Machine Learning-Assisted Phase Transition Temperatures from Generalized Replica Exchange Simulations of Dry Martini Lipid Bilayers

Abstract

Accurate estimation of phase transition temperatures has been a longstanding challenge for molecular simulations. Recently, the generalized Replica Exchange technique for estimating phase transition temperatures has allowed for improved sampling of the phase transition; however, it requires a significant number of simultaneous replicas both inside and outside of the transition region leading to costly computational expense. In this work, the recently developed machine learning-assisted lipid phase analysis technique for learning the phase of individual lipids has been combined with generalized Replica Exchange Molecular Dynamics to reduce the overall computational expense of evaluating transition temperatures. This technique is then applied to eight different Dry Martini lipids to demonstrate its ability to describe transition temperatures as a function of chain length and tail saturation.

Thermal-controlled cellular uptake of “hot” nanoparticles

“…4a), suggesting the significant disturbance of “hot” NPs on the lipid arrangement. Taking into account the gel-to-fluid transition temperature of DPPC, which is about 335 K for the dry Martini model, 57 phase transition of the membrane occurred during the NP–membrane interactions in this condition, which is supposed to be responsible for the decreased S cd of lipids. Consequently, the membrane becomes more flexible, which is helpful for the membrane deformation and ultimate wrapping of NPs.…”

Machine Learning in Soft Matter: From Simulations to Experiments

“…This tool has been used to model the transition process from coils to globule in polymer systems. Walter and Piskulich [231,232] successfully developed an algorithm to classify individual lipid configurations and integrated it with generalized replica exchange MD to reduce the overall computational cost of evaluating transition temperatures.…”

Coacervation-Induced Remodeling of Nanovesicles

“…Interestingly, here we also discover an enhanced local gel-like ordering of the lipid tails in the vicinity of the polymer coatings where the other parts show liquid-like disordered tails (Figure b,d). This happens to increase the polymer–lipid contact as gelation reduces the area per lipid . Therefore, a larger number of lipids can be packed in the vicinity of the polymers, which provides enthalpic stabilization at the cost of entropy loss.…”