Structural insights into mRNA reading frame regulation by tRNA modification and slippery codon-anticodon pairing
Abstract
Modifications in the tRNA anticodon loop, adjacent to the three-nucleotide anticodon, influence translation fidelity by stabilizing the tRNA to allow for accurate reading of the mRNA genetic code. One example is the N1-methylguaonosine modification at guanine nucleotide 37 (m1G37) located in the anticodon loop, immediately adjacent to the anticodon nucleotides 34-36. The absence of m1G37 in tRNAPro causes +1 frameshifting on polynucleotide, slippery codons. Here, we report structures of the bacterial ribosome containing tRNAPro bound to either cognate or slippery codons to determine how the m1G37 modification prevents mRNA frameshifting. The structures reveal that certain codon-anticodon contexts and m1G37 destabilize interactions of tRNAPro with the peptidyl site, causing large conformational changes typically only seen during EF-G mediated translocation of the mRNA-tRNA pairs. These studies provide molecular insights into how m1G37 stabilizes the interactions of tRNAPro with the ribosome and the influence of slippery codons on the mRNA reading frame.
Data availability
Crystallography, atomic coordinates, and structure factors have been deposited in the Protein Data Bank, www.pdb.org (PDB codes 6NTA, 6NSH, 6NUO, 6NWY, 6O3M, 6OSI)
-
Structures of 70S bound to frameshift-prone tRNAPro6NTA, 6NSH, 6NUO, 6NWY, 6O3M, 6OSI.
Article and author information
Author details
Funding
National Institutes of Health (R01GM093278)
- Christine M Dunham
National Institutes of Health (R01GM119386)
- Ruben L Gonzalez
National Science Foundation (MCB-1915843)
- Paul Whitford
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Hoffer et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,685
- views
-
- 374
- downloads
-
- 34
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Biochemistry and Chemical Biology
- Structural Biology and Molecular Biophysics
Nature has inspired the design of improved inhibitors for cancer-causing proteins.
-
- Biochemistry and Chemical Biology
Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.