Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders

Proteins can specifically bind to curved membranes through curvature-induced hydrophobic lipid packing defects. The chemical diversity among such curvature ‘sensors’ challenges our understanding of how they differ from general membrane ‘binders’, that bind without curvature selectivity. Here, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations (Evo-MD) to – for the first time – resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and neural networks (NN) can enhance the identification and discovery of curvature sensing peptides and proteins. To this aim, we benchmark a physics-trained NN model against experimental data and show that we can correctly identify known ‘sensors’ and ‘binders’. We illustrate that sensing and binding are in fact phenomena that lie on the same thermodynamic contin-uum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors. Teaser AI-based design helps explain curvature-selective membrane binding behavior.