Proteome-scale analysis of phase-separated proteins in immunofluorescence images. Issue 3 (2nd September 2020)

• Record Type: Journal Article
• Authors: Yu, Chunyu; Shen, Boyan; You, Kaiqiang; Huang, Qi; Shi, Minglei; Wu, Congying; Chen, Yang; Zhang, Chaolin; Li, Tingting
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 3(2021)
• Publication Date: 2020-09-02
• Subjects: liquid-liquid phase separation -- immunofluorescence images -- deep learning -- sequence analysis; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa187
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Phase separation is an important mechanism that mediates the spatial distribution of proteins in different cellular compartments. While phase-separated proteins share certain sequence characteristics, including intrinsically disordered regions (IDRs) and prion-like domains, such characteristics are insufficient for making accurate predictions; thus, a proteome-wide understanding of phase separation is currently lacking. Here, we define phase-separated proteomes based on the systematic analysis of immunofluorescence images of 12 073 proteins in the Human Protein Atlas. The analysis of these proteins reveals that phase-separated candidate proteins exhibit higher IDR contents, higher mean net charge and lower hydropathy and prefer to bind to RNA. Kinases and transcription factors are also enriched among these candidate proteins. Strikingly, both phase-separated kinases and phase-separated transcription factors display significantly reduced substrate specificity. Our work provides the first global view of the phase-separated proteome and suggests that the spatial proximity resulting from phase separation reduces the requirement for motif specificity and expands the repertoire of substrates. The source code and data are available at https://github.com/cheneyyu/deepphase .