Ruben L. Gonzalez

Coupling of Ribosomal L1 Stalk and tRNA Dynamics during Translation Elongation

By using single-molecule fluorescence resonance energy transfer (smFRET), we observe the real-time dynamic coupling between the ribosome, labeled at the L1 stalk, and transfer RNA (tRNA). We find that an interaction between the ribosomal L1 stalk and the newly deacylated tRNA is established spontaneously upon peptide bond formation; this event involves coupled movements of the L1 stalk and tRNAs as well as ratcheting of the ribosome. In the absence of elongation factor G, the entire pretranslocation ribosome fluctuates between just two states: a nonratcheted state, with tRNAs in their classical configuration and no L1 stalk-tRNA interaction, and a ratcheted state, with tRNAs in an intermediate hybrid configuration and a direct L1 stalk-tRNA interaction. We demonstrate that binding of EF-G shifts the equilibrium toward the ratcheted state. Real-time smFRET experiments reveal that the L1 stalk-tRNA interaction persists throughout the translocation reaction, suggesting that the L1 stalk acts to direct tRNA movements during translocation.

Label-free single-molecule detection of DNA-hybridization kinetics with a carbon nanotube field-effect transistor

Single-molecule measurements of biomolecules can provide information about the molecular interactions and kinetics that are hidden in ensemble measurements. However, there is a requirement for techniques with improved sensitivity and time resolution for use in exploring biomolecular systems with fast dynamics. Here, we report the detection of DNA hybridization at the single-molecule level using a carbon nanotube field-effect transistor. By covalently attaching a single-stranded probe DNA sequence to a point defect in a carbon nanotube, we are able to measure two-level fluctuations in the conductance of the nanotube in the presence of a complementary DNA target. The kinetics of the system are studied as a function of temperature, allowing the measurement of rate constants, melting curves and activation energies for different sequences and target concentrations. The kinetics demonstrate non-Arrhenius behaviour, in agreement with DNA hybridization experiments using fluorescence correlation spectroscopy. This technique is label-free and could be used to probe single-molecule dynamics at microsecond timescales.

Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

Time series data provided by single-molecule Förster resonance energy transfer (smFRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g., rate constants, but also information about the model itself, e.g., the number of conformational states. Resolving whether such states exist or how many of them exist requires a careful approach to the problem of model selection, here meaning discrimination among models with differing numbers of states. The most straightforward approach to model selection generalizes the common idea of maximum likelihood--selecting the most likely parameter values--to maximum evidence: selecting the most likely model. In either case, such an inference presents a tremendous computational challenge, which we here address by exploiting an approximation technique termed variational Bayesian expectation maximization. We demonstrate how this technique can be applied to temporal data such as smFRET time series; show superior statistical consistency relative to the maximum likelihood approach; compare its performance on smFRET data generated from experiments on the ribosome; and illustrate how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics. Source code used in this analysis, including a graphical user interface, is available open source via http://vbFRET.sourceforge.net.

Structure and dynamics of a processive Brownian motor: the translating ribosome.

There is mounting evidence indicating that protein synthesis is driven and regulated by mechanisms that direct stochastic, large-scale conformational fluctuations of the translational apparatus. This mechanistic paradigm implies that a free-energy landscape governs the conformational states that are accessible to and sampled by the translating ribosome. This scenario presents interdependent opportunities and challenges for structural and dynamic studies of protein synthesis. Indeed, the synergism between cryogenic electron microscopic and X-ray crystallographic structural studies, on the one hand, and single-molecule fluorescence resonance energy transfer (smFRET) dynamic studies, on the other, is emerging as a powerful means for investigating the complex free-energy landscape of the translating ribosome and uncovering the mechanisms that direct the stochastic conformational fluctuations of the translational machinery. In this review, we highlight the principal insights obtained from cryogenic electron microscopic, X-ray crystallographic, and smFRET studies of the elongation stage of protein synthesis and outline the emerging themes, questions, and challenges that lie ahead in mechanistic studies of translation.

Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation

Determining the mechanism by which tRNAs rapidly and precisely transit through the ribosomal A, P, and E sites during translation remains a major goal in the study of protein synthesis. Here, we report the real-time dynamics of the L1 stalk, a structural element of the large ribosomal subunit that is implicated in directing tRNA movements during translation. Within pretranslocation ribosomal complexes, the L1 stalk exists in a dynamic equilibrium between open and closed conformations. Binding of elongation factor G (EF-G) shifts this equilibrium toward the closed conformation through one of at least two distinct kinetic mechanisms, where the identity of the P-site tRNA dictates the kinetic route that is taken. Within posttranslocation complexes, L1 stalk dynamics are dependent on the presence and identity of the E-site tRNA. Collectively, our data demonstrate that EF-G and the L1 stalk allosterically collaborate to direct tRNA translocation from the P to the E sites, and suggest a model for the release of E-site tRNA.

Site-specific labeling of the ribosome for single-molecule spectroscopy.

Single-molecule fluorescence spectroscopy can reveal mechanistic and kinetic details that may not be observed in static structural and bulk biochemical studies of protein synthesis. One approach requires site-specific and stable attachment of fluorophores to the components of translation machinery. Fluorescent tagging of the ribosome is a prerequisite for the observation of dynamic changes in ribosomal conformation during translation using fluorescence methods. Modifications of the ribosomal particle are difficult due to its complexity and high degree of sequence and structural conservation. We have developed a general method to label specifically the prokaryotic ribosome by hybridization of fluorescent oligonucleotides to mutated ribosomal RNA. Functional, modified ribosomes can be purified as a homogenous population, and fluorescence can be monitored from labeled ribosomal complexes immobilized on a derivatized quartz surface.

Biological mechanisms, one molecule at a time

The last 15 years have witnessed the development of tools that allow the observation and manipulation of single molecules. The rapidly expanding application of these technologies for investigating biological systems of ever-increasing complexity is revolutionizing our ability to probe the mechanisms of biological reactions. Here, we compare the mechanistic information available from single-molecule experiments with the information typically obtained from ensemble studies and show how these two experimental approaches interface with each other. We next present a basic overview of the toolkit for observing and manipulating biology one molecule at a time. We close by presenting a case study demonstrating the impact that single-molecule approaches have had on our understanding of one of lifes most fundamental biochemical reactions: the translation of a messenger RNA into its encoded protein by the ribosome.

Translation factors direct intrinsic ribosome dynamics during translation termination and ribosome recycling

Characterizing the structural dynamics of the translating ribosome remains a major goal in the study of protein synthesis. Deacylation of peptidyl-tRNA during translation elongation triggers fluctuations of the pretranslocation ribosomal complex between two global conformational states. Elongation factor G–mediated control of the resulting dynamic conformational equilibrium helps to coordinate ribosome and tRNA movements during elongation and is thus a crucial mechanistic feature of translation. Beyond elongation, deacylation of peptidyl-tRNA also occurs during translation termination, and this deacylated tRNA persists during ribosome recycling. Here we report that specific regulation of the analogous conformational equilibrium by translation release and ribosome recycling factors has a critical role in the termination and recycling mechanisms. Our results support the view that specific regulation of the global state of the ribosome is a fundamental characteristic of all translation factors and a unifying theme throughout protein synthesis.

Transfer RNA–mediated regulation of ribosome dynamics during protein synthesis

Translocation of tRNAs through the ribosome during protein synthesis involves large-scale structural rearrangement of the ribosome and ribosome-bound tRNAs that is accompanied by extensive and dynamic remodeling of tRNA-ribosome interactions. How the rearrangement of individual tRNA-ribosome interactions influences tRNA movement during translocation, however, remains largely unknown. To address this question, we used single-molecule FRET to characterize the dynamics of ribosomal pretranslocation (PRE) complex analogs carrying either wild-type or systematically mutagenized tRNAs. Our data reveal how specific tRNA-ribosome interactions regulate the rate of PRE complex rearrangement into a critical, on-pathway translocation intermediate and how these interactions control the stability of the resulting configuration. Notably, our results suggest that the conformational flexibility of the tRNA molecule has a crucial role in directing the structural dynamics of the PRE complex during translocation.

Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments

Many single-molecule experiments aim to characterize biomolecular processes in terms of kinetic models that specify the rates of transition between conformational states of the biomolecule. Estimation of these rates often requires analysis of a population of molecules, in which the conformational trajectory of each molecule is represented by a noisy, time-dependent signal trajectory. Although hidden Markov models (HMMs) may be used to infer the conformational trajectories of individual molecules, estimating a consensus kinetic model from the population of inferred conformational trajectories remains a statistically difficult task, as inferred parameters vary widely within a population. Here, we demonstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments: 1), the characterization of changes in rates across a series of experiments performed under variable conditions; and 2), the detection of degenerate states that exhibit the same FRET efficiency but differ in their rates of transition. We apply this newly developed methodology to two studies of the bacterial ribosome, each exemplary of one of these two analysis tasks. We conclude with a discussion of model-selection techniques for determination of the appropriate number of conformational states. The code used to perform this analysis and a basic graphical user interface front end are available as open source software.