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rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J 2022; 121:142-156. [PMID: 34798137 PMCID: PMC8758408 DOI: 10.1016/j.bpj.2021.11.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/23/2021] [Accepted: 11/10/2021] [Indexed: 01/07/2023] Open
Abstract
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.
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Xu G, Wang Q, Ma J. OPUS-Fold: An Open-Source Protein Folding Framework Based on Torsion-Angle Sampling. J Chem Theory Comput 2020; 16:3970-3976. [DOI: 10.1021/acs.jctc.0c00186] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
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Xu G, Wang Q, Ma J. OPUS-Refine: A Fast Sampling-Based Framework for Refining Protein Backbone Torsion Angles and Global Conformation. J Chem Theory Comput 2020; 16:1359-1366. [DOI: 10.1021/acs.jctc.9b01054] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
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Long S, Tian P. A simple neural network implementation of generalized solvation free energy for assessment of protein structural models. RSC Adv 2019; 9:36227-36233. [PMID: 35540566 PMCID: PMC9074945 DOI: 10.1039/c9ra05168f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/14/2019] [Indexed: 11/21/2022] Open
Abstract
Rapid and accurate assessment of protein structural models is essential for protein structure prediction and design. Great progress has been made in this regard, especially by recent application of "knowledge-based" potentials. Various machine learning based protein structural model quality assessment methods are also quite successful. However, performance of traditional "physics-based" models has not been as effective. Based on our analysis of the fundamental computational limitation behind unsatisfactory performance of "physics-based" models, we propose a generalized solvation free energy (GSFE) framework, which is intrinsically flexible for multi-scale treatments and is amenable for machine learning implementation. Finally, we implemented a simple example of backbone-based residue level GSFE with neural network, which was found to have competitive performance when compared with highly complex latest "knowledge-based" atomic potentials in distinguishing native structures from decoys.
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Affiliation(s)
- Shiyang Long
- School of Chemistry, Jilin University Changchun China
| | - Pu Tian
- School of Life Science and School of Artificial Intelligence, Jilin University 2699 Qianjin Street Changchun China 130012
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Xu G, Ma T, Du J, Wang Q, Ma J. OPUS-Rota2: An Improved Fast and Accurate Side-Chain Modeling Method. J Chem Theory Comput 2019; 15:5154-5160. [PMID: 31412199 DOI: 10.1021/acs.jctc.9b00309] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Side-chain modeling plays a critical role in protein structure prediction. However, in many current methods, balancing the speed and accuracy is still challenging. In this paper, on the basis of our previous work OPUS-Rota (Protein Sci. 2008, 17, 1576-1585), we introduce a new side-chain modeling method, OPUS-Rota2, which is tested on both a 65-protein test set (DB65) in the OPUS-Rota paper and a 379-protein test set (DB379) in the SCWRL4 paper. If the main chain is native, OPUS-Rota2 is more accurate than OPUS-Rota, SCWRL4, and OSCAR-star but slightly less accurate than OSCAR-o. Also, if the main chain is non-native, OPUS-Rota2 is more accurate than any other method. Moreover, OPUS-Rota2 is significantly faster than any other method, in particular, 2 orders of magnitude faster than OSCAR-o. Thus, the combination of higher accuracy and speed of OPUS-Rota2 in modeling side chains on both the native and non-native main chains makes OPUS-Rota2 a very useful tool in protein structure modeling.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems , Fudan University , Shanghai 200433 , China.,School of Life Sciences , Tsinghua University , Beijing 100084 , China
| | | | - Junqing Du
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology , Baylor College of Medicine , One Baylor Plaza, BCM-125 , Houston , Texas 77030 , United States
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology , Baylor College of Medicine , One Baylor Plaza, BCM-125 , Houston , Texas 77030 , United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems , Fudan University , Shanghai 200433 , China.,School of Life Sciences , Tsinghua University , Beijing 100084 , China.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology , Baylor College of Medicine , One Baylor Plaza, BCM-125 , Houston , Texas 77030 , United States.,School of Life Sciences , Fudan University , Shanghai 200433 , China
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