中国物理B ›› 2018, Vol. 27 ›› Issue (3): 38701-038701.doi: 10.1088/1674-1056/27/3/038701
• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇 下一篇
A novel knowledge-based potential for RNA 3D structure evaluation
Yi Yang(杨毅), Qi Gu(辜琦), Ben-Gong Zhang(张本龚), Ya-Zhou Shi(时亚洲), Zhi-Gang Shao(邵志刚)
- 1 Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, SPTE, South China Normal University, Guangzhou 510006, China;
2 Center for Theoretical Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China;
3 Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430200, China
A novel knowledge-based potential for RNA 3D structure evaluation
Yi Yang(杨毅)1,2, Qi Gu(辜琦)2, Ben-Gong Zhang(张本龚)3, Ya-Zhou Shi(时亚洲)2,3, Zhi-Gang Shao(邵志刚)1
- 1 Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, SPTE, South China Normal University, Guangzhou 510006, China;
2 Center for Theoretical Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China;
3 Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430200, China
摘要：
Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation.
中图分类号: (RNA)
- 87.14.gn