ArticleBiophysics and physicobiology2019
Building a macro-mixing dual-basin Gō model using the Multistate Bennett Acceptance Ratio.
Article in Biophysics and physicobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models.The journal of physical chemistry. B · 2024Article
- Use of multistate Bennett acceptance ratio method for free-energy calculations from enhanced sampling and free-energy perturbation.Biophysical reviews · 2022Review
- Unraveling the Coupling between Conformational Changes and Ligand Binding in Ribose Binding Protein Using Multiscale Molecular Dynamics and Free-Energy Calculations.The journal of physical chemistry. B · 2021Article
- Exploring Large Domain Motions in Proteins Using Atomistic Molecular Dynamics with Enhanced Conformational Sampling.International journal of molecular sciences · 2020Article
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Authors and funding
4 authors.
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Abstract
The dual-basin Gō-model is a structural-based coarsegrained model for simulating a conformational transition between two known structures of a protein. Two parameters are required to produce a dual-basin potential mixed using two single-basin potentials, although the determination of mixing parameters is usually not straightforward. Here, we have developed an efficient scheme to determine the mixing parameters using the Multistate Bennett Acceptance Ratio (MBAR) method after short simulations with a set of parameters. In the scheme, MBAR allows us to predict observables at various unsimulated conditions, which are useful to improve the mixing parameters in the next round of iterative simulations. The number of iterations that are necessary for obtaining the converged mixing parameters are significantly reduced in the scheme. We applied the scheme to two proteins, the glutamine binding protein and the ribose binding protein, for showing the effectiveness in the parameter determination. After obtaining the converged parameters, both proteins show frequent conformational transitions between open and closed states, providing the theoretical basis to investigate structure-dynamics-function relationships of the proteins.
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