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.

RNA adapts its flexibility to efficiently fold and resist unfolding.

Recent studies have demonstrated that the mechanisms through which biopolymers like RNA interconvert between multiple folded structures are critical for their cellular functions. A major obstacle to elucidating these mechanisms is the lack of experimental approaches that can resolve these interconversions between functionally relevant biomolecular structures. Here, using a nano-electronic device with microsecond time resolution, we dissect the complete set of structural rearrangements executed by an ultra-stable RNA, the UUCG stem-loop, at the single-molecule level. We show that the stem-loop samples at least four conformations along two folding pathways leading to two distinct folded structures, only one of which has been previously observed. By modulating its flexibility, the stem-loop can adaptively select between these pathways, enabling it to both fold rapidly and resist unfolding. This paradigm of stabilization through compensatory changes in flexibility broadens our understanding of stable RNA structures and is expected to serve as a general strategy employed by all biopolymers.

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.