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

作　　者：Ling Song Shixiong Yu Xunxun Wang Ya-Lan Tan Zhi-Jie Tan 

机构地区：[1]Department of Physics and Key Laboratory of Artificial Micro&Nano-structures of Education,School of Physics and Technology,Wuhan University,Wuhan 430072,China [2]Research Center of Nonlinear Science,School of Mathematical and Physical Sciences,Wuhan Textile University,Wuhan 430073,China

出　　处：《Communications in Theoretical Physics》2022年第7期129-137,共9页理论物理通讯（英文版）

基　　金：supported by grants from the National Science Foundation of China(12075171,11774272)。

摘　　要：Knowledge of RNA 3-dimensional(3 D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3 D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3 D 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.

关 键 词：RNA structure prediction statistical potential structure evaluation 

分 类 号：Q522[生物学—生物化学]