Precise prediction of phase-separation key residues by machine learning

Jun Sun, Jiale Qu, Cai Zhao, Xinyao Zhang, Xinyu Liu, Jia Wang, Chao Wei, Xinyi Liu, Mulan Wang, Pengguihang Zeng, Xiuxiao Tang, Xiaoru Ling, Li Qing, Shaoshuai Jiang, Jiahao Chen, Tara S. R. Chen, Yalan Kuang, Jinhang Gao, Xiaoxi Zeng, Dongfeng Huang, Yong Yuan (), Lili Fan (), Haopeng Yu () and Junjun Ding ()
Additional contact information
Jun Sun: Sichuan University
Jiale Qu: Sun Yat-sen University
Cai Zhao: Sun Yat-sen University
Xinyao Zhang: Sun Yat-sen University
Xinyu Liu: Sun Yat-sen University
Jia Wang: Sun Yat-sen University
Chao Wei: Sun Yat-sen University
Xinyi Liu: Sun Yat-sen University
Mulan Wang: Sun Yat-sen University
Pengguihang Zeng: Sun Yat-sen University
Xiuxiao Tang: Sun Yat-sen University
Xiaoru Ling: Sun Yat-sen University
Li Qing: Sun Yat-sen University
Shaoshuai Jiang: Sun Yat-sen University
Jiahao Chen: Sun Yat-sen University
Tara S. R. Chen: Sun Yat-Sen University
Yalan Kuang: Sichuan University
Jinhang Gao: Sichuan University
Xiaoxi Zeng: Sichuan University
Dongfeng Huang: Sun Yat-Sen University
Yong Yuan: Sichuan University
Lili Fan: School of Traditional Chinese Medicine, Jinan University
Haopeng Yu: Sichuan University
Junjun Ding: Sichuan University

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function have remained elusive. We develop PSPHunter, which can precisely predict these key residues based on machine learning scheme. In vivo and in vitro validations demonstrate that truncating just 6 key residues in GATA3 disrupts phase separation, enhancing tumor cell migration and inhibiting growth. Glycine and its motifs are enriched in spacer and key residues, as revealed by our comprehensive analysis. PSPHunter identifies nearly 80% of disease-associated phase-separating proteins, with frequent mutated pathological residues like glycine and proline often residing in these key residues. PSPHunter thus emerges as a crucial tool to uncover key residues, facilitating insights into phase separation mechanisms governing transcriptional control, cell fate transitions, and disease development.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-024-46901-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46901-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-46901-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().