期刊
ELIFE
卷 10, 期 -, 页码 -出版社
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.68068
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资金
- National Institutes of Health [R01-DK121366, F32-GM126704, R01-AI021144]
- U.S. Department of Energy Scientific Discovery through Advanced Computing (SciDAC) program
- National Institutes of Health 4D Nucleome Grant [U01HL130010, U01HL156059]
- National Institutes of Health Encyclopedia of DNA Elements Mapping Center Award [UM1HG009375]
- National Science Foundation, Center for Theoretical Biological Physics [PHY-2019745, PHY-1427654]
- Welch Foundation [Q-1866]
- McNair Medical Institute Scholar Award
- United States - Israel Binational Science Foundation [2019276]
- U.S. Department of Defense National Defense Science & Engineering Graduate (NDSEG) Fellowship
- U.S. Department of Energy Office of Science Graduate Student Research (SCGSR) program [DE-SC0014664]
- National Science Foundation, Behavioral Plasticity Research Institute [DBI-2021795]
150 ADs were identified, with most (73%) binding to the Med15 subunit of Mediator, and binding strength correlated with activation. AD-Mediator interaction is a dynamic 'fuzzy' binding process without shape complementarity. Mutagenesis revealed biochemical and structural constraints, and a neural network accurately predicted ADs in human proteins and other yeast proteins.
Gene activator proteins comprise distinct DNA-binding and transcriptional activation domains (ADs). Because few ADs have been described, we tested domains tiling all yeast transcription factors for activation in vivo and identified 150 ADs. By mRNA display, we showed that 73% of ADs bound the Med15 subunit of Mediator, and that binding strength was correlated with activation. AD-Mediator interaction in vitro was unaffected by a large excess of free activator protein, pointing to a dynamic mechanism of interaction. Structural modeling showed that ADs interact with Med15 without shape complementarity ('fuzzy' binding). ADs shared no sequence motifs, but mutagenesis revealed biochemical and structural constraints. Finally, a neural network trained on AD sequences accurately predicted ADs in human proteins and in other yeast proteins, including chromosomal proteins and chromatin remodeling complexes. These findings solve the longstanding enigma of AD structure and function and provide a rationale for their role in biology.
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