Abstract
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.
| Original language | English |
|---|---|
| Article number | 4051 |
| Journal | Nature Communications |
| Volume | 16 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 30 Apr 2025 |
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In: Nature Communications, Vol. 16, No. 1, 4051, 30.04.2025.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - GōMartini 3
T2 - From large conformational changes in proteins to environmental bias corrections
AU - Souza, Paulo C.T.
AU - Borges-Araújo, Luís
AU - Brasnett, Christopher
AU - Moreira, Rodrigo A.
AU - Grünewald, Fabian
AU - Park, Peter
AU - Wang, Liguo
AU - Razmazma, Hafez
AU - Borges-Araújo, Ana C.
AU - Cofas-Vargas, Luis Fernando
AU - Monticelli, Luca
AU - Mera-Adasme, Raúl
AU - Melo, Manuel N.
AU - Wu, Sangwook
AU - Marrink, Siewert J.
AU - Poma, Adolfo B.
AU - Thallmair, Sebastian
N1 - 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 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.
PY - 2025/4/30
Y1 - 2025/4/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=105004349692&partnerID=8YFLogxK
U2 - 10.1038/s41467-025-58719-0
DO - 10.1038/s41467-025-58719-0
M3 - Article
C2 - 40307210
AN - SCOPUS:105004349692
SN - 2041-1723
VL - 16
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 4051
ER -