PSTP: accurate residue-level phase separation prediction using protein conformational and language model embeddings.

ABSTRACT: Phase separation (PS) is essential in cellular processes and disease mechanisms, highlighting the need for predictive algorithms to analyze uncharacterized sequences and accelerate experimental validation. Current high-accuracy methods often rely on extensive annotations or handcrafted features, limiting their generalizability to sequences lacking such annotations and making it difficult to identify key protein regions involved in PS. We introduce Phase Separation's Transfer-learning Prediction (PSTP), which combines conformational embeddings with large language model embeddings, enabling state-of-the-art PS predictions from protein sequences alone. PSTP performs well across various prediction scenarios and shows potential for predicting novel-designed artificial proteins. Additionally, PS

SUBMITTER: Feng M 

PROVIDER: S-EPMC12047702 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature