A machine learning assessment of the two states model for lipid bilayer phase transitions - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2020

A machine learning assessment of the two states model for lipid bilayer phase transitions

Viven Walter
  • Fonction : Auteur
Céline Ruscher
Olivier Benzerara
Fabrice Thalmann

Résumé

We have adapted a set of classification algorithms, also known as Machine Learning, to the identification of fluid and gel domains close to the main transition of dipalmitoyl-phosphatidylcholine (DPPC) bilayers. Using atomistic molecular dynamics conformations in the low and high temperature phases as learning sets, the algorithm was trained to categorize individual lipid configurations as fluid or gel, in relation with the usual two-states phenomenological description of the lipid melting transition. We demonstrate that our machine can learn and sort lipids according to their most likely state without prior assumption regarding the nature of the order parameter of the transition. Results from our machine learning approach provides strong support in favor of a two-states model approach of membrane fluidity.

Dates et versions

hal-03030189 , version 1 (29-11-2020)

Identifiants

Citer

Viven Walter, Céline Ruscher, Olivier Benzerara, Carlos M. Marques, Fabrice Thalmann. A machine learning assessment of the two states model for lipid bilayer phase transitions. 2020. ⟨hal-03030189⟩
21 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More