Computational design of ligand-binding proteins with high affinity and selectivity
- PMID: 24005320
- PMCID: PMC3898436
- DOI: 10.1038/nature12443
Computational design of ligand-binding proteins with high affinity and selectivity
Abstract
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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Comment in
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Computational biology: A recipe for ligand-binding proteins.Nature. 2013 Sep 12;501(7466):177-8. doi: 10.1038/nature12463. Epub 2013 Sep 4. Nature. 2013. PMID: 24005323 No abstract available.
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Protein designers go small.Science. 2013 Sep 6;341(6150):1052. doi: 10.1126/science.341.6150.1052-b. Science. 2013. PMID: 24009368 No abstract available.
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Designer binders.Nat Methods. 2013 Nov;10(11):1057. doi: 10.1038/nmeth.2719. Nat Methods. 2013. PMID: 24344382 No abstract available.
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