Two decades of Martini: Better beads, broader scope

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-06-24 DOI:10.1002/wcms.1620
Siewert J. Marrink, Luca Monticelli, Manuel N. Melo, Riccardo Alessandri, D. Peter Tieleman, Paulo C. T. Souza
{"title":"Two decades of Martini: Better beads, broader scope","authors":"Siewert J. Marrink,&nbsp;Luca Monticelli,&nbsp;Manuel N. Melo,&nbsp;Riccardo Alessandri,&nbsp;D. Peter Tieleman,&nbsp;Paulo C. T. Souza","doi":"10.1002/wcms.1620","DOIUrl":null,"url":null,"abstract":"<p>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.</p><p>This article is categorized under:\n </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 1","pages":""},"PeriodicalIF":16.8000,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1620","citationCount":"45","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews: Computational Molecular Science","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/wcms.1620","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 45

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:

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
二十年的马提尼:更好的珠子,更广阔的范围
马提尼模型是一种用于分子动力学模拟的粗粒度力场,已经存在了近20年。Martini模型最初是由Marrink和Tieleman团队为基于脂类的系统开发的,多年来作为一个社区的努力,Martini模型已经扩展到当前通用力场的水平。除了减少计算成本的明显好处之外,该模型的流行很大程度上是由于作为模型基础的系统而直观的构建块方法,以及开发的开放性及其持续验证。在广泛使用的Gromacs软件套件中轻松实现也很有帮助。自2002年提出以来,马提尼模型经历了头部相互作用的逐步完善和应用范围的扩大。在这篇回顾中,我们回顾了这一发展,最终在2021年发布了马提尼3版本。该模型的强大功能通过Martini在生物和材料科学中最近的重要发现的关键例子来说明,以及来自粗粒度分辨率至关重要的领域的例子,即高通量应用,具有大复杂性的系统,以及接近整个细胞规模的模拟。本文分类如下:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
期刊最新文献
Issue Information Embedded Many-Body Green's Function Methods for Electronic Excitations in Complex Molecular Systems ROBERT: Bridging the Gap Between Machine Learning and Chemistry Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy Computational design of energy-related materials: From first-principles calculations to machine learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1