Article

Different aa-tRNAs Are Selected Uniformly on the Ribosome

Department of Biochemistry, Molecular Biology, and Cell Biology, Northwestern University, Evanston, IL 60208, USA.
Molecular cell (Impact Factor: 14.46). 08/2008; 31(1):114-23. DOI: 10.1016/j.molcel.2008.04.026
Source: PubMed

ABSTRACT Ten E. coli aminoacyl-tRNAs (aa-tRNAs) were assessed for their ability to decode cognate codons on E. coli ribosomes by using three assays that evaluate the key steps in the decoding pathway. Despite a wide variety of structural features, each aa-tRNA exhibited similar kinetic and thermodynamic properties in each assay. This surprising kinetic and thermodynamic uniformity is likely to reflect the importance of ribosome conformational changes in defining the rates and affinities of the decoding process as well as the evolutionary "tuning" of each aa-tRNA sequence to modify their individual interactions with the ribosome at each step.

1 Follower
 · 
87 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Studies of RNA recognition and catalysis typically involve measurement of rate constants for reactions of individual RNA sequence variants by fitting changes in substrate or product concentration to exponential or linear functions. A complementary approach is determination of relative rate constants by internal competition, which involves quantifying the time dependent changes in substrate or product ratios in reactions containing multiple substrates. Here, we review approaches for determining of relative rate constants by analysis of both substrate and product ratios and illustrate their application using the in vitro processing of precursor tRNA by ribonuclease P as a model system. The presence of inactive substrate populations is a common complicating factor in analysis of reactions involving RNA substrates, and approaches for quantitative correction of observed rate constants for these effects are illustrated. These results together with recent applications in the literature indicate that internal competition offers an alternate method for analyzing RNA processing kinetics using standard molecular biology methods that directly quantifies substrate specificity and may be extended to a range of applications.
    Analytical Biochemistry 08/2014; 467. DOI:10.1016/j.ab.2014.08.022 · 2.31 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The three-nucleotide mRNA reading frame is tightly regulated during translation to ensure accurate protein expression. Translation errors that lead to aberrant protein production can result from the uncoupled movement of the tRNA in either the 5' or 3' direction on mRNA. Here, we report the biochemical and structural characterization of +1 frameshift suppressor tRNA(SufJ), a tRNA known to decode four, instead of three, nucleotides. Frameshift suppressor tRNA(SufJ) contains an insertion 5' to its anticodon, expanding the anticodon loop from seven to eight nucleotides. Our results indicate that the expansion of the anticodon loop of either ASL(SufJ) or tRNA(SufJ) does not affect its affinity for the A site of the ribosome. Structural analyses of both ASL(SufJ) and ASL(Thr) bound to the Thermus thermophilus 70S ribosome demonstrate both ASLs decode in the zero frame. Although the anticodon loop residues 34-37 are superimposable with canonical seven-nucleotide ASLs, the single C31.5 insertion between nucleotides 31 and 32 in ASL(SufJ) imposes a conformational change of the anticodon stem, that repositions and tilts the ASL toward the back of the A site. Further modeling analyses reveal that this tilting would cause a distortion in full-length A-site tRNA(SufJ) during tRNA selection and possibly impede gripping of the anticodon stem by 16S rRNA nucleotides in the P site. Together, these data implicate tRNA distortion as a major driver of noncanonical translation events such as frameshifting.
    RNA 10/2014; 20(12). DOI:10.1261/rna.046953.114 · 4.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The molecular machinery of life relies on complex multistep processes that involve numerous individual transitions, such as molecular association and dissociation steps, chemical reactions, and mechanical movements. The corresponding transition rates can be typically measured in vitro but not in vivo. Here, we develop a general method to deduce the in-vivo rates from their in-vitro values. The method has two basic components. First, we introduce the kinetic distance, a new concept by which we can quantitatively compare the kinetics of a multistep process in different environments. The kinetic distance depends logarithmically on the transition rates and can be interpreted in terms of the underlying free energy barriers. Second, we minimize the kinetic distance between the in-vitro and the in-vivo process, imposing the constraint that the deduced rates reproduce a known global property such as the overall in-vivo speed. In order to demonstrate the predictive power of our method, we apply it to protein synthesis by ribosomes, a key process of gene expression. We describe the latter process by a codon-specific Markov model with three reaction pathways, corresponding to the initial binding of cognate, near-cognate, and non-cognate tRNA, for which we determine all individual transition rates in vitro. We then predict the in-vivo rates by the constrained minimization procedure and validate these rates by three independent sets of in-vivo data, obtained for codon-dependent translation speeds, codon-specific translation dynamics, and missense error frequencies. In all cases, we find good agreement between theory and experiment without adjusting any fit parameter. The deduced in-vivo rates lead to smaller error frequencies than the known in-vitro rates, primarily by an improved initial selection of tRNA. The method introduced here is relatively simple from a computational point of view and can be applied to any biomolecular process, for which we have detailed information about the in-vitro kinetics.
    PLoS Computational Biology 10/2014; 10(10):e1003909. DOI:10.1371/journal.pcbi.1003909 · 4.83 Impact Factor

Preview

Download
0 Downloads
Available from