Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins. (February 2022) - Vennelakanti, Vyshnavi; Nazemi, Azadeh; Mehmood, Rimsha; Steeves, Adam H.; Kulik, Heather J.
Abstract: Computational prediction of enzyme mechanism and protein function requires accurate physics-based models and suitable sampling. We discuss recent advances in large-scale quantum mechanical (QM) modeling of biochemical systems that have reduced the cost of high-accuracy models. Tradeoffs between sampling and accuracy have motivated modeling with molecular mechanics (MM) in a multiscale QM/MM or iterative approach. Limitations to both conventional density-functional theory and classical MM force fields remain for describing noncovalent interactions in comparison to experiment or wavefunction theory. Because predictions of enzyme action (i.e. electrostatics), free energy barriers, and mechanisms are sensitive to the protocol and embedding method in QM/MM, convergence tests and systematic methods for quantifying QM-level interactions are a needed, active area of development. Highlights: Tradeoffs in sampling, system size, and method accuracy must be evaluated. Small QM regions suitable for sampling are insufficient for property determination. Slow property convergence with radial QM region in QM/MM are improved by systematic methods that detect essential residues. Free energy simulations are costly but highlight the role of dynamic electronic structure fluctuations in enzyme action.