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.

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.

Design and proof of concept for targeted phage-based COVID-19 vaccination strategies with a streamlined cold-free supply chain.

Development of effective vaccines against coronavirus disease 2019 (COVID-19) is a global imperative. Rapid immunization of the entire human population against a widespread, continually evolving, and highly pathogenic virus is an unprecedented challenge, and different vaccine approaches are being pursued. Engineered filamentous bacteriophage (phage) particles have unique potential in vaccine development due to their inherent immunogenicity, genetic plasticity, stability, cost-effectiveness for large-scale production, and proven safety profile in humans. Herein we report the development and initial evaluation of two targeted phage-based vaccination approaches against SARS-CoV-2: dual ligand peptide-targeted phage and adeno-associated virus/phage (AAVP) particles. For peptide-targeted phage, we performed structure-guided antigen design to select six solvent-exposed epitopes of the SARS-CoV-2 spike (S) protein. One of these epitopes displayed on the major capsid protein pVIII of phage induced a specific and sustained humoral response when injected in mice. These phage were further engineered to simultaneously display the peptide CAKSMGDIVC on the minor capsid protein pIII to enable their transport from the lung epithelium into the systemic circulation. Aerosolization of these "dual-display" phage into the lungs of mice generated a systemic and specific antibody response. In the second approach, targeted AAVP particles were engineered to deliver the entire S protein gene under the control of a constitutive CMV promoter. This induced tissue-specific transgene expression, stimulating a systemic S protein-specific antibody response in mice. With these proof-of-concept preclinical experiments, we show that both targeted phage- and AAVP-based particles serve as robust yet versatile platforms that can promptly yield COVID-19 vaccine prototypes for translational development.

Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection.

Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662-C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662-C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662-C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.

pH and the Breast Cancer Recurrent Mutation D538G Affect the Process of Activation of Estrogen Receptor α.

Estrogen receptor α (ERα) is a regulatory protein that can access a set of distinct structural configurations. ERα undergoes extensive remodeling as it interacts with different agonists and antagonists, as well as transcription activation and repression factors. Moreover, breast cancer tumors resistant to hormone therapy have been associated with the imbalance between the active and inactive ERα states. Cancer-activating mutations in ERα play a crucial role in this imbalance and can promote the progression of cancer. However, the rate of this progression can also be increased by dysregulated pH in the tumor microenvironment. Many molecular aspects of the process of activation of ERα that can be affected by these pH changes and mutations are still unclear. Thus, we applied computational and experimental techniques to explore the activation process dynamics of ER for environments with different pHs and in the presence of one of the most recurrent cancer-activating mutations, D538G. Our results indicated that the effect of the pH increase associated with the D538G mutation promoted a robust stabilization of the active state of ER. We were also able to determine the main protein regions that have the most potential to influence the activation process under different pH conditions, which may provide targets of future therapeutics for the treatment of hormone-resistant breast cancer tumors. Finally, the approach used here can be applied for proteins associated with the proliferation of other cancer types, which can also have their function affected by small pH changes.

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.

How Nanopore Translocation Experiments Can Measure RNA Unfolding.

Electrokinetic translocation of biomolecules through solid-state nanopores represents a label-free single-molecule technique that may be used to measure biomolecular structure and dynamics. Recent investigations have attempted to distinguish individual transfer RNA (tRNA) species based on the associated pore translocation times, ion-current noise, and blockage currents. By manufacturing sufficiently smaller pores, each tRNA is required to undergo a deformation to translocate. Accordingly, differences in nanopore translocation times and distributions may be used to infer the mechanical properties of individual tRNA molecules. To bridge our understanding of tRNA structural dynamics and nanopore measurements, we apply molecular dynamics simulations using a simplified "structure-based" energetic model. Calculating the free-energy landscape for distinct tRNA species implicates transient unfolding of the terminal RNA helix during nanopore translocation. This provides a structural and energetic framework for interpreting current experiments, which can aid the design of methods for identifying macromolecules using nanopores.

Studying ribosome dynamics with simplified models.

With the broad accessibility of high-performance computing resources, the significance of a molecular dynamics simulation is now rarely limited by hardware and/or software availability. Rather, the scientific value of each calculation is determined by the principles that underlie the theoretical model. The current review addresses this topic in the context of simplified models applied to large-scale (∼20-100 Å) dynamics in the ribosome. Specifically, we focus on applications of the "SMOG" class of structure-based models, which can be used to simulate spontaneous (i.e. non-targeted) conformational rearrangements in complex molecular assemblies. Here, we aim to provide an entry-level assessment of the methods, which can help bridge conceptual and communication gaps between the experimental and computational communities. In addition, inspecting the strategies that have been deployed previously can provide guidelines for future computational investigations into the relationship between structure, energetics and dynamics in other assemblies.

Diffusion of tRNA inside the ribosome is position-dependent.

In recent years, there has been a growing interest to quantify the energy landscape that governs ribosome dynamics. However, in order to quantitatively integrate theoretical predictions and experimental measurements, it is essential that one has a detailed understanding of the associated diffusive properties. Here, all-atom explicit-solvent simulations (50 µs of aggregate sampling) predict that the diffusion coefficient of a tRNA molecule will depend on its position within the ribosome. Specifically, during aa-tRNA accommodation (i.e., the process by which tRNA enters the ribosome), the apparent diffusion coefficient decreases by approximately an order of magnitude. By comparing these to values obtained with an energetically "smooth" model, we show that the observed nonuniform behavior likely arises from electrostatic and solvation interactions between the tRNA and ribosome. These calculations also reveal the hierarchical character of ribosomal energetics, where steric interactions induce a large-scale free-energy barrier, and short-scale roughness determines the rate of diffusive movement across the landscape.

Drift-diffusion (DrDiff) framework determines kinetics and thermodynamics of two-state folding trajectory and tunes diffusion models.

The stochastic drift-diffusion (DrDiff) theory is an approach used to characterize the dynamical properties of simulation data. With new features in transition times analyses, the framework characterized the thermodynamic free-energy profile [F(Q)], the folding time (τf), and transition path time (τTP) by determining the coordinate-dependent drift-velocity [v(Q)] and diffusion [D(Q)] coefficients from trajectory time traces. In order to explore the DrDiff approach and to tune it with two other methods (Bayesian analysis and fep1D algorithm), a numerical integration of the Langevin equation with known D(Q) and F(Q) was performed and the inputted coefficients were recovered with success by the diffusion models. DrDiff was also applied to investigate the prion protein (PrP) kinetics and thermodynamics by analyzing folding/unfolding simulations. The protein structure-based model, the well-known Go¯-model, was employed in a coarse-grained Cα level to generate long constant-temperature time series. PrP was chosen due to recent experimental single-molecule studies in D and τTP that stressed the importance and the difficulty of probing these quantities and the rare transition state events related to prion misfolding and aggregation. The PrP thermodynamic double-well F(Q) profile, the "X" shape of τf(T), and the linear shape of τTP(T) were predicted with v(Q) and D(Q) obtained by the DrDiff algorithm. With the advance of single-molecule techniques, the DrDiff framework might be a useful ally for determining kinetic and thermodynamic properties by analyzing time observables of biomolecular systems. The code is freely available at https://github.com/ronaldolab/DrDiff.

Disorder guides domain rearrangement in elongation factor Tu.

Elongation factor Tu (EF-Tu) is a three-domain protein that is responsible for delivering aminoacyl-tRNA (aa-tRNA) molecules to the ribosome. During the delivery process, EF-Tu undergoes a large-scale (~50Å) conformational transition that results in rearrangement of domain I, relative to the II/III superdomain. Despite the central role of EF-Tu during protein synthesis, little is known about the structural and energetic properties of this reordering process. To study the physical-chemical properties of domain motion, we constructed a multi-basin structure-based (i.e., Go-like) model, with which we have simulated hundreds of spontaneous conformational rearrangements. By analyzing the statistical properties of these events, we show that EF-Tu is likely to adopt a disordered intermediate ensemble during this transition. We further show that this disordered intermediate will favor a specific sequence of conformational substeps when bound to the ribosome, and the disordered ensemble can influence the kinetics of the incoming aa-tRNA molecule. Overall, this study highlights the dynamic nature of EF-Tu by revealing a relationship between conformational disorder and biological function.

SMOG 2: A Versatile Software Package for Generating Structure-Based Models.

Molecular dynamics simulations with coarse-grained or simplified Hamiltonians have proven to be an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Originally developed in the context of protein folding, structure-based models (SBMs) have since been extended to probe a diverse range of biomolecular processes, spanning from protein and RNA folding to functional transitions in molecular machines. The hallmark feature of a structure-based model is that part, or all, of the potential energy function is defined by a known structure. Within this general class of models, there exist many possible variations in resolution and energetic composition. SMOG 2 is a downloadable software package that reads user-designated structural information and user-defined energy definitions, in order to produce the files necessary to use SBMs with high performance molecular dynamics packages: GROMACS and NAMD. SMOG 2 is bundled with XML-formatted template files that define commonly used SBMs, and it can process template files that are altered according to the needs of each user. This computational infrastructure also allows for experimental or bioinformatics-derived restraints or novel structural features to be included, e.g. novel ligands, prosthetic groups and post-translational/transcriptional modifications. The code and user guide can be downloaded at http://smog-server.org/smog2.

Order and disorder control the functional rearrangement of influenza hemagglutinin.

Influenza hemagglutinin (HA), a homotrimeric glycoprotein crucial for membrane fusion, undergoes a large-scale structural rearrangement during viral invasion. X-ray crystallography has shown that the pre- and postfusion configurations of HA2, the membrane-fusion subunit of HA, have disparate secondary, tertiary, and quaternary structures, where some regions are displaced by more than 100 Å. To explore structural dynamics during the conformational transition, we studied simulations of a minimally frustrated model based on energy landscape theory. The model combines structural information from both the pre- and postfusion crystallographic configurations of HA2. Rather than a downhill drive toward formation of the central coiled-coil, we discovered an order-disorder transition early in the conformational change as the mechanism for the release of the fusion peptides from their burial sites in the prefusion crystal structure. This disorder quickly leads to a metastable intermediate with a broken threefold symmetry. Finally, kinetic competition between the formation of the extended coiled-coil and C-terminal melting results in two routes from this intermediate to the postfusion structure. Our study reiterates the roles that cracking and disorder can play in functional molecular motions, in contrast to the downhill mechanical interpretations of the "spring-loaded" model proposed for the HA2 conformational transition.

Head swivel on the ribosome facilitates translocation by means of intra-subunit tRNA hybrid sites.

The elongation cycle of protein synthesis involves the delivery of aminoacyl-transfer RNAs to the aminoacyl-tRNA-binding site (A site) of the ribosome, followed by peptide-bond formation and translocation of the tRNAs through the ribosome to reopen the A site. The translocation reaction is catalysed by elongation factor G (EF-G) in a GTP-dependent manner. Despite the availability of structures of various EF-G-ribosome complexes, the precise mechanism by which tRNAs move through the ribosome still remains unclear. Here we use multiparticle cryoelectron microscopy analysis to resolve two previously unseen subpopulations within Thermus thermophilus EF-G-ribosome complexes at subnanometre resolution, one of them with a partly translocated tRNA. Comparison of these substates reveals that translocation of tRNA on the 30S subunit parallels the swivelling of the 30S head and is coupled to unratcheting of the 30S body. Because the tRNA maintains contact with the peptidyl-tRNA-binding site (P site) on the 30S head and simultaneously establishes interaction with the exit site (E site) on the 30S platform, a novel intra-subunit 'pe/E' hybrid state is formed. This state is stabilized by domain IV of EF-G, which interacts with the swivelled 30S-head conformation. These findings provide direct structural and mechanistic insight into the 'missing link' in terms of tRNA intermediates involved in the universally conserved translocation process.

Generalized Manning Condensation Model Captures the RNA Ion Atmosphere.

RNA is highly sensitive to the ionic environment and typically requires Mg(2+) to form compact structures. There is a need for models capable of describing the ion atmosphere surrounding RNA with quantitative accuracy. We present a model of RNA electrostatics and apply it within coarse-grained molecular dynamics simulation. The model treats Mg(2+) ions explicitly to account for ion-ion correlations neglected by mean-field theories. Since mean-field theories capture KCl well, it is treated implicitly by a generalized Manning counterion condensation model. The model extends Manning condensation to deal with arbitrary RNA conformations, nonlimiting KCl concentrations, and the ion inaccessible volume of RNA. The model is tested against experimental measurements of the excess Mg(2+) associated with the RNA, Γ(2+), because Γ(2+) is directly related to the Mg(2+)-RNA interaction free energy. The excellent agreement with experiment demonstrates that the model captures the ionic dependence of the RNA free energy landscape.

Topography of funneled landscapes determines the thermodynamics and kinetics of protein folding.

The energy landscape approach has played a fundamental role in advancing our understanding of protein folding. Here, we quantify protein folding energy landscapes by exploring the underlying density of states. We identify three quantities essential for characterizing landscape topography: the stabilizing energy gap between the native and nonnative ensembles δE, the energetic roughness ΔE, and the scale of landscape measured by the entropy S. We show that the dimensionless ratio between the gap, roughness, and entropy of the system Λ=δE/(ΔE√(2S)) accurately predicts the thermodynamics, as well as the kinetics of folding. Large Λ implies that the energy gap (or landscape slope towards the native state) is dominant, leading to more funneled landscapes. We investigate the role of topological and energetic roughness for proteins of different sizes and for proteins of the same size, but with different structural topologies. The landscape topography ratio Λ is shown to be monotonically correlated with the thermodynamic stability against trapping, as characterized by the ratio of folding temperature versus trapping temperature. Furthermore, Λ also monotonically correlates with the folding kinetic rates. These results provide the quantitative bridge between the landscape topography and experimental folding measurements.

Reduced model captures Mg(2+)-RNA interaction free energy of riboswitches.

The stability of RNA tertiary structures depends heavily on Mg(2+). The Mg(2+)-RNA interaction free energy that stabilizes an RNA structure can be computed experimentally through fluorescence-based assays that measure Γ2+, the number of excess Mg(2+) associated with an RNA molecule. Previous explicit-solvent simulations predict that the majority of excess Mg(2+) ions interact closely and strongly with the RNA, unlike monovalent ions such as K(+), suggesting that an explicit treatment of Mg(2+) is important for capturing RNA dynamics. Here we present a reduced model that accurately reproduces the thermodynamics of Mg(2+)-RNA interactions. This model is able to characterize long-timescale RNA dynamics coupled to Mg(2+) through the explicit representation of Mg(2+) ions. KCl is described by Debye-Hückel screening and a Manning condensation parameter, which represents condensed K(+) and models its competition with condensed Mg(2+). The model contains one fitted parameter, the number of condensed K(+) ions in the absence of Mg(2+). Values of Γ2+ computed from molecular dynamics simulations using the model show excellent agreement with both experimental data on the adenine riboswitch and previous explicit-solvent simulations of the SAM-I riboswitch. This agreement confirms the thermodynamic accuracy of the model via the direct relation of Γ2+ to the Mg(2+)-RNA interaction free energy, and provides further support for the predictions from explicit-solvent calculations. This reduced model will be useful for future studies of the interplay between Mg(2+) and RNA dynamics.

Connecting the kinetics and energy landscape of tRNA translocation on the ribosome.

Functional rearrangements in biomolecular assemblies result from diffusion across an underlying energy landscape. While bulk kinetic measurements rely on discrete state-like approximations to the energy landscape, single-molecule methods can project the free energy onto specific coordinates. With measures of the diffusion, one may establish a quantitative bridge between state-like kinetic measurements and the continuous energy landscape. We used an all-atom molecular dynamics simulation of the 70S ribosome (2.1 million atoms; 1.3 microseconds) to provide this bridge for specific conformational events associated with the process of tRNA translocation. Starting from a pre-translocation configuration, we identified sets of residues that collectively undergo rotary rearrangements implicated in ribosome function. Estimates of the diffusion coefficients along these collective coordinates for translocation were then used to interconvert between experimental rates and measures of the energy landscape. This analysis, in conjunction with previously reported experimental rates of translocation, provides an upper-bound estimate of the free-energy barriers associated with translocation. While this analysis was performed for a particular kinetic scheme of translocation, the quantitative framework is general and may be applied to energetic and kinetic descriptions that include any number of intermediates and transition states.

Excited states of ribosome translocation revealed through integrative molecular modeling.

The dynamic nature of biomolecules leads to significant challenges when characterizing the structural properties associated with function. While X-ray crystallography and imaging techniques (such as cryo-electron microscopy) can reveal the structural details of stable molecular complexes, strategies must be developed to characterize configurations that exhibit only marginal stability (such as intermediates) or configurations that do not correspond to minima on the energy landscape (such as transition-state ensembles). Here, we present a methodology (MDfit) that utilizes molecular dynamics simulations to generate configurations of excited states that are consistent with available biophysical and biochemical measurements. To demonstrate the approach, we present a sequence of configurations that are suggested to be associated with transfer RNA (tRNA) movement through the ribosome (translocation). The models were constructed by combining information from X-ray crystallography, cryo-electron microscopy, and biochemical data. These models provide a structural framework for translocation that may be further investigated experimentally and theoretically to determine the precise energetic character of each configuration and the transition dynamics between them.