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ABSTRACT: 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.
Nature Structural & Molecular Biology 08/2011; 18(9):1043-51. · 12.71 Impact Factor
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ABSTRACT: We present a microfluidic approach for single-molecule studies of the temperature-dependent behavior of biomolecules, using a platform that combines microfluidic sample handling, on-chip temperature control, and total internal reflection fluorescence (TIRF) microscopy of surface-immobilized biomolecules. With efficient, rapid, and uniform heating by microheaters and in situ temperature measurements within a microfluidic flowcell by micro temperature sensors, closed-loop, accurate temperature control is achieved. To demonstrate its utility, the temperature-controlled microfluidic flowcell is coupled to a prism-based TIRF microscope and is used to investigate the temperature-dependence of ribosome and transfer RNA (tRNA) structural dynamics that are required for the rapid and precise translocation of tRNAs through the ribosome during protein synthesis. Our studies reveal that the previously characterized, thermally activated transitions between two global conformational states of the pre-translocation (PRE) ribosomal complex persist at physiological temperature. In addition, the temperature-dependence of the rates of transition between these two global conformational states of the PRE complex reveal well-defined, measurable, and disproportionate effects, providing a robust experimental framework for investigating the thermodynamic activation parameters that underlie transitions across these barriers.
Lab on a Chip 10/2010; 11(2):274-81. · 5.67 Impact Factor
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ABSTRACT: Background: The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well. Results: The VBEM algorithm returns the model's evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model's parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem. Conclusions: The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics. Comment: 22 pages, 5 figures; Journal special issue for workshop papers from "New Problems and Methods in Computational Biology" A workshop at the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS 2009) Whistler, BC, Canada, December 11 or 12, 2009. (cf. http://videolectures.net/nipsworkshops09_computational_biology/)
09/2010;
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ABSTRACT: Abstract Background The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well. Results The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem. Conclusions The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.
BMC Bioinformatics. 01/2010;
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ABSTRACT: The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.
The VBEM algorithm returns the model's evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model's parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.
The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.
BMC Bioinformatics 01/2010; 11 Suppl 8:S2. · 2.75 Impact Factor
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ABSTRACT: Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a powerful tool for mechanistic investigations of increasingly complex biochemical systems. Recently, we and others have successfully used smFRET to directly investigate the role of structural dynamics in the function and regulation of the cellular protein synthesis machinery. A significant challenge to these experiments, and to analogous experiments in similarly complex cellular machineries, is the need for specific and efficient fluorescent labeling of the biochemical system at locations that are both mechanistically informative and minimally perturbative to the biological activity. Here, we describe the development of a highly purified, fluorescently labeled in vitro translation system that we have successfully designed for smFRET studies of protein synthesis. The general approaches we outline should be amenable to single-molecule fluorescence studies of other complex biochemical systems.
Methods in enzymology 01/2010; 472:221-59. · 1.90 Impact Factor
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ABSTRACT: 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.
Biophysical Journal 12/2009; 97(12):3196-205. · 3.65 Impact Factor
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ABSTRACT: 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.
Proceedings of the National Academy of Sciences 09/2009; 106(37):15702-7. · 9.68 Impact Factor
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ABSTRACT: 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.
Nature Structural & Molecular Biology 09/2009; 16(8):861-8. · 12.71 Impact Factor
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ABSTRACT: 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.
Molecular cell 06/2008; 30(3):348-59. · 14.61 Impact Factor
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ABSTRACT: This paper reports a temperature-controlled microfluidic platform for single-molecule studies. Parallel microchannels are formed between glass and quartz, and are integrated with on-chip heaters and temperature sensors. Combined with surface derivatization and fluorescent labeling of biomolecules, temperature-dependent changes in the rates of fluorescence photobleaching are measured. The results demonstrate the potential of the device to provide an ideal environment for temperature-dependent single-molecule studies.
Nano/Micro Engineered and Molecular Systems, 2007. NEMS '07. 2nd IEEE International Conference on; 02/2007
