A machine learning study of the two states model for lipid bilayer phase transitions
| Autor: | Vivien Walter, Olivier Benzerara, Fabrice Thalmann, Céline Ruscher, Carlos M. Marques |
|---|---|
| Přispěvatelé: | Institut Charles Sadron (ICS), Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), King‘s College London, University of British Columbia (UBC), Marques, Carlos, Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Physics
Phase transition 010304 chemical physics business.industry General Physics and Astronomy 010402 general chemistry Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences [PHYS.COND.CM-SCM] Physics [physics]/Condensed Matter [cond-mat]/Soft Condensed Matter [cond-mat.soft] Set (abstract data type) Molecular dynamics Statistical classification Order (biology) 0103 physical sciences Membrane fluidity sort Artificial intelligence Physical and Theoretical Chemistry business Lipid bilayer computer [PHYS.COND.CM-SCM]Physics [physics]/Condensed Matter [cond-mat]/Soft Condensed Matter [cond-mat.soft] ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Physical Chemistry Chemical Physics Physical Chemistry Chemical Physics, Royal Society of Chemistry, 2020, 22 (34), pp.19147-19154. ⟨10.1039/D0CP02058C⟩ Physical Chemistry Chemical Physics, 2020, 22 (34), pp.19147-19154. ⟨10.1039/D0CP02058C⟩ |
| ISSN: | 1463-9076 1463-9084 |
| DOI: | 10.1039/D0CP02058C⟩ |
| Popis: | 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 categorise 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 study provide strong support in favour of a two-states model approach of membrane fluidity. |
| Databáze: | OpenAIRE |
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