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Zhang D, Gong L, Weng J, Li Y, Wang A, Li G. RNA Folding Based on 5 Beads Model and Multiscale Simulation. Interdiscip Sci 2023:10.1007/s12539-023-00561-3. [PMID: 37115389 DOI: 10.1007/s12539-023-00561-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 04/29/2023]
Abstract
RNA folding prediction is very meaningful and challenging. The molecular dynamics simulation (MDS) of all atoms (AA) is limited to the folding of small RNA molecules. At present, most of the practical models are coarse grained (CG) model, and the coarse-grained force field (CGFF) parameters usually depend on known RNA structures. However, the limitation of the CGFF is obvious that it is difficult to study the modified RNA. Based on the 3 beads model (AIMS_RNA_B3), we proposed the AIMS_RNA_B5 model with three beads representing a base and two beads representing the main chain (sugar group and phosphate group). We first run the all atom molecular dynamic simulation (AAMDS), and fit the CGFF parameter with the AA trajectory. Then perform the coarse-grained molecular dynamic simulation (CGMDS). AAMDS is the foundation of CGMDS. CGMDS is mainly to carry out the conformation sampling based on the current AAMDS state and improve the folding speed. We simulated the folding of three RNAs, which belong to hairpin, pseudoknot and tRNA respectively. Compared to the AIMS_RNA_B3 model, the AIMS_RNA_B5 model is more reasonable and performs better.
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Affiliation(s)
- Dinglin Zhang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lidong Gong
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian, 116029, China
| | - Junben Weng
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yan Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Anhui Wang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Guohui Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
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2
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Zhang D, Li Y, Zhong Q, Wang A, Weng J, Gong L, Li G. Ribonucleic Acid Folding Prediction Based on Iterative Multiscale Simulation. J Phys Chem Lett 2022; 13:9957-9966. [PMID: 36260782 DOI: 10.1021/acs.jpclett.2c01342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
RNA folding prediction is a challenge. Currently, many RNA folding models are coarse-grained (CG) with the potential derived from the known RNA structures. However, this potential is not suitable for modified and entirely new RNA. It is also not suitable for the folding simulation of RNA in the real cellular environment, including many kinds of molecular interactions. In contrast, our proposed model has the potential to address these issues, which is a multiscale simulation scheme based on all-atom (AA) force fields. We fit the CG force field using the trajectories generated by the AA force field and then iteratively perform molecular dynamics (MD) simulations of the two scales. The all-atom molecular dynamics (AAMD) simulation is mainly responsible for the correction of RNA structure, and the CGMD simulation is mainly responsible for efficient conformational sampling. On the basis of this scheme, we can successfully fold three RNAs belonging to a hairpin, a pseudoknot, and a four-way junction.
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Affiliation(s)
- Dinglin Zhang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing100049, P. R. China
| | - Yan Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China
| | - Qinglu Zhong
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China
| | - Anhui Wang
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China
| | - Junben Weng
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China
- Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing100049, P. R. China
| | - Lidong Gong
- School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian116029, China
| | - Guohui Li
- Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China
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3
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López CA, Zhang X, Aydin F, Shrestha R, Van QN, Stanley CB, Carpenter TS, Nguyen K, Patel LA, Chen D, Burns V, Hengartner NW, Reddy TJE, Bhatia H, Di Natale F, Tran TH, Chan AH, Simanshu DK, Nissley DV, Streitz FH, Stephen AG, Turbyville TJ, Lightstone FC, Gnanakaran S, Ingólfsson HI, Neale C. Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework. J Chem Theory Comput 2022; 18:5025-5045. [PMID: 35866871 DOI: 10.1021/acs.jctc.2c00168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.
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Affiliation(s)
- Cesar A López
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Xiaohua Zhang
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Fikret Aydin
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Rebika Shrestha
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Que N Van
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Christopher B Stanley
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Timothy S Carpenter
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Kien Nguyen
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Lara A Patel
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.,Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - De Chen
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Violetta Burns
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicolas W Hengartner
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Tyler J E Reddy
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Harsh Bhatia
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Francesco Di Natale
- Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Timothy H Tran
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Albert H Chan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Dhirendra K Simanshu
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Dwight V Nissley
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Frederick H Streitz
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Andrew G Stephen
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Thomas J Turbyville
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Felice C Lightstone
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Sandrasegaram Gnanakaran
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Helgi I Ingólfsson
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Chris Neale
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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4
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Gupta C, Sarkar D, Tieleman DP, Singharoy A. The ugly, bad, and good stories of large-scale biomolecular simulations. Curr Opin Struct Biol 2022; 73:102338. [PMID: 35245737 DOI: 10.1016/j.sbi.2022.102338] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/29/2021] [Accepted: 01/24/2022] [Indexed: 12/20/2022]
Abstract
Molecular modeling of large biomolecular assemblies exemplifies a disruptive area holding both promises and contentions. Propelled by peta and exascale computing, several simulation methodologies have now matured into user-friendly tools that are successfully employed for modeling viruses, membranous nano-constructs, and key pieces of the genetic machinery. We present three unifying biophysical themes that emanate from some of the most recent multi-million atom simulation endeavors. Despite connecting molecular changes with phenotypic outcomes, the quality measures of these simulations remain questionable. We discuss the existing and upcoming strategies for constructing representative ensembles of large systems, how new computing technologies will boost this area, and make a point that integrative modeling guided by experimental data is the future of biomolecular computations.
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Affiliation(s)
- Chitrak Gupta
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University at Tempe, Tempe, AZ, 85282, USA; Biodesign Institute, Tempe, AZ, 85281, USA. https://twitter.com/ChitrakGupta2
| | - Daipayan Sarkar
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University at Tempe, Tempe, AZ, 85282, USA; MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824-1319, USA. https://twitter.com/17Dsarkar
| | - D Peter Tieleman
- Centre for Molecular Simulation and Department of Biological Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada.
| | - Abhishek Singharoy
- School of Molecular Sciences, Center for Applied Structural Discovery, Arizona State University at Tempe, Tempe, AZ, 85282, USA; Biodesign Institute, Tempe, AZ, 85281, USA.
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