Rapid Prediction of Lipid Interaction Sites on Pleckstrin Homology Domains Using Deep Graph Neural Networks and Molecular Dynamics Simulations

Abstract

Interactions between membrane proteins and specific lipid molecules play a major role in cellular biology, but characterizing these interactions can be challenging due to the complexity and physicochemical properties of membranes. Molecular dynamics (MD) simulations allow researchers to predict protein-lipid interaction sites and generate testable models. MD simulations are however computationally expensive and require specialist expertise. In this study, we demonstrate that graph neural networks trained on coarse-grained MD simulation data can predict phosphoinositide lipid interaction sites on Pleckstrin Homology (PH) domain structures, a large family of membrane binding domains. The predictions are comparable to the results of simulations and require only seconds to compute. Comparison with experimental data shows that the model can predict known phosphoinositide interaction sites and can be used to form hypotheses for PH domains for which there is no experimental data. This model is a next generation tool for predicting protein-lipid interactions of PH domains and offers a basis for further development of models applicable to other membrane protein classes.