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Journal Article


Department of Physics


Enzymes function by stabilizing reaction transition states; therefore, comparison of the transition states of enzymatic and nonenzymatic model reactions can provide insight into biological catalysis. Catalysis of RNA 2′-O-transphosphorylation by ribonuclease A is proposed to involve electrostatic stabilization and acid/base catalysis, although the structure of the rate-limiting transition state is uncertain. Here, we describe coordinated kinetic isotope effect (KIE) analyses, molecular dynamics simulations, and quantum mechanical calculations to model the transition state and mechanism of RNase A. Comparison of the 18O KIEs on the 2′O nucleophile, 5′O leaving group, and nonbridging phosphoryl oxygens for RNase A to values observed for hydronium- or hydroxide-catalyzed reactions indicate a late anionic transition state. Molecular dynamics simulations using an anionic phosphorane transition state mimic suggest that H-bonding by protonated His12 and Lys41 stabilizes the transition state by neutralizing the negative charge on the nonbridging phosphoryl oxygens. Quantum mechanical calculations consistent with the experimental KIEs indicate that expulsion of the 5′O remains an integral feature of the rate-limiting step both on and off the enzyme. Electrostatic interactions with positively charged amino acid site chains (His12/Lys41), together with proton transfer from His119, render departure of the 5′O less advanced compared with the solution reaction and stabilize charge buildup in the transition state. The ability to obtain a chemically detailed description of 2′-O-transphosphorylation transition states provides an opportunity to advance our understanding of biological catalysis significantly by determining how the catalytic modes and active site environments of phosphoryl transferases influence transition state structure.

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Proceedings of the National Academy of Sciences of the United States of America

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National Academy of Sciences

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This work was supported by the Case Western Reserve University Center for Proteomics and Bioinformatics Center and by National Institutes of Health (NIH) Grant GM096000 (to M.E.H.), NIH Grant AI081987 (to J.A.P.), Hong Kong Baptist University (HKBU) startup and Faculty Research Grant funds (38-40-088 and 40-49-495) to K.-Y.W., and NIH Grant GM064288 (to D.M.Y.). D.L.K. was supported by NIH Training Grant T32-GM008056. This work was partially supported by the computing resources of the Minnesota Supercomputing Institute, by the High Performance Cluster Computing Centre and the Information Technology Office at HKBU (sciblade and jiraiya), and by the National Science Foundation through TeraGrid resources provided by Ranger, Texas Advanced Computing Center, and Kraken at the National Institute for Computational Sciences under Grant TG-CHE100072.



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Copyright © 2013 National Academy of Sciences

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JA-5229-25592_suppl.pdf (489 kB)

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