PMIpred: A physics-informed web server for quantitative Protein-Membrane Interaction prediction

Abstract

AbstractMotivationMany membrane peripheral proteins have evolved to transiently interact with the surface of (curved) lipid bilayers. Currently, methods toquantitativelypredict sensing and binding free energies for protein sequences or structures are lacking, and such tools could greatly benefit the discovery of membrane-interacting motifs, as well as theirde novodesign.ResultsHere, we trained a transformer neural network model on molecular dynamics data for>50,000 peptides that is able to accurately predict the (relative) membrane-binding free energy for any given amino acid sequence. Using this information, our physics-informed model is able to classify a peptide’s membrane-associative activity as either non-binding, curvature sensing, or membrane binding. Moreover, this method can be applied to detect membraneinteraction regions in a wide variety of proteins, with comparable predictive performance as state-of-the-art data-driven tools like DREAMM, PPM3, and MODA, but with a wider applicability regarding protein diversity, and the added feature to distinguish curvature sensing from general membrane binding.AvailabilityWe made these tools available as a web server, coined Protein-Membrane Interaction predictor (PMIpred), which can be accessed athttps://pmipred.fkt.physik.tu-dortmund.de.