Protein-Protein interactions are important contacts through which the function and activity of proteins are often modulated as well as being involved in signal transduction and other biological functions. While protein docking methods are powerful computational tools to study protein-protein interactions, most existing approaches are based on rigid proteins or protein models with limited flexibility and desolvation contributions are often only empirically included. To overcome these limitations, the Site-Identification by Ligand Competitive Saturation (SILCS) framework may be applied towards protein-protein interaction prediction. SILCS is based on grid free energy functional group affinity maps for proteins (GFE FragMaps) generated using explicit solvent all-atom Grand Canonical-Monte Carlo/molecular dynamics simulations. The GFE FragMaps, which intrinsically include contributions from protein flexibility and desolvation effects, identify favorable binding patterns on proteins and thus may be used to guide protein-protein docking. As the GFE FragMaps are grid-based, Fast-Fourier transform techniques are used to identify the best matches between the individual protein maps and GFE term is used to quantify the resulting docking poses. In the present study, five protein-protein complexes from the protein docking benchmark 5.0 are tested showing the SILCS-based protein docking method to effectively predict crystallographically identified binding orientations. Moreover, the method can be use to predict probability distributions of possible protein-protein interactions over the entire surface of the respective proteins. These results suggest that the SILCS based protein docking protocol will be of utility for the study of protein-protein interactomics.

Using the site-identification by ligand competitive saturation (SILCS) method to explore protein-protein interactions

254th National Meeting (2017)

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