Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction.

Abstract

An accurate energy scoring function is crucial for protein structure prediction. Given the increasing number of experimentally determined structures, knowledge-based approaches have been widely used to develop scoring functions for protein structure prediction in the past three decades. However, current scoring functions often only consider nonbonded interactions and neglect bonded potentials like covalent bonds and angles for the sake of speed and simplicity. Although such scoring functions may be successful on fully relaxed conformations, they would have difficulties in ranking those decoys with distorted bonds or angles, especially when being used for conformational sampling in structure prediction. Therefore, such a scoring function may perform well on one or several decoy sets, but it often has limited accuracy on large diverse sets. Addressing the limitation, we have developed a composite knowledge-based scoring function, named as ITCPS, by integrating bonded and nonbonded potentials as well as orientation-dependent and hydrophobic interactions. Our scoring function ITCPS was extensively evaluated on 18 decoy sets of 927 proteins including three sets of 3DRobot, AMBER benchmarking set, HR, CASP5-8, CASP9-13, eight sets of Decoy 'R' Us, MOULDER, ROSETTA, and I-TASSER set and compared with 51 other scoring functions. It was shown that overall ITCPS performed the best among the 52 scoring functions and achieved a good performance on all the test sets. Of 927 proteins, ITCPS recognized the native structures for 842 proteins, giving a success rate of 90.8% and an average Z-score of 3.36. Moreover, ITCPS also exhibited a strong ability to distinguish the best near-native structure among decoys and achieved a significantly better performance than other tested scoring functions. The present model is expected to be beneficial for the development of scoring functions for other interactions.