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Hsu WT, Shirts MR. Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability. J Chem Theory Comput 2024; 20:6062-6081. [PMID: 39007702 DOI: 10.1021/acs.jctc.4c00484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Generalized ensemble methods such as Hamiltonian replica exchange (HREX) and expanded ensemble (EE) have been shown effective in free energy calculations for various contexts, given their ability to circumvent free energy barriers via nonphysical pathways defined by states with different modified Hamiltonians. However, both HREX and EE methods come with drawbacks, such as limited flexibility in parameter specification or the lack of parallelizability for more complicated applications. To address this challenge, we present the method of replica exchange of expanded ensembles (REXEE), which integrates the principles of HREX and EE methods by periodically exchanging coordinates of EE replicas sampling different yet overlapping sets of alchemical states. With the solvation free energy calculation of anthracene and binding free energy calculation of the CB7-10 binding complex, we show that the REXEE method achieves the same level of accuracy in free energy calculations as the HREX and EE methods, while offering enhanced flexibility and parallelizability. Additionally, we examined REXEE simulations with various setups to understand how different exchange frequencies and replica configurations influence the sampling efficiency in the fixed-weight phase and the weight convergence in the weight-updating phase. The REXEE approach can be further extended to support asynchronous parallelization schemes, allowing looser communications between larger numbers of loosely coupled processors such as cloud computing and therefore promising much more scalable and adaptive executions of alchemical free energy calculations. All algorithms for the REXEE method are available in the Python package ensemble_md, which offers an interface for REXEE simulation management without modifying the source code in GROMACS.
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Affiliation(s)
- Wei-Tse Hsu
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80305, United States
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Li Z, Song G, Zhu J, Mu J, Sun Y, Hong X, Choi T, Cui X, Chen HF. Excited-Ground-State Transition of the RNA Strand Slippage Mechanism Captured by the Base-Specific Force Field. J Chem Theory Comput 2024; 20:6082-6097. [PMID: 38980289 DOI: 10.1021/acs.jctc.4c00497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Excited-ground-state transition and strand slippage of RNA play key roles in transcription and translation of central dogma. Due to limitation of current experimental techniques, the dynamic structure ensembles of RNA remain inadequately understood. Molecular dynamics simulations offer a promising complementary approach, whose accuracy depends on the force field. Here, we develop the new version of RNA base-specific force field (BSFF2) to address underestimation of base pairing stability and artificial backbone conformations. Extensive evaluations on typical RNA systems have comprehensively confirmed the accuracy of BSFF2. Furthermore, BSFF2 demonstrates exceptional efficiency in de novo folding of tetraloops and reproducing base pair reshuffling transition between RNA excited and ground states. Then, we explored the RNA strand slippage mechanism with BSFF2. We conducted a comprehensive three-dimensional structural investigation into the strand slippage of the most complex r(G4C2)9 repeat element and presented the molecular details in the dynamic transition along with the underlying mechanism. Our results of capturing the strand slippage, excited-ground transition, de novo folding, and simulations for various typical RNA motifs indicate that BSFF2 should be one of valuable tools for dynamic conformation research and structure prediction of RNA, and a future contribution to RNA-targeted drug design as well as RNA therapy development.
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Affiliation(s)
- Zhengxin Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ge Song
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junjie Zhu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junxi Mu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yutong Sun
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaokun Hong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, Fujian 350116, China
| | - Taeyoung Choi
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaochen Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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Hsu WT, Piomponi V, Merz PT, Bussi G, Shirts MR. Alchemical Metadynamics: Adding Alchemical Variables to Metadynamics to Enhance Sampling in Free Energy Calculations. J Chem Theory Comput 2023; 19:1805-1817. [PMID: 36853624 DOI: 10.1021/acs.jctc.2c01258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Performing alchemical transformations, in which one molecular system is nonphysically changed to another system, is a popular approach adopted in performing free energy calculations associated with various biophysical processes, such as protein-ligand binding or the transfer of a molecule between environments. While the sampling of alchemical intermediate states in either parallel (e.g., Hamiltonian replica exchange) or serial manner (e.g., expanded ensemble) can bridge the high-probability regions in the configurational space between two end states of interest, alchemical methods can fail in scenarios where the most important slow degrees of freedom in the configurational space are, in large part, orthogonal to the alchemical variable, or if the system gets trapped in a deep basin extending in both the configurational and alchemical space. To alleviate these issues, we propose to use alchemical variables as an additional dimension in metadynamics, making it possible to both sample collective variables and to enhance sampling in free energy calculations simultaneously. In this study, we validate our implementation of "alchemical metadynamics" in PLUMED with test systems and alchemical processes with varying complexities and dimensionalities of collective variable space, including the interconversion between the torsional metastable states of a toy system and the methylation of a nucleoside both in the isolated form and in a duplex. We show that multidimensional alchemical metadynamics can address the challenges mentioned above and further accelerate sampling by introducing configurational collective variables. The method can trivially be combined with other metadynamics-based algorithms implemented in PLUMED. The necessary PLUMED code changes have already been released for general use in PLUMED 2.8.
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Affiliation(s)
- Wei-Tse Hsu
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, Colorado 80305, United States
| | - Valerio Piomponi
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy
| | - Pascal T Merz
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, Colorado 80305, United States
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, Colorado 80305, United States
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Bernetti M, Bussi G. Integrating experimental data with molecular simulations to investigate RNA structural dynamics. Curr Opin Struct Biol 2023; 78:102503. [PMID: 36463773 DOI: 10.1016/j.sbi.2022.102503] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 12/05/2022]
Abstract
Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule Förster resonance energy transfer, or even thermal or mechanical denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field.
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Affiliation(s)
- Mattia Bernetti
- Computational and Chemical Biology, Italian Institute of Technology, 16152 Genova, Italy; Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, 40126 Bologna, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136, Trieste, Italy.
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