ArticleThe journal of physical chemistry. B2014
Single molecule conformational memory extraction: p5ab RNA hairpin.
Article in The journal of physical chemistry. B, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 35 citations in OpenAlex.
- Single-molecule reaction-diffusion.Biophysical journal · 2025Article
- An accurate probabilistic step finder for time-series analysis.Biophysical journal · 2024Article
- Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels.The Journal of chemical physics · 2023Article
- An accurate probabilistic step finder for time-series analysis.bioRxiv : the preprint server for biology · 2023Article
- Single-Molecule Reaction-Diffusion.bioRxiv : the preprint server for biology · 2023Article
- MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division.Biophysical journal · 2023Article
- Single-photon smFRET: II. Application to continuous illumination.Biophysical reports · 2023Article
- Single-photon smFRET. I: Theory and conceptual basis.Biophysical reports · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Learning continuous potentials from smFRET.Biophysical journal · 2023Article
- Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap.iScience · 2022Article
- A Python Toolbox for Unbiased Statistical Analysis of Fluorescence Intermittency of Multilevel Emitters.The journal of physical chemistry. C, Nanomaterials and interfaces · 2021Article
- Extraction of rapid kinetics from smFRET measurements using integrative detectors.Cell reports. Physical science · 2021Article
- Residence time analysis of RNA polymerase transcription dynamics: A Bayesian sticky HMM approach.Biophysical journal · 2021Article
- Inferring effective forces for Langevin dynamics using Gaussian processes.The Journal of chemical physics · 2020Article
- Single-Molecule Test for Markovianity of the Dynamics along a Reaction Coordinate.The journal of physical chemistry letters · 2018Article
- Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber.Biophysical journal · 2017Article
- Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis.Chemical reviews · 2017Article
Corrections and comments
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Authors and funding
8 authors at 7 institutions in 2 countries.
Funding
Abstract
Extracting kinetic models from single molecule data is an important route to mechanistic insight in biophysics, chemistry, and biology. Data collected from force spectroscopy can probe discrete hops of a single molecule between different conformational states. Model extraction from such data is a challenging inverse problem because single molecule data are noisy and rich in structure. Standard modeling methods normally assume (i) a prespecified number of discrete states and (ii) that transitions between states are Markovian. The data set is then fit to this predetermined model to find a handful of rates describing the transitions between states. We show that it is unnecessary to assume either (i) or (ii) and focus our analysis on the zipping/unzipping transitions of an RNA hairpin. The key is in starting with a very broad class of non-Markov models in order to let the data guide us toward the best model from this very broad class. Our method suggests that there exists a folding intermediate for the P5ab RNA hairpin whose zipping/unzipping is monitored by force spectroscopy experiments. This intermediate would not have been resolved if a Markov model had been assumed from the onset. We compare the merits of our method with those of others.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.