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GōMartini 3

2025-04-30Artigo científico Acesso aberto
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dc.contributor.authorSouza, Paulo C.T.
dc.contributor.authorBorges-Araújo, Luís
dc.contributor.authorBrasnett, Christopher
dc.contributor.authorMoreira, Rodrigo A.
dc.contributor.authorGrünewald, Fabian
dc.contributor.authorPark, Peter
dc.contributor.authorWang, Liguo
dc.contributor.authorRazmazma, Hafez
dc.contributor.authorBorges-Araújo, Ana C.
dc.contributor.authorCofas-Vargas, Luis Fernando
dc.contributor.authorMonticelli, Luca
dc.contributor.authorMera-Adasme, Raúl
dc.contributor.authorMelo, Manuel N.
dc.contributor.authorWu, Sangwook
dc.contributor.authorMarrink, Siewert J.
dc.contributor.authorPoma, Adolfo B.
dc.contributor.authorThallmair, Sebastian
dc.contributor.institutionInstituto de Tecnologia Química e Biológica António Xavier (ITQB)
dc.contributor.pblNature Portfolio
dc.date.accessioned2025-10-07T21:50:51Z
dc.date.available2025-10-07T21:50:51Z
dc.date.issued2025-04-30
dc.descriptionFunding Information: This work was granted access to the HPC resources of IDRIS and TGCC under the allocations 2022-A0120713456 (P.C.T.S) and 2023-A0140713456 (P.C.T.S.) made by GENCI. We also acknowledge the support of the Center Blaise Pascal\u2019s IT test platform at ENS de Lyon (Lyon, France) for the computer facilities. The platform operates the SIDUS solution developed by Emmanuel Quemener123. We thank the Center for Information Technology of the University of Groningen for providing access to the Peregrine high-performance computing cluster. We also acknowledge the National Computing Facilities Foundation of The Netherlands Organization for Scientific Research (NWO) for providing computing time. L.B.A. and P.C.T.S. would like to thank the support of the French National Center for Scientific Research (CNRS) and the funding from research collaboration agreements with PharmCADD. S.J.M. received funding from the European Research Council (ERC) through an ERC Advanced grant \u201CCOMP-MICR-CROW-MEM\u201D. S.T. acknowledges the support from the European Commission via a Marie Sk\u0142odowska-Curie Actions individual fellowship (MicroMod-PSII, grant agreement 748895), the Center for Multiscale Modeling in Life Sciences (CMMS), the Alfons und Gertrud Kassel Foundation, and the Dr. Rolf M. Schwiete Foundation. L.M. acknowledges funding by the Institut National de la Sant\u00E9 et de la Recherche M\u00E9dicale (INSERM), CC-IN2P3 (https://cc.in2p3.fr), and the French supercomputing centers CINES and TGCC, supported by Grand Equipement National de Calcul Intensif (GENCI), for computing resources and services (grants number A0120710138, A0140710138). M.N.M. acknowledges Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia for fellowship CEECIND/04124/2017/CP1428/CT0008. A.B.P and R.A.M. acknowledge Marek Cieplak for sharing the source code of the OV + rCSU contact map in Fortran. P.P. acknowledges FAPESP support (grant 2019/26557-8). A.B.P. acknowledges financial support from the National Science Center, Poland, under grant 2022/45/B/NZ1/02519 and gratefully acknowledges Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2023/016519. R.M.-A. thanks, ANID-Chile, for financial support under FONDECYT N. 1200200. Funding Information: This work was granted access to the HPC resources of IDRIS and TGCC under the allocations 2022-A0120713456 (P.C.T.S) and 2023-A0140713456 (P.C.T.S.) made by GENCI. We also acknowledge the support of the Center Blaise Pascal\u2019s IT test platform at ENS de Lyon (Lyon, France) for the computer facilities. The platform operates the SIDUS solution developed by Emmanuel Quemener. We thank the Center for Information Technology of the University of Groningen for providing access to the Peregrine high-performance computing cluster. We also acknowledge the National Computing Facilities Foundation of The Netherlands Organization for Scientific Research (NWO) for providing computing time. L.B.A. and P.C.T.S. would like to thank the support of the French National Center for Scientific Research (CNRS) and the funding from research collaboration agreements with PharmCADD. S.J.M. received funding from the European Research Council (ERC) through an ERC Advanced grant \u201CCOMP-MICR-CROW-MEM\u201D. S.T. acknowledges the support from the European Commission via a Marie Sk\u0142odowska-Curie Actions individual fellowship (MicroMod-PSII, grant agreement 748895), the Center for Multiscale Modeling in Life Sciences (CMMS), the Alfons und Gertrud Kassel Foundation, and the Dr. Rolf M. Schwiete Foundation. L.M. acknowledges funding by the Institut National de la Sant\u00E9 et de la Recherche M\u00E9dicale (INSERM), CC-IN2P3 ( https://cc.in2p3.fr ), and the French supercomputing centers CINES and TGCC, supported by Grand Equipement National de Calcul Intensif (GENCI), for computing resources and services (grants number A0120710138, A0140710138). M.N.M. acknowledges Funda\u00E7\u00E3o para a Ci\u00EAncia e a Tecnologia for fellowship CEECIND/04124/2017/CP1428/CT0008. A.B.P and R.A.M. acknowledge Marek Cieplak for sharing the source code of the OV\u2009+\u2009rCSU contact map in Fortran. P.P. acknowledges FAPESP support (grant 2019/26557-8). A.B.P. acknowledges financial support from the National Science Center, Poland, under grant 2022/45/B/NZ1/02519 and gratefully acknowledges Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2023/016519. R.M.-A. thanks, ANID-Chile, for financial support under FONDECYT N. 1200200. Publisher Copyright: © The Author(s) 2025.
dc.description.abstractCoarse-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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent6030995
dc.identifier.doi10.1038/s41467-025-58719-0
dc.identifier.issn2041-1723
dc.identifier.otherPURE: 116872088
dc.identifier.otherPURE UUID: 6754eed0-39fd-42d4-957c-1d6f4710b39e
dc.identifier.otherScopus: 105004349692
dc.identifier.otherPubMed: 40307210
dc.identifier.urihttp://hdl.handle.net/10362/189101
dc.identifier.urlhttps://www.scopus.com/pages/publications/105004349692
dc.language.isoeng
dc.peerreviewedyes
dc.subjectGeneral Chemistry
dc.subjectGeneral Biochemistry,Genetics and Molecular Biology
dc.subjectGeneral Physics and Astronomy
dc.titleGōMartini 3en
dc.title.subtitleFrom large conformational changes in proteins to environmental bias correctionsen
dc.typejournal article
degois.publication.issue1
degois.publication.titleNature Communications
degois.publication.volume16
dspace.entity.typePublication
rcaap.rightsopenAccess

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