Verma, A. R. | Search bioRxiv

bioRxiv2024

Translational T-box riboswitches bind tRNA by modulating conformational flexibility

T-box riboswitches, paradigmatic non-coding RNA elements involved in genetic regulation in most Gram-positive bacteria, are adept at monitoring amino acid metabolism through direct interactions with specific tRNAs. T-box riboswitches assess tRNA aminoacylation status, subsequently regulating the transcription or translation of downstream genes involved in amino acid metabolism. Here we present single-molecule FRET studies of the Mycobacterium tuberculosis IleS T-box riboswitch, a model of T-box translational regulation. The data supports a two-step binding model where the tRNA anticodon is recognized first, followed by interactions with the NCCA sequence. Specifically, after anticodon recognition, tRNA in the partially bound state can transiently dock into the discriminator domain, resembling the fully bound state, even in the absence of the tRNA NCCA-discriminator interactions. Establishment of the NCCA-discriminator interactions significantly stabilizes the fully bound state. Collectively, the data suggests higher conformational flexibility in translation-regulating T-box riboswitches, compared to transcription-regulating ones, and supports a conformational selection model for NCCA recognition. Furthermore, it was found that the conserved RAG sequence is pivotal in maintaining specific interactions with the tRNA NCCA sequence by preventing sampling of an aberrant conformational state, while Stem IIA/B-linker interactions impact the conformational dynamics and the stability of both the partially bound and fully bound states. The present study provides a critical kinetic basis for how specific sequences and structural elements in T-box riboswitches enable the binding efficiency and specificity required to achieve gene regulation.

biophysics↗

bioRxiv2023

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. Statement of SignificanceThe power of time-dependent single-molecule biophysical experiments lies in their ability to uncover the molecular mechanisms governing experimental systems by computationally applying kinetic models to the data. While many software solutions have been developed to estimate the optimal parameters of such models, the results reported here show that the models themselves are often inherently mismatched with the molecular mechanisms they are being used to analyze. To investigate these mismatches and demonstrate how to best model the kinetics of a molecular mechanism, we have used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), an open-source software platform we have developed that, among other features, enables the analysis of single-molecule datasets using different kinetic models within a single, extensible, and customizable pipeline.

biophysics↗