Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids.

ABSTRACT: 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

SUBMITTER: Chen YT 

PROVIDER: S-EPMC8159235 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature