De novo design of biocatalysts
Abstract
The challenging field of de novo enzyme design is beginning to produce exciting results. The application of powerful computational methods to functional protein design has recently succeeded at engineering target activities. In addition, efforts in directed evolution continue to expand the transformations that can be accomplished by existing enzymes. The engineering of completely novel catalytic activity requires traversing inactive sequence space in a fitness landscape, a feat that is better suited to computational design. Optimizing activity, which can include subtle alterations in backbone conformation and protein motion, is better suited to directed evolution, which is highly effective at scaling fitness landscapes towards maxima. Improved rational design efforts coupled with directed evolution should dramatically improve the scope of de novo enzyme design.
Additional Information
© 2002 Elsevier Science Ltd. Published online 7th February 2002. This research was supported by the Howard Hughes Medical Institute, the Ralph M Parsons Foundation and a Shared University Research Grant from IBM (SLM), the Helen G and Arthur McCallum Foundation, the Evelyn Sharp Graduate Fellowship, and grant GM07616 from the National Institutes of Health (DNB), a National Science Foundation graduate research fellowship and a California Institute of Technology Initiative in Computational Molecular Biology funded by Burroughs Wellcome (CAV).Additional details
- Eprint ID
- 25477
- Resolver ID
- CaltechAUTHORS:20110928-152719152
- Howard Hughes Medical Institute (HHMI)
- Ralph M. Parsons Foundation
- IBM Shared University Research Grant
- Helen G. and Arthur McCallum Foundation
- Evelyn Sharp Graduate Fellowship
- GM07616
- NIH
- NSF Graduate Research Fellowship
- Burroughs Wellcome Fund/Caltech Initiative in Computational Molecular Biology
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2011-10-04Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field