# Decoding Protein–Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations

## TL;DR

This paper introduces MaSIF-PMP, a deep learning model that predicts where proteins bind to cell membranes, improving accuracy and interpretability with molecular dynamics simulations.

## Contribution

MaSIF-PMP is a novel geometric deep learning model that outperforms existing methods in predicting protein–membrane binding sites.

## Key findings

- MaSIF-PMP achieves superior performance in predicting interfacial binding sites of peripheral membrane proteins.
- Molecular dynamics simulations validate model predictions and refine binding site labels.
- The model reveals distinct determinants for protein–membrane versus protein–protein interactions.

## Abstract

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 the
model accuracy and biological interpretability.