Precise prediction of phase-separation key residues by machine learning

© 2024. The Author(s)..

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:15

Enthalten in:

Nature communications - 15(2024), 1 vom: 26. März, Seite 2662

Sprache:

Englisch

Beteiligte Personen:

Sun, Jun [VerfasserIn]
Qu, Jiale [VerfasserIn]
Zhao, Cai [VerfasserIn]
Zhang, Xinyao [VerfasserIn]
Liu, Xinyu [VerfasserIn]
Wang, Jia [VerfasserIn]
Wei, Chao [VerfasserIn]
Liu, Xinyi [VerfasserIn]
Wang, Mulan [VerfasserIn]
Zeng, Pengguihang [VerfasserIn]
Tang, Xiuxiao [VerfasserIn]
Ling, Xiaoru [VerfasserIn]
Qing, Li [VerfasserIn]
Jiang, Shaoshuai [VerfasserIn]
Chen, Jiahao [VerfasserIn]
Chen, Tara S R [VerfasserIn]
Kuang, Yalan [VerfasserIn]
Gao, Jinhang [VerfasserIn]
Zeng, Xiaoxi [VerfasserIn]
Huang, Dongfeng [VerfasserIn]
Yuan, Yong [VerfasserIn]
Fan, Lili [VerfasserIn]
Yu, Haopeng [VerfasserIn]
Ding, Junjun [VerfasserIn]

Links:

Volltext

Themen:

Glycine
Journal Article
Proteins
TE7660XO1C

Anmerkungen:

Date Completed 28.03.2024

Date Revised 29.03.2024

published: Electronic

Citation Status MEDLINE

doi:

10.1038/s41467-024-46901-9

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM370214013