Interpretable Prediction of Phase Separation and Disease Variant Effects in Intrinsically Disordered Regions
An interpretable ensemble machine-learning framework that integrates protein language model embeddings of sequence and predicted structure to predict LLPS propensity and classify proteins as self-separating or partner-dependent and identifies critical phase-separating regions and quantifies mutation-induced perturbations in LLPS.