Predicting the dynamic interaction between intrinsically disordered proteins

Abstract

AbstractInteractions between intrinsically disordered proteins (IDPs) are highly dynamic, their interaction interfaces are diverse and affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-LSTM Network to capture the dynamic nature of IDP conformation and predict the behavior of IDP interaction. The training data for our method is obtained from atomistic molecular dynamics (MD) simulations. Our method models multiple conformations of IDP as a dynamic graph which can effectively describe the fluctuation of its flexible conformation. GSALIDP can effectively predict the interaction sites of IDP as well as the contact residue pairs between IDPs. Its performance to predict IDP interaction is on par with or even better than the conventional models to predict the interaction of structural proteins. To the best of our knowledge, this is the first machine-learning method to realize the prediction of interaction behavior of IDPs. Our framework can be exploited to study other IDP-mediated processes such as folding-upon-binding and fuzzy interaction, wherein the dynamic nature of IDP conformation is also essential.