Translational initiation factor eIF5 replaces eIF1 on the 40S ribosomal subunit to promote start-codon recognition

  1. José Luis Llácer
  2. Tanweer Hussain
  3. Adesh K Saini
  4. Jagpreet Singh Nanda
  5. Sukhvir Kaur
  6. Yuliya Gordiyenko
  7. Rakesh Kumar
  8. Alan G Hinnebusch  Is a corresponding author
  9. Jon R Lorsch  Is a corresponding author
  10. Venki Ramakrishnan  Is a corresponding author
  1. MRC Laboratory of Molecular Biology, United Kingdom
  2. Indian Institute of Science, India
  3. Shoolini University of Biotechnology and Management Sciences, India
  4. National Institutes of Health, United States

Abstract

In eukaryotic translation initiation AUG recognition of the mRNA requires accommodation of Met-tRNAi in a 'PIN' state, which is antagonized by the factor eIF1. eIF5 is a GTPase activating protein (GAP) of eIF2 that additionally promotes stringent AUG selection, but the molecular basis of its dual function was unknown. We present a cryo-electron microscopy (cryo-EM) reconstruction of a yeast 48S pre-initiation complex (PIC), at an overall resolution of 3.0 Å, featuring the N-terminal domain (NTD) of eIF5 bound to the 40S subunit at the location vacated by eIF1. eIF5 interacts with and allows a more accommodated orientation of Met-tRNAi. Substitutions of eIF5 residues involved in the eIF5-NTD/tRNAi interaction influenced initiation at near-cognate UUG codons in vivo, and the closed/open PIC conformation in vitro, consistent with direct stabilization of the codon:anticodon duplex by the wild-type eIF5-NTD. The present structure reveals the basis for a key role of eIF5 in start-codon selection.

Data availability

Five maps have been deposited in the EMDB with accession codes EMDB: 4328, EMDB: 4330, EMDB: 4331, EMDB: 4327, EMDB: 4329, for the sample 1 map, Map A, Map B, Map C1 and Map C2, respectively. Two atomic coordinate models have been deposited in the PDB with accession codes PDB: 6FYX, PDB: 6FYY, for models showing TC in conformation 1 and conformation 2, respectively.All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Tables 4 and 5 and Figures 4 and 5

The following data sets were generated

Article and author information

Author details

  1. José Luis Llácer

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5304-1795
  2. Tanweer Hussain

    Molecular Reproduction, Development and Genetics (MRDG), Indian Institute of Science, Bangalore, India
    Competing interests
    No competing interests declared.
  3. Adesh K Saini

    Shoolini University of Biotechnology and Management Sciences, Solan, India
    Competing interests
    No competing interests declared.
  4. Jagpreet Singh Nanda

    Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
    Competing interests
    No competing interests declared.
  5. Sukhvir Kaur

    Shoolini University of Biotechnology and Management Sciences, Solan, India
    Competing interests
    No competing interests declared.
  6. Yuliya Gordiyenko

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  7. Rakesh Kumar

    Shoolini University of Biotechnology and Management Sciences, Solan, India
    Competing interests
    No competing interests declared.
  8. Alan G Hinnebusch

    Laboratory of Gene Regulation and Development, Eunice K. Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
    For correspondence
    alanh@mail.nih.gov
    Competing interests
    Alan G Hinnebusch, Reviewing editor, eLife.
  9. Jon R Lorsch

    Laboratory on the Mechanism and Regulation of Protein Synthesis, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
    For correspondence
    jon.lorsch@nih.gov
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4521-4999
  10. Venki Ramakrishnan

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    For correspondence
    ramak@mrc-lmb.cam.ac.uk
    Competing interests
    No competing interests declared.

Funding

Medical Research Council (MC_U105184332)

  • Venki Ramakrishnan

Wellcome (WT096570)

  • Venki Ramakrishnan

Agouron Institute

  • Venki Ramakrishnan

Department of Science and Technology, Ministry of Science and Technology (Int/NZ/P-2/13)

  • Adesh K Saini

National Institutes of Health (GM62128)

  • Jon R Lorsch

Human Frontier Science Program (RGP-0028/2009)

  • Alan G Hinnebusch

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 4,743
    views
  • 747
    downloads
  • 84
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

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)

  1. José Luis Llácer
  2. Tanweer Hussain
  3. Adesh K Saini
  4. Jagpreet Singh Nanda
  5. Sukhvir Kaur
  6. Yuliya Gordiyenko
  7. Rakesh Kumar
  8. Alan G Hinnebusch
  9. Jon R Lorsch
  10. Venki Ramakrishnan
(2018)
Translational initiation factor eIF5 replaces eIF1 on the 40S ribosomal subunit to promote start-codon recognition
eLife 7:e39273.
https://doi.org/10.7554/eLife.39273

Share this article

https://doi.org/10.7554/eLife.39273

Further reading

    1. Structural Biology and Molecular Biophysics
    Johannes Elferich, Lingli Kong ... Nikolaus Grigorieff
    Research Advance

    Images taken by transmission electron microscopes are usually affected by lens aberrations and image defocus, among other factors. These distortions can be modeled in reciprocal space using the contrast transfer function (CTF). Accurate estimation and correction of the CTF is essential for restoring the high-resolution signal in cryogenic electron microscopy (cryoEM). Previously, we described the implementation of algorithms for this task in the cisTEM software package (Grant et al., 2018). Here we show that taking sample characteristics, such as thickness and tilt, into account can improve CTF estimation. This is particularly important when imaging cellular samples, where measurement of sample thickness and geometry derived from accurate modeling of the Thon ring pattern helps judging the quality of the sample. This improved CTF estimation has been implemented in CTFFIND5, a new version of the cisTEM program CTFFIND. We evaluated the accuracy of these estimates using images of tilted aquaporin crystals and eukaryotic cells thinned by focused ion beam milling. We estimate that with micrographs of sufficient quality CTFFIND5 can measure sample tilt with an accuracy of 3° and sample thickness with an accuracy of 5 nm.

    1. Structural Biology and Molecular Biophysics
    Sneha Menon, Subinoy Adhikari, Jagannath Mondal
    Research Article

    The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defined ligand-binding pockets in its disordered structure. Here, we implement a deep artificial neural network-based machine learning approach, which is able to statistically distinguish the fuzzy ensemble of conformational substates of αS in neat water from those in aqueous fasudil (small molecule of interest) solution. In particular, the presence of fasudil in the solvent either modulates pre-existing states of αS or gives rise to new conformational states of αS, akin to an ensemble-expansion mechanism. The ensembles display strong conformation-dependence in residue-wise interaction with the small molecule. A thermodynamic analysis indicates that small-molecule modulates the structural repertoire of αS by tuning protein backbone entropy, however entropy of the water remains unperturbed. Together, this study sheds light on the intricate interplay between small molecules and IDPs, offering insights into entropic modulation and ensemble expansion as key biophysical mechanisms driving potential therapeutics.