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Issue 39, 2018
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Understanding chain looping kinetics in polymer solutions: crowding effects of microviscosity and collapse

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Abstract

A theoretical framework based on a generalized Langevin equation with fractional Gaussian noise is presented to describe the looping kinetics of chains in polymer solutions. Particular attention is paid to quantitatively revealing crowding effects on the loop formation rate in terms of microviscosity and collapse. By the aid of empirical relations for these two crowding associated physical quantities, we explicitly investigate the relationship between the looping rate and polymer concentration, the degree of polymerization, and system parameters. According to our analysis, the dependence of the looping rate on the crowder volume fraction exhibits three typical regimes: monotonic decreasing, a non-monotonic trend and monotonic increasing. We reveal that these non-trivial behaviors can be attributed to the competition between the two opposing factors of viscosity-associated inhibition and collapse-induced facilitation of loop formation. We apply our theory to analyze the kinetics of single-stranded DNA hairpin base pairing in polyethylene glycol solutions. The theoretical results can reproduce the experimental data on the closing rate of hairpins quantitatively to a certain degree with reasonable fitting parameters. The unexpected increase of the closing rate upon the addition of increasing amounts of polymer is well rationalised. Such good agreements clearly demonstrate the validity of our theory, appropriately addressing the very role of crowding effects in the relevant kinetics.

Graphical abstract: Understanding chain looping kinetics in polymer solutions: crowding effects of microviscosity and collapse

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Article information


Submitted
22 Jul 2018
Accepted
06 Sep 2018
First published
10 Sep 2018

Soft Matter, 2018,14, 8060-8072
Article type
Paper

Understanding chain looping kinetics in polymer solutions: crowding effects of microviscosity and collapse

Y. Bian, X. Cao, P. Li and N. Zhao, Soft Matter, 2018, 14, 8060
DOI: 10.1039/C8SM01499J

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