MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes

TY - JOUR

T1 - MLLPA

T2 - A Machine Learning-assisted Python module to study phase-specific events in lipid membranes

AU - Walter, Vivien

AU - Ruscher, Céline

AU - Benzerara, Olivier

AU - Thalmann, Fabrice

N1 - Funding Information: Vivien Walter warmly thanks Adrien Gola for helping with Ovito set‐up and conversion scripts. The authors gratefully acknowledge support from the high performance cluster (HPC) Equip@Meso from the Université de Strasbourg, through grant no. G2019A131C, and use of the research computing facility at King's College London, Rosalind ( https://rosalind.kcl.ac.uk ). Publisher Copyright: © 2021 Wiley Periodicals LLC. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5/15

Y1 - 2021/5/15

N2 - Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all-atom (e.g., CHARMM36) or coarse-grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.

AB - Machine Learning-assisted Lipid Phase Analysis (MLLPA) is a new Python 3 module developed to analyze phase domains in a lipid membrane based on lipid molecular states. Reading standard simulation coordinate and trajectory files, the software first analyze the phase composition of the lipid membrane by using machine learning tools to label each individual molecules with respect to their state, and then decompose the simulation box using Voronoi tessellations to analyze the local environment of all the molecules of interest. MLLPA is versatile as it can read from multiple format (e.g., GROMACS, LAMMPS) and from either all-atom (e.g., CHARMM36) or coarse-grain models (e.g., Martini). It can also analyze multiple geometries of membranes (e.g., bilayers, vesicles). Finally, the software allows for training with more than two phases, allowing for multiple phase coexistence analysis.

KW - lipid membrane analysis

KW - machine learning

KW - molecular dynamics

KW - phase transition

KW - tessellation

UR - http://www.scopus.com/inward/record.url?scp=85102183531&partnerID=8YFLogxK

U2 - 10.1002/jcc.26508

DO - 10.1002/jcc.26508

M3 - Article

C2 - 33675541

AN - SCOPUS:85102183531

SN - 0192-8651

VL - 42

SP - 930

EP - 943

JO - JOURNAL OF COMPUTATIONAL CHEMISTRY

JF - JOURNAL OF COMPUTATIONAL CHEMISTRY

IS - 13

ER -