Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines

Protein-protein interactions are critically dependent on just a few 'hot spot' residues at the interface. Hot spots make a dominant contribution to the free energy of binding and they can disrupt the interaction if mutated to alanine. Here, we present HSPred, a support vector machine(SVM)-based method to predict hot spot residues, given the structure of a complex. HSPred represents an improvement over a previously described approach (Lise et al, BMC Bioinformatics 2009, 10: 365). It achieves higher accuracy by treating separately predictions involving either an arginine or a glutamic acid residue. These are the amino acid types on which the original model did not perform well. We have therefore developed two additional SVM classifiers, specifically optimised for these cases. HSPred reaches an overall precision and recall respectively of 61% and 69%, which roughly corresponds to a 10% improvement. An implementation of the described method is available as a web server at http://bioinf.cs.ucl.ac.uk/hspred. It is free to non-commercial users

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

BACKGROUND: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS: To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS: eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi

Computational study and peptide inhibitors design for the CDK9 – cyclin T1 complex

Cyclin dependent kinase 9 (CDK9) is a protein that belongs to the cyclin-dependent kinases family, and its main role is in the regulation of the cell transcription processes. Since the increased activity of CDK9 is connected with the development of pathological processes such as tumor growth and survival and HIV-1 replication, inhibition of the CDK9 could be of particular interest for treating such diseases. The activation of CDK9 is initiated by the formation of CDK9/cyclin T1 complex, therefore disruption of its formation could be a promising strategy for the design of CDK9 inhibitors. In order to assist in the design of potential inhibitors of CDK9/cyclin T1 complex formation, a computational study of the CDK9/cyclin T1 interface was conducted. Ten peptides were designed using the information from the analysis of the complex, hot spot residues and fragment based design. The designed peptides were docked to CDK9 structures obtained by molecular dynamics simulations of CDK9/cyclin T1 complex and the CDK9 alone and their binding affinities were evaluated using molecular mechanics Poisson Boltzman surface area (MM-PBSA) method and steered molecular dynamics (SMD). Designed peptide sequences LQTLGF and ESIILQ, both derived from the surface of cyclin T1, as well as the peptide sequence PRWPE, derived from fragment based design, showed the most favorable binding properties and were selected for our further studies