Tan Ya-Lan, Wang Xunxun, Yu Shixiong, Zhang Bengong, Tan Zhi-Jie
Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430073, China.
Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China.
NAR Genom Bioinform. 2023 Mar 3;5(1):lqad016. doi: 10.1093/nargab/lqad016. eCollection 2023 Mar.
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. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset-RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, 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.
基于知识的统计势对于RNA三维（3D）结构预测和评估非常重要。近年来，已经开发了各种粗粒度（CG）和全原子模型来预测RNA的3D结构，然而，仍然缺乏可靠的CG统计势，不仅用于CG结构评估，也无法高效地用于全原子结构评估。在这项工作中，我们针对RNA 3D结构评估，在不同的CG水平上开发了一系列基于残基间距的CG统计势，即cgRNASP，它由基于残基间距的长程和短程相互作用组成。与新开发的全原子rsRNASP相比，cgRNASP中的短程相互作用涉及得更精细、更完整。我们的研究表明，cgRNASP的性能随CG水平而变化，与rsRNASP相比，cgRNASP在多种类型的测试数据集上具有相似的良好性能，并且在真实数据集——RNA-Puzzles数据集上可能具有稍好的性能。此外，cgRNASP比全原子统计势/评分函数的效率显著更高，并且在RNA-Puzzles数据集上明显优于其他从神经网络训练得到的全原子统计势和评分函数。cgRNASP可在https://github.com/Tan-group/cgRNASP获取。
