Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology

Semiempirical methods like density
functional tight-binding (DFTB)
allow extensive phase space sampling, making it possible to generate
free energy surfaces of complex reactions in condensed-phase environments.
Such a high efficiency often comes at the cost of reduced accuracy,
which may be improved by developing a specific reaction parametrization
(SRP) for the particular molecular system. Thiol–disulfide
exchange is a nucleophilic substitution reaction that occurs in a
large class of proteins. Its proper description requires a high-level
ab initio method, while DFT-GAA and hybrid functionals were shown
to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop
an SRP for thiol–disulfide exchange based on an artificial
neural network (ANN) implementation in the DFTB+ software and compare
its performance to that of a standard SRP approach applied to DFTB.
As an application, we use both new DFTB-SRP as components of a QM/MM
scheme to investigate thiol–disulfide exchange in two molecular
complexes: a solvated model system and a blood protein. Demonstrating
the strengths of the methodology, highly accurate free energy surfaces
are generated at a low cost, as the augmentation of DFTB with an ANN
only adds a small computational overhead.