| Autor: |
Claudia L. Gómez-Flores, Denis Maag, Mayukh Kansari, Van-Quan Vuong, Stephan Irle, Frauke Gräter, Tomáš Kubař, Marcus Elstner |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Journal of chemical theory and computation. 18(2) |
| ISSN: |
1549-9626 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
