The mechanisms by which transcriptional activation domains (tADs) initiate eukaryotic gene expression have been an enigma for decades because most tADs lack specificity in sequence, structure, and interactions with targets. Machine learning analysis of data sets of tAD sequences generated elucidated several functionality rules: the functional tAD sequences should (i) be devoid of or depleted with basic amino acid residues, (ii) be enriched with aromatic and acidic residues, (iii) be with aromatic residues localized mostly near the terminus of the sequence, and acidic residues localized more internally within a span of 20-30 amino acids, (iv) be with both aromatic and acidic residues preferably spread out in th... More
The mechanisms by which transcriptional activation domains (tADs) initiate eukaryotic gene expression have been an enigma for decades because most tADs lack specificity in sequence, structure, and interactions with targets. Machine learning analysis of data sets of tAD sequences generated elucidated several functionality rules: the functional tAD sequences should (i) be devoid of or depleted with basic amino acid residues, (ii) be enriched with aromatic and acidic residues, (iii) be with aromatic residues localized mostly near the terminus of the sequence, and acidic residues localized more internally within a span of 20-30 amino acids, (iv) be with both aromatic and acidic residues preferably spread out in the sequence and not clustered, and (v) not be separated by occasional basic residues. These and other more subtle rules are not absolute, reflecting absence of a tAD consensus sequence, enormous variability, and consistent with surfactant-like tAD biochemical properties. The findings are compatible with the paradigm-shifting nucleosome detergent mechanism of gene expression activation, contributing to the development of the liquid-liquid phase separation model and the biochemistry of near-stochastic functional allosteric interactions.
