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Zhao Y, Zhang J, Zhang H, Gu S, Deng Y, Tu Y, Hou T, Kang Y. Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems. J Phys Chem Lett 2023; 14:1103-1112. [PMID: 36700836 DOI: 10.1021/acs.jpclett.2c03688] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Gaussian accelerated molecular dynamics (GaMD) is recognized as a popular enhanced sampling method for tackling long-standing challenges in biomolecular simulations. Inspired by GaMD, Sigmoid accelerated molecular dynamics (SaMD) is proposed in this work by adding a Sigmoid boost potential to improve the balance between the highest acceleration and accurate reweighting. Compared with GaMD, SaMD extends the accessible time scale and improves the computational efficiency as tested in three tasks. In the alanine dipeptide task, SaMD can produce the free energy landscape with better accuracy and efficiency. In the chignolin folding task, the estimated Gibbs free energy difference can converge to the experimental value ∼30% faster. In the protein-ligand binding task, the bound conformations are closer to the crystal structure with a minimal ligand root-mean-square deviation of 1.7 Å. The binding of the ligand XK263 to the HIV protease is reproduced by SaMD in ∼60% less simulation time.
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Affiliation(s)
- Yihao Zhao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
| | - Jintu Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
- CarbonSilicon AI Technology Company, Ltd., Hangzhou310018, Zhejiang, China
| | - Haotian Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
- CarbonSilicon AI Technology Company, Ltd., Hangzhou310018, Zhejiang, China
| | - Shukai Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Company, Ltd., Hangzhou310018, Zhejiang, China
| | - Yaoquan Tu
- Division of Theoretical Chemistry and Biology, Department of Chemistry, KTH Royal Institute of Technology, 114 28Stockholm, Sweden
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou310058, Zhejiang, China
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Empereur-Mot C, Capelli R, Perrone M, Caruso C, Doni G, Pavan GM. Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG. J Chem Phys 2022; 156:024801. [PMID: 35032979 DOI: 10.1063/5.0079044] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. However, the parameters obtained via such automatic methods often make use of specifically designed interaction potentials and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here, we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here, we use lipids for which we can avail reference experimental data (area per lipid and bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights into the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs and to improve the existing ones.
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Affiliation(s)
- Charly Empereur-Mot
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Riccardo Capelli
- Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Mattia Perrone
- Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Cristina Caruso
- Politecnico di Torino, Department of Applied Science and Technology, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Giovanni Doni
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
| | - Giovanni M Pavan
- Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland
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