Evolutionary turnover of key amino acids explains conservation of function without conservation of sequence in transcriptional activation domains
Claire J. LeBlanc, Jordan Stefani, Melvin Soriano, Angelica W. Y. Lam, Marissa A. Zintel, Sanjana R. Kotha, Emily P. Chase, Giovani Pimentel-Solorio, Aditya Vunnum, Gean Hu, Katherine L. Flug, Aaron Fultineer, Niklas Hummel, Max V. Staller, Michael Guertin, Michael Guertin

TL;DR
This study shows how transcription factors can maintain their function over long evolutionary times despite significant changes in their protein sequences.
Contribution
The paper reveals that evolutionary turnover of key amino acids in disordered regions maintains function without sequence conservation.
Findings
The central activation domain of Gcn4 shows strong functional conservation despite sequence divergence.
Evolutionary turnover of acidic and aromatic residues and short linear motifs contributes to functional conservation.
Turnover of entire activation domains in full-length transcription factors supports functional stability over time.
Abstract
In folded protein domains, protein function is frequently more conserved than amino acid sequence because highly diverged sequences can fold into equivalent 3D structures with identical function. During evolution, intrinsically disordered protein regions (IDRs) often experience rapid amino acid sequence divergence, but because they do not fold into stable 3D structures, it remains largely unknown when and how function is conserved. As a model system for studying the evolution of IDRs, we examined transcriptional activation domains, the regions of transcription factors that bind to coactivator complexes. We systematically identified activation domains on 502 homologs of the transcriptional activator Gcn4 spanning 600 MY of fungal evolution in the Ascomycota. We found that the central activation domain shows strong conservation of function without conservation of sequence. This…
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Taxonomy
TopicsFungal and yeast genetics research · Protein Structure and Dynamics · Bioinformatics and Genomic Networks
