Building a Macro-mixing Dual-basin Go Model using the Multistate Bennett Acceptance Ratio

Abstract

Molecular dynamics (MD) simulations of biomolecules are widely used to investigate conformational dynamics and structure-function relationship. All-atom (AA) models provide the most accurate description of the underlying dynamics. However at present, even with the fastest computers, the time-scale obtainable with an atomistic simulation is up to a millisecond, whereas biologically relevant motions occur at the time scale of milliseconds to seconds. To overcome this, coarse-grained (CG) modeling can be utilized. The use of CG models reduces the computational time by several orders of magnitude, allowing access to biologically relevant time-scales. In this work, we use the dual-basin Gō-model, which is a structural-based CG model, for simulating conformational transitions between two known structures of a protein. The dual-basin model is formed by mixing two single-basin potentials and includes system-dependent parameters which determine the barrier height and the energetic offsets between the two basins. The determination of parameters however, is usually not straightforward and can be time consuming. 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 is significantly reduced in the scheme. We applied the scheme to several proteins, for showing the effectiveness in parameter determination. After obtaining the converged parameters, the proteins show frequent conformational transitions between open and closed states, providing the theoretical basis to investigate structure-dynamics-function relationships of the proteins.