Ligand Binding Site Prediction Research Articles

Prediction of Protein-compound Complex Structures by Docking Simulation

Protein-compound docking is a key technology in structure-guided drug development (SGDD), and some combinations of computational methods including the protein-compound docking have been used in the SGDD. Ligand-binding site prediction is the first step in the SGDD. The second step is the ensemble docking, in which compounds in a database are docked onto the protein surfaces of a structural ensemble of the target protein. The third step is the information analysis of the docking results. Finally, suitable docking results are selected for an assay experiment. Topics around the protein-compound docking and the SGDD are briefly explained.

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-010-9402-0) contains supplementary material, which is available to authorized users.

Molecular docking study of bark-derived components of Cinnamomum cassia on aldose reductase

Aldose reductase (AR) is an enzyme associated with retinopathy of both type 1 and type 2 diabetic patients. AR is inhibited by giving chemical drugs to prevent diabetic retinopathy, but is associated with deleterious side effects. Compounds isolated from plants are safer than chemical drugs and have a lot of potential. In the present study, three components viz., cinnamaldehyde, cinnamic acid and cinnamyl alcohol derived from the bark of Cinnamomum cassia were used to evaluate their efficiency in inhibiting AR activity. For this, the AR protein structure database was downloaded from PDB and its pictorial database was downloaded from PDBsum. Structural visualization of AR was done by RasMol. Using Q-SiteFinder, prediction of ligand binding site was done. The three dimensional structures of inhibitors viz., cinnamaldehyde, cinnamic acid and cinnamyl alcohol were downloaded from ChemSketch. Docking studies were carried out using ArgusLab software. Docking studies of cinnamaldehyde, cinnamic acid and cinnamyl alcohol against AR was undertaken to gain insight into the binding mode of the investigated compounds at the active site of AR. All compounds were found to be active against AR as indicated by docking results; the best being cinnamaldehyde. Results suggest that cinnamaldehyde, cinnamic acid and cinnamyl alcohol should be evaluated further for therapeutic use in combination with other diabetic drugs. Further, this may be confirmed by drug trials in animal models to find out the optimum dose and its efficiency in inhibiting AR activity to treat diabetic related complications. Keywords: Diabetes, aldose reductase, cinnamon, molecular docking.

Molecular docking study of bark-derived components of Cinnamomum cassia on aldose reductase

Aldose reductase (AR) is an enzyme associated with retinopathy of both type 1 and type 2 diabetic patients. AR is inhibited by giving chemical drugs to prevent diabetic retinopathy, but is associated with deleterious side effects. Compounds isolated from plants are safer than chemical drugs and have a lot of potential. In the present study, three components viz ., cinnamaldehyde, cinnamic acid and cinnamyl alcohol derived from the bark of Cinnamomum cassia were used to evaluate their efficiency in inhibiting AR activity. For this, the AR protein structure database was downloaded from PDB and its pictorial database was downloaded from PDBsum. Structural visualization of AR was done by RasMol. Using Q-SiteFinder, prediction of ligand binding site was done. The three dimensional structures of inhibitors viz ., cinnamaldehyde, cinnamic acid and cinnamyl alcohol were downloaded from ChemSketch. Docking studies were carried out using ArgusLab software. Docking studies of cinnamaldehyde, cinnamic acid and cinnamyl alcohol against AR was undertaken to gain insight into the binding mode of the investigated compounds at the active site of AR. All compounds were found to be active against AR as indicated by docking results; the best being cinnamaldehyde. Results suggest that cinnamaldehyde, cinnamic acid and cinnamyl alcohol should be evaluated further for therapeutic use in combination with other diabetic drugs. Further, this may be confirmed by drug trials in animal models to find out the optimum dose and its efficiency in inhibiting AR activity to treat diabetic related complications

The binding site distance test score: a robust method for the assessment of predicted protein binding sites

We propose a novel method for scoring the accuracy of protein binding site predictions-the Binding-site Distance Test (BDT) score. Recently, the Matthews Correlation Coefficient (MCC) has been used to evaluate binding site predictions, both by developers of new methods and by the assessors for the community-wide prediction experiment-CASP8. While being a rigorous scoring method, the MCC does not take into account the actual 3D location of the predicted residues from the observed binding site. Thus, an incorrectly predicted site that is nevertheless close to the observed binding site will obtain an identical score to the same number of non-binding residues predicted at random. The MCC is somewhat affected by the subjectivity of determining observed binding residues and the ambiguity of choosing distance cutoffs. By contrast the BDT method produces continuous scores ranging between 0 and 1, relating to the distance between the predicted and observed residues. Residues predicted close to the binding site will score higher than those more distant, providing a better reflection of the true accuracy of predictions. The CASP8 function predictions were evaluated using both the MCC and BDT methods and the scores were compared. The BDT was found to strongly correlate with the MCC scores while also being less susceptible to the subjectivity of defining binding residues. We therefore suggest that this new simple score is a potentially more robust method for future evaluations of protein-ligand binding site predictions. http://www.reading.ac.uk/bioinf/downloads/.

3DLigandSite: predicting ligand-binding sites using similar structures

3DLigandSite is a web server for the prediction of ligand-binding sites. It is based upon successful manual methods used in the eighth round of the Critical Assessment of techniques for protein Structure Prediction (CASP8). 3DLigandSite utilizes protein-structure prediction to provide structural models for proteins that have not been solved. Ligands bound to structures similar to the query are superimposed onto the model and used to predict the binding site. In benchmarking against the CASP8 targets 3DLigandSite obtains a Matthew’s correlation co-efficient (MCC) of 0.64, and coverage and accuracy of 71 and 60%, respectively, similar results to our manual performance in CASP8. In further benchmarking using a large set of protein structures, 3DLigandSite obtains an MCC of 0.68. The web server enables users to submit either a query sequence or structure. Predictions are visually displayed via an interactive Jmol applet. 3DLigandSite is available for use at http://www.sbg.bio.ic.ac.uk/3dligandsite.

Ligand-binding site prediction of proteins based on known fragment–fragment interactions

Motivation: The identification of putative ligand-binding sites on proteins is important for the prediction of protein function. Knowledge-based approaches using structure databases have become interesting, because of the recent increase in structural information. Approaches using binding motif information are particularly effective. However, they can only be applied to well-known ligands that frequently appear in the structure databases.Results: We have developed a new method for predicting the binding sites of chemically diverse ligands, by using information about the interactions between fragments. The selection of the fragment size is important. If the fragments are too small, then the patterns derived from the binding motifs cannot be used, since they are many-body interactions, while using larger fragments limits the application to well-known ligands. In our method, we used the main and side chains for proteins, and three successive atoms for ligands, as fragments. After superposition of the fragments, our method builds the conformations of ligands and predicts the binding sites. As a result, our method could accurately predict the binding sites of chemically diverse ligands, even though the Protein Data Bank currently contains a large number of nucleotides. Moreover, a further evaluation for the unbound forms of proteins revealed that our building up procedure was robust to conformational changes induced by ligand binding.Availability: Our method, named ‘BUMBLE’, is available at http://bumble.hgc.jp/Contact: kasahara@cb.k.u-tokyo.ac.jpSupplementary information: Supplementary Material is available at Bioinformatics online.

In Silico Prediction of Binding Sites on Proteins

The majority of biological processes involve the association of proteins or binding of other ligands to proteins. The accurate prediction of putative binding sites on the protein surface can be very helpful for rational drug design on target proteins of medical relevance, for predicting the geometry of protein-protein as well as protein-ligand complexes and for evaluating the tendency of proteins to aggregate or oligomerize. A variety of computational methods to rapidly predict protein-protein binding interfaces or binding sites for small drug-like molecules have been developed in recent years. The principles of methods available for protein interface and pocket detection are summarized, including approaches based on sequence conservation, as well as geometric and physicochemical surface properties. The performance of several Webaccessible methods for ligand binding site prediction has been compared using protein structures in bound and unbound conformation and homology modeled proteins. All methods tested gave very promising predictions even on unbound and homology modeled protein structures, thus indicating that current methods are robust in relation to modest conformational changes associated with the ligand binding process.

Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere

Prediction of ligand binding sites of proteins is significant as it can provide insight into biological functions and reaction mechanisms of proteins. It is also a prerequisite for protein-ligand docking and an important step in structure-based drug design. We present a new algorithm, Roll, implemented in a program named POCASA, which can predict binding sites by detecting pockets and cavities of proteins with a rolling sphere. To evaluate the performance of POCASA, a test with the same data set as used in several existing methods was carried out. POCASA achieved a high success rate of 77%. In addition, the test results indicated that POCASA can predict good shapes of ligand binding sites. A web version of POCASA is freely available at http://altair.sci.hokudai.ac.jp/g6/Research/POCASA_e.html.

MetaPocket: A Meta Approach to Improve Protein Ligand Binding Site Prediction

The identification of ligand-binding sites is often the starting point for protein function annotation and structure-based drug design. Many computational methods for the prediction of ligand-binding sites have been developed in recent decades. Here we present a consensus method metaPocket, in which the predicted sites from four methods: LIGSITE(cs), PASS, Q-SiteFinder, and SURFNET are combined together to improve the prediction success rate. All these methods are evaluated on two datasets of 48 unbound/bound structures and 210 bound structures. The comparison results show that metaPocket improves the success rate from approximately 70 to 75% at the top 1 prediction. MetaPocket is available at http://metapocket.eml.org .

FINDSITE: a combined evolution/structure-based approach to protein function prediction

A key challenge of the post-genomic era is the identification of the function(s) of all the molecules in a given organism. Here, we review the status of sequence and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the approximately 50% of ORFs of unassigned function in an average proteome. We then describe FINDSITE, a recently developed algorithm for ligand binding site prediction, ligand screening and molecular function prediction, which is based on binding site conservation across evolutionary distant proteins identified by threading. Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used.

Prediction of ligand binding sites using homologous structures and conservation at CASP8

The critical assessment of protein structure prediction experiment is a blind assessment of the prediction of protein structure and related topics including function prediction. We present our results in the function/binding site prediction category. Our approach to identify binding sites combined the use of the predicted structure of the targets with both residue conservation and the location of ligands bound to homologous structures. We obtained an average coverage of 83% and 56% accuracy. Analysis of our predictions suggests that over-prediction reduces the accuracy obtained due to large areas of conservation around the binding site that do not bind the ligand. In some proteins such conserved residues may have a functional role. A server version of our method will soon be available.

Protein‐binding site prediction based on three‐dimensional protein modeling

Structural information of a protein can guide one to understand the function of the protein, and ligand binding is one of the major biochemical functions of proteins. We have applied a two-stage template-based ligand binding site prediction method to CASP8 targets and achieved high quality results with accuracy/coverage = 70/80 (LEE). First, templates are used for protein structure modeling and then for binding site prediction by structural clustering of ligand-containing templates to the predicted protein model. Remarkably, the results are only a few percent worse than those one can obtain from native structures, which were available only after the prediction. Prediction was performed without knowing identity of ligands, and consequently, in many cases the ligand molecules used for prediction were different from the actual ligands, and yet we find that the prediction was quite successful. The current approach can be easily combined with experiments to investigate protein activities in a systematic way.

2P-033 タンパク質立体構造上でのリガンド構造アゼンブルによるリガンド結合部位の予測(蛋白質-構造機能相関,第47回日本生物物理学会年会)

2P-033 タンパク質立体構造上でのリガンド構造アゼンブルによるリガンド結合部位の予測(蛋白質-構造機能相関,第47回日本生物物理学会年会)

MODBASE, a database of annotated comparative protein structure models and associated resources.

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence–structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5 152 695 reliable models for domains in 1 593 209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/).

A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

The detection of ligand-binding sites is often the starting point for protein function identification and drug discovery. Because of inaccuracies in predicted protein structures, extant binding pocket-detection methods are limited to experimentally solved structures. Here, FINDSITE, a method for ligand-binding site prediction and functional annotation based on binding-site similarity across groups of weakly homologous template structures identified from threading, is described. For crystal structures, considering a cutoff distance of 4 A as the hit criterion, the success rate is 70.9% for identifying the best of top five predicted ligand-binding sites with a ranking accuracy of 76.0%. Both high prediction accuracy and ability to correctly rank identified binding sites are sustained when approximate protein models (<35% sequence identity to the closest template structure) are used, showing a 67.3% success rate with 75.5% ranking accuracy. In practice, FINDSITE tolerates structural inaccuracies in protein models up to a rmsd from the crystal structure of 8-10 A. This is because analysis of weakly homologous protein models reveals that about half have a rmsd from the native binding site <2 A. Furthermore, the chemical properties of template-bound ligands can be used to select ligand templates associated with the binding site. In most cases, FINDSITE can accurately assign a molecular function to the protein model.

Automated prediction of ligand‐binding sites in proteins

We present a method, termed AutoLigand, for the prediction of ligand-binding sites in proteins of known structure. The method searches the space surrounding the protein and finds the contiguous envelope with the specified volume of atoms, which has the largest possible interaction energy with the protein. It uses a full atomic representation, with atom types for carbon, hydrogen, oxygen, nitrogen and sulfur (and others, if desired), and is designed to minimize the need for artificial geometry. Testing on a set of 187 diverse protein-ligand complexes has shown that the method is successful in predicting the location and approximate volume of the binding site in 73% of cases. Additional testing was performed on a set of 96 protein-ligand complexes with crystallographic structures of apo and holo forms, and AutoLigand was able to predict the binding site in 80% of the apo structures.

A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites

BackgroundAn accurate description of protein shape derived from protein structure is necessary to establish an understanding of protein-ligand interactions, which in turn will lead to improved methods for protein-ligand docking and binding site analysis. Most current shape descriptors characterize only the local properties of protein structure using an all-atom representation and are slow to compute. We need new shape descriptors that have the ability to capture both local and global structural information, are robust for application to models and low quality structures and are computationally efficient to permit high throughput analysis of protein structures.ResultsWe introduce a new shape description that requires only the Cα atoms to represent the protein structure, thus making it both fast and suitable for use on models and low quality structures. The notion of a geometric potential is introduced to quantitatively describe the shape of the structure. This geometric potential is dependent on both the global shape of the protein structure as well as the surrounding environment of each residue. When applying the geometric potential for binding site prediction, approximately 85% of known binding sites can be accurately identified with above 50% residue coverage and 80% specificity. Moreover, the algorithm is fast enough for proteome-scale applications. Proteins with fewer than 500 amino acids can be scanned in less than two seconds.ConclusionThe reduced representation of the protein structure combined with the geometric potential provides a fast, quantitative description of protein-ligand binding sites with potential for use in large-scale predictions, comparisons and analysis.

Detection of pockets on protein surfaces using small and large probe spheres to find putative ligand binding sites

One of the simplest ways to predict ligand binding sites is to identify pocket-shaped regions on the protein surface. Many programs have already been proposed to identify these pocket regions. Examination of their algorithms revealed that a pocket intrinsically has two arbitrary properties, "size" and "depth". We proposed a new definition for pockets using two explicit adjustable parameters that correspond to these two arbitrary properties. A pocket region is defined as a space into which a small probe can enter, but a large probe cannot. The radii of small and large probe spheres are the two parameters that correspond to the "size" and "depth" of the pockets, respectively. These values can be adjusted individual putative ligand molecule. To determine the optimal value of the large probe spheres radius, we generated pockets for thousands of protein structures in the database, using several size of large probe spheres, examined the correspondence of these pockets with known binding site positions. A new measure of shallowness, a minimum inaccessible radius, R(inaccess), indicated that binding sites of coenzymes are very deep, while those for adenine/guanine mononucleotide have only medium shallowness and those for short peptides and oligosaccharides are shallow. The optimal radius of large probe spheres was 3-4 A for the coenzymes, 4 A for adenine/guanine mononucleotides, and 5 A or more for peptides/oligosaccharides. Comparison of our program with two other popular pocket-finding programs showed that our program had a higher performance of detecting binding pockets, although it required more computational time.

Docking of novel reversible monoamine oxidase-B inhibitors: efficient prediction of ligand binding sites and estimation of inhibitors thermodynamic properties

Monoamine oxidase (MAO, EC 1.4.3.4) is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known target for antidepressant, Parkinson's disease and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for molecular modeling studies. In this research 12 reversible and MAO-B selective inhibitors have been docked computationally to the active site of the MAO-B enzyme. AutoDock 3.0.5 was employed to perform the automated molecular docking. The result of docking studies generated thermodynamic properties, such as free energy of bindings (DeltaG(b)) and inhibition constants (K (i)) for the inhibitors. Moreover, 3D pictures of inhibitor-enzyme complexes afforded valuable data regarding the binding orientation of each inhibitor in the active site of MAO-B.