Enzyme-assisted high throughput sequencing of an expanded genetic alphabet at single base resolution

Bang Wang, Kevin M. Bradley, Myong-Jung Kim, Roberto Laos, Cen Chen, Dietlind L. Gerloff, Luran Manfio, Zunyi Yang () and Steven A. Benner ()
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Bang Wang: Foundation for Applied Molecular Evolution
Kevin M. Bradley: LLC
Myong-Jung Kim: LLC
Roberto Laos: Foundation for Applied Molecular Evolution
Cen Chen: Foundation for Applied Molecular Evolution
Dietlind L. Gerloff: Foundation for Applied Molecular Evolution
Luran Manfio: Foundation for Applied Molecular Evolution
Zunyi Yang: Foundation for Applied Molecular Evolution
Steven A. Benner: Foundation for Applied Molecular Evolution

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract With just four building blocks, low sequence information density, few functional groups, poor control over folding, and difficulties in forming compact folds, natural DNA and RNA have been disappointing platforms from which to evolve receptors, ligands, and catalysts. Accordingly, synthetic biology has created “artificially expanded genetic information systems” (AEGIS) to add nucleotides, functionality, and information density. With the expected improvements seen in AegisBodies and AegisZymes, the task for synthetic biologists shifts to developing for expanded DNA the same analytical tools available to natural DNA. Here we report one of these, an enzyme-assisted sequencing of expanded genetic alphabet (ESEGA) method to sequence six-letter AEGIS DNA. We show how ESEGA analyses this DNA at single base resolution, and applies it to optimized conditions for six-nucleotide PCR, assessing the fidelity of various DNA polymerases, and extending this to AEGIS components with functional groups. This supports the renewed exploitation of expanded DNA alphabets in biotechnology.

Date: 2024
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DOI: 10.1038/s41467-024-48408-9

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