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

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...

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Bibliographic Details
Published in:Journal of computational chemistry Vol. 42; no. 13; pp. 930 - 943
Main Authors: Walter, Vivien, Ruscher, Céline, Benzerara, Olivier, Thalmann, Fabrice
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 15.05.2021
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Subjects:
Condensed Matter
lipid membrane analysis
Lipids
Machine learning
Membranes
Modules
molecular dynamics
Phase composition
phase transition
Physics
Soft Condensed Matter
Software
tessellation
lipid membrane analysis
phase transition
tessellation
machine learning
molecular dynamics
Molecular dynamics
Tessellation
Lipid membrane analysis
Machine Learning
Phase transition
ISSN:0192-8651, 1096-987X, 1096-987X
Online Access:Get full text
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Summary: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. A new python software uses machine learning to characterize efficiently the individual thermodynamic states of lipids in membranes. It offers new routes for microscopic exploration of lipids mixtures and for the investigation of the interaction between lipids and external molecules or macromolecules.
Bibliography:Funding information
Université de Strasbourg, Grant/Award Number: G2019A131C
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ISSN:0192-8651
1096-987X
1096-987X
DOI:10.1002/jcc.26508