Karlsruhe Inst. of Technology (KIT) (Germany). Inst. of Physical Chemistry
Univ. of Tennessee, Knoxville, TN (United States). Bredesen Center for Interdisciplinary Research and Graduate Education
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); US Department of Energy (USDOE), Washington DC (United States). National Virtual Biotechnology Lab.
Heidelberg Institute for Theoretical Studies (Germany)
Karlsruhe Inst. of Technology (KIT) (Germany). Inst. of Physical Chemistry; Karlsruhe Inst. of Technology (KIT) (Germany). Inst. of Biological Interfaces
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. Furthermore, 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.
Gómez-Flores, Claudia L., et al. "Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology." Journal of Chemical Theory and Computation, vol. 18, no. 2, Jan. 2022. https://doi.org/10.1021/acs.jctc.1c00811
Gómez-Flores, Claudia L., Maag, Denis, Kansari, Mayukh, Vuong, Van-Quan, Irle, Stephan, Gräter, Frauke, Kubař, Tomáš, & Elstner, Marcus (2022). Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology. Journal of Chemical Theory and Computation, 18(2). https://doi.org/10.1021/acs.jctc.1c00811
Gómez-Flores, Claudia L., Maag, Denis, Kansari, Mayukh, et al., "Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology," Journal of Chemical Theory and Computation 18, no. 2 (2022), https://doi.org/10.1021/acs.jctc.1c00811
@article{osti_1863306,
author = {Gómez-Flores, Claudia L. and Maag, Denis and Kansari, Mayukh and Vuong, Van-Quan and Irle, Stephan and Gräter, Frauke and Kubař, Tomáš and Elstner, Marcus},
title = {Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology},
annote = {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. Furthermore, 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.},
doi = {10.1021/acs.jctc.1c00811},
url = {https://www.osti.gov/biblio/1863306},
journal = {Journal of Chemical Theory and Computation},
issn = {ISSN 1549-9618},
number = {2},
volume = {18},
place = {United States},
publisher = {American Chemical Society},
year = {2022},
month = {01}}
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 372, Issue 2011https://doi.org/10.1098/rsta.2012.0483