Publication Date:
2013-09-06
Description:
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 beta-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.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898436/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉 〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898436/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Tinberg, Christine E -- Khare, Sagar D -- Dou, Jiayi -- Doyle, Lindsey -- Nelson, Jorgen W -- Schena, Alberto -- Jankowski, Wojciech -- Kalodimos, Charalampos G -- Johnsson, Kai -- Stoddard, Barry L -- Baker, David -- P41 GM103533/GM/NIGMS NIH HHS/ -- R01 GM049857/GM/NIGMS NIH HHS/ -- T32 HG000035/HG/NHGRI NIH HHS/ -- T32 HG00035/HG/NHGRI NIH HHS/ -- England -- Nature. 2013 Sep 12;501(7466):212-6. doi: 10.1038/nature12443. Epub 2013 Sep 4.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24005320" target="_blank"〉PubMed〈/a〉
Keywords:
Binding Sites
;
Biotechnology
;
*Computer Simulation
;
Crystallography, X-Ray
;
Digoxigenin/chemistry/*metabolism
;
*Drug Design
;
Estradiol/chemistry/metabolism
;
Ligands
;
Models, Molecular
;
Progesterone/chemistry/metabolism
;
Protein Binding
;
Proteins/*chemistry/*metabolism
;
Reproducibility of Results
;
Substrate Specificity
Print ISSN:
0028-0836
Electronic ISSN:
1476-4687
Topics:
Biology
,
Chemistry and Pharmacology
,
Medicine
,
Natural Sciences in General
,
Physics
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