A dynamic compositional equilibrium governs mRNA recognition by eIF3.

Eukaryotic translation initiation factor (eIF) 3 is a multi-subunit protein complex that binds both ribosomes and messenger RNAs (mRNAs) in order to drive a diverse set of mechanistic steps during translation. Despite its importance, a unifying framework explaining how eIF3 performs these numerous activities is lacking. Using single-molecule light scattering microscopy, we demonstrate that Saccharomyces cerevisiae eIF3 is an equilibrium mixture of the full complex, subcomplexes, and subunits. By extending our microscopy approach to an in vitro reconstituted eIF3 and complementing it with biochemical assays, we define the subspecies comprising this equilibrium and show that, rather than being driven by the full complex, mRNA binding by eIF3 is instead driven by the eIF3a subunit within eIF3a-containing subcomplexes. Our findings provide a mechanistic model for the role of eIF3 in the mRNA recruitment step of translation initiation and establish a mechanistic framework for explaining and investigating the other activities of eIF3.

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.

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.

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.

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.

Mechanistic studies of non-canonical amino acid mutagenesis.

Over the past decade, harnessing the cellular protein synthesis machinery to incorporate non-canonical amino acids (ncAAs) into tailor-made peptides has significantly advanced many aspects of molecular science. More recently, groundbreaking progress in our ability to engineer this machinery for improved ncAA incorporation has led to significant enhancements of this powerful tool for biology and chemistry. By revealing the molecular basis for the poor or improved incorporation of ncAAs, mechanistic studies of ncAA incorporation by the protein synthesis machinery have tremendous potential for informing and directing such engineering efforts. In this chapter, we describe a set of complementary biochemical and single-molecule fluorescence assays that we have adapted for mechanistic studies of ncAA incorporation. Collectively, these assays provide data that can guide engineering of the protein synthesis machinery to expand the range of ncAAs that can be incorporated into peptides and increase the efficiency with which they can be incorporated, thereby enabling the full potential of ncAA mutagenesis technology to be realized.

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.

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 tRNAPro causes +1 frameshifting on polynucleotide, slippery codons. Here, we report structures of the bacterial ribosome containing tRNAPro 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 tRNAPro 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 tRNAPro 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.

Multiplexed genomic encoding of non-canonical amino acids for labeling large complexes.

Stunning advances in the structural biology of multicomponent biomolecular complexes (MBCs) have ushered in an era of intense, structure-guided mechanistic and functional studies of these complexes. Nonetheless, existing methods to site-specifically conjugate MBCs with biochemical and biophysical labels are notoriously impracticable and/or significantly perturb MBC assembly and function. To overcome these limitations, we have developed a general, multiplexed method in which we genomically encode non-canonical amino acids (ncAAs) into multiple, structure-informed, individual sites within a target MBC; select for ncAA-containing MBC variants that assemble and function like the wildtype MBC; and site-specifically conjugate biochemical or biophysical labels to these ncAAs. As a proof-of-principle, we have used this method to generate unique single-molecule fluorescence resonance energy transfer (smFRET) signals reporting on ribosome structural dynamics that have thus far remained inaccessible to smFRET studies of translation.

The alarmones (p)ppGpp directly regulate translation initiation during entry into quiescence.

Many bacteria exist in a state of metabolic quiescence where energy consumption must be minimized so as to maximize available resources over a potentially extended period of time. As protein synthesis is the most energy intensive metabolic process in a bacterial cell, it would be an appropriate target for down-regulation during the transition from growth to quiescence. We observe that when Bacillus subtilis exits rapid growth, a subpopulation of cells emerges with very low protein synthetic activity. This phenotypic heterogeneity requires the production of the nucleotides (p)ppGpp, which we show are sufficient to inhibit protein synthesis in vivo. We then show that one of these molecules, ppGpp, inhibits protein synthesis by preventing the allosteric activation of the essential GTPase Initiation Factor 2 (IF2) during translation initiation. Finally, we demonstrate that the observed attenuation of protein synthesis during the entry into quiescence is a consequence of the direct interaction of (p)ppGpp and IF2.

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.