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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\u2019s 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, PSTP provides residue-level predictions that are highly correlated with experimentally validated PS regions. By analyzing 160\u00a0000+ variants, PSTP characterizes the strong link between the incidence of pathogenic variants and residue-level PS propensities in unconserved intrinsically disordered regions, offering insights into underexplored mutation effects. PSTP\u2019s sliding-window optimization reduces its memory usage to a few hundred megabytes, facilitating rapid execution on typical CPUs and GPUs. Offered via both a web server and an installable Python package, PSTP provides a versatile tool for decoding protein PS behavior and supporting disease-focused research.<\/jats:p>","DOI":"10.1093\/bib\/bbaf171","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T07:47:16Z","timestamp":1745826436000},"source":"Crossref","is-referenced-by-count":5,"title":["PSTP: accurate residue-level phase separation prediction using protein conformational and language model embeddings"],"prefix":"10.1093","volume":"26","author":[{"given":"Mofan","family":"Feng","sequence":"first","affiliation":[{"name":"Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University , No. 1954 Huashan Road, Xuhui District, Shanghai 200030 ,","place":["China"]},{"name":"Shanghai Institute of Medical Genetics, Shanghai Children\u2019s Hospital, Shanghai Jiao Tong University School of Medicine , No. 24 Lane 1400 West 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