@article{Hedger:2026:10.1016/j.sbi.2026.103226,
author = {Hedger, G and Lyman, E and Rouse, SL},
doi = {10.1016/j.sbi.2026.103226},
journal = {Current Opinion in Structural Biology},
title = {Ligand-like lipid interactions with membrane proteins: simulations and machine learning},
url = {http://dx.doi.org/10.1016/j.sbi.2026.103226},
volume = {97},
year = {2026}
}

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TY  - JOUR
AB  - Membrane lipids can bind to specific sites on membrane proteins in a ligand-like manner and modulate protein structure and function. Molecular dynamics simulations encompass a suite of approaches to identify, characterise, and explain the atomic-level mechanisms that underlie the functional effects of ligand-like lipids on membrane proteins. Simulations have shown good agreement with available structural data on lipid-protein interactions. Building on successes, simulations are now used to identify new interactions and mechanisms de novo for a given membrane protein. In this age of abundance, it is increasingly possible to analyse patterns across large groups of proteins and in ever more complex membrane environments. The dawn of machine learning approaches in lipid-protein cofolding holds considerable promise to synergistically capitalise on this availability of simulation data and uncover new facets of ligand-like lipid biology.
AU  - Hedger,G
AU  - Lyman,E
AU  - Rouse,SL
DO  - 10.1016/j.sbi.2026.103226
PY  - 2026///
SN  - 0959-440X
TI  - Ligand-like lipid interactions with membrane proteins: simulations and machine learning
T2  - Current Opinion in Structural Biology
UR  - http://dx.doi.org/10.1016/j.sbi.2026.103226
UR  - https://doi.org/10.1016/j.sbi.2026.103226
VL  - 97
ER  - 

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