Protein Interactions: Integrating Computational Methods and Experimental Data for Understanding the Binding Specificity
Starts 8 Jun 2016 11:00
Ends 8 Jun 2016 12:00
Central European Time
Central Area, 2nd floor, old SISSA building
Protein-protein interactions play crucial roles in many biological processes and responsible for smooth functioning of the machinery in living organisms. Predicting the binding affinity of protein-protein complexes and understanding the recognition mechanism are challenging problems in computational and molecular biology (1,2). We have developed a generalized energy based approach for identifying the binding site residues and interacting pairs in all types of protein complexes. We observed that the residues with charged and aromatic side chains are important for binding in protein-protein complexes. These residues influence to form cation–p, electrostatic and aromatic interactions. Our observations have been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments (3). Further, we have developed algorithms for discriminating protein-protein complexes based on their binding affinities (4) and predicting the binding affinity (5). We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions (6).
Recently, we have developed a protocol to identify the novel inhibitors for cYes kinase, which is a target for colorectal cancer using homology modeling, docking and virtual screening (7). The lead compounds have been verified with experiments, which show the inhibition rate of 30-70% and the IC50 in the range of 5-30 µM. The salient features of the results will be discussed.
1. M.M. Gromiha (2010) Protein Bioinformatics, Elsevier Publishers/Academic Press
2. M.M. Gromiha and K. Yugandhar (2016) Prog. Biophys. Mol. Biol. (in press)
3. M.M. Gromiha, K. Yokota and K Fukui (2009) Mol. Biosystems5: 1779-1786.
4. K. Yugandhar and M.M. Gromiha (2014) Proteins. 82: 2088-96.
5. K. Yugandhar and M.M. Gromiha (2014) Bioinformatics.30: 3583-9.
6. K. Yugandhar and M.M. Gromiha (2016) Curr. Prot. Pept. Sci. 17, 72-81
7. S. Chiba, K. Ikeda, T. Ishida, M.M Gromiha et al. (2015) Sci. Rep. 5, 17209
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