Accelerated Computation of Free Energy Profile at ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semi-Empirical Reference Potential. I. Weighted Thermodynamics Perturbation

TL;DR

This paper introduces a new MBAR+wTP method that significantly accelerates the computation of ab initio free energy profiles by combining semi-empirical calculations with thermodynamic perturbation corrections, improving efficiency for reaction mechanism studies.

Contribution

The paper presents a novel MBAR+wTP approach that efficiently computes ab initio free energy profiles using semi-empirical data and thermodynamic perturbation, reducing computational cost substantially.

Findings

Method enhances efficiency of free energy profile calculations by several orders of magnitude.

Weighted thermodynamic perturbation improves accuracy of semi-empirical to ab initio corrections.

Approach validated on chemical and enzymatic reactions, showing promising results.

Abstract

Free energy profile (FE Profile) is an essential quantity for the estimation of reaction rate and the validation of reaction mechanism. For chemical reactions in condensed phase or enzymatic reactions, the computation of FE profile at ab initio (ai) quantum mechanical/molecular mechanics (QM/MM) level is still far too expensive. Semiempirical (SE) method can be hundreds or thousands of times faster than the ai methods. However, the accuracy of SE methods is often unsatisfactory, due to the approximations that have been adopted in these methods. In this work, we proposed a new method termed MBAR+wTP, in which the ai QM/MM free energy profile is computed by a weighted thermodynamic perturbation (TP) correction to the SE profile generated by the multistate Bennett acceptance ratio (MBAR) analysis of the trajectories from umbrella samplings (US). The weight factors used in the TP calculations are a byproduct of the MBAR analysis in the post-processing of the US trajectories, which are often discarded after the free energy calculations. The results show that this approach can enhance the efficiency of ai FE profile calculations by several orders of magnitude.