CgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

Abstract

Knowledge of RNA 3-dimensional (3D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3D structure evaluation. For this purpose, we developed a statistical potential based on a minimal coarse-grained representation and residue separation, where every nucleotide is represented by C4’ atom for backbone and N1 (or N9) atom for base. In analogy to the newly developed all-atom rsRNASP, cgRNASP-CN is composed of short-ranged and long-ranged potentials, and the short-ranged one was involved more subtly. The examination indicates that the performance of cgRNASP-CN is close to that of the all-atom rsRNASP and is superior to other top all-atom traditional statistical potentials and scoring functions trained from neural networks, for two realistic test datasets including the RNA-Puzzles dataset. Very importantly, cgRNASP-CN is about 100 times more efficient than existing all-atom statistical potentials/scoring functions including rsRNASP. cgRNASP-CN is available at website: https://github.com/Tan-group/cgRNASP-CN.