Data and code from: Decoding protein–membrane binding interfaces from surface-fingerprint-based geometric deep learning and molecular dynamics simulations

Predicting protein–membrane interactions is a formidable challenge due to
the subtle physicochemical features that distinguish membrane-binding
regions of a protein surface, as well as the scarcity of experimentally
resolved membrane-bound protein conformations. Here, we present MaSIF-PMP,
a geometric deep learning model that leverages molecular surface
fingerprints to predict interfacial binding sites (IBSs) of peripheral
membrane proteins (PMPs). MaSIF-PMP integrates geometric and chemical
surface features to produce spatially resolved IBS predictions. Compared
to existing models, MaSIF-PMP achieves superior performance for IBS
classification, while feature ablation studies and transfer learning
analyses reveal distinct determinants governing protein–membrane versus
protein–protein interactions. We further show that molecular dynamics (MD)
simulations can validate model predictions, refine IBS labels, and capture
composition-dependent membrane binding patterns. These results establish
MaSIF-PMP as an effective framework for IBS prediction and highlight the
potential of incorporating conformational dynamics from MD to improve both
model accuracy and biological interpretability.