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

Computational protein design is used to create a protein that binds the steroid digoxigenin (DIG) with high affinity and selectivity; the computational design methods described here should help to enable the development of a new generation of small molecule receptors for synthetic biology, diagnostics and therapeutics. Current approaches to the design of ligand-binding proteins for medical or biotechnological applications involve raising antibodies against a target antigen in immunized animals and/or performing directed evolution experiments on proteins with pre-existing, low affinity for the desired ligand. This paper describes a general method for the computational design of small molecule binding proteins that the authors use to design high-affinity and highly selective binding sites for the steroid digoxigenin, a cardiac glycoside used to treat heart disease. Using this method it should be possible to rapidly create small molecule receptors for synthetic biology applications, therapeutic scavengers for toxic compounds in vivo and robust ligand-binding domains for diagnostic devices. 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.