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  • Open Access

Stochastic Sensing of Polynucleotides Using Patterned Nanopores

Jack A. Cohen1,*, Abhishek Chaudhuri1,2,†, and Ramin Golestanian1,‡

  • 1Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3NP, United Kingdom
  • 2Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, United Kingdom

  • *j.cohen@physics.ox.ac.uk
  • †a.chaudhuri1@physics.ox.ac.uk
  • ‡ramin.golestanian@physics.ox.ac.uk
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Phys. Rev. X 2, 021002 – Published 5 April, 2012

DOI: https://doi.org/10.1103/PhysRevX.2.021002

Abstract

The effect of the microscopic structure of a pore on polymer translocation is studied using Langevin dynamics simulation, and the consequence of introducing patterned stickiness inside the pore is investigated. It is found that the translocation process is extremely sensitive to the detailed structure of such patterns with faster than exponential dependence of translocation times on the stickiness of the pore. The stochastic nature of the translocation process leads to discernible differences between how polymers with different sequences go through specifically patterned pores. This notion is utilized to propose a stochastic sensing protocol for polynucleotides, and it is demonstrated that the method, which would be significantly faster than the existing methods, could be made arbitrarily robust.

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Popular Summary

A single-stranded DNA or RNA molecule can be driven through a nanoscale pore connecting two separated solution chambers under the action of an electric voltage across the pore. It turns out that this translocation can be detected in the changes of a tiny electric current passing through the nanopore and used to analyze the sequence of the translocated molecule. However, one major challenge in the quest for efficient sequencing techniques is noise, arising from the inherent random nature of the process and leading to irreproducibility of individual events observed. Almost all the approaches being developed focus on finding ways to reduce the effect of the noise. In this paper, we propose a radically different approach: Instead of trying to eliminate the effect of random noises, we actually take full advantage of the very stochasticity of the translocation process for gains towards fast and accurate sequencing of polynucleotides, by exploiting the idea of nanopore engineering and with fundamental theoretical understanding.

The inherent randomness of the translocation process leads to a range (or a distribution) of translocation times even for identical molecules. Desired information about the sequence of the translocating molecule is hidden in that distribution. Our idea is to engineer both the qualitative shape and the quantitative features of that distribution in such a way that the hidden information is brought to the forefront. How should such engineering be achieved? The approach we propose is to structurally pattern the inside of the pores with “spots” of sticky interactions with translocating molecules. Using a recently developed coarse-grained, but still microscopic, model for polymer translocation, we have shown that the translocation process is extremely sensitive to the detailed structure of such interaction patterns, with a faster-than-exponential dependence of the translocation time on the stickiness of the pore. Exploring two types of different pores with distinct translocation-time distributions, we demonstrate theoretically that the sequence of an unknown polynucleotide can be determined with arbitrary accuracy from the stochastic translocation-time readout obtained from the driven, sequential translocation of the polynucleotide through a sufficient number of pores with different interaction patterns.

We believe that our approach will open a new front in the development of the nanopore-based molecular sequencing techniques.

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Phys. Rev. X 2, 021002– Published 5 April, 2012
  • Received 21 December 2011
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This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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