Predicting Heteropolymer Phase Separation Using Two-Chain Contact Maps.

Abstract

Phase separation in polymer solutions often correlates with single-chain and two-chain properties, such as the single-chain radius of gyration, $R_{\text{g}}$ , and the pairwise second virial coefficient, $B_{22}$ . However, recent studies have shown that these metrics can fail to distinguish phase-separating from non-phase-separating heteropolymers, including intrinsically disordered proteins (IDPs). Here we introduce an approach to predict heteropolymer phase separation from two-chain simulations by analyzing contact maps, which capture how often specific monomers from the two chains are in physical proximity. Whereas $B_{22}$ summarizes the overall attraction between two chains, contact maps preserve spatial information about their interactions. To compare these metrics, we train phase-separation classifiers for both a minimal heteropolymer model and a chemically specific, residue-level IDP model. Remarkably, simple statistical properties of two-chain contact maps predict phase separation with high accuracy, vastly outperforming classifiers based on $R_{\text{g}}$ and $B_{22}$ alone. Our results thus establish a transferable and computationally efficient method to uncover key driving forces of IDP phase behavior based on their physical interactions in dilute solution.