Paulo C. T. Souza, Luís Borges-Araújo, Christopher Brasnett, Rodrigo A. Moreira, Fabian Grünewald, Peter Park, Liguo Wang, Hafez Razmazma, Ana C. Borges-Araújo, Luis Fernando Cofas-Vargas, Luca Monticelli, Raúl Mera-Adasme, Manuel N. Melo, Sangwook Wu, Siewert J. Marrink, Adolfo B. Poma, Sebastian Thallmair
{"title":"GōMartini 3:从蛋白质的大构象变化到环境偏差修正","authors":"Paulo C. T. Souza, Luís Borges-Araújo, Christopher Brasnett, Rodrigo A. Moreira, Fabian Grünewald, Peter Park, Liguo Wang, Hafez Razmazma, Ana C. Borges-Araújo, Luis Fernando Cofas-Vargas, Luca Monticelli, Raúl Mera-Adasme, Manuel N. Melo, Sangwook Wu, Siewert J. Marrink, Adolfo B. Poma, Sebastian Thallmair","doi":"10.1038/s41467-025-58719-0","DOIUrl":null,"url":null,"abstract":"<p>Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"12 1","pages":""},"PeriodicalIF":15.7000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GōMartini 3: From large conformational changes in proteins to environmental bias corrections\",\"authors\":\"Paulo C. T. Souza, Luís Borges-Araújo, Christopher Brasnett, Rodrigo A. Moreira, Fabian Grünewald, Peter Park, Liguo Wang, Hafez Razmazma, Ana C. Borges-Araújo, Luis Fernando Cofas-Vargas, Luca Monticelli, Raúl Mera-Adasme, Manuel N. Melo, Sangwook Wu, Siewert J. Marrink, Adolfo B. Poma, Sebastian Thallmair\",\"doi\":\"10.1038/s41467-025-58719-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.</p>\",\"PeriodicalId\":19066,\"journal\":{\"name\":\"Nature Communications\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":15.7000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Communications\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41467-025-58719-0\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-58719-0","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
GōMartini 3: From large conformational changes in proteins to environmental bias corrections
Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.