Studies of the Mechanism of Nucleosome Dynamics: A Review on Multifactorial Regulation from Computational and Experimental Cases

• Published in: Polymers Vol. 15; no. 7; p. 1763
• Main Authors: Shi, Danfeng, Huang, Yuxin, Bai, Chen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-04-2023; MDPI
• Subjects: Chromosomes; Complexity; DNA binding proteins; DNA methylation; Gene expression; Genetic transcription; mechanism studies; Methylation; Microscopy; multifactorial regulation; NMR; Nuclear magnetic resonance; nucleosome dynamics; Packaging; Proteins; Regulatory mechanisms (biology); Review; mechanism studies; multifactorial regulation; nucleosome dynamics
• ISSN: 2073-4360; 2073-4360
• DOI: 10.3390/polym15071763

The nucleosome, which organizes the long coil of genomic DNA in a highly condensed, polymeric way, is thought to be the basic unit of chromosomal structure. As the most important protein-DNA complex, its structural and dynamic features have been successively revealed in recent years. However, its regulatory mechanism, which is modulated by multiple factors, still requires systemic discussion. This study summarizes the regulatory factors of the nucleosome's dynamic features from the perspective of histone modification, DNA methylation, and the nucleosome-interacting factors (transcription factors and nucleosome-remodeling proteins and cations) and focuses on the research exploring the molecular mechanism through both computational and experimental approaches. The regulatory factors that affect the dynamic features of nucleosomes are also discussed in detail, such as unwrapping, wrapping, sliding, and stacking. Due to the complexity of the high-order topological structures of nucleosomes and the comprehensive effects of regulatory factors, the research on the functional modulation mechanism of nucleosomes has encountered great challenges. The integration of computational and experimental approaches, the construction of physical modes for nucleosomes, and the application of deep learning techniques will provide promising opportunities for further exploration.