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Backbone charge transport in double-stranded DNA

Nature Nanotechnology volume 15, pages 836–840 (2020)Cite this article

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Abstract

Understanding charge transport in DNA molecules is a long-standing problem of fundamental importance across disciplines1,2. It is also of great technological interest due to DNA’s ability to form versatile and complex programmable structures. Charge transport in DNA-based junctions has been reported using a wide variety of set-ups2,3,4, but experiments so far have yielded seemingly contradictory results that range from insulating5,6,7,8 or semiconducting9,10 to metallic-like behaviour11. As a result, the intrinsic charge transport mechanism in molecular junction set-ups is not well understood, which is mainly due to the lack of techniques to form reproducible and stable contacts with individual long DNA molecules. Here we report charge-transport measurements through single 30-nm-long double-stranded DNA (dsDNA) molecules with an experimental set-up that enables us to address individual molecules repeatedly and to measure the current–voltage characteristics from 5 K up to room temperature. Strikingly, we observed very high currents of tens of nanoamperes, which flowed through both homogeneous and non-homogeneous base-pair sequences. The currents are fairly temperature independent in the range 5–60 K and show a power-law decrease with temperature above 60 K, which is reminiscent of charge transport in organic crystals. Moreover, we show that the presence of even a single discontinuity (‘nick’) in both strands that compose the dsDNA leads to complete suppression of the current, which suggests that the backbones mediate the long-distance conduction in dsDNA, contrary to the common wisdom in DNA electronics2,3,4.

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Fig. 1: Experimental set-up and SEM images of the dsDNA junctions.
Fig. 2: Sequence dependence of the I–V characteristics.
Fig. 3: The role of the backbone in the I–V characteristics.
Fig. 4: Ab initio electronic structure calculations.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

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Acknowledgements

This research was supported by the Israel Science Foundation (ISF grant nos. 1589/14 and 2556/17) and by the Minerva Centre for Bio-Hybrid Complex Systems. D.P. thanks the Etta and Paul Schankerman Chair of Molecular Biomedicine. G.P. and S.S.S. acknowledge financial support from the University of Cyprus PhD grants and J.C.C. from the Spanish MINECO (contract no. FIS2017-84057-P). V.G. acknowledges the support of Toyota Research Institute under the auspices of which some aspects of the DFT-FE package relevant to this work were developed. L.A.Z. thanks financial support from the University of Seville through the VI PPIT-US program. This paper and work are dedicated to the memory of Professor Joseph Sperling, who passed away during the performance of this research.

Author information

Authors and Affiliations

  1. Institute of Chemistry and The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem, Israel

    Roman Zhuravel, Haichao Huang, Dvir Rotem & Danny Porath

  2. Department of Physics, University of Cyprus, Nicosia, Cyprus

    Georgia Polycarpou, Savvas Polydorides & Spiros S. Skourtis

  3. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA

    Phani Motamarri & Vikram Gavini

  4. Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India

    Phani Motamarri

  5. Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences and Center of Nanoscience and Nanotechnology, Tel Aviv University, Ramat Aviv, Israel

    Liat Katrivas & Alexander B. Kotlyar

  6. Department of Organic Chemistry, Weizmann Institute of Science, Rehovot, Israel

    Joseph Sperling

  7. Departamento de Física Teórica de la Materia Condensada and Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid, Madrid, Spain

    Linda A. Zotti & Juan Carlos Cuevas

  8. Departamento de Física Aplicada I, Escuela Politécnica Superior, Universidad de Sevilla, Seville, Spain

    Linda A. Zotti

  9. Department of Materials Science & Engineering, University of Michigan, Ann Arbor, MI, USA

    Vikram Gavini

Authors
  1. Roman Zhuravel
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Contributions

R.Z., D.R. and D.P. conceived the work. R.Z. prepared the dimers based on inspiration and guidance by J.S. R.Z. and H.H. fabricated the devices. R.Z. performed the transport experiments and analysed the data. A.B.K. and L.K. prepared the poly(dG)–poly(dC) dimers. G.P., S.P., P.M. and L.A.Z. performed the calculations under the supervision of S.S.S., V.G. and J.C.C. R.Z., A.B.K., J.C.C., S.S.S., D.R. and D.P. wrote the manuscript, with comments and input from all the authors.

Corresponding author

Correspondence to Danny Porath.

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Extended data

Extended Data Fig. 1 Dimer synthesis.

a, Scheme of dimer synthesis. Incubation of GNPs and ssDNA end-modified with thiol followed by purification of the conjugates using electrophoresis yields the mono-conjugate composed of one DNA oligonucleotide attached to the GNP. Mixing the mono-conjugates comprising complementary DNA strands results in formation of dimers. b, TEM scan of the dimer solution. Typically, 40–90% of the GNPs were found in dimers.

Extended Data Fig. 2 Temperature dependence of the current for different dsDNA molecules.

a, Arrhenius plot for a random sequence dsDNA molecule that was heated from 5 K to 125 K and cooled down back to 5 K. The transition is reversible. b, Arrhenius plot of different molecules (random sequence, with and without nicks and poly(dG)-poly(dC)). All the dsDNA molecule types (see legend) have a transition temperature at 40-80 K with nearly constant current below the transition and rapid decrease above it.

Source data

Extended Data Fig. 3 2D histograms of I-V curves for each type of DNA.

Two-dimensional (2D) histograms of I-V measurements at temperatures below the transition temperature for each type of molecule. For clarity, only data points that appear in more than one measurement are presented here. The histograms show that I-V curves from different molecules of the same type are generally similar but not identical due to the variability in the contacts and positioning. Nevertheless, the different types of dsDNA molecules, share similar trends. a, 100 base-pairs random sequence dsDNA: 217 curves from 13 molecules. b, 100 base pairs Poly(dG)-poly(dC): 43 curves from 6 molecules. c, The single-nicked DNA having a sequence identical to that in (a): 22 curves from 3 molecules.

Extended Data Fig. 4 Ab initio electronic structure calculations of B-type dsDNA molecules.

a, Highest occupied molecular orbital (HOMO), which resides in the base pairs, and the highest occupied orbital that belongs to backbone atoms for a B-type 100 base-pairs poly(dG)-poly(dC) dsDNA molecule. The energy of these two orbitals, with respect to the vacuum energy, is indicated in this panel. Notice in both cases the localized nature of these orbitals. b, The corresponding local density of states (LDOS) as a function of energy for the poly(dG)-poly(dC) dsDNA molecule showing the individual contributions of base-pair-centred states (black line) and backbone-centred states (red line). c, The computed localization length, normalized by the length of the molecule, as a function of energy for the occupied states of the poly(dG)-poly(dC) dsDNA molecule. The vertical dashed lines in panels b and c indicate the position of the HOMO. d-f, The same as in panels a-c for a B-type 100 base-pairs dsDNA molecule with the random sequence investigated experimentally. All the results were obtained using density functional theory and the DFT-FE package27.

Source data

Extended Data Fig. 5 Ab initio electronic structure calculations of nicked molecules.

a, A-type 100 base-pairs dsDNA molecule with a random sequence and featuring a nick (removal of a phosphate group in the position indicated in the frame). b, The same molecule as in panel a, but featuring two nicks (one in each strand, as indicated by the two frames). c, The local density of states (LDOS) as a function of energy for the one-nick molecule of panel a showing the individual contributions of base-pair-centred states (black line) and backbone-centred states (red line). d, The computed localization length, normalized by the total length of the molecule, as a function of energy for the occupied states of the one-nick molecule of panel a. The vertical dashed lines in panels c and d indicate the position of the HOMO. d-f, The same as in panels c-d for the two-nick molecule of panel b. All the results were obtained using density functional theory and the DFT-FE package27.

Source data

Extended Data Fig. 6 Molecular dynamics simulations of 8 base-pairs dsDNA molecules.

a, A hydrogen bond occurs when a hydrogen atom (H) and the acceptor (A) are closer than 2.4Å and the angle between D – H – A is greater than 140°. b, Schematic drawing of Watson-Crick pairs guanine:cytosine. c, The distance between neighboring nucleobases is specified by the vector connecting their centers of mass. d, The vectors \(\vec a_i,\vec b_i\) define a plane for the nucleobase and the normal to the plane is described by \(\vec c_i = \vec a_i \times \vec b_i\). The relative orientation between adjacent bases is described by \(cos\theta = \frac{{\vec c_i \cdot \vec c_{i + 1}}}{{\left| {\vec c_i} \right|\left| {\vec c_{i + 1}} \right|}}\). e,f The figures show the relative position and orientation between neighboring nucleobases in the same strand only for the bases around the nicks. e, Intact structures, poly(dG)-poly(dC) and random sequence. f, One-nicked structures, poly(dG)-poly(dC) and random sequences.

Extended Data Fig. 7 Molecular dynamics simulations of intact and nicked random sequence dsDNA molecules.

a, A-type 8-base-pairs dsDNA molecule with a random sequence. The histograms show the probability distributions of inter-base distances and angles (Extended Data Fig. 6 and Supplementary Table 8) for the base pair along a single strand that is adjacent to the phosphate group to be removed in the nicked structure. b, Probability distributions as in a for the same molecule with a nick (removal of a phosphate group) inserted between the indicated base pairs. For notation see Supplementary Table 3. The MD simulations were carried out at 300 K using the CHARMM c37b2 software package42.

Source data

Extended Data Fig. 8 Molecular dynamics simulations and DFTB electronic structure computations of intact and nicked random sequence dsDNA molecules.

A-type 8-base-pairs dsDNA molecule with a random sequence. a Ratio of 〈LL(E)〉nicked/〈LL(E)〉without nicks where \(\cdots\) denotes MD averaging (over 10 ns trajectories) and LL(E) denotes localization length for orbitals at energy E. b Ratios σ/〈LL(E)〉 for the three structures, where \(\sigma = \sqrt {(LL\left( E \right) - \langle LL(E)\rangle)^2}\), for the intact and nicked structures. The calculations were carried out using the Density Functional based Tight Binding (DFTB)28 method as implemented in the DFTB+50 program package (version 17.1).

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–8.

Supplementary Data 1

Raw data – random sequence.

Supplementary Data 2

Raw data – random sequence.

Supplementary Data 3

Raw data – random sequence.

Supplementary Data 4

Raw data – random sequence.

Supplementary Data 5

Raw data – random sequence.

Supplementary Data 6

Raw data – random sequence.

Supplementary Data 7

Raw data – random sequence.

Supplementary Data 8

Raw data – random sequence.

Supplementary Data 9

Raw data – random sequence.

Supplementary Data 10

Raw data – random sequence.

Supplementary Data 11

Raw data – random sequence.

Supplementary Data 12

Raw data – random sequence.

Supplementary Data 13

Raw data – random sequence.

Supplementary Data 14

Raw data – random sequence.

Supplementary Data 15

Raw data – one nick.

Supplementary Data 16

Raw data – one nick.

Supplementary Data 17

Raw data – one nick.

Supplementary Data 18

Raw data – poly(G)–poly(C).

Supplementary Data 19

Raw data – poly(G)–poly(C).

Supplementary Data 20

Raw data – poly(G)-poly(C).

Supplementary Data 21

Raw data – poly(G)-poly(C).

Supplementary Data 22

Raw data – poly(G)-poly(C).

Supplementary Data 23

Raw data – poly(G)-poly(C).

Source data

Source Data Fig. 2

Numerical data used to generate graphs in Fig. 2a–c

Source Data Fig. 3

Numerical data used to generate graphs in 3b

Source Data Fig. 4

Numerical data used to generate graphs in 4b,c,e,f

Source Data Extended Data Fig. 2

Numerical data used to generate graphs in Extended Data Fig. 2a,b

Source Data Extended Data Fig. 4

Numerical data used to generate graphs in Extended Data Fig. 4b,c,e,f

Source Data Extended Data Fig. 5

Numerical data used to generate graphs in Extended Data Fig. 5–f

Source Data Extended Data Fig. 7

Numerical data used to generate graphs in Extended Data Fig. 7a,b

Source Data Extended Data Fig. 8

Numerical data used to generate graphs in Extended Data Fig. 8a,b

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Zhuravel, R., Huang, H., Polycarpou, G. et al. Backbone charge transport in double-stranded DNA. Nat. Nanotechnol. 15, 836–840 (2020). https://doi.org/10.1038/s41565-020-0741-2

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