cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation

Abstract

Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely through explicitly adding the interactions between nearest neighbor residues and between next-nearest ones. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP can have similarly good performance for extensive test datasets and slightly better performance for the realistic RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom potentials such as rsRNASP, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.