Ribosomal subunit association is a key checkpoint in translation initiation but its structural dynamics are poorly understood. Here, we used a recently developed mixing-spraying, time-resolved, cryogenic electron microscopy (cryo-EM) method to study ribosomal subunit association in the sub-second time range. We have improved this method and increased the cryo-EM data yield by tenfold. Pre-equilibrium states of the association reaction were captured by reacting the mixture of ribosomal subunits for 60 ms and 140 ms. We also identified three distinct ribosome conformations in the associated ribosomes. The observed proportions of these conformations are the same in these two time points, suggesting that ribosomes equilibrate among the three conformations within less than 60 ms upon formation. Our results demonstrate that the mixing-spraying method can capture multiple states of macromolecules during a sub-second reaction. Other fast processes, such as translation initiation, decoding, and ribosome recycling, are amenable to study with this method.
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.
Single-particle cryogenic electron microscopy (cryo-EM) is a powerful tool for the study of macromolecular structures at high resolution. Classification allows multiple structural states to be extracted and reconstructed from the same sample. One classification approach is via the covariance matrix, which captures the correlation between every pair of voxels. Earlier approaches employ computing-intensive resampling and estimate only the eigenvectors of the matrix, which are then used in a separate fast classification step. We propose an iterative scheme to explicitly estimate the covariance matrix in its entirety. In our approach, the flexibility in choosing the solution domain allows us to examine a part of the molecule in greater detail. Three-dimensional covariance maps obtained in this way from experimental data (cryo-EM images of the eukaryotic pre-initiation complex) prove to be in excellent agreement with conclusions derived by using traditional approaches, revealing in addition the interdependencies of ligand bindings and structural changes.
Image formation in bright field electron microscopy can be described with the help of the contrast transfer function (CTF). In this work the authors describe the “CTF Estimation Challenge”, called by the Madrid Instruct Image Processing Center (I2PC) in collaboration with the National Center for Macromolecular Imaging (NCMI) at Houston. Correcting for the effects of the CTF requires accurate knowledge of the CTF parameters, but these have often been difficult to determine. In this challenge, researchers have had the opportunity to test their ability in estimating some of the key parameters of the electron microscope CTF on a large micrograph data set produced by well-known laboratories on a wide set of experimental conditions. This work presents the first analysis of the results of the CTF Estimation Challenge, including an assessment of the performance of the different software packages under different conditions, so as to identify those areas of research where further developments would be desirable in order to achieve high-resolution structural information.
At equilibrium, thermodynamic and kinetic information can be extracted from biomolecular energy landscapes by many techniques. However, while static, ensemble techniques yield thermodynamic data, often only dynamic, single-molecule techniques can yield the kinetic data that describe transition-state energy barriers. Here we present a generalized framework based upon dwell-time distributions that can be used to connect such static, ensemble techniques with dynamic, single-molecule techniques, and thus characterize energy landscapes to greater resolutions. We demonstrate the utility of this framework by applying it to cryogenic electron microscopy (cryo-EM) and single- molecule fluorescence resonance energy transfer (smFRET) studies of the bacterial ribosomal pre-translocation complex. Among other benefits, application of this framework to these data explains why two transient, intermediate conformations of the pre-translocation complex, which are observed in a cryo-EM study, may not be observed in several smFRET studies.
Ryanodine receptors (RyRs) mediate the rapid release of calcium (Ca2+) from intracellular stores into the cytosol, which is essential for numerous cellular functions including excitation–contraction coupling in muscle. Lack of sufficient structural detail has impeded understanding of RyR gating and regulation. Here we report the closed-state structure of the 2.3-megadalton complex of the rabbit skeletal muscle type 1 RyR (RyR1), solved by single-particle electron cryomicroscopy at an overall resolution of 4.8 Å. We fitted a polyalanine-level model to all 3,757 ordered residues in each protomer, defining the transmembrane pore in unprecedented detail and placing all cytosolic domains as tertiary folds. The cytosolic assembly is built on an extended α-solenoid scaffold connecting key regulatory domains to the pore. The RyR1 pore architecture places it in the six-transmembrane ion channel superfamily. A unique domain inserted between the second and third transmembrane helices interacts intimately with paired EF-hands originating from the α-solenoid scaffold, suggesting a mechanism for channel gating by Ca2+.
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.
Recently developed classification methods have enabled resolving multiple biological structures from cryo-EM data collected on heterogeneous biological samples. However, there remains the problem of how to base the decisions in the classification on the statistics of the cryo-EM data, to reduce the subjectivity in the process. Here, we propose a quantitative analysis to determine the iteration of convergence and the number of distinguishable classes, based on the statistics of the single particles in an iterative classification scheme. We start the classification with more number of classes than anticipated based on prior knowledge, and then combine the classes that yield similar reconstructions. The classes yielding similar reconstructions can be identified from the migrating particles (jumpers) during consecutive iterations after the iteration of convergence. We therefore termed the method “jumper analysis”, and applied it to the output of RELION 3D classification of a benchmark experimental dataset. This work is a step forward toward fully automated single-particle reconstruction and classification of cryo-EM data.
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.
Supramolecular machines perform their work in the cell by going through many different states, distinguished by different conformations and free-energy levels. Ideally, in order to find out how these machines work, we would create a suitable in vitro environment containing all components including energy supply that allows the machine to function. We would then aim to take a “movie,” capturing their structure at highest resolution in a continuous fashion. Keeping within that film analogy, we might consider taking a large number of “snapshots” in equal small time intervals, each short enough, as in the macroscopic world, to eliminate jarring transitions. However, we would find out that this project has flaws both on the conceptual and the practical level. Conceptually, it is incorrect to equate a molecular machine’s progress to the workings of a macroscopic machine in motion since the states are not ordered in sequence of time but are visited in a stochastic manner, with occasional irreversible events such as NTP hydrolysis as the only mark of progress. In practical terms, there are in fact two problems, one affecting the way data for any given state can be captured, the other affecting the ability to obtain coverage of states in a continuum...
Frank Lab post doc, Amédée Des Georges, has been named Assistant Professor with the Structural Biology Initiative at CUNY's newly constructed Advanced Science Research Center (ASRC) located at 85 St. Nicholas Terrace.