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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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.
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.
About this article
Nature Reviews Chemistry (2018)