'Nonlinear' Biochemistry of Nucleosome Detergents. Issue 12 (December 2018)

• Record Type: Journal Article
• Authors: Erkine, Alexandre M.
• Is Part Of: Trends in biochemical sciences. Volume 43:Issue 12(2018)
• Page Start: 951
• Page End: 959
• Publication Date: 2018-12
• Subjects: intrinsically disordered regions -- transcriptional activation domains -- machine learning -- chromatin remodeling -- nucleosome detergents; Biochemistry -- Periodicals; 572
• DOI: 10.1016/j.tibs.2018.09.006
• Languages: English
• ISSNs: 0968-0004

Abstract

Abstract : The transcriptional activation domains (TADs) are critical for life, yet intrinsically disordered polypeptides with no specific consensus sequence, interacting with multiple targets via low-specificity fuzzy contacts. The recent integration of machine learning approaches in biochemistry allows analysis of large experimental datasets of functional TADs as a whole and clear observation of TAD features. The emerging picture describes TADs as sequences without consensus but with a variety of detergent-like mini-motifs enriched in negatively charged and aromatic amino acids. Comparison of the canonical direct coactivator recruitment model and a new model describing TADs as nucleosome detergents that trigger chromatin remodeling during gene activation helps solve a fundamental enigma of molecular biology spanning 30 years. Highlights: Introduction of machine learning in analyzing TADs led to the realization that the main feature of TAD sequences is presence of a variety of detergent-like mini-motifs comprising negatively charged and aromatic extremities separated by variable spacers. Interchangeability of TADs, absence of a discernable consensus sequence, and high frequency (1/100) of functional TADs appearance in random sequence pools suggests that TADs operate at the near-stochastic level of interactions. While approximation of interactions to the near-stochastic level renders the direct coactivator recruitment model for TADs unworkable, it fits perfectly with the gene promoter nucleosome detergent model. Acceptance of functionality at near-stochastic levels is challenging for canonical biochemistry and requires a drastic revision of mentality and methodology.