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



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 problem1. Current approaches for designing ligand-binding proteins for medical2 and biotechnological uses rely on raising antibodies against a target antigen in immunized animals3,4 and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand5,6,7, 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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Figure 1: Computational design methodology and experimental binding validation.
Figure 2: Binding characterization and affinity maturation.
Figure 3: Crystal structures of DIG10.2–DIG and DIG10.3–DIG.
Figure 4: Steroid binding selectivity.

Accession codes


Protein Data Bank

Data deposits

The crystal structures of DIG10.2 and DIG10.3 bound to DIG have been deposited in the RCSB Protein Data Bank under the accession codes 4J8T (DIG10.2) and 4J9A (DIG10.3).


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We thank E.-M. Strauch and P.-S. Huang for providing the ZZ/pETCON and S2/pETCON plasmids, respectively, and B. Shen for assistance with data processing, modelling and refinement of the X-ray crystal structures. We thank J. P. Sumida for assistance with analytical ultracentrifugation data collection, processing, and analysis. Analytical ultracentrifugation experiments were performed at the University of Washington Analytical Biopharmacy Core, which is supported by the Washington State Life Sciences Discovery Fund and the Center for the Intracellular Delivery of Biologics. We thank S. Fleishman, O. Khersonsky and P.-S. Huang for comments on the manuscript. J.W.N. acknowledges a pre-doctoral fellowship from the National Human Genome Research Institute under the Interdisciplinary Training in Genome Sciences program (T32 HG00035). This work was supported by grants from DTRA and DARPA to D.B., a grant from the Swiss National Science Foundation to K.J., and National Science Foundation grant MCB1121896 to C.G.K.

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Authors and Affiliations



C.E.T., S.D.K. and D.B. designed the research. S.D.K., C.E.T. and J.D. performed computations. S.D.K. wrote program code. C.E.T. experimentally characterized the designs, constructed libraries, performed affinity maturation and deep sequencing selections, and conducted binding and biochemical studies of DIG10. J.D. characterized DIG5. J.W.N. analysed deep sequencing data. C.E.T. and J.D. prepared protein samples for crystallographic analysis. L.D. and B.S. crystallized the protein samples and analysed crystallographic data. A.S. and K.J. synthesized DIG-PEG3-biotin and DIG-PEG3-Alexa488. C.E.T. prepared protein samples for ITC studies, and W.J. and C.G.K. performed ITC experiments and analysed ITC data. C.E.T., S.D.K. and D.B. analysed data and wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to David Baker.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Figure legends 1-2, Supplementary Methods, Supplementary Tables 1-22, Supplementary Figures 1-21 with legends, and Supplementary Data. Supplementary Methods contains additional computational and experimental methods. Supplementary Tables contain information about design metrics, experimental observations and statistics, and primer sequences. Supplementary Figures provide additional experimental results. Supplementary Data contains command lines and protocols for running design calculations. (PDF 12410 kb)

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Tinberg, C., Khare, S., Dou, J. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).

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