sDFIRE: Sequence-Specific Statistical Energy Function for Protein Structure Prediction by Decoy Selections

Abstract

An important unsolved problem in molecular and structural biology is the protein folding and structure prediction problem. One major bottleneck for solving this is the lack of an accurate energy to discriminate near‐native conformations against other possible conformations. Here we have developed sDFIRE energy function, which is an optimized linear combination of DFIRE (the Distance‐scaled Finite Ideal gas Reference state based Energy), the orientation dependent (polar‐polar and polar‐nonpolar) statistical potentials, and the matching scores between predicted and model structural properties including predicted main‐chain torsion angles and solvent accessible surface area. The weights for these scoring terms are optimized by three widely used decoy sets consisting of a total of 134 proteins. Independent tests on CASP8 and CASP9 decoy sets indicate that sDFIRE outperforms other state‐of‐the‐art energy functions in selecting near native structures and in the Pearson's correlation coefficient between the energy score and structural accuracy of the model (measured by TM‐score).