Robert Langlois

Structure of the Mammalian Ribosomal 43S Preinitiation Complex Bound to the Scanning Factor DHX29

Eukaryotic translation initiation begins with assembly of a 43S preinitiation complex. First, methionylated initiator methionine transfer RNA (Met-tRNAi(Met)), eukaryotic initiation factor (eIF) 2, and guanosine triphosphate form a ternary complex (TC). The TC, eIF3, eIF1, and eIF1A cooperatively bind to the 40S subunit, yielding the 43S preinitiation complex, which is ready to attach to messenger RNA (mRNA) and start scanning to the initiation codon. Scanning on structured mRNAs additionally requires DHX29, a DExH-box protein that also binds directly to the 40S subunit. Here, we present a cryo-electron microscopy structure of the mammalian DHX29-bound 43S complex at 11.6xa0Å resolution. It reveals that eIF2 interacts with the 40S subunit via its α subunit and supports Met-tRNAi(Met) in an unexpected P/I orientation (eP/I). The structural core of eIF3 resides on the back of the 40S subunit, establishing two principal points of contact, whereas DHX29 binds around helix 16. The structure provides insights into eukaryote-specific aspects of translation, including the mechanism of action of DHX29.

Hepatitis-C-virus-like internal ribosome entry sites displace eIF3 to gain access to the 40S subunit

Hepatitis C virus (HCV) and classical swine fever virus (CSFV) messenger RNAs contain related (HCV-like) internal ribosome entry sites (IRESs) that promote 5′-end independent initiation of translation, requiring only a subset of the eukaryotic initiation factors (eIFs) needed for canonical initiation on cellular mRNAs. Initiation on HCV-like IRESs relies on their specific interaction with the 40S subunit, which places the initiation codon into the P site, where it directly base-pairs with eIF2-bound initiator methionyl transfer RNA to form a 48S initiation complex. However, all HCV-like IRESs also specifically interact with eIF3 (refs 2, 5, 6, 7, 9, 10, 11, 12), but the role of this interaction in IRES-mediated initiation has remained unknown. During canonical initiation, eIF3 binds to the 40S subunit as a component of the 43S pre-initiation complex, and comparison of the ribosomal positions of eIF3 and the HCV IRES revealed that they overlap, so that their rearrangement would be required for formation of ribosomal complexes containing both components. Here we present a cryo-electron microscopy reconstruction of a 40S ribosomal complex containing eIF3 and the CSFV IRES. Remarkably, although the position and interactions of the CSFV IRES with the 40S subunit in this complex are similar to those of the HCV IRES in the 40S–IRES binary complex, eIF3 is completely displaced from its ribosomal position in the 43S complex, and instead interacts through its ribosome-binding surface exclusively with the apical region of domain III of the IRES. Our results suggest a role for the specific interaction of HCV-like IRESs with eIF3 in preventing ribosomal association of eIF3, which could serve two purposes: relieving the competition between the IRES and eIF3 for a common binding site on the 40S subunit, and reducing formation of 43S complexes, thereby favouring translation of viral mRNAs.

Trajectories of the ribosome as a Brownian nanomachine

Significance Many functions in the cell are performed by Brownian machines, macromolecular assemblies that use energy from the thermal environment for many of the conformational changes involved in their work cycles. Here we present a new approach capable of mapping the continuous motions of such nanomachines along their trajectories in the free-energy landscape and demonstrate this capability in the context of experimental cryogenic electron microscope snapshots of the ribosome, the nanomachine responsible for protein synthesis in all living organisms. We believe our approach constitutes a universal platform for the analysis of free-energy landscapes and conformational motions of molecular nanomachines and their dependencies on temperature, buffer conditions, and regulatory factors. A Brownian machine, a tiny device buffeted by the random motions of molecules in the environment, is capable of exploiting these thermal motions for many of the conformational changes in its work cycle. Such machines are now thought to be ubiquitous, with the ribosome, a molecular machine responsible for protein synthesis, increasingly regarded as prototypical. Here we present a new analytical approach capable of determining the free-energy landscape and the continuous trajectories of molecular machines from a large number of snapshots obtained by cryogenic electron microscopy. We demonstrate this approach in the context of experimental cryogenic electron microscope images of a large ensemble of nontranslating ribosomes purified from yeast cells. The free-energy landscape is seen to contain a closed path of low energy, along which the ribosome exhibits conformational changes known to be associated with the elongation cycle. Our approach allows model-free quantitative analysis of the degrees of freedom and the energy landscape underlying continuous conformational changes in nanomachines, including those important for biological function.

Automated particle picking for low-contrast macromolecules in cryo-electron microscopy.

Cryo-electron microscopy is an increasingly popular tool for studying the structure and dynamics of biological macromolecules at high resolution. A crucial step in automating single-particle reconstruction of a biological sample is the selection of particle images from a micrograph. We present a novel algorithm for selecting particle images in low-contrast conditions; it proves more effective than the human eye on close-to-focus micrographs, yielding improved or comparable resolution in reconstructions of two macromolecular complexes.

Activation of GTP hydrolysis in mRNA-tRNA translocation by elongation factor G.

Cryo-EM study reveals key molecular structural features for activation of guanosine triphosphate cleavage by EF-G during translocation. During protein synthesis, elongation of the polypeptide chain by each amino acid is followed by a translocation step in which mRNA and transfer RNA (tRNA) are advanced by one codon. This crucial step is catalyzed by elongation factor G (EF-G), a guanosine triphosphatase (GTPase), and accompanied by a rotation between the two ribosomal subunits. A mutant of EF-G, H91A, renders the factor impaired in guanosine triphosphate (GTP) hydrolysis and thereby stabilizes it on the ribosome. We use cryogenic electron microscopy (cryo-EM) at near-atomic resolution to investigate two complexes formed by EF-G H91A in its GTP state with the ribosome, distinguished by the presence or absence of the intersubunit rotation. Comparison of these two structures argues in favor of a direct role of the conserved histidine in the switch II loop of EF-G in GTPase activation, and explains why GTP hydrolysis cannot proceed with EF-G bound to the unrotated form of the ribosome.

Affinity grid-based cryo-EM of PKC binding to RACK1 on the ribosome

Affinity grids (AG) are specialized EM grids that bind macromolecular complexes containing tagged proteins to obtain maximum occupancy for structural analysis through single-particle EM. In this study, utilizing AG, we show that His-tagged activated PKC βII binds to the small ribosomal subunit (40S). We reconstructed a cryo-EM map which shows that PKC βII interacts with RACK1, a seven-bladed β-propeller protein present on the 40S and binds in two different regions close to blades 3 and 4 of RACK1. This study is a first step in understanding the molecular framework of PKC βII/RACK1 interaction and its role in translation.

Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy

Reference-based methods have dominated the approaches to the particle selection problem, proving fast, and accurate on even the most challenging micrographs. A reference volume, however, is not always available and compiling a set of reference projections from the micrographs themselves requires significant effort to attain the same level of accuracy. We propose a reference-free method to quickly extract particles from the micrograph. The method is augmented with a new semi-supervised machine-learning algorithm to accurately discriminate particles from contaminants and noise.

A Clarification of the Terms Used in Comparing Semi-automated Particle Selection Algorithms in Cryo-EM

Many cyro-EM datasets are heterogeneous stemming from molecules undergoing conformational changes. The need to characterize each of the substrates with sufficient resolution entails a large increase in the data flow and motivates the development of more effective automated particle selection algorithms. Concepts and procedures from the machine-learning field are increasingly employed toward this end. However, a review of recent literature has revealed a discrepancy in terminology of the performance scores used to compare particle selection algorithms, and this has subsequently led to ambiguities in the meaning of claimed performance. In an attempt to curtail the perpetuation of this confusion and to disentangle past mistakes, we review the performance of published particle selection efforts with a set of explicitly defined performance scores using the terminology established and accepted within the field of machine learning.

High-resolution Cryo-EM Structure of the Trypanosoma brucei Ribosome: A Case Study

Single-particle cryo-electron microscopy has the immense advantage over crystallography in being able to image frozen-hydrated biological complexes in their “native” state, in solution. For years the ribosome has been the benchmark sample for particles without symmetry. It has witnessed steady improvement in resolution from the very first single-particle 3D reconstruction to today’s reconstructions at near-atomic resolution. In this study, we describe the different steps of sample preparation, data collection, data processing, and modeling that led to the 5A structure of the T. brucei ribosome [32]. A local resolution estimation demonstrates the extent to which resolution can be anisotropic and pinpoints regions of higher heterogeneity or structural flexibility. This study also shows an example of misuse of spatial frequency filters leading to overfitting of the data and the artifacts that can be observed in the resulting density map.

Fully Automated Particle Selection and Verification in Single-Particle Cryo-EM

Cryo-electron microscopy combined with single-particle reconstruction is a promising technique for solving the high-resolution structure of macromolecular complexes, even in the presence of conformational or compositional heterogeneity. However, the usual workflow leading to one or several structures is mired in subjective decisions that must be made by an expert. One problem, in particular, has been the difficulty finding algorithms capable of automatically selecting and verifying individual views of a macromolecular complex from the electron micrograph, due to the extremely low signal-to-noise ratio and the presence of contaminants. We present a novel machine-learning algorithm that overcomes these problems. The performance of the algorithm is demonstrated with electron micrographs of ribosomes.