Protein Binding Site Prediction Research Articles

A tool for calculating binding-site residues on proteins from PDB structures

BackgroundIn the research on protein functional sites, researchers often need to identify binding-site residues on a protein. A commonly used strategy is to find a complex structure from the Protein Data Bank (PDB) that consists of the protein of interest and its interacting partner(s) and calculate binding-site residues based on the complex structure. However, since a protein may participate in multiple interactions, the binding-site residues calculated based on one complex structure usually do not reveal all binding sites on a protein. Thus, this requires researchers to find all PDB complexes that contain the protein of interest and combine the binding-site information gleaned from them. This process is very time-consuming. Especially, combing binding-site information obtained from different PDB structures requires tedious work to align protein sequences. The process becomes overwhelmingly difficult when researchers have a large set of proteins to analyze, which is usually the case in practice.ResultsIn this study, we have developed a tool for calculating binding-site residues on proteins, TCBRP . For an input protein, TCBRP can quickly find all binding-site residues on the protein by automatically combining the information obtained from all PDB structures that consist of the protein of interest. Additionally, TCBRP presents the binding-site residues in different categories according to the interaction type. TCBRP also allows researchers to set the definition of binding-site residues.ConclusionThe developed tool is very useful for the research on protein binding site analysis and prediction.

Defining and characterizing protein surface using alpha shapes

The alpha shape of a molecule is a geometrical representation that provides a unique surface decomposition and a means to filter atomic contacts. We used it to revisit and unify the definition and computation of surface residues, contiguous patches, and curvature. These descriptors are evaluated and compared with former approaches on 85 proteins for which both bound and unbound forms are available. Based on the local density of interactions, the detection of surface residues shows a sensibility of 98%, whereas preserving a well-formed protein core. A novel conception of surface patch is defined by traveling along the surface from a central residue or atom. By construction, all surface patches are contiguous and, therefore, allows to cope with common problems of wrong and nonselection of neighbors. In the case of protein-binding site prediction, this new definition has improved the signal-to-noise ratio by 2.6 times compared with a widely used approach. With most common approaches, the computation of surface curvature can be locally biased by the presence of subsurface cavities and local variations of atomic densities. A novel notion of surface curvature is specifically developed to avoid such bias and is parametrizable to emphasize either local or global features. It defines a molecular landscape composed on average of 38% knobs and 62% clefts where interacting residues (IR) are 30% more frequent in knobs. A statistical analysis shows that residues in knobs are more charged, less hydrophobic and less aromatic than residues in clefts. IR in knobs are, however, much more hydrophobic and aromatic and less charged than noninteracting residues (non-IR) in knobs. Furthermore, IR are shown to be more accessible than non-IR both in clefts and knobs. The use of the alpha shape as a unifying framework allows for formal definitions, and fast and robust computations desirable in large-scale projects. This swiftness is not achieved to the detriment of quality, as proven by valid improvements compared with former approaches. In addition, our approach is general enough to be applied on nucleic acids and any other biomolecules.

Using protein binding site prediction to improve protein docking

Predicting protein interaction interfaces and protein complexes are two important related problems. For interface prediction, there are a number of tools, such as PPI–Pred, PPISP, PINUP, Promate, and SPPIDER, which predict enzyme–inhibitor interfaces with success rates of 23% to 55% and other interfaces with 10% to 28% on a benchmark dataset of 62 complexes. Here, we develop, metaPPI, a meta server for interface prediction. It significantly improves prediction success rates to 70% for enzyme–inhibitor and 44% for other interfaces. As shown with Promate, predicted interfaces can be used to improve protein docking. Here, we follow this idea using the meta server instead of individual predictions. We confirm that filtering with predicted interfaces significantly improves candidate generation in rigid-body docking based on shape complementarity. Finally, we show that the initial ranking of candidate solutions in rigid-body docking can be further improved for the class of enzyme–inhibitor complexes by a geometrical scoring which rewards deep pockets. A web server of metaPPI is available at scoppi.tu-dresden.de/metappi. The source code of our docking algorithm BDOCK is also available at www.biotec.tu-dresden.de/~bhuang/bdock.

The interface of protein-protein complexes: analysis of contacts and prediction of interactions.

Specific protein-protein interactions are essential for cellular functions. Experimentally determined three-dimensional structures of protein-protein complexes offer the possibility to characterize binding interfaces in terms of size, shape and packing density. Comparison with crystal-packing interfaces representing nonspecific protein-protein contacts gives insight into how specific binding differs from nonspecific low-affinity binding. An overview is given on empirical structural rules for specific protein-protein recognition derived from known complex structures. Although single parameters such as interface size, shape or surface complementary show clear trends for different interface types, each parameter alone is insufficient to fully distinguish between specific versus crystal-packing contacts. A combination of interface parameters is, however, well suited to characterize a specific interface. This knowledge provides us with the essential ingredients that make up a specific protein recognition site. It is also of great value for the prediction of protein binding sites and for the evaluation of predicted complex structures.

ProMateus--an open research approach to protein-binding sites analysis

The development of bioinformatic tools by individual labs results in the abundance of parallel programs for the same task. For example, identification of binding site regions between interacting proteins is done using: ProMate, WHISCY, PPI-Pred, PINUP and others. All servers first identify unique properties of binding sites and then incorporate them into a predictor. Obviously, the resulting prediction would improve if the most suitable parameters from each of those predictors would be incorporated into one server. However, because of the variation in methods and databases, this is currently not feasible. Here, the protein-binding site prediction server is extended into a general protein-binding sites research tool, ProMateus. This web tool, based on ProMate's infrastructure enables the easy exploration and incorporation of new features and databases by the user, providing an evaluation of the benefit of individual features and their combination within a set framework. This transforms the individual research into a community exercise, bringing out the best from all users for optimized predictions. The analysis is demonstrated on a database of protein protein and protein-DNA interactions. This approach is basically different from that used in generating meta-servers. The implications of the open-research approach are discussed. ProMateus is available at http://bip.weizmann.ac.il/promate.

Protein−Protein Binding-Sites Prediction by Protein Surface Structure Conservation

A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal the location of binding sites on this protein. This approach, which has previously been used to predict binding sites for small ligands, is now extended to predict protein-protein binding sites. Examples of binding-site predictions for a set of proteins, which have previously been studied for sequence conservation in protein-protein interfaces, are given. The predicted binding sites and the actual binding sites are in good agreement. Our algorithm for finding conserved surface structures in a set of similar proteins is a useful tool for the prediction of protein-protein binding sites.

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

BackgroundThe ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.ResultsTwo B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper.ConclusionIt may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.

Protemot: prediction of protein binding sites with automatically extracted geometrical templates

Geometrical analysis of protein tertiary substructures has been an effective approach employed to predict protein binding sites. This article presents the Protemot web server that carries out prediction of protein binding sites based on the structural templates automatically extracted from the crystal structures of protein–ligand complexes in the PDB (Protein Data Bank). The automatic extraction mechanism is essential for creating and maintaining a comprehensive template library that timely accommodates to the new release of PDB as the number of entries continues to grow rapidly. The design of Protemot is also distinctive by the mechanism employed to expedite the analysis process that matches the tertiary substructures on the contour of the query protein with the templates in the library. This expediting mechanism is essential for providing reasonable response time to the user as the number of entries in the template library continues to grow rapidly due to rapid growth of the number of entries in PDB. This article also reports the experiments conducted to evaluate the prediction power delivered by the Protemot web server. Experimental results show that Protemot can deliver a superior prediction power than a web server based on a manually curated template library with insufficient quantity of entries. Availability: .

SuperLigands - a database of ligand structures derived from the Protein Data Bank.

BackgroundCurrently, the PDB contains approximately 29,000 protein structures comprising over 70,000 experimentally determined three-dimensional structures of over 5,000 different low molecular weight compounds. Information about these PDB ligands can be very helpful in the field of molecular modelling and prediction, particularly for the prediction of protein binding sites and function.DescriptionHere we present an Internet accessible database delivering PDB ligands in the MDL Mol file format which, in contrast to the PDB format, includes information about bond types. Structural similarity of the compounds can be detected by calculation of Tanimoto coefficients and by three-dimensional superposition. Topological similarity of PDB ligands to known drugs can be assessed via Tanimoto coefficients.ConclusionSuperLigands supplements the set of existing resources of information about small molecules bound to PDB structures. Allowing for three-dimensional comparison of the compounds as a novel feature, this database represents a valuable means of analysis and prediction in the field of biological and medical research.

PredictRegulon: a web server for the prediction of the regulatory protein binding sites and operons in prokaryote genomes.

An interactive web server is developed for predicting the potential binding sites and its target operons for a given regulatory protein in prokaryotic genomes. The program allows users to submit known or experimentally determined binding sites of a regulatory protein as ungapped multiple sequence alignments. It analyses the upstream regions of all genes in a user-selected prokaryote genome and returns the potential binding sites along with the downstream co-regulated genes (operons). The known binding sites of a regulatory protein can also be used to identify its orthologue binding sites in phylogeneticaly related genomes where the trans-acting regulator protein and cognate cis-acting DNA sequences could be conserved. PredictRegulon can be freely accessed from a link on our world wide web server: http://www.cdfd.org.in/predictregulon/.