Increasing the accuracy of single-molecule data analysis using tMAVEN

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physicochemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule data set and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series modeling, analysis, and visualization environment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from preprocessing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule data set with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule data sets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physicochemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule data sets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

Increasing the accuracy of single-molecule data analysis using tMAVEN

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.

The mechanism of mRNA activation

During translation initiation, messenger RNA molecules must be identified and activated for loading into a ribosome. In this rate-limiting step, the heterotrimeric protein eukaryotic initiation factor eIF4F must recognize and productively interact with the 7-methylguanosine cap at the 5' end of the messenger RNA and subsequently activate the message. Despite its fundamental, regulatory role in gene expression, the molecular events underlying cap recognition and messenger RNA activation remain mysterious. Here, we generate a unique, single-molecule fluorescence imaging system to interrogate the dynamics with which eIF4F discriminates productive and non-productive locations on full-length, native messenger RNA molecules. At the single-molecule level, we observe stochastic sampling of eIF4F along the length of the messenger RNA and identify allosteric communication between the eIF4F subunits which ultimately drive cap-recognition and subsequent activation of the message. Our experiments uncover novel functions for each subunit of eIF4F and we conclude by presenting a model for messenger RNA activation which precisely defines the composition of the activated message. This model provides a general framework for understanding how messenger RNA molecules may be discriminated from one another, and how other RNA-binding proteins may control the efficiency of translation initiation.

Comparative genetic, biochemical, and biophysical analyses of the four E. coli ABCF paralogs support distinct functions related to mRNA translation

Multiple paralogous ABCF ATPases are encoded in most genomes, but the physiological functions remain unknown for most of them. We herein compare the four Escherichia coli K12 ABCFs - EttA, Uup, YbiT, and YheS - using assays previously employed to demonstrate EttA gates the first step of polypeptide elongation on the ribosome dependent on ATP/ADP ratio. A Δ uup knockout, like Δ ettA , exhibits strongly reduced fitness when growth is restarted from long-term stationary phase, but neither Δ ybiT nor Δ yheS exhibits this phenotype. All four proteins nonetheless functionally interact with ribosomes based on in vitro translation and single-molecule fluorescence resonance energy transfer experiments employing variants harboring glutamate-to-glutamine active-site mutations (EQ ₂ ) that trap them in the ATP-bound conformation. These variants all strongly stabilize the same global conformational state of a ribosomal elongation complex harboring deacylated tRNA ^Val in the P site. However, EQ ₂ -Uup uniquely exchanges on/off the ribosome on a second timescale, while EQ ₂ -YheS-bound ribosomes uniquely sample alternative global conformations. At sub-micromolar concentrations, EQ ₂ -EttA and EQ ₂ -YbiT fully inhibit in vitro translation of an mRNA encoding luciferase, while EQ ₂ -Uup and EQ ₂ -YheS only partially inhibit it at ~10-fold higher concentrations. Moreover, tripeptide synthesis reactions are not inhibited by EQ ₂ -Uup or EQ ₂ -YheS, while EQ ₂ -YbiT inhibits synthesis of both peptide bonds and EQ ₂ -EttA specifically traps ribosomes after synthesis of the first peptide bond. These results support the four E. coli ABCF paralogs all having different activities on translating ribosomes, and they suggest that there remains a substantial amount of functionally uncharacterized "dark matter" involved in mRNA translation.

Entropic control of the free-energy landscape of an archetypal biomolecular machine

Biomolecular machines are complex macromolecular assemblies that utilize thermal and chemical energy to perform essential, multistep, cellular processes. Despite possessing different architectures and functions, an essential feature of the mechanisms of action of all such machines is that they require dynamic rearrangements of structural components. Surprisingly, biomolecular machines generally possess only a limited set of such motions, suggesting that these dynamics must be repurposed to drive different mechanistic steps. Although ligands that interact with these machines are known to drive such repurposing, the physical and structural mechanisms through which ligands achieve this remain unknown. Using temperature-dependent, single-molecule measurements analyzed with a time-resolution-enhancing algorithm, here, we dissect the free-energy landscape of an archetypal biomolecular machine, the bacterial ribosome, to reveal how its dynamics are repurposed to drive distinct steps during ribosome-catalyzed protein synthesis. Specifically, we show that the free-energy landscape of the ribosome encompasses a network of allosterically coupled structural elements that coordinates the motions of these elements. Moreover, we reveal that ribosomal ligands which participate in disparate steps of the protein synthesis pathway repurpose this network by differentially modulating the structural flexibility of the ribosomal complex (i.e., the entropic component of the free-energy landscape). We propose that such ligand-dependent entropic control of free-energy landscapes has evolved as a general strategy through which ligands may regulate the functions of all biomolecular machines. Such entropic control is therefore an important driver in the evolution of naturally occurring biomolecular machines and a critical consideration for the design of synthetic molecular machines.

Characterizing the Conformational Free-Energy Landscape of RNA Stem-Loops Using Single-Molecule Field-Effect Transistors

We have developed and used single-molecule field-effect transistors (smFETs) to characterize the conformational free-energy landscape of RNA stem-loops. Stem-loops are one of the most common RNA structural motifs and serve as building blocks for the formation of complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been interpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or mechanical (un)folding forces, they provide a unique approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops (un)fold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which unfolded conformations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several misfolded conformations or the natively folded conformation. Our findings highlight the extremely rugged landscape on which even the simplest RNA structural elements fold and demonstrate that smFETs are a unique and powerful approach for characterizing the conformational free-energy of RNA.

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results-especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes' rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated 'engine' capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

NusG-mediated Coupling of Transcription and Translation Enhances Gene Expression by Suppressing RNA Polymerase Backtracking

In bacteria, transcription is coupled to, and can be regulated by, translation. Although recent structural studies suggest that the N-utilization substance G (NusG) transcription factor can serve as a direct, physical link between the transcribing RNA polymerase (RNAP) and the lead ribosome, mechanistic studies investigating the potential role of NusG in mediating transcription-translation coupling are lacking. Here, we report development of a cellular extract- and reporter gene-based, in vitro biochemical system that supports transcription-translation coupling as well as the use of this system to study the role of NusG in coupling. Our findings show that NusG is required for coupling and that the enhanced gene expression that results from coupling is dependent on the ability of NusG to directly interact with the lead ribosome. Moreover, we provide strong evidence that NusG-mediated coupling enhances gene expression through a mechanism in which the lead ribosome that is tethered to the RNAP by NusG suppresses spontaneous backtracking of the RNAP on its DNA template that would otherwise inhibit transcription.

FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current 'state of the art' from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage 'open science' practices.

Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments

Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

Insights into genome recoding from the mechanism of a classic +1-frameshifting tRNA

While genome recoding using quadruplet codons to incorporate non-proteinogenic amino acids is attractive for biotechnology and bioengineering purposes, the mechanism through which such codons are translated is poorly understood. Here we investigate translation of quadruplet codons by a +1-frameshifting tRNA, SufB2, that contains an extra nucleotide in its anticodon loop. Natural post-transcriptional modification of SufB2 in cells prevents it from frameshifting using a quadruplet-pairing mechanism such that it preferentially employs a triplet-slippage mechanism. We show that SufB2 uses triplet anticodon-codon pairing in the 0-frame to initially decode the quadruplet codon, but subsequently shifts to the +1-frame during tRNA-mRNA translocation. SufB2 frameshifting involves perturbation of an essential ribosome conformational change that facilitates tRNA-mRNA movements at a late stage of the translocation reaction. Our results provide a molecular mechanism for SufB2-induced +1 frameshifting and suggest that engineering of a specific ribosome conformational change can improve the efficiency of genome recoding.

Genome recoding with quadruplet codons requires a +1-frameshift-suppressor tRNA able to insert an amino acid at quadruplet codons of interest. Here the authors identify the mechanisms resulting in +1 frameshifting and the steps of the elongation cycle in which it occurs.

Structural insights into mRNA reading frame regulation by tRNA modification and slippery codon-anticodon pairing

Modifications in the tRNA anticodon loop, adjacent to the three-nucleotide anticodon, influence translation fidelity by stabilizing the tRNA to allow for accurate reading of the mRNA genetic code. One example is the N1-methylguanosine modification at guanine nucleotide 37 (m¹G37) located in the anticodon loop andimmediately adjacent to the anticodon nucleotides 34, 35, 36. The absence of m¹G37 in tRNA^Pro causes +1 frameshifting on polynucleotide, slippery codons. Here, we report structures of the bacterial ribosome containing tRNA^Pro bound to either cognate or slippery codons to determine how the m¹G37 modification prevents mRNA frameshifting. The structures reveal that certain codon–anticodon contexts and the lack of m¹G37 destabilize interactions of tRNA^Pro with the P site of the ribosome, causing large conformational changes typically only seen during EF-G-mediated translocation of the mRNA-tRNA pairs. These studies provide molecular insights into how m¹G37 stabilizes the interactions of tRNA^Pro with the ribosome in the context of a slippery mRNA codon.

Elongation inhibitors do not prevent the release of puromycylated nascent polypeptide chains from ribosomes

Puromycin is an amino-acyl transfer RNA analog widely employed in studies of protein synthesis. Since puromycin is covalently incorporated into nascent polypeptide chains, anti-puromycin immunofluorescence enables visualization of nascent protein synthesis. A common assumption in studies of local messenger RNA translation is that the anti-puromycin staining of puromycylated nascent polypeptides in fixed cells accurately reports on their original site of translation, particularly when ribosomes are stalled with elongation inhibitors prior to puromycin treatment. However, when we attempted to implement a proximity ligation assay to detect ribosome-puromycin complexes, we found no evidence to support this assumption. We further demonstrated, using biochemical assays and live cell imaging of nascent polypeptides in mammalian cells, that puromycylated nascent polypeptides rapidly dissociate from ribosomes even in the presence of elongation inhibitors. Our results suggest that attempts to define precise subcellular translation sites using anti-puromycin immunostaining may be confounded by release of puromycylated nascent polypeptide chains prior to fixation.

5'-UTR recruitment of the translation initiation factor eIF4GI or DAP5 drives cap-independent translation of a subset of human mRNAs

During unfavorable conditions (e.g. tumor hypoxia or viral infection), canonical, cap-dependent mRNA translation is suppressed in human cells. Nonetheless, a subset of physiologically important mRNAs (e.g. hypoxia-inducible factor 1α [HIF-1α], fibroblast growth factor 9 [FGF-9], and p53) is still translated by an unknown, cap-independent mechanism. Additionally, expression levels of eukaryotic translation initiation factor 4GI (eIF4GI) and of its homolog, death-associated protein 5 (DAP5), are elevated. By examining the 5' UTRs of HIF-1α, FGF-9, and p53 mRNAs and using fluorescence anisotropy binding studies, luciferase reporter-based in vitro translation assays, and mutational analyses, we demonstrate here that eIF4GI and DAP5 specifically bind to the 5' UTRs of these cap-independently translated mRNAs. Surprisingly, we found that the eIF4E-binding domain of eIF4GI increases not only the binding affinity but also the selectivity among these mRNAs. We further demonstrate that the affinities of eIF4GI and DAP5 binding to these 5' UTRs correlate with the efficiency with which these factors drive cap-independent translation of these mRNAs. Integrating the results of our binding and translation assays, we conclude that eIF4GI or DAP5 is critical for recruitment of a specific subset of mRNAs to the ribosome, providing mechanistic insight into their cap-independent translation.

Late steps in bacterial translation initiation visualized using time-resolved cryo-EM

The initiation of bacterial translation involves the tightly regulated joining of the 50S ribosomal subunit to an initiator transfer RNA (fMet-tRNAfMet)-containing 30S ribosomal initiation complex to form a 70S initiation complex, which subsequently matures into a 70S elongation-competent complex. Rapid and accurate formation of the 70S initiation complex is promoted by initiation factors, which must dissociate from the 30S initiation complex before the resulting 70S elongation-competent complex can begin the elongation of translation1. Although comparisons of the structures of the 30S2-5 and 70S4,6-8 initiation complexes have revealed that the ribosome, initiation factors and fMet-tRNAfMet can acquire different conformations in these complexes, the timing of conformational changes during formation of the 70S initiation complex, the structures of any intermediates formed during these rearrangements, and the contributions that these dynamics might make to the mechanism and regulation of initiation remain unknown. Moreover, the absence of a structure of the 70S elongation-competent complex formed via an initiation-factor-catalysed reaction has precluded an understanding of the rearrangements to the ribosome, initiation factors and fMet-tRNAfMet that occur during maturation of a 70S initiation complex into a 70S elongation-competent complex. Here, using time-resolved cryogenic electron microscopy9, we report the near-atomic-resolution view of how a time-ordered series of conformational changes drive and regulate subunit joining, initiation factor dissociation and fMet-tRNAfMet positioning during formation of the 70S elongation-competent complex. Our results demonstrate the power of time-resolved cryogenic electron microscopy to determine how a time-ordered series of conformational changes contribute to the mechanism and regulation of one of the most fundamental processes in biology.

Bayesian-Estimated Hierarchical HMMs Enable Robust Analysis of Single-Molecule Kinetic Heterogeneity

Single-molecule kinetic experiments allow the reaction trajectories of individual biomolecules to be directly observed, eliminating the effects of population averaging and providing a powerful approach for elucidating the kinetic mechanisms of biomolecular processes. A major challenge to the analysis and interpretation of these experiments, however, is the kinetic heterogeneity that almost universally complicates the recorded single-molecule signal versus time trajectories (i.e., signal trajectories). Such heterogeneity manifests as changes and/or differences in the transition rates that are observed within individual signal trajectories or across a population of signal trajectories. Because characterizing kinetic heterogeneity can provide critical mechanistic information, we have developed a computational method that effectively and comprehensively enables such analysis. To this end, we have developed a computational algorithm and software program, hFRET, that uses the variational approximation for Bayesian inference to estimate the parameters of a hierarchical hidden Markov model, thereby enabling robust identification and characterization of kinetic heterogeneity. Using simulated signal trajectories, we demonstrate the ability of hFRET to accurately and precisely characterize kinetic heterogeneity. In addition, we use hFRET to analyze experimentally recorded signal trajectories reporting on the conformational dynamics of ribosomal pre-translocation (PRE) complexes. The results of our analyses demonstrate that PRE complexes exhibit kinetic heterogeneity, reveal the physical origins of this heterogeneity, and allow us to expand the current model of PRE complex dynamics. The methods described here can be applied to signal trajectories generated using any type of signal and can be easily extended to the analysis of signal trajectories exhibiting more complex kinetic behaviors. Moreover, variations of our approach can be easily developed to integrate kinetic data obtained from different experimental constructs and/or from molecular dynamics simulations of a biomolecule of interest.

Increasing the Time Resolution of Single-Molecule Experiments with Bayesian Inference

Many time-resolved single-molecule biophysics experiments seek to characterize the kinetics of biomolecular systems exhibiting dynamics that challenge the time resolution of the given technique. Here, we present a general, computational approach to this problem that employs Bayesian inference to learn the underlying dynamics of such systems, even when they are much faster than the time resolution of the experimental technique being used. By accurately and precisely inferring rate constants, our Bayesian inference for the analysis of subtemporal resolution dynamics approach effectively enables the experimenter to super-resolve the poorly resolved dynamics that are present in their data.

d-Amino Acid-Mediated Translation Arrest Is Modulated by the Identity of the Incoming Aminoacyl-tRNA

A complete understanding of the determinants that restrict d-amino acid incorporation by the ribosome, which is of interest to both basic biologists and the protein engineering community, remains elusive. Previously, we demonstrated that d-amino acids are successfully incorporated into the C-terminus of the nascent polypeptide chain. Ribosomes carrying the resulting peptidyl-d-aminoacyl-tRNA (peptidyl-d-aa-tRNA) donor substrate, however, partition into subpopulations that either undergo translation arrest through inactivation of the ribosomal peptidyl-transferase center (PTC) or remain translationally competent. The proportion of each subpopulation is determined by the identity of the d-amino acid side chain. Here, we demonstrate that the identity of the aminoacyl-tRNA (aa-tRNA) acceptor substrate that is delivered to ribosomes carrying a peptidyl-d-aa-tRNA donor further modulates this partitioning. Our discovery demonstrates that it is the pairing of the peptidyl-d-aa-tRNA donor and the aa-tRNA acceptor that determines the activity of the PTC. Moreover, we provide evidence that both the amino acid and tRNA components of the aa-tRNA acceptor contribute synergistically to the extent of arrest. The results of this work deepen our understanding of the mechanism of d-amino acid-mediated translation arrest and how cells avoid this precarious obstacle, reveal similarities to other translation arrest mechanisms involving the PTC, and provide a new route for improving the yields of engineered proteins containing d-amino acids.

A conformational switch in initiation factor 2 controls the fidelity of translation initiation in bacteria

Initiation factor (IF) 2 controls the fidelity of translation initiation by selectively increasing the rate of 50S ribosomal subunit joining to 30S initiation complexes (ICs) that carry an N-formyl-methionyl-tRNA (fMet-tRNA^fMet). Previous studies suggest that rapid 50S subunit joining involves a GTP- and fMet-tRNA^fMet-dependent "activation" of IF2, but a lack of data on the structure and conformational dynamics of 30S IC-bound IF2 has precluded a mechanistic understanding of this process. Here, using an IF2-tRNA single-molecule fluorescence resonance energy transfer signal, we directly observe the conformational switch that is associated with IF2 activation within 30S ICs that lack IF3. Based on these results, we propose a model of IF2 activation that reveals how GTP, fMet-tRNA^fMet, and specific structural elements of IF2 drive and regulate this conformational switch. Notably, we find that domain III of IF2 plays a pivotal, allosteric, role in IF2 activation, suggesting that this domain can be targeted for the development of novel antibiotics.

Abstract A17: Probing a translational switch involved in tumor angiogenesis

As a tumor proliferates, it rapidly outgrows its blood supply, creating an oxygen-starved environment. Low oxygenation (hypoxia) suppresses the canonical biochemical pathway through which the translation of messenger RNAs (mRNAs) into proteins by the ribosome is typically initiated. Despite shutting down this general mechanism for initiating protein synthesis, the tumor can continue to initiate translation of a subset of mRNAs that are involved in angiogenesis and tumor survival. To explain this observation, it has been proposed that, in addition to the eukaryotic mRNA-specific 7-methyl guanosine cap at the 5' end, these pro-angiogenic mRNAs, of which Fibroblast Growth Factor-9 (FGF9) and Hypoxia Inducible Factor 1α (HIF1α) are prototypical members, contain highly stable structures in their 5' untranslated regions. These structures, also referred to as Cap Independent Translation Enhancers (CITEs), form part of a switch that activates a non-canonical pathway for initiating translation, presumably by binding directly to eukaryotic initiation factor 4G (eIF4G), thereby recruiting ribosomes in a manner distinct from the canonical 5' cap-dependent mechanism. To test this hypothesis, here we report the binding affinities of pro-angiogenic mRNAs, as well as control mRNAs lacking CITEs, to eIF4G as a measure of their ability to initiate translation in the hypoxic environment of a tumor. Understanding the dynamics of eIF4G-CITE interactions can inform the design and development of novel angiogenesis inhibitors that function by targeting these interactions.

Citation Format: Maya Ramachandran, Somdeb Mitra, Ruben L. Gonzalez, Jr. Probing a translational switch involved in tumor angiogenesis. [abstract]. In: Proceedings of the AACR Special Conference on Translational Control of Cancer: A New Frontier in Cancer Biology and Therapy; 2016 Oct 27-30; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2017;77(6 Suppl):Abstract nr A17.

Precisely and Accurately Inferring Single-Molecule Rate Constants

The kinetics of biomolecular systems can be quantified by calculating the stochastic rate constants that govern the biomolecular state vs time trajectories (i.e., state trajectories) of individual biomolecules. To do so, the experimental signal vs time trajectories (i.e., signal trajectories) obtained from observing individual biomolecules are often idealized to generate state trajectories by methods such as thresholding or hidden Markov modeling. Here, we discuss approaches for idealizing signal trajectories and calculating stochastic rate constants from the resulting state trajectories. Importantly, we provide an analysis of how the finite length of signal trajectories restricts the precision of these approaches and demonstrate how Bayesian inference-based versions of these approaches allow rigorous determination of this precision. Similarly, we provide an analysis of how the finite lengths and limited time resolutions of signal trajectories restrict the accuracy of these approaches, and describe methods that, by accounting for the effects of the finite length and limited time resolution of signal trajectories, substantially improve this accuracy. Collectively, therefore, the methods we consider here enable a rigorous assessment of the precision, and a significant enhancement of the accuracy, with which stochastic rate constants can be calculated from single-molecule signal trajectories.

Single-Molecule Reaction Chemistry in Patterned Nanowells

A new approach to synthetic chemistry is performed in ultraminiaturized, nanofabricated reaction chambers. Using lithographically defined nanowells, we achieve single-point covalent chemistry on hundreds of individual carbon nanotube transistors, providing robust statistics and unprecedented spatial resolution in adduct position. Each device acts as a sensor to detect, in real-time and through quantized changes in conductance, single-point functionalization of the nanotube as well as consecutive chemical reactions, molecular interactions, and molecular conformational changes occurring on the resulting single-molecule probe. In particular, we use a set of sequential bioconjugation reactions to tether a single-strand of DNA to the device and record its repeated, reversible folding into a G-quadruplex structure. The stable covalent tether allows us to measure the same molecule in different solutions, revealing the characteristic increased stability of the G-quadruplex structure in the presence of potassium ions (K(+)) versus sodium ions (Na(+)). Nanowell-confined reaction chemistry on carbon nanotube devices offers a versatile method to isolate and monitor individual molecules during successive chemical reactions over an extended period of time.

Tadalafil for Endometrial Growth in Clomiphene Citrate stimulated cycles in an IUI programma: A pilot study

AIM OF THE STUDY

The objective of this study was to assess the impact of tadalafil on endometrial growth, the uterine artery pulsatility index (PI) and the uterine artery resistance index (RI) in patients under clomiphene ovarian stimulation for intrauterine insemination (IUI).

METHODS

This randomized crossover study included 30 patients with a normal endometrium over 53 cycles, and 46 of those cycles in 23 patients were included in the analysis. In group A the patients were under 100 mg clomiphene daily for five days (2-6) and 5 mg tadalafil daily for 7 days (4-10). For Group B (control) the patients only received clomiphene. Measurements of the endometrium, PI, RI and estradiol determinations were taken on cycle days 4, 8 and 10.

RESULTS

We observed a better endometrial growth in Group A compared to Group B: 7.5 ± 2.1 mm vs 5.5 ± 1.2 mm, P < 0.0002 and 8.9 ± 1.8 mm vs 6.3 ± 1.8 mm, P < 0.0002 on days 8 and 10, respectively. Additionally, a progressive decrease in the RI was observed in Group A but not in Group B from day 8 (0.77 ± 0.15 vs 0.85 ± 0.18, P = 0.059) to day 10 (0.74 ± 0.20 vs 0.87 ± 0.14, P < 0.017). However, no differences were observed in PI or serum estradiol between Group A and Group B.

CONCLUSION

The use of tadalafil improved endometrial growth in patients under clomiphene ovarian stimulation with no significant effect on the uterine artery Pulsatility Index and serum estradiol.

A dynamic RNA loop in an IRES affects multiple steps of elongation factor-mediated translation initiation

Internal ribosome entry sites (IRESs) are powerful model systems to understand how the translation machinery can be manipulated by structured RNAs and for exploring inherent features of ribosome function. The intergenic region (IGR) IRESs from the Dicistroviridae family of viruses are structured RNAs that bind directly to the ribosome and initiate translation by co-opting the translation elongation cycle. These IRESs require an RNA pseudoknot that mimics a codon-anticodon interaction and contains a conformationally dynamic loop. We explored the role of this loop and found that both the length and sequence are essential for translation in different types of IGR IRESs and from diverse viruses. We found that loop 3 affects two discrete elongation factor-dependent steps in the IRES initiation mechanism. Our results show how the IRES directs multiple steps after 80S ribosome placement and highlights the often underappreciated significance of discrete conformationally dynamic elements within the context of structured RNAs.

DOI:http://dx.doi.org/10.7554/eLife.08146.001

Many viruses store their genetic information in the form of strands of ribonucleic acid (RNA), which contain building blocks called nucleotides. Once inside an infected cell, the virus hijacks the cellular structures that build proteins (called ribosomes), which forces the cell to start making viral proteins.

Many RNA viruses manipulate the cell’s ribosomes using RNA elements called Internal Ribosome Entry Sites, or IRESs. In a family of viruses called Dicistroviridae, which infect a number of insects, a section of the IRES RNA binds directly to the ribosome. Proteins called elongation factors then trigger a series of events that lead to the cell starting to make the viral proteins.

By mutating the RNA of many different Dicistroviridae viruses that infect a variety of invertebrates, Ruehle et al. have now investigated how a particular loop in the structure of the IRES helps to make cells build the viral proteins. This loop is flexible, and interacts with the ribosome to enable the IRES to move through the ribosome. Mutations that shorten the loop or alter the sequence of nucleotides in the loop prevent the occurrence of two of the steps that need to occur for the cell to make viral proteins. Both of these steps depend on elongation factors. Determining how the entire IRES might change shape as it moves through the ribosome is an important next step, since the ribosome is exquisitely sensitive to the shape and motions of its binding partners.

DOI:http://dx.doi.org/10.7554/eLife.08146.002

Structural dynamics of ribosome subunit association studied by mixing-spraying time-resolved cryogenic electron microscopy

Ribosomal subunit association is a key checkpoint in translation initiation but its structural dynamics are poorly understood. Here, we used a recently developed mixing-spraying, time-resolved, cryogenic electron microscopy (cryo-EM) method to study ribosomal subunit association in the sub-second time range. We have improved this method and increased the cryo-EM data yield by tenfold. Pre-equilibrium states of the association reaction were captured by reacting the mixture of ribosomal subunits for 60 ms and 140 ms. We also identified three distinct ribosome conformations in the associated ribosomes. The observed proportions of these conformations are the same in these two time points, suggesting that ribosomes equilibrate among the three conformations within less than 60 ms upon formation. Our results demonstrate that the mixing-spraying method can capture multiple states of macromolecules during a sub-second reaction. Other fast processes, such as translation initiation, decoding, and ribosome recycling, are amenable to study with this method.

The emerging role of rectified thermal fluctuations in initiator aa-tRNA- and start codon selection during translation initiation

Decades of genetic, biochemical, biophysical, and structural studies suggest that the conformational dynamics of the translation machinery (TM), of which the ribosome is the central component, play a fundamental role in the mechanism and regulation of translation. More recently, single-molecule fluorescence resonance energy transfer (smFRET) studies have provided a unique and powerful approach for directly monitoring the real-time dynamics of the TM. Indeed, smFRET studies of the elongation stage of translation have significantly enriched our understanding of the mechanisms through which stochastic, thermally driven conformational fluctuations of the TM are exploited to drive and regulate the individual steps of translation elongation [1]. Beyond translation elongation, smFRET studies of the conformational dynamics of the initiation stage of translation offer great potential for providing mechanistic information that has thus far remained difficult or impossible to obtain using traditional methods. This is particularly true of the mechanisms through which the accuracy of initiator tRNA- and start codon selection is established during translation initiation. Given that translation initiation is a major checkpoint for regulating the translation of mRNAs, obtaining such mechanistic information holds great promise for our understanding of the translational regulation of gene expression. Here, we provide an overview of the bacterial translation initiation pathway, summarize what is known regarding the biochemical functions of the IFs, and discuss various new and exciting mechanistic insights that have emerged from several recently published smFRET studies of the mechanisms that guide initiator tRNA- and start codon selection during translation initiation. These studies provide a springboard for future investigations of the conformational dynamics of the more complex eukaryotic translation initiation pathway and mechanistic studies of the role of translational regulation of gene expression in human health and disease.

Quantitative Connection between Ensemble Thermodynamics and Single-Molecule Kinetics: A Case Study Using Cryogenic Electron Microscopy and Single-Molecule Fluorescence Resonance Energy Transfer Investigations of the Ribosome

At equilibrium, thermodynamic and kinetic information can be extracted from biomolecular energy landscapes by many techniques. However, while static, ensemble techniques yield thermodynamic data, often only dynamic, single-molecule techniques can yield the kinetic data that describe transition-state energy barriers. Here we present a generalized framework based upon dwell-time distributions that can be used to connect such static, ensemble techniques with dynamic, single-molecule techniques, and thus characterize energy landscapes to greater resolutions. We demonstrate the utility of this framework by applying it to cryogenic electron microscopy (cryo-EM) and single-molecule fluorescence resonance energy transfer (smFRET) studies of the bacterial ribosomal pre-translocation complex. Among other benefits, application of this framework to these data explains why two transient, intermediate conformations of the pre-translocation complex, which are observed in a cryo-EM study, may not be observed in several smFRET studies.

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data

Background

Single-molecule techniques have emerged as incisive approaches for addressing a wide range of questions arising in contemporary biological research [Trends Biochem Sci 38:30–37, 2013; Nat Rev Genet 14:9–22, 2013; Curr Opin Struct Biol 2014, 28C:112–121; Annu Rev Biophys 43:19–39, 2014]. The analysis and interpretation of raw single-molecule data benefits greatly from the ongoing development of sophisticated statistical analysis tools that enable accurate inference at the low signal-to-noise ratios frequently associated with these measurements. While a number of groups have released analysis toolkits as open source software [J Phys Chem B 114:5386–5403, 2010; Biophys J 79:1915–1927, 2000; Biophys J 91:1941–1951, 2006; Biophys J 79:1928–1944, 2000; Biophys J 86:4015–4029, 2004; Biophys J 97:3196–3205, 2009; PLoS One 7:e30024, 2012; BMC Bioinformatics 288 11(8):S2, 2010; Biophys J 106:1327–1337, 2014; Proc Int Conf Mach Learn 28:361–369, 2013], it remains difficult to compare analysis for experiments performed in different labs due to a lack of standardization.

Results

Here we propose a standardized single-molecule dataset (SMD) file format. SMD is designed to accommodate a wide variety of computer programming languages, single-molecule techniques, and analysis strategies. To facilitate adoption of this format we have made two existing data analysis packages that are used for single-molecule analysis compatible with this format.

Conclusion

Adoption of a common, standard data file format for sharing raw single-molecule data and analysis outcomes is a critical step for the emerging and powerful single-molecule field, which will benefit both sophisticated users and non-specialists by allowing standardized, transparent, and reproducible analysis practices.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0429-4) contains supplementary material, which is available to authorized users.

Ribosomal initiation complex-driven changes in the stability and dynamics of initiation factor 2 regulate the fidelity of translation initiation

Joining of the large, 50S, ribosomal subunit to the small, 30S, ribosomal subunit initiation complex (IC) during bacterial translation initiation is catalyzed by the initiation factor (IF) IF2. Because the rate of subunit joining is coupled to the IF, transfer RNA (tRNA), and mRNA codon compositions of the 30S IC, the subunit joining reaction functions as a kinetic checkpoint that regulates the fidelity of translation initiation. Recent structural studies suggest that the conformational dynamics of the IF2·tRNA sub-complex forming on the intersubunit surface of the 30S IC may play a significant role in the mechanisms that couple the rate of subunit joining to the IF, tRNA, and codon compositions of the 30S IC. To test this hypothesis, we have developed a single-molecule fluorescence resonance energy transfer signal between IF2 and tRNA that has enabled us to monitor the conformational dynamics of the IF2·tRNA sub-complex across a series of 30S ICs. Our results demonstrate that 30S ICs undergoing rapid subunit joining display a high affinity for IF2 and an IF2·tRNA sub-complex that primarily samples a single conformation. In contrast, 30S ICs that undergo slower subunit joining exhibit a decreased affinity for IF2 and/or a change in the conformational dynamics of the IF2·tRNA sub-complex. These results strongly suggest that 30S IC-driven changes in the stability of IF2 and the conformational dynamics of the IF2·tRNA sub-complex regulate the efficiency and fidelity of subunit joining during translation initiation.

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.

Translation initiation factor 3 regulates switching between different modes of ribosomal subunit joining

Ribosomal subunit joining is a key checkpoint in the bacterial translation initiation pathway during which initiation factors (IFs) regulate association of the 30S initiation complex (IC) with the 50S subunit to control formation of a 70S IC that can enter into the elongation stage of protein synthesis. The GTP-bound form of IF2 accelerates subunit joining, whereas IF3 antagonizes subunit joining and plays a prominent role in maintaining translation initiation fidelity. The molecular mechanisms through which IF2 and IF3 collaborate to regulate the efficiency of 70S IC formation, including how they affect the dynamics of subunit joining, remain poorly defined. Here, we use single-molecule fluorescence resonance energy transfer to monitor the interactions between IF2 and the GTPase-associated center (GAC) of the 50S subunit during real-time subunit joining reactions in the absence and presence of IF3. In the presence of IF3, IF2-mediated subunit joining becomes reversible, and subunit joining events cluster into two distinct classes corresponding to formation of shorter- and longer-lifetime 70S ICs. Inclusion of IF3 within the 30S IC was also found to alter the conformation of IF2 relative to the GAC, suggesting that IF3's regulatory effects may stem in part from allosteric modulation of IF2-GAC interactions. The results are consistent with a model in which IF3 can exert control over the efficiency of subunit joining by modulating the conformation of the 30S IC, which in turn influences the formation of stabilizing intersubunit contacts and thus the reaction's degree of reversibility.

smFRET studies of the 'encounter' complexes and subsequent intermediate states that regulate the selectivity of ligand binding

The selectivity with which a biomolecule can bind its cognate ligand when confronted by the vast array of structurally similar, competing ligands that are present in the cell underlies the fidelity of some of the most fundamental processes in biology. Because they collectively comprise one of only a few methods that can sensitively detect the 'encounter' complexes and subsequent intermediate states that regulate the selectivity of ligand binding, single-molecule fluorescence, and particularly single-molecule fluorescence resonance energy transfer (smFRET), approaches have revolutionized studies of ligand-binding reactions. Here, we describe a widely used smFRET strategy that enables investigations of a large variety of ligand-binding reactions, and discuss two such reactions, aminoacyl-tRNA selection during translation elongation and splice site selection during spliceosome assembly, that highlight both the successes and challenges of smFRET studies of ligand-binding reactions. We conclude by reviewing a number of emerging experimental and computational approaches that are expanding the capabilities of smFRET approaches for studies of ligand-binding reactions and that promise to reveal the mechanisms that control the selectivity of ligand binding with unprecedented resolution.

EttA regulates translation by binding the ribosomal E site and restricting ribosome-tRNA dynamics

Cells express many ribosome-interacting factors whose functions and molecular mechanisms remain unknown. Here, we elucidate the mechanism of a newly characterized regulatory translation factor, Energy-dependent Translational Throttle A (EttA), which is an Escherichia coli representative of the ATP-binding cassette F (ABC-F) protein family. Using cryo-EM, we demonstrate that the ATP-bound form of EttA binds to the ribosomal tRNA exit (E) site, where it forms bridging interactions between the ribosomal L1 stalk and the tRNA bound in the peptidyl-tRNA binding (P) site. Using single-molecule fluorescence resonance energy transfer (smFRET), we show that the ATP-bound form of EttA restricts ribosome and tRNA dynamics required for protein synthesis. This work represents the first example, to our knowledge, where the detailed molecular mechanism of any ABC-F family protein has been determined and establishes a framework for elucidating the mechanisms of other regulatory translation factors.

Robustly passivated, gold nanoaperture arrays for single-molecule fluorescence microscopy

The optical confinement generated by metal-based nanoapertures fabricated on a silica substrate has recently enabled single-molecule fluorescence measurements to be performed at physiologically relevant background concentrations of fluorophore-labeled biomolecules. Nonspecific adsorption of fluorophore-labeled biomolecules to the metallic cladding and silica bottoms of nanoapertures, however, remains a critical limitation. To overcome this limitation, we have developed a selective functionalization chemistry whereby the metallic cladding of gold nanoaperture arrays is passivated with methoxy-terminated, thiol-derivatized polyethylene glycol (PEG), and the silica bottoms of those arrays are functionalized with a binary mixture of methoxy- and biotin-terminated, silane-derivatized PEG. This functionalization scheme enables biotinylated target biomolecules to be selectively tethered to the silica nanoaperture bottoms via biotin-streptavidin interactions and reduces the nonspecific adsorption of fluorophore-labeled ligand biomolecules. This, in turn, enables the observation of ligand biomolecules binding to their target biomolecules even under greater than 1 μM background concentrations of ligand biomolecules, thereby rendering previously impracticable biological systems accessible to single-molecule fluorescence investigations.

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.

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 life's most fundamental biochemical reactions: the translation of a messenger RNA into its encoded protein by the ribosome.