Abstract
Creatininase is a key enzyme of creatinine-metabolizing pathway in mammals, and has a great potential for diagnostic application. It catalyzes the reversible conversion of creatinine to creatine. Here, we investigated its reaction mechanism with density functional theory in conjunction with the quantum cluster approach. Three reaction pathways in which several possible proton transfers assisted by either His178 or a water ligand to Zn1 (Wat2) or both were considered. DFT calculations reveal, depending on Wat2 coordination mode at Zn1, two competitive ring-opening pathways where His178 playing a central role as a proton shuttle or both His178 and Wat2 serving as a dual catalytic role as a base and an acid, respectively. Three elementary steps were proposed for the reaction: the first involves nucleophilic attack by a bridging hydroxide to the substrate and forms a gem-diolate intermediate, followed by a proton transfer from the gem-diolate to His178 (His178 protonation is a required step for efficient proton transfers). Finally, the second proton transfer from the protonated His178 or Wat2 to the amide of substrate leads to the ring opening. The first proton transfer is the rate-limiting step of the whole reaction, in consistent with previous experimental and computational studies. A detailed understanding of the reaction mechanism of the creatininase enzyme family will also be helpful for developing a biosensor for kidney function.
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Acknowledgements
Financial supports by the Thailand Research Fund and the University of Phayao (Grants RSA6280104 and FF64-RIB002) are gratefully acknowledged. The School of Science, University of Phayao (Grant PBTSC65006) is also acknowledged. The authors acknowledge National e-Science Infrastructure Consortium for providing computing resources that have partly contributed to the research results reported within this paper (www.e-science.in.th).
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Meelua, W., Oláh, J. & Jitonnom, J. Role of water coordination at zinc binding site and its catalytic pathway of dizinc creatininase: insights from quantum cluster approach. J Comput Aided Mol Des 36, 279–289 (2022). https://doi.org/10.1007/s10822-022-00451-8
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DOI: https://doi.org/10.1007/s10822-022-00451-8