'Nonlinear' Biochemistry of Nucleosome Detergents. Issue 12 (December 2018) - Erkine, Alexandre M.
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.