Abstract
Efficiency and substrate specificity of proteases in the Potyviridae family have not been comprehensively profiled. Here we develop a model that learns co-evolutionary features to accurately predict and experimentally validate protease performance at single amino-acid resolution. We identify and engineer several proteases that perform better than the commercially available tobacco etch virus protease. To demonstrate the resolving power of our methods, we engineer protease crosstalk to selectively trigger a synthetic cell-death program in human cells.
Data availability
The data generated in this study have been deposited in the Zenodo database under accession code https://doi.org/10.5281/zenodo.15039890. The overview of aligned Potyviridae sequences and the list of plasmids (Supplementary Data 2) used in this study are provided as Supplementary Data. The plasmid sequences and maps for all proteases, the 7-, GS-flanked 7-, and 20-amino acid substrate of TEVp, and the H2B-sfGFP reporter used here are available on Addgene: https://www.addgene.org/Dave_Dingal/. Source data are provided with this paper. The protein structural data used in this study are available in the PDB database under accession code 1LVM. The protein family profile HMM used in this study are available in the InterPro database under accession code PF00863. Source data are provided with this paper.
Code availability
To facilitate the testing of thousands of potyviral proteases against peptide targets by multiple laboratories, we created an interactive web application for ProSSpeC (https://coevolutionary.org/prosspec/). Code is available at https://github.com/morcoslab/ProSSpeC and archived on Zenodo with https://doi.org/10.5281/zenodo.1832102535.
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Acknowledgements
We thank members of the Dingal lab and Morcos lab for their advice, expertise, and discussions. We thank Elliott Joe, Ahmed Adookkattil, and Shashwat Singh for data analysis support. We also thank the UTD Flow Cytometry Core for infrastructure and support. We acknowledge the UTD Office of Information Technology Cyberinfrastructure Research Computing for providing high-performance computing and services. M.B.L. is supported by the UTD Eugene McDermott Graduate Fellowship. This research was supported by a UTD Startup Fund and National Institutes of Health-NIGMS awards to the labs of P.C.D.P.D. (R35GM150967) and of F.M. (R35GM133631). F.M. acknowledges support from the National Science Foundation (MCB-1943442).
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M.B.L., Z.S., A.S.Y., A.T., R.R., J.N., and J.K. performed experiments. M.B.L. and P.C.D.P.D. analyzed experimental results. Computational modeling and full stack development of the ProSSpeC web app performed by C.Z. Conceptual planning and resources provided by P.C.D.P.D. and F.M. M.B.L., C.Z., F.M., and P.C.D.P.D. authored and edited this manuscript, including illustrations. The final version of this manuscript is approved by all authors.
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The Board of Regents of The University of Texas System have filed a pending patent application on behalf of co-inventors P.C.D.P.D., F.M., C.Z., and M.B.L. of the engineered proteases described (US Provisional Application No. 63/885,099). The remaining authors declare no competing interests.
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Lim Suan, M.B., Ziegler, C., Syed, Z. et al. Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69961-5
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DOI: https://doi.org/10.1038/s41467-026-69961-5
