Jin Jessica, Oliver Wesley, Webb Michael A, Jacobs William M
Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA.
Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA.
J Chem Phys. 2025 Jul 7;163(1). doi: 10.1063/5.0269504.
Phase separation in polymer solutions often correlates with single-chain and two-chain properties, such as the single-chain radius of gyration, Rg, and the pairwise second virial coefficient, B22. 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. While B22 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 Rg and B22 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.
聚合物溶液中的相分离通常与单链和双链性质相关，例如单链回转半径Rg和成对的第二维里系数B22。然而，最近的研究表明，这些指标可能无法区分相分离的杂聚物和非相分离的杂聚物，包括内在无序蛋白（IDP）。在这里，我们介绍一种通过分析接触图从双链模拟预测杂聚物相分离的方法，接触图能捕捉两条链中特定单体在物理上接近的频率。虽然B22总结了两条链之间的整体吸引力，但接触图保留了它们相互作用的空间信息。为了比较这些指标，我们针对一个最小杂聚物模型和一个化学特定的、残基水平的IDP模型训练相分离分类器。值得注意的是，双链接触图的简单统计特性能高精度地预测相分离，大大优于仅基于Rg和B22的分类器。因此，我们的结果建立了一种可转移且计算高效的方法，以基于IDP在稀溶液中的物理相互作用揭示其相行为的关键驱动力。
