期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 18, 期 2, 页码 1213-1226出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.1c00811
关键词
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资金
- German Science Foundation (DFG) [GRK 2450]
- state of BadenWurttemberg through bwHPC
- DFG [INST 40/467-1 FUGG]
- DOE Office of Science through the National Virtual Biotechnology Laboratory
- DFTB
- Coronavirus CARES Act
This paper investigates the generation of free energy surfaces in complex reactions using the semiempirical method DFTB and improves its accuracy by developing a specific reaction parametrization (SRP). Through the implementation of an artificial neural network (ANN), the authors successfully generate highly accurate free energy surfaces for thiol-disulfide exchange in two molecular complexes.
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.
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