標題: Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids
作者: Chen, Yi-Tsao
Yang, Haw
Chu, Jhih-Wei
交大名義發表
生物資訊及系統生物研究所
National Chiao Tung University
Institude of Bioinformatics and Systems Biology
公開日期: 21-五月-2020
摘要: The mechanical properties of nucleic acids underlie biological processes ranging from genome packaging to gene expression, but tracing their molecular origin has been difficult due to the structural and chemical complexity. We posit that concepts from machine learning can help to tackle this long-standing challenge. Here, we demonstrate the feasibility and advantage of this strategy through developing a structure-mechanics statistical learning scheme to elucidate how local rigidity in double-stranded (ds)DNA and dsRNA may lead to their global flexibility in bend, stretch, and twist. Specifically, the mechanical parameters in a heavy-atom elastic network model are computed from the trajectory data of all-atom molecular dynamics simulation. The results show that the inter-atomic springs for backbone and ribose puckering in dsRNA are stronger than those in dsDNA, but are similar in strengths for base-stacking and base-pairing. Our analysis shows that the experimental observation of dsDNA being easier to bend but harder to stretch than dsRNA comes mostly from the respective B- and A-form topologies. The computationally resolved composition of local rigidity indicates that the flexibility of both nucleic acids is mostly due to base-stacking. But for properties like twist-stretch coupling, backbone springs are shown to play a major role instead. The quantitative connection between local rigidity and global flexibility sets foundation for understanding how local binding and chemical modification of genetic materials effectuate longer-ranged regulatory signals.
URI: http://dx.doi.org/10.1039/d0sc00480d
http://hdl.handle.net/11536/154603
ISSN: 2041-6520
DOI: 10.1039/d0sc00480d
期刊: CHEMICAL SCIENCE
Volume: 11
Issue: 19
起始頁: 4969
結束頁: 4979
顯示於類別:期刊論文