Pré-Publication, Document De Travail Année : 2024

GōMartini 3: From large conformational changes in proteins to environmental bias corrections

Luís Borges-Araújo
Chris 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
  • Fonction : Auteur
  • PersonId : 913058
Sebastian Thallmair

Résumé

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 new 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.

Fichier principal
Vignette du fichier
2024.04.15.589479v1.full.pdf (2.66 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04745078 , version 1 (20-10-2024)

Licence

Identifiants

Citer

Paulo C T Souza, Luís Borges-Araújo, Chris Brasnett, Rodrigo A Moreira, Fabian Grünewald, et al.. GōMartini 3: From large conformational changes in proteins to environmental bias corrections. 2024. ⟨hal-04745078⟩
73 Consultations
146 Téléchargements

Altmetric

Partager

  • More