Automated fitting of transition state force fields for biomolecular simulations
The generation of surrogate potential energy functions (PEF) that are orders of magnitude faster to compute but as accurate as the underlying training data from high-level electronic structure methods is one of the most promising applications of fitting procedures in chemistry. In previous work, we...
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| Veröffentlicht in: | PloS one 2022-03, Vol.17 (3), p.e0264960-e0264960 |
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| creator | Quinn, Taylor R Patel, Himani N Koh, Kevin H Haines, Brandon E Norrby, Per-Ola Helquist, Paul Wiest, Olaf |
| description | The generation of surrogate potential energy functions (PEF) that are orders of magnitude faster to compute but as accurate as the underlying training data from high-level electronic structure methods is one of the most promising applications of fitting procedures in chemistry. In previous work, we have shown that transition state force fields (TSFFs), fitted to the functional form of MM3* force fields using the quantum guided molecular mechanics (Q2MM) method, provide an accurate description of transition states that can be used for stereoselectivity predictions of small molecule reactions. Here, we demonstrate the applicability of the method for fit TSFFs to the well-established Amber force field, which could be used for molecular dynamics studies of enzyme reaction. As a case study, the fitting of a TSFF to the second hydride transfer in Pseudomonas mevalonii 3-hydroxy-3-methylglutaryl coenzyme A reductase (PmHMGR) is used. The differences and similarities to fitting of small molecule TSFFs are discussed. |
| doi_str_mv | 10.1371/journal.pone.0264960 |
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The differences and similarities to fitting of small molecule TSFFs are discussed.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0264960</identifier><identifier>PMID: 35271647</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Automation ; Biochemistry ; Biology and Life Sciences ; Chemistry ; Coenzyme A ; Electronic structure ; Engineering and Technology ; Enzymes ; Hydroxymethylglutaryl-CoA reductase ; Mechanics ; Methods ; Molecular dynamics ; Molecular Dynamics Simulation ; Physical Sciences ; Potential energy ; R&D ; Reductases ; Research & development ; Simulation ; Stereoselectivity</subject><ispartof>PloS one, 2022-03, Vol.17 (3), p.e0264960-e0264960</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Quinn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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| subjects | Analysis Automation Biochemistry Biology and Life Sciences Chemistry Coenzyme A Electronic structure Engineering and Technology Enzymes Hydroxymethylglutaryl-CoA reductase Mechanics Methods Molecular dynamics Molecular Dynamics Simulation Physical Sciences Potential energy R&D Reductases Research & development Simulation Stereoselectivity |
| title | Automated fitting of transition state force fields for biomolecular simulations |
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