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BAR-Based Multi-Dimensional Nonequilibrium Pulling for Indirect Construction of QM/MM Free Energy Landscape

By Xiaohui Wang (5949518), Qiaole He (5954726) and Zhaoxi Sun (5949509)


<p>Construction of free energy landscapes at Quantum mechanics (QM) level is computationally demanding. As shown in previous studies, by employing an indirect scheme (i.e. constructing a thermodynamic cycle connecting QM states via an alchemical pathway), simulations are converged with much less computational burden. The indirect scheme makes QM/ molecular mechanics (MM) free energy simulation orders of magnitude faster than the direct QM/MM schemes. However, the indirect QM/MM simulations were mostly equilibrium sampling based and the nonequilibrium methods were merely exploited in one-dimensional alchemical QM/MM end-state correction at two end states. In this work, we represent a multi-dimensional nonequilibrium pulling scheme for indirect QM/MM free energy simulations, where the whole free energy simulation is performed only with nonequilibrium methods. The collective variable (CV) space we explore is a combination of one alchemical CV and one physically meaningful CV. The current nonequilibrium indirect QM/MM simulation method can be seen as the generalization of equilibrium perturbation based indirect QM/MM methods. The test systems include one backbone dihedral case and one distance case. The two cases are significantly different in size, enabling us to investigate the dependence of the speedup of the indirect scheme on the size of the system. It is shown that the speedup becomes larger when the size of the system becomes larger, which is consistent with the scaling behavior of QM Hamiltonians. </p

Topics: Free Energy, Indirect QM/MM Free Energy Simulation, Enhanced Sampling Simulations, Nonequilibrium, Thermodynamics, Steered Molecular Dynamics, Bennett Acceptance Ratio, Enhanced Sampling Technique, Bidirectional Reweighting, Multi-dimensional Enhanced Sampling Method, Convergence Criterion, Deca-alanine, Free Energy Landscape
Year: 2019
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Provided by: FigShare
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