Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41748587.
- Also identified by DOI 10.1038/s41467-026-69961-5 and PMC identifier 13062108.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Protein Engineering
- Peptide Hydrolases
- Potyvirus
- Viral Proteins