000188387 001__ 188387
000188387 005__ 20180317092946.0
000188387 0247_ $$2doi$$a10.1038/nature12443
000188387 022__ $$a1476-4687
000188387 02470 $$2ISI$$a000324244900039
000188387 037__ $$aARTICLE
000188387 245__ $$aComputational design of ligand-binding proteins with high affinity and selectivity
000188387 260__ $$aLondon$$bNature Publishing Group$$c2013
000188387 269__ $$a2013
000188387 300__ $$a7
000188387 336__ $$aJournal Articles
000188387 520__ $$aThe 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.
000188387 700__ $$aTinberg, Christine E.
000188387 700__ $$aKhare, Sagar D.
000188387 700__ $$aDou, Jiayi
000188387 700__ $$aDoyle, Lindsey
000188387 700__ $$aNelson, Jorgen W.
000188387 700__ $$0244191$$aSchena, Alberto$$g196846
000188387 700__ $$aJankowski, Wojciech
000188387 700__ $$aKalodimos, Charalampos G.
000188387 700__ $$0240057$$aJohnsson, Kai$$g123155
000188387 700__ $$aStoddard, Barry L.
000188387 700__ $$aBaker, David
000188387 773__ $$j501$$k7466$$q212-6$$tNature
000188387 909CO $$ooai:infoscience.tind.io:188387$$particle$$pSB
000188387 909C0 $$0252027$$pLIP$$xU10102
000188387 917Z8 $$x123155
000188387 917Z8 $$x123155
000188387 937__ $$aEPFL-ARTICLE-188387
000188387 973__ $$aEPFL$$rNON-REVIEWED$$sPUBLISHED
000188387 980__ $$aARTICLE