Authors: Hilten, Niek van
Methorst, Jeroen
Verwei, Nino
Risselada, Herre Jelger
Title: Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders
Language (ISO): en
Abstract: 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 resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and a neural network (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 phenomena that lie on the same thermodynamic continuum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors.
Subject Headings (RSWK): Membranproteine
Biomembran
Molekulardynamik
Molekulare Biophysik
Simulation
Neuronales Netz
URI: http://hdl.handle.net/2003/41365
http://dx.doi.org/10.17877/DE290R-23208
Issue Date: 2023-03-17
Rights link: https://creativecommons.org/licenses/by/4.0/
Appears in Collections:Fakultät für Physik

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