PocketAnnotate: towards site-based function annotation.

A computational pipeline PocketAnnotate for functional annotation of proteins at the level of binding sites has been proposed in this study. The pipeline integrates three in-house algorithms for site-based function annotation: PocketDepth, for prediction of binding sites in protein structures; PocketMatch, for rapid comparison of binding sites and PocketAlign, to obtain detailed alignment between pair of binding sites. A novel scheme has been developed to rapidly generate a database of non-redundant binding sites. For a given input protein structure, putative ligand-binding sites are identified, matched in real time against the database and the query substructure aligned with the promising hits, to obtain a set of possible ligands that the given protein could bind to. The input can be either whole protein structures or merely the substructures corresponding to possible binding sites. Structure-based function annotation at the level of binding sites thus achieved could prove very useful for cases where no obvious functional inference can be obtained based purely on sequence or fold-level analyses. An attempt has also been made to analyse proteins of no known function from Protein Data Bank. PocketAnnotate would be a valuable tool for the scientific community and contribute towards structure-based functional inference. The web server can be freely accessed at http://proline.biochem.iisc.ernet.in/pocketannotate/.

Structural annotation of Mycobacterium tuberculosis proteome.

Of the ∼4000 ORFs identified through the genome sequence of Mycobacterium tuberculosis (TB) H37Rv, experimentally determined structures are available for 312. Since knowledge of protein structures is essential to obtain a high-resolution understanding of the underlying biology, we seek to obtain a structural annotation for the genome, using computational methods. Structural models were obtained and validated for ∼2877 ORFs, covering ∼70% of the genome. Functional annotation of each protein was based on fold-based functional assignments and a novel binding site based ligand association. New algorithms for binding site detection and genome scale binding site comparison at the structural level, recently reported from the laboratory, were utilized. Besides these, the annotation covers detection of various sequence and sub-structural motifs and quaternary structure predictions based on the corresponding templates. The study provides an opportunity to obtain a global perspective of the fold distribution in the genome. The annotation indicates that cellular metabolism can be achieved with only 219 folds. New insights about the folds that predominate in the genome, as well as the fold-combinations that make up multi-domain proteins are also obtained. 1728 binding pockets have been associated with ligands through binding site identification and sub-structure similarity analyses. The resource (http://proline.physics.iisc.ernet.in/Tbstructuralannotation), being one of the first to be based on structure-derived functional annotations at a genome scale, is expected to be useful for better understanding of TB and for application in drug discovery. The reported annotation pipeline is fairly generic and can be applied to other genomes as well.

PocketAlign a novel algorithm for aligning binding sites in protein structures.

A fundamental task in bioinformatics involves a transfer of knowledge from one protein molecule onto another by way of recognizing similarities. Such similarities are obtained at different levels, that of sequence, whole fold, or important substructures. Comparison of binding sites is important to understand functional similarities among the proteins and also to understand drug cross-reactivities. Current methods in literature have their own merits and demerits, warranting exploration of newer concepts and algorithms, especially for large-scale comparisons and for obtaining accurate residue-wise mappings. Here, we report the development of a new algorithm, PocketAlign, for obtaining structural superpositions of binding sites. The software is available as a web-service at http://proline.physics.iisc.ernet.in/pocketalign/. The algorithm encodes shape descriptors in the form of geometric perspectives, supplemented by chemical group classification. The shape descriptor considers several perspectives with each residue as the focus and captures relative distribution of residues around it in a given site. Residue-wise pairings are computed by comparing the set of perspectives of the first site with that of the second, followed by a greedy approach that incrementally combines residue pairings into a mapping. The mappings in different frames are then evaluated by different metrics encoding the extent of alignment of individual geometric perspectives. Different initial seed alignments are computed, each subsequently extended by detecting consequential atomic alignments in a three-dimensional grid, and the best 500 stored in a database. Alignments are then ranked, and the top scoring alignments reported, which are then streamed into Pymol for visualization and analyses. The method is validated for accuracy and sensitivity and benchmarked against existing methods. An advantage of PocketAlign, as compared to some of the existing tools available for binding site comparison in literature, is that it explores different schemes for identifying an alignment thus has a better potential to capture similarities in ligand recognition abilities. PocketAlign, by finding a detailed alignment of a pair of sites, provides insights as to why two sites are similar and which set of residues and atoms contribute to the similarity.

Structural bioinformatics: deriving biological insights from protein structures.

Structural bioinformatics can be described as an approach that will help decipher biological insights from protein structures. As an important component of structural biology, this area promises to provide a high resolution understanding of biology by assisting comprehension and interpretation of a large amount of structural data. Biological function of protein molecules can be inferred from their three-dimensional structures by comparing structures, classifying them and transferring function from a related protein or family. It is well known now that the structure space of protein molecules is more conserved than the sequence space, making it important to seek functional associations at the structural level. An added advantage of structural bioinformatics over simpler sequence-based methods is that the former also provides ultimate insights into the mechanisms by which various biological events take place. A bird's eye-view of the different aspects of structural bioinformatics is given here along with various recent advances in the area including how knowledge obtained from structural bioinformatics can be applied in drug discovery.

An automated framework for understanding structural variations in the binding grooves of MHC class II molecules.

BACKGROUND: MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them. RESULTS: Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch13. CONCLUSION: The systematic framework for understanding structural variations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.

targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis.

BACKGROUND: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. RESULTS: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. CONCLUSION: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

PocketMatch: a new algorithm to compare binding sites in protein structures.

BACKGROUND: Recognizing similarities and deriving relationships among protein molecules is a fundamental requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison. RESULTS: Here we present a new algorithm PocketMatch for comparison of binding sites in a frame invariant manner. Each binding site is represented by 90 lists of sorted distances capturing shape and chemical nature of the site. The sorted arrays are then aligned using an incremental alignment method and scored to obtain PMScores for pairs of sites. A comprehensive sensitivity analysis and an extensive validation of the algorithm have been carried out. A comparison with other site matching algorithms is also presented. Perturbation studies where the geometry of a given site was retained but the residue types were changed randomly, indicated that chance similarities were virtually non-existent. Our analysis also demonstrates that shape information alone is insufficient to discriminate between diverse binding sites, unless combined with chemical nature of amino acids. CONCLUSION: A new algorithm has been developed to compare binding sites in accurate, efficient and high-throughput manner. Though the representation used is conceptually simplistic, we demonstrate that along with the new alignment strategy used, it is sufficient to enable binding comparison with high sensitivity. Novel methodology has also been presented for validating the algorithm for accuracy and sensitivity with respect to geometry and chemical nature of the site. The method is also fast and takes about 1/250th second for one comparison on a single processor. A parallel version on BlueGene has also been implemented.