Exploitation of binding energy for catalysis and design

By Summer B. Thyme, Jordan Jarjour, R. Takeuchi, James J. Havranek, Justin Ashworth, A. Scharenberg, Barry L. Stoddard, David Baker. 2009

106 citations recorded by the source corpus.

Enzymes use substrate-binding energy both to promote ground-state association and to stabilize the reaction transition state selectively. The monomeric homing endonuclease I-AniI cleaves with high sequence specificity in the centre of a 20-base-pair (bp) DNA target site, with the amino (N)-terminal domain of the enzyme making extensive binding interactions with the left (-) side of the target site and the similarly structured carboxy (C)-terminal domain interacting with the right (+) side. Here we show that, despite the approximate twofold symmetry of the enzyme–DNA complex, there is almost complete segregation of interactions responsible for substrate binding to the (-) side of the interface and interactions responsible for transition-state stabilization to the (+) side. Although single base-pair substitutions throughout the entire DNA target site reduce catalytic efficiency, mutations in the (-) DNA half-site almost exclusively increase the dissociation constant (KD) and the Michaelis constant under single-turnover conditions (KM*), and those in the (+) half-site primarily decrease the turnover number (kcat*). The reduction of activity produced by mutations on the (-) side, but not mutations on the (+) side, can be suppressed by tethering the substrate to the endonuclease displayed on the surface of yeast. This dramatic asymmetry in the use of enzyme–substrate binding energy for catalysis has direct relevance to the redesign of endonucleases to cleave genomic target sites for gene therapy and other applications. Computationally redesigned enzymes that achieve new specificities on the (-) side do so by modulating KM*, whereas redesigns with altered specificities on the (+) side modulate kcat*. Our results illustrate how classical enzymology and modern protein design can each inform the other.

Subfields: Molecular Biology

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In Molecular Biology

1. David Baker
2. Po-Ssu Huang
3. I. Samish
4. Tanja Kortemme
5. Philip Bradley
6. Summer B. Thyme
7. Barry L. Stoddard
8. Yifan Song

Citations in the corpus

• Computational reprogramming of homing endonuclease specificity at multiple adjacent base pairs (2010, methodology): “The computational methodology for the prediction and redesign of homing endonuclease specificity has been described previously (6,11).”
• Achievements and Challenges in Computational Protein Design. (2017, methodology): “Using a CPD scheme previously applied to the protein [58, 76], concerted design for all simultaneous substitutions was more successful than a modular approach against individual substitutions, highlighting the importance of contextdependent redesign and optimization of protein–DNA interactions.”
• Reprogramming homing endonuclease specificity through computational design and directed evolution (2013, methodology): “Experiments measuring kinetics always used the fulllength plasmid substrates to maintain consistency with previously collected kinetic data (12).”
• Improved Modeling of Side-Chain–Base Interactions and Plasticity in Protein–DNA Interface Design (2012, background): “18 The importance of these three C-terminal residues for cleavage of this particular target DNA is underscored by their complete conservation in homologues of I-AniI predicted to cleave a very similar target DNA sequence, even in those with sequence identity of less than 50%.”
• Massively parallel determination and modeling of endonuclease substrate specificity (2014, methodology): “Previously, we carried out standard DNA cleavage assays to collect kinetic data on each single base-pair substitution in the target site of the I-AniI homing endonuclease and found that distinct interface domains function in groundstate and transition-state formation during the reaction (2).”
• RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design (2011, background): “Computational protein design tools to date have been useful for engineering proteins with a wide range of functions, including DNA binding [1,2,3,4], co-factor binding [5], catalysis [6,7,8,9], fluorescence spectral change [10], peptide-protein specificity [11,12], and protein-protein interaction [1”