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Two decades of Martini: Better beads, broader scope

‪Siewert J. MarrinkGroningen Biomolecular Sciences and Biotechnology Institute & Zernike Institute for Advanced Materials University of Groningen Groningen The NetherlandsLuca MonticelliMolecular Microbiology and Structural Biochemistry (MMSB ‐ UMR 5086) CNRS & University of Lyon Lyon FranceManuel N. MeloInstituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa Oeiras PortugalRiccardo AlessandriPritzker School of Molecular Engineering University of Chicago Chicago Illinois USAD. Peter TielemanCentre for Molecular Simulation and Department of Biological Sciences University of Calgary Alberta CanadaPaulo C. T. SouzaMolecular Microbiology and Structural Biochemistry (MMSB ‐ UMR 5086) CNRS & University of Lyon Lyon France
2022en
ABI

Аннотация

Abstract The Martini model, a coarse‐grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipid‐based systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general‐purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building‐block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse‐grained resolution is essential, namely high‐throughput applications, systems with large complexity, and simulations approaching the scale of whole cells. This article is categorized under: Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Computational Biochemistry and Biophysics

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