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Issue 19, 2020
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Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids

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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 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.

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

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Supplementary files

Article information


Submitted
27 Jan 2020
Accepted
22 Apr 2020
First published
23 Apr 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020,11, 4969-4979
Article type
Edge Article

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

Y. Chen, H. Yang and J. Chu, Chem. Sci., 2020, 11, 4969
DOI: 10.1039/D0SC00480D

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

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    [Original citation] - Published by The Royal Society of Chemistry.

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