4J9A

Engineered Digoxigenin binder DIG10.3

Summary for 4J9A

• Entry DOI: 10.2210/pdb4j9a/pdb
• Related: 4J8T
• Descriptor: Engineered Digoxigenin binder protein DIG10.3, DIGOXIGENIN (2 entities in total)
• Functional Keywords: engineered, computationally designed, digoxigenin-binding, digoxigenin binding protein
• Biological source: Pseudomonas aeruginosa
• Total number of polymer chains: 9
• Total formula weight: 147488.57

Authors

Stoddard, B.L.,Doyle, L.A. (deposition date: 2013-02-15, release date: 2013-06-26, Last modification date: 2024-02-28)

Primary citation

Tinberg, C.E.,Khare, S.D.,Dou, J.,Doyle, L.,Nelson, J.W.,Schena, A.,Jankowski, W.,Kalodimos, C.G.,Johnsson, K.,Stoddard, B.L.,Baker, D.
Computational design of ligand-binding proteins with high affinity and selectivity.
Nature, 501:212-216, 2013

PubMed 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.

PubMed: 24005320
DOI: 10.1038/nature12443

Experimental method

X-RAY DIFFRACTION (3.2 Å)