Activation of gene expression by detergent-like protein domains
| Autor: | Daniel A. Coil, Thomas M. Wagner, Daisuke Kihara, Theodore P. Maris, Andrew T. Gutierrez, Xiao Wang, Bradley K. Broyles, Alexandre M. Erkine, Caleb A. Class |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Molecular biology
Bioinformatics gene activation neural network Science Protein domain IDR Article Gene expression Consensus sequence Transcriptional regulation Nucleosome transcriptional regulation Regulation of gene expression chemistry.chemical_classification Multidisciplinary Chemistry nucleosome Computational bioinformatics Chromatin Amino acid Biological sciences machine learning Biochemistry chromatin |
| Zdroj: | iScience iScience, Vol 24, Iss 9, Pp 103017-(2021) |
| ISSN: | 2589-0042 |
| Popis: | Summary 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 in vivo 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. Graphical abstract Highlights • Transcriptional activation domain features are analyzed by machine learning • Absence of basic and redundant presence of acidic and aromatic residues is important • Among hydrophobic residues, only aromatics are significantly more represented • C-terminal localization is essential for aromatic but not for acidic residues Biological sciences; Molecular biology; Bioinformatics; Computational bioinformatics |
| Databáze: | OpenAIRE |
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