amyloid-predict and LLPS-predict: Predicting phase separation propensities in the intrinsically disordered proteome.

Amyloid formation and liquid-liquid phase separation (LLPS) are two important phenomena in cellular biology, linked to both normal physiological functions and various pathologies. Here, we present a computational framework that scores amyloid propensities (amyloid-predict) or LLPS propensities (LLPS-predict) from protein language model embeddings, enabling rapid proteome-wide annotation of peptides and residues. amyloid-predict achieves classification performance that exceeds existing AI and physics-based tools on a hexapeptide benchmark while enabling substantially faster high-throughput screening; notably, amyloid-predict is sensitive to subtle mutational effects and is influenced by sequence patterning and context rather than amino acid composition alone. We apply these protein language model classifiers to all the IDRs in the human proteome and uncover several protein categories with significant enhancement in amyloid and/or LLPS propensity, suggesting insights into the biological roles of these protein categories. For example, signaling receptors, carbohydrate-binding proteins, and Ca