Literature DB >> 22957855

Catalysis of tRNA aminoacylation: single turnover to steady-state kinetics of tRNA synthetases.

Mantu Santra1, Biman Bagchi.   

Abstract

Aminoacyl-tRNA synthetases (aaRS) catalyze the bimolecular association reaction between amino acid and tRNA by specifically and unerringly choosing the cognate amino acid and tRNA. There are two classes of such synthetases that perform tRNA-aminoacylation reaction. Interestingly, these two classes of aminoacyl-tRNA synthetases differ not only in their structures but they also exhibit remarkably distinct kinetics under pre-steady-state condition. The class I synthetases show initial burst of product formation followed by a slower steady-state rate. This has been argued to represent the influence of slow product release. In contrast, there is no burst in the case of class II enzymes. The tight binding of product with enzyme for class I enzymes is correlated with the enhancement of rate in presence of elongation factor EF-TU. In spite of extensive experimental studies, there is no detailed theoretical analysis that can provide a quantitative understanding of this important problem. In this article, we present a theoretical investigation of enzyme kinetics for both classes of aminoacyl-tRNA synthetases. We present an augmented kinetic scheme and then employ the methods of time-dependent probability statistics to obtain expressions for the first passage time distribution that gives both the time-dependent and the steady-state rates. The present study quantitatively explains all the above experimental observations. We propose an alternative path way in the case of class II enzymes showing the tRNA-dependent amino acid activation and the discrepancy between the single-turnover and steady-state rate.

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Year:  2012        PMID: 22957855     DOI: 10.1021/jp305045w

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  3 in total

1.  Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling.

Authors:  Tomoaki Matsuura; Naoki Tanimura; Kazufumi Hosoda; Tetsuya Yomo; Yoshihiro Shimizu
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-06       Impact factor: 11.205

2.  Kinetic proofreading at single molecular level: aminoacylation of tRNA(Ile) and the role of water as an editor.

Authors:  Mantu Santra; Biman Bagchi
Journal:  PLoS One       Date:  2013-06-20       Impact factor: 3.240

3.  A stochastic chemical dynamic approach to correlate autoimmunity and optimal vitamin-D range.

Authors:  Susmita Roy; Krishna Shrinivas; Biman Bagchi
Journal:  PLoS One       Date:  2014-06-27       Impact factor: 3.240

  3 in total

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