Summarize this content to 100 words: Neurath, H. & Walsh, K. A. Role of proteolytic enzymes in biological regulation (a review). Proc. Natl. Acad. Sci. USA. 73, 3825–3832 (1976).
Google Scholar
Kapust, R. B. & Waugh, D. S. Controlled intracellular processing of fusion proteins by TEV protease. Protein Expr. Purif. 19, 312–318 (2000).
Google Scholar
Rawlings, N. D. & Salvesen, G. Handbook of Proteolytic Enzymes, 1–3 (Academic Press, 2013).Xie, M. & Fussenegger, M. Designing cell function: assembly of synthetic gene circuits for cell biology applications. Nat. Rev. Mol. Cell Biol. 19, 507–525 (2018).
Google Scholar
Fernandez-Rodriguez, J. & Voigt, C. A. Post-translational control of genetic circuits using potyvirus proteases. Nucleic Acids Res. 44, 6493–6502 (2016).
Google Scholar
Fink, T. et al. Design of fast proteolysis-based signaling and logic circuits in mammalian cells. Nat. Chem. Biol. 15, 115–122 (2018).
Google Scholar
Sanchez, M. I. & Ting, A. Y. Directed evolution improves the catalytic efficiency of TEV protease. Nat. Methods 17, 167–174 (2019).
Google Scholar
Carrington, J. C. & Dougherty, W. G. Small nuclear inclusion protein encoded by a plant potyvirus genome is a protease. J. Virol. 61, 2540–2548 (1987).
Google Scholar
Morcos, F. et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl. Acad. Sci. USA. 108, E1293–E1301 (2011).
Google Scholar
Dos Santos, R. N., Morcos, F., Jana, B., Andricopulo, A. D. & Onuchic, J. N. Dimeric interactions and complex formation using direct coevolutionary couplings. Sci. Rep. 5, 1–10 (2015).
Google Scholar
Morcos, F., Jana, B., Hwa, T. & Onuchic, J. N. Coevolutionary signals across protein lineages help capture multiple protein conformations. Proc. Natl. Acad. Sci. USA. 110, 20533–20538 (2013).
Google Scholar
Cheng, R. R., Morcos, F., Levine, H. & Onuchic, J. N. Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information. Proc. Natl. Acad. Sci. USA. 111, E563–E571 (2014).Jiang, X. L., Dimas, R. P., Chan, C. T. Y. & Morcos, F. Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions. Nat. Commun. 12, 5592 (2021).Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
Savinov, A., Swanson, S., Keating, A. E. & Li, G.-W. High-throughput discovery of inhibitory protein fragments with AlphaFold. Proc. Natl. Acad. Sci. USA. 122, e2322412122 (2025).
Google Scholar
Zhou, Q. et al. Global pairwise RNA interaction landscapes reveal core features of protein recognition. Nat. Commun. 9, 2511 (2018).Dimas, R. P., Jiang, X. L., De La Paz, J. A., Morcos, F. & Chan, C. T. Y. Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset. Nucleic Acids Res. 47, 5449–5463 (2019).Kipniss, N. H. et al. Engineering cell sensing and responses using a GPCR-coupled CRISPR-Cas system. Nat. Commun. 2017 8, 1–10 (2017).
Google Scholar
Kapust, R. B. et al. Tobacco etch virus protease: mechanism of autolysis and rational design of stable mutants with wild-type catalytic proficiency. Protein Eng. 14, 993–1000 (2001).
Google Scholar
Xia, S. et al. Synthetic protein circuits for programmable control of mammalian cell death. Cell 187, 2785–2800.e16 (2024).
Google Scholar
Gao, X. J., Chong, L. S., Kim, M. S. & Elowitz, M. B. Programmable protein circuits in living cells. Science 361, 1252–1258 (2018).
Google Scholar
Goh, C. J. & Hahn, Y. Analysis of proteolytic processing sites in potyvirus polyproteins revealed differential amino acid preferences of NIa-pro protease in each of seven cleavage sites. PLoS ONE 16, e0245853 (2021).Kapust, R. B., Toözseór, J., Copeland, T. D. & Waugh, D. S. The P1′ specificity of tobacco etch virus protease. Biochem. Biophys. Res. Commun. 294, 949–955 (2002).Huber, L. et al. Data-driven protease engineering by DNA-recording and epistasis-aware machine learning. Nat. Commun. 16, 5466 (2025).Beaumont, L. P., Mehalko, J., Johnson, A., Wall, V. E. & Esposito, D. Unexpected tobacco etch virus (TEV) protease cleavage of recombinant human proteins. Protein Expr. Purif. 220, 106488 (2024).Song, J. et al. PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites. PLoS ONE 7, e50300 (2012).Song, J. et al. IProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief. Bioinform. 20, 638–658 (2019).Song, J. et al. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. Bioinformatics 34, 684–687 (2018).Gasteiger, E. et al. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31, 3784–3788 (2003).Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
Google Scholar
Koutra, D., Shah, N., Vogelstein, J. T., Gallagher, B. & Faloutsos, C. DELTACON: principled massive-graph similarity function with attribution. ACM Trans. Knowl. Discov. Data 10, 1–43 (2016).Kumar, S. et al. MEGA12: molecular evolutionary genetic analysis version 12 for adaptive and green computing. Mol. Biol. Evol. 41, msae263 (2024).Feng, S. et al. Bright split red fluorescent proteins for the visualization of endogenous proteins and synapses. Commun. Biol. 2, 1–12 (2019).
Google Scholar
Lim Suan, M. B. et al. Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models. ProSSpeC. https://doi.org/10.5281/zenodo.18321025 (2025).
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Identification and engineering of highly functional potyviral proteases in cells using co-evolutionary models
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