The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Precision is essential for efficient catalysis in an evolved Kemp eliminase
Open accessAbstract
Linus Pauling established the conceptual framework for understanding and mimicking enzymes more than six decades ago. The notion that enzymes selectively stabilize the rate-limiting transition state of the catalysed reaction relative to the bound ground state reduces the problem of design to one of molecular recognition. Nevertheless, past attempts to capitalize on this idea, for example by using transition state analogues to elicit antibodies with catalytic activities, have generally failed to deliver true enzymatic rates. The advent of computational design approaches, combined with directed evolution, has provided an opportunity to revisit this problem. Starting from a computationally designed catalyst for the Kemp elimination—a well-studied model system for proton transfer from carbon—we show that an artificial enzyme can be evolved that accelerates an elementary chemical reaction 6 × 108-fold, approaching the exceptional efficiency of highly optimized natural enzymes such as triosephosphate isomerase. A 1.09 Å resolution crystal structure of the evolved enzyme indicates that familiar catalytic strategies such as shape complementarity and precisely placed catalytic groups can be successfully harnessed to afford such high rate accelerations, making us optimistic about the prospects of designing more sophisticated catalysts.
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Cited by
Emergence of a catalytic tetrad during evolution of a highly active artificial aldolase
Obexer, R., Godina, A., Garrabou, X., Mittl, P. R. E., Baker, D., Griffiths, A. D., & Hilvert, D. (2017) · Nature Chemistry · doi:10.1038/nchem.2596 · Cited by 312
Self-citationComputational enzyme design by catalytic motif scaffolding
Braun, M., Tripp, A., Chakatok, M., Kaltenbrunner, S., Fischer, C., Stoll, D., Bijelic, A., Elaily, W., Totaro, M. G., Moser, M., et al. (2026) · Nature · doi:10.1038/s41586-025-09747-9 · Cited by 73
Mutexa: A Computational Ecosystem for Intelligent Protein Engineering
Yang, Z. J., Shao, Q., Jiang, Y., Jurich, C., Ran, X., Juarez, R. J., Yan, B., Stull, S. L., Gollu, A., & Ding, N. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00602 · Cited by 28
Metadata
- Authors
- Blomberg, R., Kries, H., Pinkas, D. M., Mittl, P. R. E., Grütter, M. G., Privett, H. K., Mayo, S. L., & Hilvert, D.
- Venue
- Nature
- Published
- 2013-10-16
- Volume
- 503
- Issue
- 7476
- Pages
- 418-421
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2002219567
- Cited by
- 337
- References
- 38
- FWCI
- 12.91
- Type
- journal article
- Language
- en
- Open access
- green
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