rsRNASP1: a distance and dihedral dependent statistical potential for RNA 3D structure evaluation.

Abstract

Knowledge-based statistical potentials have been shown to be rather important for RNA 3-dimensional (3D) structure prediction and evaluation. Recently, several statistical potentials/scoring functions have been developed for RNA 3D structure evaluation, while their performances are still at an unsatisfied level for the datasets from various 3D structure prediction methods. In this work, we developed an all-atom distance and torsion-angle dependent statistical potential with residue separation for RNA 3D structure evaluation, named as rsRNASP1, through considering torsion angles for the backbone, sugar ring and base to involve local structure features. The extensive examinations against available RNA test datasets show that rsRNASP1 has an overall higher performance than existing top statistical potentials/scoring functions in identifying native/near-native structures and ranking the decoy structures. Especially, rsRNASP1 shows the apparently improved performance on a new dataset from the CASP15 competition. rsRNASP1 is available at https://github.com/Tan-group/rsRNASP1.