The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Computational design of ligand-binding proteins with high affinity and selectivity
Open accessAbstract
The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein–small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medical and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Here we describe a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG). Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and β-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics.
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Cites
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Self-citationKemp elimination catalysts by computational enzyme design
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Self-citationGenetic circuit design automation
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Generalized biomolecular modeling and design with RoseTTAFold All-Atom
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Metadata
- Authors
- Tinberg, C. E., Khare, S. D., Dou, J., Doyle, L., Nelson, J. W., Schena, A., Jankowski, W., Kalodimos, C. G., Johnsson, K., Stoddard, B. L., & Baker, D.
- Venue
- Nature
- Published
- 2013-09
- Volume
- 501
- Issue
- 7466
- Pages
- 212-216
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2086361102
- Cited by
- 431
- References
- 37
- FWCI
- 17.21
- Type
- journal article
- Language
- en
- Open access
- green
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- Tinberg, C. E., Khare, S. D., Dou, J., Doyle, L., Nelson, J. W., Schena, A., Jankowski, W., Kalodimos, C. G., Johnsson, K., Stoddard, B. L., & Baker, D. (2013). Computational design of ligand-binding proteins with high affinity and selectivity. Nature. https://doi.org/10.1038/nature12443
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