Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China; Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Department of Physics and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, China.
Biophys J. 2022 Jan 4;121(1):142-156. doi: 10.1016/j.bpj.2021.11.016. Epub 2021 Nov 17.
Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at a low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models, including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. In addition, rsRNASP is superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available to the public.
基于知识的统计势在蛋白质三维（3D）结构评估和预测中已被证明非常有效。最近，已经开发了几种用于 RNA 3D 结构评估的统计势，但其性能要么对于来自结构预测模型的测试数据集仍然处于较低水平，要么依赖于通过神经网络的“黑盒”过程。在这项工作中，我们开发了一种基于残基分离的全原子距离相关统计势用于 RNA 3D 结构评估，即 rsRNASP，它由通过残基分离区分的短程和长程势组成。对现有 RNA 测试数据集的广泛评估表明，rsRNASP 对于来自结构预测模型的大型 RNA 的实际测试数据集具有明显更高的性能，包括新发布的 RNA-Puzzles 数据集，并且与用于具有小 RNA 或近天然诱饵的测试数据集的现有顶级统计势相当。此外，rsRNASP 优于最近通过 3D 卷积神经网络开发的评分函数 RNA3DCNN。rsRNASP 和相关数据库可供公众使用。
