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] |
---|
Links: |
---|
Themen: |
---|
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 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | NLM370214013 | ||
003 | DE-627 | ||
005 | 20240330001453.0 | ||
007 | cr uuu---uuuuu | ||
008 | 240328s2024 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1038/s41467-024-46901-9 |2 doi | |
028 | 5 | 2 | |a pubmed24n1355.xml |
035 | |a (DE-627)NLM370214013 | ||
035 | |a (NLM)38531854 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Sun, Jun |e verfasserin |4 aut | |
245 | 1 | 0 | |a Precise prediction of phase-separation key residues by machine learning |
264 | 1 | |c 2024 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 28.03.2024 | ||
500 | |a Date Revised 29.03.2024 | ||
500 | |a published: Electronic | ||
500 | |a Citation Status MEDLINE | ||
520 | |a © 2024. The Author(s). | ||
520 | |a 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 | ||
650 | 4 | |a Journal Article | |
650 | 7 | |a Proteins |2 NLM | |
650 | 7 | |a Glycine |2 NLM | |
650 | 7 | |a TE7660XO1C |2 NLM | |
700 | 1 | |a Qu, Jiale |e verfasserin |4 aut | |
700 | 1 | |a Zhao, Cai |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Xinyao |e verfasserin |4 aut | |
700 | 1 | |a Liu, Xinyu |e verfasserin |4 aut | |
700 | 1 | |a Wang, Jia |e verfasserin |4 aut | |
700 | 1 | |a Wei, Chao |e verfasserin |4 aut | |
700 | 1 | |a Liu, Xinyi |e verfasserin |4 aut | |
700 | 1 | |a Wang, Mulan |e verfasserin |4 aut | |
700 | 1 | |a Zeng, Pengguihang |e verfasserin |4 aut | |
700 | 1 | |a Tang, Xiuxiao |e verfasserin |4 aut | |
700 | 1 | |a Ling, Xiaoru |e verfasserin |4 aut | |
700 | 1 | |a Qing, Li |e verfasserin |4 aut | |
700 | 1 | |a Jiang, Shaoshuai |e verfasserin |4 aut | |
700 | 1 | |a Chen, Jiahao |e verfasserin |4 aut | |
700 | 1 | |a Chen, Tara S R |e verfasserin |4 aut | |
700 | 1 | |a Kuang, Yalan |e verfasserin |4 aut | |
700 | 1 | |a Gao, Jinhang |e verfasserin |4 aut | |
700 | 1 | |a Zeng, Xiaoxi |e verfasserin |4 aut | |
700 | 1 | |a Huang, Dongfeng |e verfasserin |4 aut | |
700 | 1 | |a Yuan, Yong |e verfasserin |4 aut | |
700 | 1 | |a Fan, Lili |e verfasserin |4 aut | |
700 | 1 | |a Yu, Haopeng |e verfasserin |4 aut | |
700 | 1 | |a Ding, Junjun |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Nature communications |d 2010 |g 15(2024), 1 vom: 26. März, Seite 2662 |w (DE-627)NLM199274525 |x 2041-1723 |7 nnns |
773 | 1 | 8 | |g volume:15 |g year:2024 |g number:1 |g day:26 |g month:03 |g pages:2662 |
856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41467-024-46901-9 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 15 |j 2024 |e 1 |b 26 |c 03 |h 2662 |