Machine Learning approach to the discrimination of phospholipid gel and fluid states in lipid bilayers.

The two-states model of the phospholipid gel-fluid transition was introduced in the late seventies, and is still routinely used to interpret calorimetric data in the field of lipid membranes [1]. Rather than relying on traditional order parameters (segment orientation, membrane thickness) we propose to use Machine Learning classifiers to assign either a gel, or a fluid state, to any lipid molecular conformation arising from molecular dynamics simulations [2]. Using respectively the high and low temperature simulated phases as training sets, we investigate the behavior of the lipid assembly at intermediate temperatures, in the binary mixture cases and in the presence of various external compounds [4].


[1] S.J Doniach, J. Chem. Phys., 1978, 68, p4912
[2] O. Mouritsen, Chemistry and Physics of Lipids, 1991, 57, p179
[3] V. Walter, C. Ruscher, O. Benzerara, C.M. Marques and F. Thalmann, Physical Chemistry and Chemical Physics, 2020,22, p19147, http://doi.org/10.1039/d0cp02058c
[4] V. Walter, C. Ruscher, O. Benzerara and F. Thalmann, MLLPA: A Machine Learning-assisted Python module to study phase-specific events in lipid membranes, in preparation.