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.

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.

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.

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.

Observation and Analysis of RAD51 Nucleation Dynamics at Single-Monomer Resolution.

Human RAD51 promotes accurate DNA repair by homologous recombination and is involved in protection and repair of damaged DNA replication forks. The active species of RAD51 and related recombinases in all organisms is a nucleoprotein filament assembled on single-stranded DNA (ssDNA). The formation of a nucleoprotein filament competent for the recombination reaction, or for DNA replication support, is a delicate and strictly regulated process, which occurs through filament nucleation followed by filament extension. The rates of these two phases of filament formation define the capacity of RAD51 to compete with the ssDNA-binding protein RPA, as well as the lengths of the resulting filament segments. Single-molecule approaches can provide a wealth of quantitative information on the kinetics of RAD51 nucleoprotein filament assembly, internal dynamics, and disassembly. In this chapter, we describe how to set up a single-molecule total internal reflection fluorescence microscopy experiment to monitor the initial steps of RAD51 nucleoprotein filament formation in real-time and at single-monomer resolution. This approach is based on the unique, stretched-ssDNA conformation within the recombinase nucleoprotein filament and follows the efficiency of Förster resonance energy transfer (EFRET) between two DNA-conjugated fluorophores. We will discuss the practical aspects of the experimental setup, extraction of the FRET trajectories, and how to analyze and interpret the data to obtain information on RAD51 nucleation kinetics, the mechanism of nucleation, and the oligomeric species involved in filament formation.

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.

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.

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.