Abstract

We demonstrate the applicability of a computational method, Site Identification by Ligand Competitive Saturation (SILCS) to identify regions on a protein surface with which different classes of functional groups interact. The method involves MD simulations of a protein in an aqueous solution of chemically diverse small molecules. In the present application, SILCS simulations are performed with an aqueous solution of 1 M benzene and propane to map the affinity pattern of the protein for aromatic and aliphatic functional groups. In addition, water hydrogens and oxygen serve as probes for hydrogen bond donor and acceptor functionality, respectively. The method is tested using a set of proteins for which crystal structures of complexes with several high affinity inhibitors are known. SILCS simulations are performed for these proteins and the affinity pattern is obtained as 3D probability distributions of fragment atom types on a 3D-grid surrounding the protein called “FragMaps”. Good agreement is obtained between FragMaps of each type and the positions of chemically similar functional group in inhibitors as observed in the Xray crystallographic structures. For proteins for which inhibitor decoy sets are available, we demonstrate the statistical significance of the SILCS predictions by showing a significantly higher degree of overlap of the ligand atoms in the experimental conformation with the FragMaps. For a few test cases, we correlate the extent of overlap of the ligand functional groups with FragMaps to the experimental binding affinities. SILCS is also shown to capture the subtle differences in protein affinity across homologs, information which may be of utility towards specificity-guided drug design. Taken together, our results suggest that SILCS can recapitulate the location of functional groups of bound inhibitors, suggesting that the method may be of utility for rational drug design.

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