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Nature. 2013 Sep 12;501(7466):212-216. doi: 10.1038/nature12443. Epub 2013 Sep 4.

Computational design of ligand-binding proteins with high affinity and selectivity.

Author information

1
Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
2
Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.
3
Graduate Program in Biological Physics, Structure, and Design, University of Washington, Seattle, WA 98195, USA.
4
Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
5
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
6
Institute of Chemical Sciences and Engineering, Institute of Bioengineering, National Centre of Competence in Research (NCCR) of Chemical Biology, École Polytechnique Fédérale de Laussane (EPFL), Laussane, Switzerland.
7
Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA.
8
Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
#
Contributed equally

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.

PMID:
24005320
PMCID:
PMC3898436
DOI:
10.1038/nature12443
[Indexed for MEDLINE]
Free PMC Article

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