Skip to content
DraconiticResearch
Search Sources API
197,788 studies31 sourcesUpdated 38 min ago
Peer-reviewedCC BY-NC-NDNature Biotechnology

AI-guided redesign of laboratory-evolved reverse transcriptases enhances prime editing

  • Y. Allen Tao1,2,3
  • Holt A. Sakai1,2,3
  • Allen Yujie Jiang1,2,3
  • Nicholas Andrew Krasnow1,2,3
  • Vasilii Vaganov1,2,3
  • Brian H. Shim1,2,3
  • Zachary Barsdale1,2,3
  • Smriti Pandey1,2,3
  • Nouraiz Ahmed1,2,3
  • Man Na3
  • Ting-Wei Liao1,2,3
  • Keyede Oye1,2,3
5 more authors
  • Ana Cristian1,2,4,3
  • Emily Zhang1,2,3
  • J. Y. Xu1,2,3
  • M. Bulcaen1,2,3
  • David R. Liu5,6,7
7 affiliations
  1. 1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
  2. 2Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
  3. 3Merkin Institute of Transformative Technologies in Healthcare, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
  4. 4Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
  5. 5Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA. [email protected]
  6. 6Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA. [email protected]
  7. 7Merkin Institute of Transformative Technologies in Healthcare, The Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected]
Open at sourceDOICite

Abstract

Although protein engineering and laboratory evolution have been used to optimize prime editors, we show that previous changes that improve prime editor efficiency also compromise protein stability and expression level, limiting performance. To address these limitations, we apply structure-informed artificial intelligence-guided methods such as the inverse-folding network ProteinMPNN to redesign the reverse transcriptase (RT) domains of engineered and evolved prime editors while preserving regions essential for catalysis. Redesigned RTs are extensively mutated, with 30-163 amino acid substitutions, and exhibit enhanced folding stability and soluble expression and up to twofold higher intracellular prime editor protein levels following mRNA delivery. Redesigned PE8 prime editors demonstrate enhanced editing efficiencies across multiple ex vivo contexts, including in several human primary cell types and via several delivery modalities. In mice, editing efficiency is up to 2.9-fold higher than that of state-of-the-art PE6, PE7 and PEmax prime editors. These findings demonstrate a generalizable approach for augmenting laboratory evolution to improve genome editing agents.

Related

  1. A co-evolved peptide-GPCR system senses host entry to drive fungal infection
    Gabriel Mendoza-Rojas, Philip Nakonz, Min Lu +7
    BioRxivPreprintLicence not statedStructural biologyGeneral biologyHAL
  2. Computational redesign of Chlamydomonas reinhardtii photosynthetic fructose-1,6-bisphosphatase from the Calvin-Benson-Bassham cycle improves both stability and catalytic capacities in vitro
    C. Quel Pinol, T. Chenuel, J. Henri
    bioRxivPreprintCC BYFull textMolecular biology & gene regulationbioRxiv
  3. A co-evolved peptide-GPCR system senses host entry to drive fungal infection
    Gabriel Mendoza-Rojas, Philip Nakonz, Min Lu +9
    Nature CommunicationsPeer-reviewedCC BY 4.0Full text1 citedMolecular biology & gene regulationCrossref
  4. Conservation guided reverse vaccinology for the design of a broadly immunogenic multi-epitope vaccine against monkeypox virus
    Harshit Sajal, Aswin Mohan, Rajesh Raju +1
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref
  5. Molecular basis of DNA polymerase ζ’s reverse transcriptase activity
    Radhika Malik, Robert E. Johnson, Louise Prakash +2
    Nature CommunicationsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationPMCEurope PMC

Cite

BibTeXRIS
BibTeX
@article{tao2026aiguided,
  title = {{AI-guided redesign of laboratory-evolved reverse transcriptases enhances prime editing}},
  author = {Y. Allen Tao and Holt A. Sakai and Allen Yujie Jiang and Nicholas Andrew Krasnow and Vasilii Vaganov and Brian H. Shim and Zachary Barsdale and Smriti Pandey and Nouraiz Ahmed and Man Na and Ting-Wei Liao and Keyede Oye and Ana Cristian and Emily Zhang and J. Y. Xu and M. Bulcaen and David R. Liu},
  journal = {Nature Biotechnology},
  year = {2026},
  month = may,
  doi = {10.1038/s41587-026-03149-6},
  url = {https://doi.org/10.1038/s41587-026-03149-6},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - AI-guided redesign of laboratory-evolved reverse transcriptases enhances prime editing
AU  - Y. Allen Tao
AU  - Holt A. Sakai
AU  - Allen Yujie Jiang
AU  - Nicholas Andrew Krasnow
AU  - Vasilii Vaganov
AU  - Brian H. Shim
AU  - Zachary Barsdale
AU  - Smriti Pandey
AU  - Nouraiz Ahmed
AU  - Man Na
AU  - Ting-Wei Liao
AU  - Keyede Oye
AU  - Ana Cristian
AU  - Emily Zhang
AU  - J. Y. Xu
AU  - M. Bulcaen
AU  - David R. Liu
PY  - 2026
DA  - 2026/05/21
JO  - Nature Biotechnology
DO  - 10.1038/s41587-026-03149-6
UR  - https://doi.org/10.1038/s41587-026-03149-6
AB  - Although protein engineering and laboratory evolution have been used to optimize prime editors, we show that previous changes that improve prime editor efficiency also compromise protein stability and expression level, limiting performance. To address these limitations, we apply structure-informed artificial intelligence-guided methods such as the inverse-folding network ProteinMPNN to redesign the reverse transcriptase (RT) domains of engineered and evolved prime editors while preserving regions essential for catalysis. Redesigned RTs are extensively mutated, with 30-163 amino acid substitutions, and exhibit enhanced folding stability and soluble expression and up to twofold higher intracellular prime editor protein levels following mRNA delivery. Redesigned PE8 prime editors demonstrate enhanced editing efficiencies across multiple ex vivo contexts, including in several human primary cell types and via several delivery modalities. In mice, editing efficiency is up to 2.9-fold higher than that of state-of-the-art PE6, PE7 and PEmax prime editors. These findings demonstrate a generalizable approach for augmenting laboratory evolution to improve genome editing agents.
SN  - 0733-222X
SN  - 1087-0156
SN  - 1546-1696
SN  - 2331-3684
ER  -