Souza Paulo C T, Borges-Araújo Luís, Brasnett Christopher, Moreira Rodrigo A, Grünewald Fabian, Park Peter, Wang Liguo, Razmazma Hafez, Borges-Araújo Ana C, Cofas-Vargas Luis Fernando, Monticelli Luca, Mera-Adasme Raúl, Melo Manuel N, Wu Sangwook, Marrink Siewert J, Poma Adolfo B, Thallmair Sebastian
Laboratoire de Biologie et Modélisation de la Cellule, CNRS, UMR 5239, Inserm, U1293, Université Claude Bernard Lyon 1, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon, France.
Centre Blaise Pascal de Simulation et de Modélisation Numérique, Ecole Normale Supérieure de Lyon, 46 Allée d'Italie, Lyon, France.
Nat Commun. 2025 Apr 30;16(1):4051. doi: 10.1038/s41467-025-58719-0.
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.
粗粒度模型已成为补充实验测量的重要工具,能够研究基于全原子方法无法触及的时空尺度。GōMartini模型结合了基于结构和物理的粗粒度方法,在计算效率与蛋白质动力学的准确表征之间取得平衡,同时具备在不同生物环境中研究蛋白质的能力。本文介绍了一种增强型GōMartini模型,它将Gō模型的虚拟位点实现与Martini 3相结合。自Martini重新参数化版本发布以来,该实现已在学界得到广泛测试。这项工作展示了该模型在各种案例研究中的能力,涵盖从蛋白质-膜结合到蛋白质-配体相互作用以及原子力显微镜力谱计算等方面。该模型还具有通用性,因为它可以解决Martini蛋白质模型中最近报道的不准确问题。最后,本文讨论了Martini 3蛋白质模型及其与Gō模型结合的优点、局限性和未来前景。