Biology subjects

Hon, J.

Publications and source records attributed to Hon, J..

3 recordsLinked to original sources

bioRxiv2022

Characterizing the conformational free-energy landscape of RNA using single-molecule field-effect transistors

We have developed and used high-time-resolution, single-molecule field-effect transistors (smFETs) to characterize the conformational free-energy landscape of RNA stem-loops. Stem-loops are some of the most common RNA structural motifs and serve as building blocks for the formation of more 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 recently 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 on the application of mechanical (un)folding forces, they provide a unique and complementary approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 s time resolution. Our results show that under our experimental conditions, stem-loops fold and unfold 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, alternatively, the natively folded conformation. Our findings demonstrate how the combination of single-molecule sensitivity and high time resolution makes smFETs unique and powerful tools for characterizing the conformational free-energy landscape of RNA and highlight the extremely rugged landscape on which even the simplest RNA structural elements fold.

biophysics↗

bioRxiv2021

An Empirical Energy Landscape Reveals Mechanism of Proteasome in Polypeptide Translocation

The ring-like ATPase complexes in the AAA+ family perform diverse cellular functions that require coordination between the conformational transitions of their individual ATPase subunits1,2. How the energy from ATP hydrolysis is captured to perform mechanical work by these coordinated movements is not known. In this study, we developed a novel approach for delineating the nucleotide-dependent free-energy landscape (FEL) of the proteasomes heterohexameric ATPase complex based on complementary structural and kinetic measurements. We used the FEL to simulate the dynamics of the proteasome and quantitatively evaluated the predicted structural and kinetic properties. The FEL model predictions were widely consistent with experimental observations in this and previous studies and suggested novel features of the mechanism of proteasomal ATPase. We find that the cooperative movements of the ATPase subunits result from the design of the ATPase hexamer entailing a unique free-energy minimum for each nucleotide-binding state. ATP hydrolysis dictates the direction of substrate translocation by triggering an energy-dissipating conformational transition of the ATPase complex.

biophysics↗

bioRxiv2018

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. Although characterizing kinetic heterogeneity can provide critical mechanistic information, there are currently no computational methods available that effectively and/or comprehensively enable such analysis. To address this gap, 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. The hFRET source code, graphical user interface, and user manual can be downloaded as freeware at https://github.com/GonzalezBiophysicsLab/hFRET.

biophysics↗