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Published October 2004 | public
Journal Article

Selectivity and specificity of substrate binding in methionyl-tRNA synthetase

Abstract

The accuracy of in vivo incorporation of amino acids during protein biosynthesis is controlled to a significant extent by aminoacyl-tRNA synthetases (aaRS). This paper describes the application of the HierDock computational method to study the molecular basis of amino acid binding to the Escherichia coli methionyl tRNA synthetase (MetRS). Starting with the protein structure from the MetRS cocrystal, the HierDock calculations predict the binding site of methionine in MetRS to a root mean square deviation in coordinates (CRMS) of 0.55 Å for all the atoms, compared with the crystal structure. The MetRS conformation in the cocrystal structure shows good discrimination between cognate and the 19 noncognate amino acids. In addition, the calculated binding energies of a set of five methionine analogs show a good correlation (R^2 = 0.86) to the relative free energies of binding derived from the measured in vitro kinetic parameters, K_m and k_(cat). Starting with the crystal structure of MetRS without the methionine (apo-MetRS), the putative binding site of methionine was predicted. We demonstrate that even the apo-MetRS structure shows a preference for binding methionine compared with the 19 other natural amino acids. On comparing the calculated binding energies of the 20 natural amino acids for apo-MetRS with those for the cocrystal structure, we observe that the discrimination against the noncognate substrate increases dramatically in the second step of the physical binding process associated with the conformation change in the protein.

Additional Information

© 2004 The Protein Society. Manuscript Received: 7 April 2004. Manuscript Accepted: 25 June 2004. Manuscript Revised: 25 June 2004. We thank David Tirrell for his active involvement in this work and for stimulating discussions. We would also like to thank Kristi Kiick at the University of Delaware for her participation in scientific discussions. This research was supported by NIH/BioEngineering Research Group Grant GM62523. The computing facilities for this project were provided by an IBM Shared University Research grant. The facilities of the Materials and Process Simulation Center used in this project are supported also by the Department of Energy/Accelerated Strategic Computing Initiative/Academic Strategic Alliances Program, NSF grants (CHE9985574 and MRI99-77872), the NIH, the Army Research Office–Multidisciplinary University Research Initiative, Chevron Corp., the Defense Advanced Research Planning Agency, 3M, Seiko-Epson, Avery-Dennison Corp., Kellogg's, General Motors, the Beckman Institute, Asahi Chemical, and the Office of Naval Research. The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.

Additional details

Created:
August 22, 2023
Modified:
October 25, 2023