(340f) Quantum Molecular Sequencing: Unravelling Genomic Information One Molecule at a Time

Health and medicine are personal for each individual, but most of the current diagnosis and treatments follow “one size fits all” approach. Recently there has been increasing awareness to remedy this, and several initiatives have been started towards personalized medicine, precision medicine, and others, to develop customized and tailored solutions. In this talk, I will address recent advances made in my group in developing Quantum Molecular Sequencing (QM-Seq) , a new nanoelectronic molecular spectroscopy technique that can provide a transformative solutions for detection, diagnosis and treatment of diseases. Particularly, my talk will focus on developing a new paradigm in molecular recognition and nanoelectronic spectroscopy that will provide useful and relevant genomic information for development of personalized medicine and represent a unified platform for multiomics: genomic, transcriptomic, epigenomic, and RNA structure.

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). In this talk I will present recent results on Quantum Molecular Sequencing, which utilizes nanoelectronic spectroscopy to develop a molecular recognition technique, using new 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. Besides better characterization of molecular orbitals using the new biophysical parameters and specific biochemical conditions, Quantum Molecular Sequencing combines new algorithms and customized machine learning techniques for sequencing single-molecule DNA, RNA, epigenetic modifications, and RNA structure, using a simple comparison of QM-Seq electronic spectra with a library of respective signatures without any prior expectation. These results have significant implications for developing a robust and accurate high-throughput multiomics technique.