Literature DB >> 28067976

Single Nucleobase Identification Using Biophysical Signatures from Nanoelectronic Quantum Tunneling.

Lee E Korshoj1,2, Sepideh Afsari1,2, Sajida Khan1,2, Anushree Chatterjee1,3, Prashant Nagpal1,2,3,4.   

Abstract

Nanoelectronic DNA sequencing can provide an important alternative to sequencing-by-synthesis by reducing sample preparation time, cost, and complexity as a high-throughput next-generation technique with accurate single-molecule identification. However, sample noise and signature overlap continue to prevent high-resolution and accurate sequencing results. Probing the molecular orbitals of chemically distinct DNA nucleobases offers a path for facile sequence identification, but molecular entropy (from nucleotide conformations) makes such identification difficult when relying only on the energies of lowest-unoccupied and highest-occupied molecular orbitals (LUMO and HOMO). Here, nine biophysical parameters are developed to better characterize molecular orbitals of individual nucleobases, intended for single-molecule DNA sequencing using quantum tunneling of charges. For this analysis, theoretical models for quantum tunneling are combined with transition voltage spectroscopy to obtain measurable parameters unique to the molecule within an electronic junction. Scanning tunneling spectroscopy is then used to measure these nine biophysical parameters for DNA nucleotides, and a modified machine learning algorithm identified nucleobases. The new parameters significantly improve base calling over merely using LUMO and HOMO frontier orbital energies. Furthermore, high accuracies for identifying DNA nucleobases were observed at different pH conditions. These results have significant implications for developing a robust and accurate high-throughput nanoelectronic DNA sequencing technique.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  DNA; biophysics; nucleic acids; transition voltage spectroscopy; tunneling spectroscopy

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Year:  2017        PMID: 28067976     DOI: 10.1002/smll.201603033

Source DB:  PubMed          Journal:  Small        ISSN: 1613-6810            Impact factor:   13.281


  1 in total

1.  A machine learning approach for accurate and real-time DNA sequence identification.

Authors:  Yiren Wang; Mashari Alangari; Joshua Hihath; Arindam K Das; M P Anantram
Journal:  BMC Genomics       Date:  2021-07-09       Impact factor: 3.969

  1 in total

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