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

Density-functional tight-binding Molecular dynamics simulations Near-linear scaling methods Neoantigen therapy

Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2020
Historique:
entrez: 5 2 2020
pubmed: 6 2 2020
medline: 27 1 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32016892
doi: 10.1007/978-1-0716-0282-9_10
doi:

Substances chimiques

Peptides 0
Pharmaceutical Preparations 0
Proteins 0

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S. Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

149-161

Auteurs

Stephan Irle (S)

Computational Sciences and Engineering Division & Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. irles@ornl.gov.
Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, USA. irles@ornl.gov.

Van Q Vuong (VQ)

Computational Sciences and Engineering Division & Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, USA.

Mouhmad H Elayyan (MH)

Department of Chemical Engineering, Tennessee Tech University, Cookeville, TN, USA.

Marat R Talipov (MR)

Department of Chemistry & Biochemistry, New Mexico State University, Las Cruces, NM, USA.

Steven M Abel (SM)

Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN, USA.

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Classifications MeSH