Ligand Binding Site Prediction Research Articles

Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

The recently developed field of ligand homology modeling (LHM) that extends the ideas of protein homology modeling to the prediction of ligand binding sites and for use in virtual ligand screening has emerged as a powerful new approach. Unlike traditional docking methodologies, LHM can be applied to low-to-moderate resolution predicted as well as experimental structures with little if any diminution in performance; thereby enabling ≈ 75% of an average proteome to have potentially significant virtual screening predictions. In large scale benchmarking, LHM is able to predict off-target ligand binding. Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction.

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction

SVM Prediction of Ligand-binding Sites in Bacterial Lipoproteins Employing Shape and Physio-chemical Descriptors

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.

Comparison of Different Ranking Methods in Protein-Ligand Binding Site Prediction

In recent years, although many ligand-binding site prediction methods have been developed, there has still been a great demand to improve the prediction accuracy and compare different prediction algorithms to evaluate their performances. In this work, in order to improve the performance of the protein-ligand binding site prediction method presented in our former study, a comparison of different binding site ranking lists was studied. Four kinds of properties, i.e., pocket size, distance from the protein centroid, sequence conservation and the number of hydrophobic residues, have been chosen as the corresponding ranking criterion respectively. Our studies show that the sequence conservation information helps to rank the real pockets with the most successful accuracy compared to others. At the same time, the pocket size and the distance of binding site from the protein centroid are also found to be helpful. In addition, a multi-view ranking aggregation method, which combines the information among those four properties, was further applied in our study. The results show that a better performance can be achieved by the aggregation of the complementary properties in the prediction of ligand-binding sites.

Prediction of ligand binding site by insilico approach in cold resistant protein isolated from cold resistant mutant of Pseudomonas fluorescens

Cold shock proteins perform vital functions, such as mRNA masking, coupling of transcription to translation and developmental timing and regulation, which aids in survival of microbes in cold stress. Pseudomonas fluorescens is an ecologically important bacterium which helps in plant growth promotion. Since the cold tolerant mutant of the bacterium is able to grow at the temperature ranges from 30 to 4°C, it is of interest to study the process of its survival in the extreme temperatures. Therefore, this study is focused on the three dimensional structure and molecular modeling of cold resistant protein (CRP) from P. fluorescens to predict its molecular mechanism. Investigating the structure of CRP confirmed the presence of a conserved domain characteristic of the cold shock domain (CSD) family and a single nucleotide binding domain. When 3D structure of CRP was compared with the existing cold shock proteins, major deviations were found in the loop regions connecting the β2–β3, β3–β4 and β4–β5 sheets. Docking studies showed that CRP forms a significant clamp like structure at the substrate binding cleft which stabilizes the ligand. Therefore, it can be concluded that CRP has a strong affinity for the poly thymidine (poly T) stretch and can be considered a candidate for transcription regulation.

Prediction of a New Ligand-Binding Site for Type 2 Motif based on the Crystal Structure of ALG-2 by Dry and Wet Approaches

ALG-2 is a penta-EF-hand Ca2+-binding protein and interacts with a variety of intracellular proteins. Two types of ALG-2-binding motifs have been determined: type 1, PXYPXnYP (X, variable; n = 4), in ALIX and PLSCR3; type 2, PXPGF, in Sec31A and PLSCR3. The previously solved X-ray crystal structure of the complex between ALG-2 and an ALIX peptide containing type 1 motif showed that the peptide binds to Pocket 1 and Pocket 2. Co-crystallization of ALG-2 and type 2 motif-containing peptides has not been successful. To gain insights into the molecular basis of type 2 motif recognition, we searched for a new hydrophobic cavity by computational algorithms using MetaPocket 2.0 based on 3D structures of ALG-2. The predicted hydrophobic pocket designated Pocket 3 fits with N-acetyl-ProAlaProGlyPhe-amide, a virtual penta-peptide derived from one of the two types of ALG-2-binding sites in PLSCR3 (type 2 motif), using the molecular docking software AutoDock Vina. We investigated effects of amino acid substitutions of the predicted binding sites on binding abilities by pulldown assays using glutathione-S-transferase -fused ALG-2 of wild-type and mutant proteins and lysates of cells expressing green fluorescent protein -fused PLSCR3 of wild-type and mutants. Substitution of either L52 with Ala or F148 with Ser of ALG-2 caused loss of binding abilities to PLSCR3 lacking type 1 motif but retained those to PLSCR3 lacking type 2 motif, strongly supporting the hypothesis that Pocket 3 is the binding site for type 2 motif.

FunFOLDQA: A Quality Assessment Tool for Protein-Ligand Binding Site Residue Predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Identification and analysis of conserved pockets on protein surfaces

Motivations. The interaction between proteins and ligands occurs at pockets which are often lined by conserved aminoacids. In order to make the research of new drugs as economic as possible, it is necessary to exploit "in silico" techniques, high throughput and fragment-based screenings, which require the identification of pockets on the surface of proteins, active sites or not, which might be the targets of low molecular weight drugs. Methods. We developed a tool to evaluate the conservation of each pocket detected on the protein surface by CastP. This tool was named DrosteP because it recursively searches for optimal input sequences to be used to calculate conservation. DrosteP uses a descriptor of statistical significance, Poisson p-value, as a target to optimize the choice of input sequences. To benchmark DrosteP we used monomeric or homodimer human proteins with known 3D-structure whose active site had been annotated in UNIPROT. DrosteP is able to detect the active site with high accuracy because it nearly always coincides with the most conserved pocket. We extended our analysis to all the pockets found on the surface of human proteins. Results. Several methods for predicting ligand binding sites on protein surfaces have been proposed which combine 3D-structure and evolutionary sequence conservation, but any method relying on conservation depends critically on the choice of the input sequences. DrosteP chooses how deeply distant homologs must be collected to evaluate conservation and thus optimize the identification of active site pockets. Moreover it recognize conserved pockets other than those coinciding with the sites annotated in UNIPROT which might represent useful druggable sites. Amino acid composition of conserved pockets differs significantly from that of non conserved pockets. This finding provides useful hints on the fundamental principles underlying protein-ligand interaction. Availability http://www.sbcentrostorico.unina.it/cammisa

Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles

Knowledge about the site at which a ligand binds provides an important clue for predicting the function of a protein and is also often a prerequisite for performing docking computations in virtual drug design and screening. We have previously shown that certain ligand-interacting triangles of protein atoms, called protein triangles, tend to occur more frequently at ligand-binding sites than at other parts of the protein. In this work, we describe a new ligand-binding site prediction method that was developed based on binding site-enriched protein triangles. The new method was tested on 2 benchmark datasets and on 19 targets from two recent community-based studies of such predictions, and excellent results were obtained. Where comparisons were made, the success rates for the new method for the first predicted site were significantly better than methods that are not a meta-predictor. Further examination showed that, for most of the unsuccessful predictions, the pocket of the ligand-binding site was identified, but not the site itself, whereas for some others, the failure was not due to the method itself but due to the use of an incorrect biological unit in the structure examined, although using correct biological units would not necessarily improve the prediction success rates. These results suggest that the new method is a valuable new addition to a suite of existing structure-based bioinformatics tools for studies of molecular recognition and related functions of proteins in post-genomics research. The executable binaries and a web server for our method are available from http://sourceforge.net/projects/msdock/ and http://lise.ibms.sinica.edu.tw, respectively, free for academic users.

Endo-xylanase GH11 activation by the fungal metabolite eugenitin

Eugenitin, a chromone derivative and a metabolite of the endophyte Mycoleptodiscus indicus, at 5 mM activated a recombinant GH11 endo-xylanase by 40 %. The in silico prediction of ligand-binding sites on the three-dimensional structure of the endo-xylanase revealed that eugenitin interacts mainly by a hydrogen bond with a serine residue and a stacking interaction of the heterocyclic aromatic ring system with a tryptophan residue. Eugenitin improved the GH11 endo-xylanase activity on different substrates, modified the optimal pH and temperature activities and slightly affected the kinetic parameters of the enzyme.

Novel aryl β-aminocarbonyl derivatives as inhibitors of Trypanosoma cruzi trypanothione reductase: binding mode revised by docking and GRIND2-based 3D-QSAR procedures

Trypanothione reductase has long been investigated as a promising target for chemotherapeutic intervention in Chagas disease, since it is an enzyme of a unique metabolic pathway that is exclusively present in the pathogen but not in the human host, which has the analog Glutathione reductase. In spite of the present data-set includes a small number of compounds, a combined use of flexible docking, pharmacophore perception, ligand binding site prediction, and Grid-Independent Descriptors GRIND2-based 3D-Quantitative Structure-Activity Relationships (QSAR) procedures allowed us to rationalize the different biological activities of a series of 11 aryl β-aminocarbonyl derivatives, which are inhibitors of Trypanosoma cruzi trypanothione reductase (TcTR). Three QSAR models were built and validated using different alignments, which are based on docking with the TcTR crystal structure, pharmacophore, and molecular interaction fields. The high statistical significance of the models thus obtained assures the robustness of this second generation of GRIND descriptors here used, which were able to detect the most important residues of such enzyme for binding the aryl β-aminocarbonyl derivatives, besides to rationalize distances among them. Finally, a revised binding mode has been proposed for our inhibitors and independently supported by the different methodologies here used, allowing further optimization of the lead compounds with such combined structure- and ligand-based approaches in the fight against the Chagas disease.

Comparison of Structure-based Tools for the Prediction of Ligand Binding Site Residues in Apo-structures

In this paper, a group of structure-based algorithms used in the prediction of ligand binding sites in proteins were tested on apo-structures, and their results are analyzed and discussed. In particular, we focused on the accuracy of predicting the residues at the binding sites as the main criterion for comparison. The data set used for the testing was compiled using the LigASite database and consisted entirely of apo-structures. The tools compared are LIGSITE, SURFNET, DEPTH, GHECOM, SiteHound, and Fpocket.

Improved Prediction of Protein Ligand-Binding Sites Using Random Forests

This article describes a novel method for predicting ligand-binding sites of proteins. This method uses only 8 structural properties as input vector to train 9 random forest classifiers which are combined to predict binding residues. These predicted binding residues are then clustered into some predicted ligand-binding sites. According to our measurement criterion, this method achieved a success rate of 0.914 in the bound state dataset and 0.800 in the unbound state dataset, which are better than three other methods: Q-SiteFinder, SCREEN and Morita's method. It indicates that the proposed method here is successful for predicting ligand-binding sites.

The fungal metabolite eugenitin as additive for Aspergillus niveus glucoamylase activation

Abstract Endophytic microorganisms live inside tissues of host plants apparently do not causing warning to them, and are a promising source of bioactive molecules as antimicrobial and antitumoral drugs. In this work, we report the isolation of eugenitin from cultures of the endophyte Mycoleptodiscus indicus and its potential as additive for Aspergillus niveus glucoamylase activation. The glucoamylase hydrolytic activity increased twofold using 5 mM of eugenitin and this activation could be explained by the binding mode of eugenitin with the three-dimensional structure of glucoamylase. The in silico prediction of ligand binding sites revealed at least 9 possible interaction sites able to accommodate eugenitin on glucoamylase from Hypocrea jecorina . Besides, we evaluated the effect of pH and temperature on activity and stability, as well as in the hydrolysis of different substrates and kinetic parameters either in presence or absence of eugenitin. The results displayed by eugenitin as additive to glucoamylase activation are promising and provide novel perspectives for applications of fungal metabolites.

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

BackgroundAccurate 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.ResultsHere 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.ConclusionsHemeBIND is the first specialized algorithm used to predict binding residues in protein structures for heme ligands. Extensive experiments indicated that both the structure-based and sequence-based methods have effectively identified heme binding residues while the complementary relationship between them can result in a significant improvement in prediction performance. The value of our method is highlighted through the development of HemeBIND web server that is freely accessible at http://mleg.cse.sc.edu/hemeBIND/.

Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

BackgroundThe human pathogen Helicobacter pylori (H. pylori) is a main cause for gastric inflammation and cancer. Increasing bacterial resistance against antibiotics demands for innovative strategies for therapeutic intervention.Methodology/Principal FindingsWe present a method for structure-based virtual screening that is based on the comprehensive prediction of ligand binding sites on a protein model and automated construction of a ligand-receptor interaction map. Pharmacophoric features of the map are clustered and transformed in a correlation vector (‘virtual ligand’) for rapid virtual screening of compound databases. This computer-based technique was validated for 18 different targets of pharmaceutical interest in a retrospective screening experiment. Prospective screening for inhibitory agents was performed for the protease HtrA from the human pathogen H. pylori using a homology model of the target protein. Among 22 tested compounds six block E-cadherin cleavage by HtrA in vitro and result in reduced scattering and wound healing of gastric epithelial cells, thereby preventing bacterial infiltration of the epithelium.Conclusions/SignificanceThis study demonstrates that receptor-based virtual screening with a permissive (‘fuzzy’) pharmacophore model can help identify small bioactive agents for combating bacterial infection.

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

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/.

Prediction of Binding Sites on Intrinsically Disordered Proteins

Many of the methods for analyzing intrinsically disordered proteins (IDPs) to date have been limited to sequence analysis that attempts to predict intrinsically unstructured regions of proteins. However, our research is based on developing algorithms for predicting ligand binding sites and the associated secondary structure of these binding sites in IDPs. Our algorithms are founded on parameters determined through a statistical method. This method used bound IDP structures in the Protein Data Bank to calculate the frequency of amino acids occurring at the binding regions of eight nucleic acid binding IDPs. Our primitive sequence composition algorithm for predicting binding sites, SeqCom, predicts, on average, 83.2 percent of the binding sites with 47.0 percent of the binding sites predicted representative of the native binding regions. To improve binding site prediction, we developed IUPattern. IUPattern works on the same principles as SeqCom, but it uses additional constraints to better decipher between native and non-native binding sites. IUPattern predicts, on average, 90.2 percent of the binding sites with 55.0 percent of the binding sites predicted correctly correlating the native structure. Currently, we are finalizing a dynamic programming algorithm which predicts binding sites and secondary structure on nucleic acid binding IDPs. This algorithm incorporates an annotated sequence to show our confidence in the predictions.

Assessment of ligand‐binding residue predictions in CASP9

Interactions between proteins and their ligands play central roles in many physiological processes. The structural details for most of these interactions, however, have not yet been characterized experientially. Therefore, various computational tools have been developed to predict the location of binding sites and the amino acid residues interacting with ligands. In this manuscript, we assess the performance of 33 methods participating in the ligand-binding site prediction category in CASP9. The overall accuracy of ligand-binding site predictions in CASP9 appears rather high (average Matthews correlation coefficient of 0.62 for the 10 top performing groups) and compared to previous experiments more groups performed equally well. However, this should be seen in context of a strong bias in the test data toward easy template-based models. Overall, the top performing methods have converged to a similar approach using ligand-binding site inference from related homologous structures, which limits their applicability for difficult de novo prediction targets. Here, we present the results of the CASP9 assessment of the ligand-binding site category, discuss examples for successful and challenging prediction targets in CASP9, and finally suggest changes in the format of the experiment to overcome the current limitations of the assessment.