Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 2114; p. 149
• Main Authors: Irle, Stephan, Vuong, Van Q, Elayyan, Mouhmad H, Talipov, Marat R, Abel, Steven M
• Format: Journal Article
• Language: English
• Published: United States 01-01-2020
• Subjects: Drug Discovery - methods; Molecular Dynamics Simulation; Peptides - chemistry; Pharmaceutical Preparations - chemistry; Proteins - chemistry; Quantum Theory; Neoantigen therapy; Molecular dynamics simulations; Density-functional tight-binding; Near-linear scaling methods
• ISSN: 1940-6029
• DOI: 10.1007/978-1-0716-0282-9_10

Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.