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<p>Efficiency and substrate specificity of proteases in the <i>Potyviridae</i> 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.</p>

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Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

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  • Medel B. Lim Suan Jr,
  • Cheyenne Ziegler,
  • Zain Syed,
  • Arjun Sai Yedavalli,
  • Jaimahesh Nagineni,
  • Rodrigo Raposo,
  • Ajay Tunikipati,
  • Jaideep Kaur,
  • Faruck Morcos,
  • P. C. Dave P. Dingal

摘要

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.