In spite of the differences in the underlying scaling of these transport models, both models reproduce the global confinement scalings observed in the scans equally well. These results are explained by changes in profile shapes from one end of each scan to the other. These changes in the profile shapes are caused by changes in boundary conditions, heating and particle source profiles, large scale instabilities, and transport

Binding of the Multimodal Antidepressant Drug Vortioxetine to the Human Serotonin Transporter

DEFF Research Database (Denmark)

Andersen, Jacob; Ladefoged, Lucy Kate; Wang, Danyang

2015-01-01

the orientation of vortioxetine within the central binding site and showed that only one of the proposed binding modes is functionally relevant. The findings provide important new insight about the molecular basis for high affinity recognition of vortioxetine in hSERT, which is essential for future structure...

Peeling mode relaxation ELM model

International Nuclear Information System (INIS)

Gimblett, C. G.

2006-01-01

This paper discusses an approach to modelling Edge Localised Modes (ELMs) in which toroidal peeling modes are envisaged to initiate a constrained relaxation of the tokamak outer region plasma. Relaxation produces both a flattened edge current profile (which tends to further destabilise a peeling mode), and a plasma-vacuum negative current sheet which has a counteracting stabilising influence; the balance that is struck between these two effects determines the radial extent (rE) of the ELM relaxed region. The model is sensitive to the precise position of the mode rational surfaces to the plasma surface and hence there is a 'deterministic scatter' in the results that has an accord with experimental data. The toroidal peeling stability criterion involves the edge pressure, and using this in conjunction with predictions of rE allows us to evaluate the ELM energy losses and compare with experiment. Predictions of trends with the edge safety factor and collisionality are also made

A model for prediction of fume formation rate in gas metal arc welding (GMAW), globular and spray modes, DC electrode positive.

Science.gov (United States)

Dennis, J H; Hewitt, P J; Redding, C A; Workman, A D

2001-03-01

Prediction of fume formation rate during metal arc welding and the composition of the fume are of interest to occupational hygienists concerned with risk assessment and to manufacturers of welding consumables. A model for GMAW (DC electrode positive) is described based on the welder determined process parameters (current, wire feed rate and wire composition), on the surface area of molten metal in the arc and on the partial vapour pressures of the component metals of the alloy wire. The model is applicable to globular and spray welding transfer modes but not to dip mode. Metal evaporation from a droplet is evaluated for short time increments and total evaporation obtained by summation over the life of the droplet. The contribution of fume derived from the weld pool and spatter (particles of metal ejected from the arc) is discussed, as are limitations of the model. Calculated droplet temperatures are similar to values determined by other workers. A degree of relationship between predicted and measured fume formation rates is demonstrated but the model does not at this stage provide a reliable predictive tool.

The flexible C-terminal arm of the Lassa arenavirus Z-protein mediates interactions with multiple binding partners.

Science.gov (United States)

May, Eric R; Armen, Roger S; Mannan, Aristotle M; Brooks, Charles L

2010-08-01

The arenavirus genome encodes for a Z-protein, which contains a RING domain that coordinates two zinc ions, and has been identified as having several functional roles at various stages of the virus life cycle. Z-protein binds to multiple host proteins and has been directly implicated in the promotion of viral budding, repression of mRNA translation, and apoptosis of infected cells. Using homology models of the Z-protein from Lassa strain arenavirus, replica exchange molecular dynamics (MD) was used to refine the structures, which were then subsequently clustered. Population-weighted ensembles of low-energy cluster representatives were predicted based upon optimal agreement of the chemical shifts computed with the SPARTA program with the experimental NMR chemical shifts. A member of the refined ensemble was identified to be a potential binder of budding factor Tsg101 based on its correspondence to the structure of the HIV-1 Gag late domain when bound to Tsg101. Members of these ensembles were docked against the crystal structure of human eIF4E translation initiation factor. Two plausible binding modes emerged based upon their agreement with experimental observation, favorable interaction energies and stability during MD trajectories. Mutations to Z are proposed that would either inhibit both binding mechanisms or selectively inhibit only one mode. The C-terminal domain conformation of the most populated member of the representative ensemble shielded protein-binding recognition motifs for Tsg101 and eIF4E and represents the most populated state free in solution. We propose that C-terminal flexibility is key for mediating the different functional states of the Z-protein. (c) 2010 Wiley-Liss, Inc.

Accurate Sliding-Mode Control System Modeling for Buck Converters

DEFF Research Database (Denmark)

Høyerby, Mikkel Christian Wendelboe; Andersen, Michael Andreas E.

2007-01-01

This paper shows that classical sliding mode theory fails to correctly predict the output impedance of the highly useful sliding mode PID compensated buck converter. The reason for this is identified as the assumption of the sliding variable being held at zero during sliding mode, effectively...... approach also predicts the self-oscillating switching action of the sliding-mode control system correctly. Analytical findings are verified by simulation as well as experimentally in a 10-30V/3A buck converter....

Asymmetry of 3H- imipramine binding may predict psychiatric illness

International Nuclear Information System (INIS)

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

1989-01-01

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

The quiescent H-mode regime for high performance edge localized mode-stable operation in future burning plasmas

Energy Technology Data Exchange (ETDEWEB)

Garofalo, A. M., E-mail: garofalo@fusion.gat.com; Burrell, K. H.; Meneghini, O.; Osborne, T. H.; Paz-Soldan, C.; Smith, S. P.; Snyder, P. B.; Turnbull, A. D. [General Atomics, P.O. Box 85608, San Diego, California 92186-5608 (United States); Eldon, D.; Grierson, B. A.; Solomon, W. M. [Princeton Plasma Physics Laboratory, P.O. Box 451, Princeton, New Jersey 08543-0451 (United States); Hanson, J. M. [Columbia University, 2960 Broadway, New York, New York 10027-6900 (United States); Holland, C. [University of California San Diego, 9500 Gilman Dr., La Jolla, California 92093-0417 (United States); Huijsmans, G. T. A.; Liu, F.; Loarte, A. [ITER Organization, Route de Vinon sur Verdon, 13067 St Paul Lez Durance (France); Zeng, L. [University of California Los Angeles, P.O. Box 957099, Los Angeles, California 90095-7099 (United States)

2015-05-15

For the first time, DIII-D experiments have achieved stationary quiescent H-mode (QH-mode) operation for many energy confinement times at simultaneous ITER-relevant values of beta, confinement, and safety factor, in an ITER-like shape. QH-mode provides excellent energy confinement, even at very low plasma rotation, while operating without edge localized modes (ELMs) and with strong impurity transport via the benign edge harmonic oscillation (EHO). By tailoring the plasma shape to improve the edge stability, the QH-mode operating space has also been extended to densities exceeding 80% of the Greenwald limit, overcoming the long-standing low-density limit of QH-mode operation. In the theory, the density range over which the plasma encounters the kink-peeling boundary widens as the plasma cross-section shaping is increased, thus increasing the QH-mode density threshold. The DIII-D results are in excellent agreement with these predictions, and nonlinear magnetohydrodynamic analysis of reconstructed QH-mode equilibria shows unstable low n kink-peeling modes growing to a saturated level, consistent with the theoretical picture of the EHO. Furthermore, high density operation in the QH-mode regime has opened a path to a new, previously predicted region of parameter space, named “Super H-mode” because it is characterized by very high pedestals that can be more than a factor of two above the peeling-ballooning stability limit for similar ELMing H-mode discharges at the same density.