Computation of binding energies including their enthalpy and entropy components for protein-ligand complexes using support vector machines.

Computing binding energies of protein-ligand complexes including their enthalpy and entropy terms by means of computational methods is an appealing approach for selecting initial hits and for further optimization in early stages of drug discovery. Despite the importance, computational predictions of thermodynamic components have evaded attention and reasonable solutions. In this study, support vector machines are used for developing scoring functions to compute binding energies and their enthalpy and entropy components of protein-ligand complexes. The binding energies computed from our newly derived scoring functions have better Pearson's correlation coefficients with experimental data than previously reported scoring functions in benchmarks for protein-ligand complexes from the PDBBind database. The protein-ligand complexes with binding energies dominated by enthalpy or entropy term could be qualitatively classified by the newly derived scoring functions with high accuracy. Furthermore, it is found that the inclusion of comprehensive descriptors based on ligand properties in the scoring functions improved the accuracy of classification as well as the prediction of binding energies including their thermodynamic components. The prediction of binding energies including the enthalpy and entropy components using the support vector machine based scoring functions should be of value in the drug discovery process.

Identification of protein binding surfaces using surface triplet propensities.

MOTIVATION: The ability to reliably predict protein-protein and protein-ligand interactions is important for identifying druggable binding sites and for understanding how proteins communicate. Most currently available algorithms identify cavities on the protein surface as potential ligand recognition sites. The method described here does not explicitly look for cavities but uses small surface patches consisting of triplets of adjacent surface atomic groups that can be touched simultaneously by a probe sphere representing a solvent molecule. A total of 455 different types of triplets can be identified. A training set of 309 protein-ligand protein X-ray structures has been used to generate interface propensities for the triplets, which can be used to predict their involvement in ligand-binding interactions. RESULTS: The success rate for locating protein-ligand binding sites on protein surfaces using this new surface triplet propensities (STP) algorithm is 88% which compares well with currently available grid-based and energy-based approaches. Q-SiteFinder's dataset (Laurie and Jackson, 2005. Bioinformatics, 21, 1908-1916) was used to show the favorable performance of STP. An analysis of the different triplet types showed that higher ligand binding propensity is related to more polarizable surfaces. The interaction statistics between triplet atoms on the protein surface and ligand atoms have been used to estimate statistical free energies of interaction. The ΔG(stat) for halogen atoms interacting with hydrophobic triplets is -0.6 kcal/mol and an estimate of the maximal ΔG(stat) for a ligand atom interacting with a triplet in a binding pocket is -1.45 kcal/mol. AVAILABILITY: Freely available online at http://opus.bch.ed.ac.uk/stp. Website implemented in Php, with all major browsers supported. CONTACT: m.walkinshaw@ed.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Predicting functional upstream open reading frames in Saccharomyces cerevisiae.

BACKGROUND: Some upstream open reading frames (uORFs) regulate gene expression (i.e., they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not yet fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of experimentally verified functional uORFs are needed. Unfortunately, wet-experiments to verify that uORFs are functional are expensive. RESULTS: In this paper, a new computational approach to predicting functional uORFs in the yeast Saccharomyces cerevisiae is presented. Our approach is based on inductive logic programming and makes use of a novel combination of knowledge about biological conservation, Gene Ontology annotations and genes' responses to different conditions. Our method results in a set of simple and informative hypotheses with an estimated sensitivity of 76%. The hypotheses predict 301 further genes to have 398 novel functional uORFs. Three (RPC11, TPK1, and FOL1) of these 301 genes have been hypothesised, following wet-experiments, by a related study to have functional uORFs. A comparison with another related study suggests that eleven of the predicted functional uORFs from genes LDB17, HEM3, CIN8, BCK2, PMC1, FAS1, APP1, ACC1, CKA2, SUR1, and ATH1 are strong candidates for wet-lab experimental studies. CONCLUSIONS: Learning based prediction of functional uORFs can be done with a high sensitivity. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help to elucidate the regulatory roles of uORFs.

Identification of putative regulatory upstream ORFs in the yeast genome using heuristics and evolutionary conservation.

BACKGROUND: The translational efficiency of an mRNA can be modulated by upstream open reading frames (uORFs) present in certain genes. A uORF can attenuate translation of the main ORF by interfering with translational reinitiation at the main start codon. uORFs also occur by chance in the genome, in which case they do not have a regulatory role. Since the sequence determinants for functional uORFs are not understood, it is difficult to discriminate functional from spurious uORFs by sequence analysis. RESULTS: We have used comparative genomics to identify novel uORFs in yeast with a high likelihood of having a translational regulatory role. We examined uORFs, previously shown to play a role in regulation of translation in Saccharomyces cerevisiae, for evolutionary conservation within seven Saccharomyces species. Inspection of the set of conserved uORFs yielded the following three characteristics useful for discrimination of functional from spurious uORFs: a length between 4 and 6 codons, a distance from the start of the main ORF between 50 and 150 nucleotides, and finally a lack of overlap with, and clear separation from, neighbouring uORFs. These derived rules are inherently associated with uORFs with properties similar to the GCN4 locus, and may not detect most uORFs of other types. uORFs with high scores based on these rules showed a much higher evolutionary conservation than randomly selected uORFs. In a genome-wide scan in S. cerevisiae, we found 34 conserved uORFs from 32 genes that we predict to be functional; subsequent analysis showed the majority of these to be located within transcripts. A total of 252 genes were found containing conserved uORFs with properties indicative of a functional role; all but 7 are novel. Functional content analysis of this set identified an overrepresentation of genes involved in transcriptional control and development. CONCLUSION: Evolutionary conservation of uORFs in yeasts can be traced up to 100 million years of separation. The conserved uORFs have certain characteristics with respect to length, distance from each other and from the main start codon, and folding energy of the sequence. These newly found characteristics can be used to facilitate detection of other conserved uORFs.

PROPHECY--a yeast phenome database, update 2006.

Connecting genotype to phenotype is fundamental in biomedical research and in our understanding of disease. Phenomics--the large-scale quantitative phenotypic analysis of genotypes on a genome-wide scale--connects automated data generation with the development of novel tools for phenotype data integration, mining and visualization. Our yeast phenomics database PROPHECY is available at http://prophecy.lundberg.gu.se. Via phenotyping of 984 heterozygous diploids for all essential genes the genotypes analysed and presented in PROPHECY have been extended and now include all genes in the yeast genome. Further, phenotypic data from gene overexpression of 574 membrane spanning proteins has recently been included. To facilitate the interpretation of quantitative phenotypic data we have developed a new phenotype display option, the Comparative Growth Curve Display, where growth curve differences for a large number of mutants compared with the wild type are easily revealed. In addition, PROPHECY now offers a more informative and intuitive first-sight display of its phenotypic data via its new summary page. We have also extended the arsenal of data analysis tools to include dynamic visualization of phenotypes along individual chromosomes. PROPHECY is an initiative to enhance the growing field of phenome bioinformatics.

Conformation of the exopolysaccharide of Burkholderia cepacia predicted with molecular mechanics (MM3) using genetic algorithm search.

We present a computational conformational analysis of the exopolysaccharide of Burkholderia cepacia, which is believed to play a role in colonization and persistence of B. cepacia in the lungs of cystic fibrosis patients. The repeating unit of the exopolysaccharide is a heptasaccharide with three branches, which cause significant steric restraints. Conformational searches using glygal, an in-house developed software using genetic algorithm search methods, were performed on fragments as well as on the complete repeating unit with wrap-over residues. The force field used for the calculations was MM3(96). The search showed four favored conformations for an isolated repeating unit. However, for a sequence of several repeating units, the calculations indicate a single, well-defined linear conformation.

The use of a genetic algorithm search for molecular mechanics (MM3)-based conformational analysis of oligosaccharides.

We have implemented a system called glygal that can perform conformational searches on oligosaccharides using several different genetic algorithm (GA) search methods. The searches are performed in the torsion angle conformational space, considering both the primary glycosidic linkages as well as the pendant groups (C-5-C-6 and hydroxyl groups) where energy calculations are performed using the MM3(96) force field. The system includes a graphical user interface for setting calculation parameters and incorporates a 3D molecular viewer. The system was tested using dozens of structures and we present two case studies for two previously investigated O-specific oligosaccharides of the Shigella dysenteriae type 2 and 4. The results obtained using glygal show a significant reduction in the number of structures that need to be sampled in order to find the best conformation, as compared to filtered systematic search.

PROPHECY--a database for high-resolution phenomics.

The rapid recent evolution of the field phenomics--the genome-wide study of gene dispensability by quantitative analysis of phenotypes--has resulted in an increasing demand for new data analysis and visualization tools. Following the introduction of a novel approach for precise, genome-wide quantification of gene dispensability in Saccharomyces cerevisiae we here announce a public resource for mining, filtering and visualizing phenotypic data--the PROPHECY database. PROPHECY is designed to allow easy and flexible access to physiologically relevant quantitative data for the growth behaviour of mutant strains in the yeast deletion collection during conditions of environmental challenges. PROPHECY is publicly accessible at http://prophecy.lundberg.gu.se.

Analysis of fish IL-1beta and derived peptide sequences indicates conserved structures with species-specific IL-1 receptor binding: implications for pharmacological design.

A large number of IL-1 protein sequences have become available recently from a range of vertebrate species and especially from bony fish. However, 3D structures are still only known for mammalian IL-1. In this review, we use a multiple sequence alignment of all published non-mammalian vertebrate IL-1beta proteins to locate the structurally important residues critical for maintaining the beta-trefoil fold and we investigate the degree to which functionally important residues involved in receptor binding are conserved across vertebrate species. We find that although there is a high level of variability of positions involved in receptor binding, the mode of binding and overall shape of the ligand-receptor complex is probably maintained. This implies that each species has evolved its own unique interleukin-1 signalling system through ligand-receptor co-evolution. Nonetheless, the IL-1beta processing mechanism in non-mammalian vertebrates remains unclear because, with the exception of three bony fish, all non-mammalian IL-1beta sequences discovered so far lack an ICE (Interleukin Converting Enzyme) cut site. The IL-1 system has become an important drug target because of its significance in inflammatory diseases. Research on peptides derived from IL-1beta has identified peptides that possess agonist activity in humans and in trout, and peptides with antagonist activity. The agonist peptides map to two distinct loop regions of IL-1beta that are known to interact with the flexible domain III of the corresponding receptor. Further analysis of the IL-1 system may prove useful in engineering IL-1 with improved features and in suggesting new avenues for therapeutic intervention.

Architecture of a mediator for a bioinformatics database federation.

Developments in our ability to integrate and analyze data held in existing heterogeneous data resources can lead to an increase in our understanding of biological function at all levels. However, supporting ad hoc queries across multiple data resources and correlating data retrieved from these is still difficult. To address this, we are building a mediator based on the functional data model database, P/FDM, which integrates access to heterogeneous distributed biological databases. Our architecture makes use of the existing search capabilities and indexes of the underlying databases, without infringing on their autonomy. Central to our design philosophy is the use of schemas. We have adopted a federated architecture with a five-level schema, arising from the use of the ANSI-SPARC three-level schema to describe both the existing autonomous data resources and the mediator itself. We describe the use of mapping functions and list comprehensions in query splitting, producing execution plans, code generation, and result fusion. We give an example of cross-database querying involving data held locally in P/FDM systems and external data in SRS.