Energy barriers and driving forces in tRNA translocation through the ribosome

Supercomputing in the biological sciences: Toward Zettascale and Yottascale simulations

Molecular simulations of biological systems tend to be significantly more compute-intensive than those in materials science and astrophysics, due to important contributions of long-range electrostatic forces and large numbers of time steps (>1E9) required. Simulations of biomolecular complexes of microseconds to milliseconds are considered state-of-the-art today. However, these time scales are miniscule in comparison to physiological time scales relevant to molecular machine activity, drug action, and elongation cycles for protein synthesis, RNA synthesis, and DNA synthesis (seconds to days). While an exascale supercomputer has simulated an entire virus for nanoseconds, this supercomputer would need to be 10 billion times faster to simulate that virus for 3 hours of physiological time, demonstrating the insatiable need for computing power. With growing interest in computational drug design from the pharmaceutical sector, the biological sciences are positioned to be an industry driver in computing.

Alternate conformational trajectories in ribosome translocation

Translocation in protein synthesis entails the efficient and accurate movement of the mRNA-[tRNA]2 substrate through the ribosome after peptide bond formation. An essential conformational change during this process is the swiveling of the small subunit head domain about two rRNA 'hinge' elements. Using iterative selection and molecular dynamics simulations, we derive alternate hinge elements capable of translocation in vitro and in vivo and describe their effects on the conformational trajectory of the EF-G-bound, translocating ribosome. In these alternate conformational pathways, we observe a diversity of swivel kinetics, hinge motions, three-dimensional head domain trajectories and tRNA dynamics. By finding alternate conformational pathways of translocation, we identify motions and intermediates that are essential or malleable in this process. These findings highlight the plasticity of protein synthesis and provide a more thorough understanding of the available sequence and conformational landscape of a central biological process.

Single-particle Cryo-EM and molecular dynamics simulations: A perfect match

Knowledge of the structure and dynamics of biomolecules is key to understanding the mechanisms underlying their biological functions. Single-particle cryo-electron microscopy (cryo-EM) is a powerful structural biology technique to characterize complex biomolecular systems. Here, we review recent advances of how Molecular Dynamics (MD) simulations are being used to increase and enhance the information extracted from cryo-EM experiments. We will particularly focus on the physics underlying these experiments, how MD facilitates structure refinement, in particular for heterogeneous and non-isotropic resolution, and how thermodynamic and kinetic information can be extracted from cryo-EM data.

Conformational changes of ribosomes during translation elongation resolved by molecular dynamics simulations

Molecular dynamics simulations have emerged as a powerful set of tools to unravel the intricate dynamics of ribosomes during protein synthesis. Recent advancements in this field have enabled simulations to delve deep into the conformational rearrangements of ribosomes and associated factors, providing invaluable insights into the intricacies of translation. Emphasis on simulations has recently been on translation elongation, such as tRNA selection, translocation, and ribosomal head-swivel motions. These studies have offered crucial structural interpretations of how genetic information is faithfully translated into proteins. This review outlines recent discoveries concerning ribosome conformational changes occurring during translation elongation, as elucidated through molecular dynamics simulations.

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

All-Atom Biomolecular Simulation in the Exascale Era

Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.

The energy landscape of the ribosome

The ribosome is a prototypical assembly that can be used to establish general principles and techniques for the study of biological molecular machines. Motivated by the fact that the dynamics of every biomolecule is governed by an underlying energy landscape, there has been great interest to understand and quantify ribosome energetics. In the present review, we will focus on theoretical and computational strategies for probing the interactions that shape the energy landscape of the ribosome, with an emphasis on more recent studies of the elongation cycle. These efforts include the application of quantum mechanical methods for describing chemical kinetics, as well as classical descriptions to characterize slower (microsecond to millisecond) large-scale (10-100 Å) rearrangements, where motion is described in terms of diffusion across an energy landscape. Together, these studies provide broad insights into the factors that control a diverse range of dynamical processes in this assembly.

Dynamics and Function of sRNA/mRNAs Under the Scrutiny of Computational Simulation Methods

Molecular dynamics simulations have proved extremely useful in investigating the functioning of proteins with atomic-scale resolution. Many applications to the study of RNA also exist, and their number increases by the day. However, implementing MD simulations for RNA molecules in solution faces challenges that the MD practitioner must be aware of for the appropriate use of this tool. In this chapter, we present the fundamentals of MD simulations, in general, and the peculiarities of RNA simulations, in particular. We discuss the strengths and limitations of the technique and provide examples of its application to elucidate small RNA's performance.

The many faces of ribosome translocation along the mRNA: reading frame maintenance, ribosome frameshifting and translational bypassing

In each round of translation elongation, the ribosome translocates along the mRNA by precisely one codon. Translocation is promoted by elongation factor G (EF-G) in bacteria (eEF2 in eukaryotes) and entails a number of precisely-timed large-scale structural rearrangements. As a rule, the movements of the ribosome, tRNAs, mRNA and EF-G are orchestrated to maintain the exact codon-wise step size. However, signals in the mRNA, as well as environmental cues, can change the timing and dynamics of the key rearrangements leading to recoding of the mRNA into production of trans-frame peptides from the same mRNA. In this review, we discuss recent advances on the mechanics of translocation and reading frame maintenance. Furthermore, we describe the mechanisms and biological relevance of non-canonical translocation pathways, such as hungry and programmed frameshifting and translational bypassing, and their link to disease and infection.

Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach

Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential-based on relative entropy-in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit.

Machines on Genes through the Computational Microscope

Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.

Molecular dynamic simulations identifying the mechanism of holoenzyme formation by O-GlcNAc transferase and active p38α

O-N-Acetylglucosamine transferase (OGT) can catalyze the O-GlcNAc modification of thousands of proteins. The holoenzyme formation of OGT and adaptor protein is the precondition for further recognition and glycosylation of the target protein, while the corresponding mechanism is still open. Here, static and dynamic schemes based on statistics can successfully screen the feasible identifying, approaching, and binding mechanism of OGT and its typical adaptor protein p38α. The most favorable interface, energy contribution of hotspots, and conformational changes of fragments were discovered. The hydrogen bond interactions were verified as the main driving force for the whole process. The distinct characteristic of active and inactive p38α is explored and demonstrates that the phosphorylated tyrosine and threonine will form strong ion-pair interactions with Lys714, playing a key role in the dynamic identification stage. Multiple method combinations from different points of view may be helpful for exploring other systems of the protein-protein interactions.

R, de Oliveira R, Dunham C, Whitford P. Ratchet, swivel, tilt and roll: a complete description of subunit rotation in the ribosome

Protein synthesis by the ribosome requires large-scale rearrangements of the 'small' subunit (SSU; ∼1 MDa), including inter- and intra-subunit rotational motions. However, with nearly 2000 structures of ribosomes and ribosomal subunits now publicly available, it is exceedingly difficult to design experiments based on analysis of all known rotation states. To overcome this, we developed an approach where the orientation of each SSU head and body is described in terms of three angular coordinates (rotation, tilt and tilt direction) and a single translation. By considering the entire RCSB PDB database, we describe 1208 fully-assembled ribosome complexes and 334 isolated small subunits, which span >50 species. This reveals aspects of subunit rearrangements that are universal, and others that are organism/domain-specific. For example, we show that tilt-like rearrangements of the SSU body (i.e. 'rolling') are pervasive in both prokaryotic and eukaryotic (cytosolic and mitochondrial) ribosomes. As another example, domain orientations associated with frameshifting in bacteria are similar to those found in eukaryotic ribosomes. Together, this study establishes a common foundation with which structural, simulation, single-molecule and biochemical efforts can more precisely interrogate the dynamics of this prototypical molecular machine.

Altered tRNA dynamics during translocation on slippery mRNA as determinant of spontaneous ribosome frameshifting

When reading consecutive mRNA codons, ribosomes move by exactly one triplet at a time to synthesize a correct protein. Some mRNA tracks, called slippery sequences, are prone to ribosomal frameshifting, because the same tRNA can read both 0- and –1-frame codon. Using smFRET we show that during EF-G-catalyzed translocation on slippery sequences a fraction of ribosomes spontaneously switches from rapid, accurate translation to a slow, frameshifting-prone translocation mode where the movements of peptidyl- and deacylated tRNA become uncoupled. While deacylated tRNA translocates rapidly, pept-tRNA continues to fluctuate between chimeric and posttranslocation states, which slows down the re-locking of the small ribosomal subunit head domain. After rapid release of deacylated tRNA, pept-tRNA gains unconstrained access to the –1-frame triplet, resulting in slippage followed by recruitment of the –1-frame aa-tRNA into the A site. Our data show how altered choreography of tRNA and ribosome movements reduces the translation fidelity of ribosomes translocating in a slow mode.

Combining NMR, SAXS and SANS to characterize the structure and dynamics of protein complexes

Understanding the structure and dynamics of biological macromolecules is essential to decipher the molecular mechanisms that underlie cellular functions. The description of structure and conformational dynamics often requires the integration of complementary techniques. In this review, we highlight the utility of combining nuclear magnetic resonance (NMR) spectroscopy with small angle scattering (SAS) to characterize these challenging biomolecular systems. NMR can assess the structure and conformational dynamics of multidomain proteins, RNAs and biomolecular complexes. It can efficiently provide information on interaction surfaces, long-distance restraints and relative domain orientations at residue-level resolution. Such information can be readily combined with high-resolution structural data available on subcomponents of biomolecular assemblies. Moreover, NMR is a powerful tool to characterize the dynamics of biomolecules on a wide range of timescales, from nanoseconds to seconds. On the other hand, SAS approaches provide global information on the size and shape of biomolecules and on the ensemble of all conformations present in solution. Therefore, NMR and SAS provide complementary data that are uniquely suited to investigate dynamic biomolecular assemblies. Here, we briefly review the type of data that can be obtained by both techniques and describe different approaches that can be used to combine them to characterize biomolecular assemblies. We then provide guidelines on which experiments are best suited depending on the type of system studied, ranging from fully rigid complexes, dynamic structures that interconvert between defined conformations and systems with very high structural heterogeneity.

Identifying Strategies to Experimentally Probe Multidimensional Dynamics in the Ribosome

The ribosome is a complex biomolecular machine that utilizes large-scale conformational rearrangements to synthesize proteins. For example, during the elongation cycle, the "head" domain of the ribosomal small subunit (SSU) is known to undergo transient rotation events that allow for movement of tRNA molecules (i.e., translocation). While the head may exhibit rigid-body-like properties, the precise relationship between experimentally accessible probes and multidimensional rotations has yet to be established. To address this gap, we perform molecular dynamics simulations of the translocation step of the elongation cycle in the ribosome, where the SSU head spontaneously undergoes rotation and tilt-like motions. With this data set (1250 simulated events), we used statistical and information-theory-based measures to identify possible single-molecule probes that can isolate SSU head rotation and head tilting. This analysis provides a molecular interpretation for previous single-molecule measurements, while establishing a framework for the design of next-generation experiments that may precisely probe the mechanistic and kinetic aspects of the ribosome.

Visualizing translation dynamics at atomic detail inside a bacterial cell

Translation is the fundamental process of protein synthesis and is catalysed by the ribosome in all living cells1. Here we use advances in cryo-electron tomography and sub-tomogram analysis2,3 to visualize the structural dynamics of translation inside the bacterium Mycoplasma pneumoniae. To interpret the functional states in detail, we first obtain a high-resolution in-cell average map of all translating ribosomes and build an atomic model for the M. pneumoniae ribosome that reveals distinct extensions of ribosomal proteins. Classification then resolves 13 ribosome states that differ in their conformation and composition. These recapitulate major states that were previously resolved in vitro, and reflect intermediates during active translation. On the basis of these states, we animate translation elongation inside native cells and show how antibiotics reshape the cellular translation landscapes. During translation elongation, ribosomes often assemble in defined three-dimensional arrangements to form polysomes4. By mapping the intracellular organization of translating ribosomes, we show that their association into polysomes involves a local coordination mechanism that is mediated by the ribosomal protein L9. We propose that an extended conformation of L9 within polysomes mitigates collisions to facilitate translation fidelity. Our work thus demonstrates the feasibility of visualizing molecular processes at atomic detail inside cells.

Hyper-swivel head domain motions are required for complete mRNA-tRNA translocation and ribosome resetting

Translocation of messenger RNA (mRNA) and transfer RNA (tRNA) substrates through the ribosome during protein synthesis, an exemplar of directional molecular movement in biology, entails a complex interplay of conformational, compositional, and chemical changes. The molecular determinants of early translocation steps have been investigated rigorously. However, the elements enabling the ribosome to complete translocation and reset for subsequent protein synthesis reactions remain poorly understood. Here, we have combined molecular simulations with single-molecule fluorescence resonance energy transfer imaging to gain insights into the rate-limiting events of the translocation mechanism. We find that diffusive motions of the ribosomal small subunit head domain to hyper-swivelled positions, governed by universally conserved rRNA, can maneuver the mRNA and tRNAs to their fully translocated positions. Subsequent engagement of peptidyl-tRNA and disengagement of deacyl-tRNA from mRNA, within their respective small subunit binding sites, facilitate the ribosome resetting mechanism after translocation has occurred to enable protein synthesis to resume.

Selection of start codon during mRNA scanning in eukaryotic translation initiation

Accurate and high-speed scanning and subsequent selection of the correct start codon are important events in protein synthesis. Eukaryotic mRNAs have long 5′ UTRs that are inspected for the presence of a start codon by the ribosomal 48S pre-initiation complex (PIC). However, the conformational state of the 48S PIC required for inspecting every codon is not clearly understood. Here, atomistic molecular dynamics (MD) simulations and energy calculations suggest that the scanning conformation of 48S PIC may reject all but 4 (GUG, CUG, UUG and ACG) of the 63 non-AUG codons, and initiation factor eIF1 is crucial for this discrimination. We provide insights into the possible role of initiation factors eIF1, eIF1A, eIF2α and eIF2β in scanning. Overall, the study highlights how the scanning conformation of ribosomal 48S PIC acts as a coarse selectivity checkpoint for start codon selection and scans long 5′ UTRs in eukaryotic mRNAs with accuracy and high speed.

Molecular simulations of start codon selection by the eukaryotic ribosome during mRNA scanning provide further insight into high speed of scanning and how initiation factors contribute toward codon-anticodon-ribosome network stability.

Diffuse Ions Coordinate Dynamics in a Ribonucleoprotein Assembly

Proper ionic concentrations are required for the functional dynamics of RNA and ribonucleoprotein (RNP) assemblies. While experimental and computational techniques have provided many insights into the properties of chelated ions, less is known about the energetic contributions of diffuse ions to large-scale conformational rearrangements. To address this, we present a model that is designed to quantify the influence of diffuse monovalent and divalent ions on the dynamics of biomolecular assemblies. This model employs all-atom (non-H) resolution and explicit ions, where effective potentials account for hydration effects. We first show that the model accurately predicts the number of excess Mg²⁺ ions for prototypical RNA systems, at a level comparable to modern coarse-grained models. We then apply the model to a complete ribosome and show how the balance between diffuse Mg²⁺ and K⁺ ions can control the dynamics of tRNA molecules during translation. The model predicts differential effects of diffuse ions on the free-energy barrier associated with tRNA entry and the energy of tRNA binding to the ribosome. Together, this analysis reveals the direct impact of diffuse ions on the dynamics of an RNP assembly.

GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design

We assess costs and efficiency of state-of-the-art high-performance cloud computing and compare the results to traditional on-premises compute clusters. Our use case is atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a particular focus on alchemical protein-ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems, we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs, including hardware, personnel, room, energy, and cooling, producing MD trajectories in the cloud can be about as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19 872 independent simulations or ∼200 μs of combined simulation trajectory, we made use of diverse hardware available in the cloud at the time of the study. The computations scaled-up to reach peak performance using more than 4 000 instances, 140 000 cores, and 3 000 GPUs simultaneously. Our simulation ensemble finished in about 2 days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes.

Effects of cryo-EM cooling on structural ensembles

Structure determination by cryo electron microscopy (cryo-EM) provides information on structural heterogeneity and ensembles at atomic resolution. To obtain cryo-EM images of macromolecules, the samples are first rapidly cooled down to cryogenic temperatures. To what extent the structural ensemble is perturbed during cooling is currently unknown. Here, to quantify the effects of cooling, we combined continuum model calculations of the temperature drop, molecular dynamics simulations of a ribosome complex before and during cooling with kinetic models. Our results suggest that three effects markedly contribute to the narrowing of the structural ensembles: thermal contraction, reduced thermal motion within local potential wells, and the equilibration into lower free-energy conformations by overcoming separating free-energy barriers. During cooling, barrier heights below 10 kJ/mol were found to be overcome, which is expected to reduce B-factors in ensembles imaged by cryo-EM. Our approach now enables the quantification of the heterogeneity of room-temperature ensembles from cryo-EM structures.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

High-resolution structures of a thermophilic eukaryotic 80S ribosome reveal atomistic details of translocation

Ribosomes are complex and highly conserved ribonucleoprotein assemblies catalyzing protein biosynthesis in every organism. Here we present high-resolution cryo-EM structures of the 80S ribosome from a thermophilic fungus in two rotational states, which due to increased 80S stability provide a number of mechanistic details of eukaryotic translation. We identify a universally conserved ‘nested base-triple knot’ in the 26S rRNA at the polypeptide tunnel exit with a bulged-out nucleotide that likely serves as an adaptable element for nascent chain containment and handover. We visualize the structure and dynamics of the ribosome protective factor Stm1 upon ribosomal 40S head swiveling. We describe the structural impact of a unique and essential m1acp3 Ψ 18S rRNA hyper-modification embracing the anticodon wobble-position for eukaryotic tRNA and mRNA translocation. We complete the eEF2-GTPase switch cycle describing the GDP-bound post-hydrolysis state. Taken together, our data and their integration into the structural landscape of 80S ribosomes furthers our understanding of protein biogenesis.

Probing Interplays between Human XBP1u Translational Arrest Peptide and 80S Ribosome

The ribosome stalling mechanism is a crucial biological process, yet its atomistic underpinning is still elusive. In this framework, the human XBP1u translational arrest peptide (AP) plays a central role in regulating the unfolded protein response (UPR) in eukaryotic cells. Here, we report multimicrosecond all-atom molecular dynamics simulations designed to probe the interactions between the XBP1u AP and the mammalian ribosome exit tunnel, both for the wild type AP and for four mutant variants of different arrest potencies. Enhanced sampling simulations allow investigating the AP release process of the different variants, shedding light on this complex mechanism. The present outcomes are in qualitative/quantitative agreement with available experimental data. In conclusion, we provide an unprecedented atomistic picture of this biological process and clear-cut insights into the key AP–ribosome interactions.

The energetics of subunit rotation in the ribosome

Protein synthesis in the cell is controlled by an elaborate sequence of conformational rearrangements in the ribosome. The composition of a ribosome varies by species, though they typically contain ∼ 50-100 RNA and protein molecules. While advances in structural techniques have revolutionized our understanding of long-lived conformational states, a vast range of transiently visited configurations can not be directly observed. In these cases, computational/simulation methods can be used to understand the mechanical properties of the ribosome. Insights from these approaches can then help guide next-generation experimental measurements. In this short review, we discuss theoretical strategies that have been deployed to quantitatively describe the energetics of collective rearrangements in the ribosome. We focus on efforts to probe large-scale subunit rotation events, which involve the coordinated displacement of large numbers of atoms (tens of thousands). These investigations are revealing how the molecular structure of the ribosome encodes the mechanical properties that control large-scale dynamics.

Structural mechanism of GTPase-powered ribosome-tRNA movement

GTPases are regulators of cell signaling acting as molecular switches. The translational GTPase EF-G stands out, as it uses GTP hydrolysis to generate force and promote the movement of the ribosome along the mRNA. The key unresolved question is how GTP hydrolysis drives molecular movement. Here, we visualize the GTPase-powered step of ongoing translocation by time-resolved cryo-EM. EF-G in the active GDP-Pi form stabilizes the rotated conformation of ribosomal subunits and induces twisting of the sarcin-ricin loop of the 23 S rRNA. Refolding of the GTPase switch regions upon Pi release initiates a large-scale rigid-body rotation of EF-G pivoting around the sarcin-ricin loop that facilitates back rotation of the ribosomal subunits and forward swiveling of the head domain of the small subunit, ultimately driving tRNA forward movement. The findings demonstrate how a GTPase orchestrates spontaneous thermal fluctuations of a large RNA-protein complex into force-generating molecular movement.

Time-Lagged Independent Component Analysis of Random Walks and Protein Dynamics

Time-lagged independent component analysis (tICA) is a widely used dimension reduction method for the analysis of molecular dynamics (MD) trajectories and has proven particularly useful for the construction of protein dynamics Markov models. It identifies those “slow” collective degrees of freedom onto which the projections of a given trajectory show maximal autocorrelation for a given lag time. Here we ask how much information on the actual protein dynamics and, in particular, the free energy landscape that governs these dynamics the tICA-projections of MD-trajectories contain, as opposed to noise due to the inherently stochastic nature of each trajectory. To answer this question, we have analyzed the tICA-projections of high dimensional random walks using a combination of analytical and numerical methods. We find that the projections resemble cosine functions and strongly depend on the lag time, exhibiting strikingly complex behavior. In particular, and contrary to previous studies of principal component projections, the projections change noncontinuously with increasing lag time. The tICA-projections of selected 1 μs protein trajectories and those of random walks are strikingly similar, particularly for larger proteins, suggesting that these trajectories contain only little information on the energy landscape that governs the actual protein dynamics. Further the tICA-projections of random walks show clusters very similar to those observed for the protein trajectories, suggesting that clusters in the tICA-projections of protein trajectories do not necessarily reflect local minima in the free energy landscape. We also conclude that, in addition to the previous finding that certain ensemble properties of nonconverged protein trajectories resemble those of random walks; this is also true for their time correlations.

Differential Contribution of Protein Factors and 70S Ribosome to Elongation

The growth of the polypeptide chain occurs due to the fast and coordinated work of the ribosome and protein elongation factors, EF-Tu and EF-G. However, the exact contribution of each of these components in the overall balance of translation kinetics remains not fully understood. We created an in vitro translation system Escherichia coli replacing either elongation factor with heterologous thermophilic protein from Thermus thermophilus. The rates of the A-site binding and decoding reactions decreased an order of magnitude in the presence of thermophilic EF-Tu, indicating that the kinetics of aminoacyl-tRNA delivery depends on the properties of the elongation factor. On the contrary, thermophilic EF-G demonstrated the same translocation kinetics as a mesophilic protein. Effects of translocation inhibitors (spectinomycin, hygromycin B, viomycin and streptomycin) were also similar for both proteins. Thus, the process of translocation largely relies on the interaction of tRNAs and the ribosome and can be efficiently catalysed by thermophilic EF-G even at suboptimal temperatures.

The dynamics of subunit rotation in a eukaryotic ribosome

Protein synthesis by the ribosome is coordinated by an intricate series of large-scale conformational rearrangements. Structural studies can provide information about long-lived states, however biological kinetics are controlled by the intervening free-energy barriers. While there has been progress describing the energy landscapes of bacterial ribosomes, very little is known about the energetics of large-scale rearrangements in eukaryotic systems. To address this topic, we constructed an all-atom model with simplified energetics and performed simulations of subunit rotation in the yeast ribosome. In these simulations, the small subunit (SSU; ~1MDa) undergoes spontaneous and reversible rotations (~8°). By enabling the simulation of this rearrangement under equilibrium conditions, these calculations provide initial insights into the molecular factors that control dynamics in eukaryotic ribosomes. Through this, we are able to identify specific inter-subunit interactions that have a pronounced influence on the rate-limiting free-energy barrier. We also show that, as a result of changes in molecular flexibility, the thermodynamic balance between the rotated and unrotated states is temperature-dependent. This effect may be interpreted in terms of differential molecular flexibility within the rotated and unrotated states. Together, these calculations provide a foundation, upon which the field may begin to dissect the energetics of these complex molecular machines.

Organic nanoelectronics inside us: charge transport and localization in RNA could orchestrate ribosome operation

Translation - protein synthesis at the ribonucleic acid (RNA) based molecular machine, the ribosome, - proceeds in a similar manner in all life forms. However, despite several decades of research, the physics underlying this process remains enigmatic. Specifically, during translation, a ribosome undergoes large-scale conformational changes of its distant parts, and these motions are coordinated by an unknown mechanism. In this study, we suggest that such a mechanism could be related to charge (electron hole) transport along and between the RNA molecules, localization of these charges at certain sites and successive relaxation of the molecular geometry. Thus, we suppose that RNA-based molecular machines, e.g., the ribosome, could be electronically controlled, having "wires", "actuators", "a battery", and other "circuitry". Taking transfer RNA as an example, we justify the reasonability of our suggestion using ab initio and atomistic simulations. Specifically, very large hole transfer integrals between the nucleotides (up to above 100 meV) are observed so that the hole can migrate over nearly the whole tRNA molecule. Hole localization at several guanines located at functionally important sites (G27, G10, G34 and G63) is predicted, which is shown to induce geometry changes in these sites, their neighborhoods and even rather distant moieties. If our hypothesis is right, we anticipate that our findings will qualitatively advance the understanding of the key biological processes and could inspire novel approaches in medicine.

Energy-dependent protein folding: modeling how a protein folding machine may work

Background: Proteins fold robustly and reproducibly in vivo, but many cannot fold in vitro in isolation from cellular components. Despite the remarkable progress that has been achieved by the artificial intelligence approaches in predicting the protein native conformations, the pathways that lead to such conformations, either in vitro or in vivo, remain largely unknown. The slow progress in recapitulating protein folding pathways in silico may be an indication of the fundamental deficiencies in our understanding of folding as it occurs in nature. Here we consider the possibility that protein folding in living cells may not be driven solely by the decrease in Gibbs free energy and propose that protein folding in vivo should be modeled as an active energy-dependent process. The mechanism of action of such a protein folding machine might include direct manipulation of the peptide backbone. Methods: To show the feasibility of a protein folding machine, we conducted molecular dynamics simulations that were augmented by the application of mechanical force to rotate the C-terminal amino acid while simultaneously limiting the N-terminal amino acid movements. Results: Remarkably, the addition of this simple manipulation of peptide backbones to the standard molecular dynamics simulation indeed facilitated the formation of native structures in five diverse alpha-helical peptides. Steric clashes that arise in the peptides due to the forced directional rotation resulted in the behavior of the peptide backbone no longer resembling a freely jointed chain. Conclusions: These simulations show the feasibility of a protein folding machine operating under the conditions when the movements of the polypeptide backbone are restricted by applying external forces and constraints. Further investigation is needed to see whether such an effect may play a role during co-translational protein folding in vivo and how it can be utilized to facilitate folding of proteins in artificial environments.

From integrative structural biology to cell biology

Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.

A steric gate controls P/E hybrid-state formation of tRNA on the ribosome

The ribosome is a biomolecular machine that undergoes multiple large-scale structural rearrangements during protein elongation. Here, we focus on a conformational rearrangement during translocation, known as P/E hybrid-state formation. Using a model that explicitly represents all non-hydrogen atoms, we simulated more than 120 spontaneous transitions, where the tRNA molecule is displaced between the P and E sites of the large subunit. In addition to predicting a free-energy landscape that is consistent with previous experimental observations, the simulations reveal how a six-residue gate-like region can limit P/E formation, where sub-angstrom structural perturbations lead to an order-of-magnitude change in kinetics. Thus, this precisely defined set of residues represents a novel target that may be used to control functional dynamics in bacterial ribosomes. This theoretical analysis establishes a direct relationship between ribosome structure and large-scale dynamics, and it suggests how next-generation experiments may precisely dissect the energetics of hybrid formation on the ribosome.

The ribosome undergoes multiple large-scale structural rearrangements during protein elongation. Here the authors present an all-atom model of the ribosome to study the energetics of P/E hybrid-state formation, an early conformational rearrangement occurring during translocation.

An Information-Theory-Based Approach for Optimal Model Reduction of Biomolecules

In theoretical modeling of a physical system, a crucial step consists of the identification of those degrees of freedom that enable a synthetic yet informative representation of it. While in some cases this selection can be carried out on the basis of intuition and experience, straightforward discrimination of the important features from the negligible ones is difficult for many complex systems, most notably heteropolymers and large biomolecules. We here present a thermodynamics-based theoretical framework to gauge the effectiveness of a given simplified representation by measuring its information content. We employ this method to identify those reduced descriptions of proteins, in terms of a subset of their atoms, that retain the largest amount of information from the original model; we show that these highly informative representations share common features that are intrinsically related to the biological properties of the proteins under examination, thereby establishing a bridge between protein structure, energetics, and function.

Sampling of the conformational landscape of small proteins with Monte Carlo methods

Computer simulation provides an increasingly realistic picture of large-scale conformational change of proteins, but investigations remain fundamentally constrained by the femtosecond timestep of molecular dynamics simulations. For this reason, many biologically interesting questions cannot be addressed using accessible state-of-the-art computational resources. Here, we report the development of an all-atom Monte Carlo approach that permits the modelling of the large-scale conformational change of proteins using standard off-the-shelf computational hardware and standard all-atom force fields. We demonstrate extensive thermodynamic characterization of the folding process of the α-helical Trp-cage, the Villin headpiece and the β-sheet WW-domain. We fully characterize the free energy landscape, transition states, energy barriers between different states, and the per-residue stability of individual amino acids over a wide temperature range. We demonstrate that a state-of-the-art intramolecular force field can be combined with an implicit solvent model to obtain a high quality of the folded structures and also discuss limitations that still remain.

A GPU-Accelerated Fast Multipole Method for GROMACS: Performance and Accuracy

An important and computationally demanding part of molecular dynamics simulations is the calculation of long-range electrostatic interactions. Today, the prevalent method to compute these interactions is particle mesh Ewald (PME). The PME implementation in the GROMACS molecular dynamics package is extremely fast on individual GPU nodes. However, for large scale multinode parallel simulations, PME becomes the main scaling bottleneck as it requires all-to-all communication between the nodes; as a consequence, the number of exchanged messages scales quadratically with the number of involved nodes in that communication step. To enable efficient and scalable biomolecular simulations on future exascale supercomputers, clearly a method with a better scaling property is required. The fast multipole method (FMM) is such a method. As a first step on the path to exascale, we have implemented a performance-optimized, highly efficient GPU FMM and integrated it into GROMACS as an alternative to PME. For a fair performance comparison between FMM and PME, we first assessed the accuracies of the methods for various sets of input parameters. With parameters yielding similar accuracies for both methods, we determined the performance of GROMACS with FMM and compared it to PME for exemplary benchmark systems. We found that FMM with a multipole order of 8 yields electrostatic forces that are as accurate as PME with standard parameters. Further, for typical mixed-precision simulation settings, FMM does not lead to an increased energy drift with multipole orders of 8 or larger. Whereas an ≈50 000 atom simulation system with our FMM reaches only about a third of the performance with PME, for systems with large dimensions and inhomogeneous particle distribution, e.g., aerosol systems with water droplets floating in a vacuum, FMM substantially outperforms PME already on a single node.

Structural Heterogeneities of the Ribosome: New Frontiers and Opportunities for Cryo-EM

The extent of ribosomal heterogeneity has caught increasing interest over the past few years, as recent studies have highlighted the presence of structural variations of the ribosome. More precisely, the heterogeneity of the ribosome covers multiple scales, including the dynamical aspects of ribosomal motion at the single particle level, specialization at the cellular and subcellular scale, or evolutionary differences across species. Upon solving the ribosome atomic structure at medium to high resolution, cryogenic electron microscopy (cryo-EM) has enabled investigating all these forms of heterogeneity. In this review, we present some recent advances in quantifying ribosome heterogeneity, with a focus on the conformational and evolutionary variations of the ribosome and their functional implications. These efforts highlight the need for new computational methods and comparative tools, to comprehensively model the continuous conformational transition pathways of the ribosome, as well as its evolution. While developing these methods presents some important challenges, it also provides an opportunity to extend our interpretation and usage of cryo-EM data, which would more generally benefit the study of molecular dynamics and evolution of proteins and other complexes.

Converting GTP hydrolysis into motion: versatile translational elongation factor G

Elongation factor G (EF-G) is a translational GTPase that acts at several stages of protein synthesis. Its canonical function is to catalyze tRNA movement during translation elongation, but it also acts at the last step of translation to promote ribosome recycling. Moreover, EF-G has additional functions, such as helping the ribosome to maintain the mRNA reading frame or to slide over non-coding stretches of the mRNA. EF-G has an unconventional GTPase cycle that couples the energy of GTP hydrolysis to movement. EF-G facilitates movement in the GDP-Pi form. To convert the energy of hydrolysis to movement, it requires various ligands in the A site, such as a tRNA in translocation, an mRNA secondary structure element in ribosome sliding, or ribosome recycling factor in post-termination complex disassembly. The ligand defines the direction and timing of EF-G-facilitated motion. In this review, we summarize recent advances in understanding the mechanism of EF-G action as a remarkable force-generating GTPase.

Structural insights into mammalian mitochondrial translation elongation catalyzed by mtEFG1

Mitochondria are eukaryotic organelles of bacterial origin where respiration takes place to produce cellular chemical energy. These reactions are catalyzed by the respiratory chain complexes located in the inner mitochondrial membrane. Notably, key components of the respiratory chain complexes are encoded on the mitochondrial chromosome and their expression relies on a dedicated mitochondrial translation machinery. Defects in the mitochondrial gene expression machinery lead to a variety of diseases in humans mostly affecting tissues with high energy demand such as the nervous system, the heart, or the muscles. The mitochondrial translation system has substantially diverged from its bacterial ancestor, including alterations in the mitoribosomal architecture, multiple changes to the set of translation factors and striking reductions in otherwise conserved tRNA elements. Although a number of structures of mitochondrial ribosomes from different species have been determined, our mechanistic understanding of the mitochondrial translation cycle remains largely unexplored. Here, we present two cryo-EM reconstructions of human mitochondrial elongation factor G1 bound to the mammalian mitochondrial ribosome at two different steps of the tRNA translocation reaction during translation elongation. Our structures explain the mechanism of tRNA and mRNA translocation on the mitoribosome, the regulation of mtEFG1 activity by the ribosomal GTPase-associated center, and the basis of decreased susceptibility of mtEFG1 to the commonly used antibiotic fusidic acid.

Principles for Integrative Structural Biology Studies

Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.

Will Cryo-Electron Microscopy Shift the Current Paradigm in Protein Structure Prediction?

Protein dynamics is undoubtedly a pervasive ingredient in all biological functions. However, structural biology has been strongly driven by a static-centered view of protein architecture. We argue that the recent advances of cryo-electron microscopy (EM) have the potential to more broadly explore the conformational landscapes of protein complexes and therefore will enhance our ability to predict the diverse conformations of tertiary and quaternary protein structures that are functionally relevant in physiological conditions.

tRNA Dissociation from EF-Tu after GTP Hydrolysis: Primary Steps and Antibiotic Inhibition

In each round of ribosomal translation, the translational GTPase elongation factor Tu (EF-Tu) delivers a transfer RNA (tRNA) to the ribosome. After successful decoding, EF-Tu hydrolyzes GTP, which triggers a conformational change that ultimately results in the release of the tRNA from EF-Tu. To identify the primary steps of these conformational changes and how they are prevented by the antibiotic kirromycin, we employed all-atom explicit-solvent molecular dynamics simulations of the full ribosome-EF-Tu complex. Our results suggest that after GTP hydrolysis and Pi release, the loss of interactions between the nucleotide and the switch 1 loop of EF-Tu allows domain D1 of EF-Tu to rotate relative to domains D2 and D3 and leads to an increased flexibility of the switch 1 loop. This rotation induces a closing of the D1-D3 interface and an opening of the D1-D2 interface. We propose that the opening of the D1-D2 interface, which binds the CCA tail of the tRNA, weakens the crucial EF-Tu-tRNA interactions, which lowers tRNA binding affinity, representing the first step of tRNA release. Kirromycin binds within the D1-D3 interface, sterically blocking its closure, but does not prevent hydrolysis. The resulting increased flexibility of switch 1 explains why it is not resolved in kirromycin-bound structures.

Structural basis for ribosome recycling by RRF and tRNA

The bacterial ribosome is recycled into subunits by two conserved proteins, elongation factor G (EF-G) and the ribosome recycling factor (RRF). The molecular basis for ribosome recycling by RRF and EF-G remains unclear. Here, we report the crystal structure of a posttermination Thermus thermophilus 70S ribosome complexed with EF-G, RRF and two transfer RNAs at a resolution of 3.5 Å. The deacylated tRNA in the peptidyl (P) site moves into a previously unsuspected state of binding (peptidyl/recycling, p/R) that is analogous to that seen during initiation. The terminal end of the p/R-tRNA forms nonfavorable contacts with the 50S subunit while RRF wedges next to central inter-subunit bridges, illuminating the active roles of tRNA and RRF in dissociation of ribosomal subunits. The structure uncovers a missing snapshot of tRNA as it transits between the P and exit (E) sites, providing insights into the mechanisms of ribosome recycling and tRNA translocation.

Dynamics of the context-specific translation arrest by chloramphenicol and linezolid

Chloramphenicol (CHL) and linezolid (LZD) are antibiotics that inhibit translation. Both were thought to block peptide bond formation between all combinations of amino acids. Yet recently, a strong nascent peptide context-dependency of CHL- and LZD-induced translation arrest was discovered. Here, we probed the mechanism of action of CHL and LZD by using single-molecule Förster resonance energy transfer spectroscopy (smFRET) to monitor translation arrest induced by antibiotics. The presence of CHL or LZD does not significantly alter dynamics of protein synthesis until the arrest-motif of the nascent peptide is generated. Inhibition of peptide-bond formation compels the fully accommodated A-site tRNA to undergo repeated rounds of dissociation and non-productive rebinding. The glycyl amino-acid moiety on the A-site Gly-tRNA manages to overcome the arrest by CHL. Our results illuminate the mechanism of CHL and LZD action through their interactions with the ribosome, the nascent peptide and the incoming amino acid, perturbing elongation dynamics.

Active role of elongation factor G in maintaining the mRNA reading frame during translation

During translation, the ribosome moves along the mRNA one codon at a time with the help of elongation factor G (EF-G). Spontaneous changes in the translational reading frame are extremely rare, yet how the precise triplet-wise step is maintained is not clear. Here, we show that the ribosome is prone to spontaneous frameshifting on mRNA slippery sequences, whereas EF-G restricts frameshifting. EF-G helps to maintain the mRNA reading frame by guiding the A-site transfer RNA during translocation due to specific interactions with the tip of EF-G domain 4. Furthermore, EF-G accelerates ribosome rearrangements that restore the ribosome's control over the codon-anticodon interaction at the end of the movement. Our data explain how the mRNA reading frame is maintained during translation.

An Efficient Timer and Sizer of Biomacromolecular Motions

Life ticks as fast as how proteins move. Computationally expensive molecular dynamics simulation has been the only theoretical tool to gauge the time and sizes of these motions, though barely to their slowest ends. Here, we convert a computationally cheap elastic network model (ENM) into a molecular timer and sizer to gauge the slowest functional motions of structured biomolecules. Quasi-harmonic analysis, fluctuation profile matching, and the Wiener-Khintchine theorem are used to define the "time periods," t, for anharmonic principal components (PCs), which are validated by nuclear magnetic resonance (NMR) order parameters. The PCs with their respective "time periods" are mapped to the eigenvalues (λ_ENM) of the corresponding ENM modes. Thus, the power laws t(ns) = 56.1λ_ENM^-1.6 and σ²(Ų) = 32.7λ_ENM^-3.0 can be established allowing the characterization of the timescales of NMR-resolved conformers, crystallographic anisotropic displacement parameters, and important ribosomal motions, as well as motional sizes of the latter.