Precise Normal Mode Analysis and Frequency Prediction of DNA Nanostructure through Mass-Weighted Chemical Elastic Network Model

Abstract

In our previous study on self-assembly mechanism of DNA nanostructures, using elastic network model (ENM) based normal mode analysis (NMA), we successfully explained the reason why planar DNA tiles were self-assembled as a helical nanotube, which was totally contrast to our preconception such that they could form a planar lattice nanostructure. Although this traditional ENM based NMA is very useful to catch collective motions of biomolecules in a timely fashion, it is impossible to predict vibration frequency due to its dimensionless uniform mass and spring constants.We propose a mass-weighted chemical elastic network model (MWCENM) which takes inertia effect and chemical bond information into account. For example, lumped masses are assigned to the representative atoms and different spring constants are given to bonded and non-bonded interactions including covalent bond, hydrogen bond, and van der Waals interaction.Several comparison studies convince us that MWCENM is able to catch DNA dynamics more precisely and more effectively. It is also expected that one can use MWCENM based NMA results, which include both predicted frequencies and corresponding mode shapes, as a fingerprint to characterize molecular dynamics by a comparison with vibration spectrum data.