NEWS AND VIEWS08 July 2025A computational workflow designs proteins with catalytic efficiencies comparable to those of some natural enzymes — a landmark result for the field.ByZhuofan Shen0 &Sagar D. Khare1Zhuofan ShenZhuofan Shen is in the Department of Chemistry and Chemical Biology, and at the Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA.View author publicationsSearch author on: PubMed Google ScholarSagar D. KhareSagar D. Khare is in the Department of Chemistry and Chemical Biology, and at the Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA.View author publicationsSearch author on: PubMed Google ScholarEnzymes are proteins that catalyse the chemical transformations essential to life. Through billions of years of evolution, nature has sculpted them to be remarkably efficient and specific for their substrates and reactions. For example, the ‘perfect’ enzyme triosephosphate isomerase (TIM) increases the rate of a reaction involved in glycolysis, a ubiquitous metabolic process, by more than one-billion-fold relative to the rate of the uncatalysed reaction at neutral pH (ref. 1)1. Researchers aim to fast-track the evolutionary process through computational design, but the resulting enzymes are less efficient catalytically than their natural counterparts. Writing in Nature, Listov et al.2 report a computational workflow that designs enzymes with catalytic efficiencies approaching those of some natural enzymes.Access optionsAccess Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription27,99 € / 30 dayscancel any timeLearn moreSubscribe to this journalReceive 51 print issues and online access199,00 € per yearonly 3,90 € per issueLearn moreRent or buy this articlePrices vary by article typefrom$1.95to$39.95Learn morePrices may be subject to local taxes which are calculated during checkoutdoi: https://doi.org/10.1038/d41586-025-02054-3ReferencesHall, A. & Knowles, J. R. Biochemistry 14, 4348–4352 (1975).Article PubMed Google Scholar Listov, D. et al. Nature https://doi.org/10.1038/s41586-025-09136-2 (2025).Article Google Scholar Korendovych, I. & DeGrado, W. F. Curr. Opin. Struct. Biol. 27, 113–121 (2014).Article PubMed Google Scholar Lamba, V. et al. Biochemistry 56, 582–591 (2017).Article PubMed Google Scholar Röthlisberger, D. et al. Nature 453, 190–195 (2008).Article PubMed Google Scholar Khersonsky, O. et al. Proc. Natl Acad. Sci. USA 109, 10358–10363 (2012).Article PubMed Google Scholar Privett, H. K. et al. Proc. Natl Acad. Sci. USA 109, 3790–3795 (2012).Article PubMed Google Scholar Blomberg, R. et al. Nature 503, 418–421 (2013).Article PubMed Google Scholar Otten, R. et al. Science 370, 1442–1446 (2020).Article PubMed Google Scholar Lipsh-Sokolik, R. et al. Science 379, 195–201 (2023).Article PubMed Google Scholar Gutierrez-Rus, L. I. et al. J. Am. Chem. Soc. 147, 14978–14996 (2025).Article PubMed Google Scholar Download referencesCompeting InterestsThe authors declare no competing interests. Read the paper: Complete computational design of high-efficiency Kemp elimination enzymes ‘Remarkable’ new enzymes built by algorithm with physics know-how AI-designed proteins tackle century-old problem — making snake antivenomsSee all News & ViewsSubjectsComputational biology and bioinformaticsBiochemistryChemical biologyLatest on:Jobs Garvan Faculty MemberThe Garvan Institute invites applicants for Faculty-level positions in the Immune Biotherapies and Precision Immunology Programs.Sydney (LGA), New South Wales (AU)The Garvan InstituteInsect NeuroscientistPioneer the development of insect-based biorobots—join SWARM Biotactics as an Insect Neuroscientist.Kassel, Hessen (DE)SWARM Biotactics