Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology.
Claudia L Gómez-FloresDenis MaagMayukh KansariVan Quan VuongStephan IrleFrauke GräterTomáš KubařMarcus ElstnerPublished in: Journal of chemical theory and computation (2022)
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.
Keyphrases
- neural network
- low cost
- high efficiency
- density functional theory
- high resolution
- healthcare
- molecular docking
- primary care
- blood brain barrier
- escherichia coli
- binding protein
- single molecule
- quality improvement
- mass spectrometry
- staphylococcus aureus
- transcription factor
- molecular dynamics simulations
- candida albicans
- crystal structure