4.4 Article

Simultaneous parametrization of torsional and third-neighbor interaction terms in force-field development: The LLS-SC algorithm

期刊

JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 43, 期 9, 页码 644-653

出版社

WILEY
DOI: 10.1002/jcc.26819

关键词

algorithms; energy; force field; molecular mechanics; optimization

资金

  1. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
  2. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior [001]
  3. Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro
  4. Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung [200021-175944]

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This article presents a new algorithm named LLS-SC for the simultaneous optimization of torsional and third-neighbor interaction parameters. The algorithm relies on fitting relative conformational energies against quantum-mechanical values and utilizes a self-consistent procedure involving linear least-squares regression and geometry optimization.
The calibration of torsional interaction terms by fitting relative gas-phase conformational energies against their quantum-mechanical values is a common procedure in force-field development. However, much less attention has been paid to the optimization of third-neighbor nonbonded interaction parameters, despite their strong coupling with the torsions. This article introduces an algorithm termed LLS-SC, aimed at simultaneously parametrizing torsional and third-neighbor interaction terms based on relative conformational energies. It relies on a self-consistent (SC) procedure where each iteration involves a linear least-squares (LLS) regression followed by a geometry optimization of the reference structures. As a proof-of-principle, this method is applied to obtain torsional and third-neighbor interaction parameters for aliphatic chains in the context of the GROMOS 53A6 united-atom force field. The optimized parameter set is compared to the original one, which has been fitted manually against thermodynamic properties for small linear alkanes. The LLS-SC implementation is freely available under .

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