An Automated Bayesian Pipeline for Rapid Analysis of Single-Molecule Binding Data

Smith, Carlas; Jouravleva, Karina; Huisman, Maximiliaan; Jolly, Samson M.; Zamore, Phillip D.; Grünwald, David

doi:10.1101/261917

Cited by 5 publications

(8 citation statements)

References 44 publications

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“…Experimentally, one could further investigate the nature of intersegmental transfer through a combined tweezer-fluorescence single-molecule assay, where forces strong enough to pull on entropically coiled ssDNA can be applied 17,37 . Furthermore, theoretical modelling and additional experiments are required in order to establish to what extent partitioning the search modes on different length scales will allow nucleic-acid-guided proteins to optimize the search process [52][53][54][55] since the absence of cooperative binding was recently reported for another Ago system 29 .…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown that certain DNA/RNA-guided proteins interact with DNA through non-specific electrostatic interactions [21][22][23] , but the strength of these interactions and their behavior on roadblocks and secondary structures is not understood. Since these interactions are typically short-ranged [24][25][26] and short-lived 14,21,23,[26][27][28][29] , a method offering high spatiotemporal resolution is required to study these interactions. Here we make use of single molecule Förster Resonance Energy Transfer (FRET) to elucidate the mechanism of ssDNA target search by a mesophilic Ago from the bacterium Clostridium butyricum (CbAgo).…”

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confidence: 99%

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Argonaute bypasses cellular obstacles without hindrance during target search

et al. 2019

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Argonaute (Ago) proteins are key players in both gene regulation (eukaryotes) and host defense (prokaryotes). Acting on single-stranded nucleic-acid substrates, Ago relies on base pairing between a small nucleic-acid guide and its complementary target sequences for specificity. To efficiently scan nucleic-acid chains for targets, Ago diffuses laterally along the substrate and must bypass secondary structures as well as protein barriers. Using single-molecule FRET in conjunction with kinetic modelling, we reveal that target scanning is mediated through loose protein-nucleic acid interactions, allowing Ago to slide short distances over secondary structures, as well as to bypass protein barriers via intersegmental transfer. Our combined single-molecule experiment and kinetic modelling approach may serve as a platform to dissect search processes and study the effect of sequence on search kinetics for other nucleic acid-guided proteins.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Argonaute bypasses cellular obstacles without hindrance during target search

et al. 2019

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“…where the posterior is assumed to be a multivariate Gaussian distribution centered at the (15,36). In addition to these benefits, the real power of VB inference methods is that they also provide an estimate of the true evidence (in the form of the ELBO).…”

Section: The Likelihood: How An Experiments Relates To a Modelmentioning

confidence: 99%

“…Practically, this means that, at some point during an investigation, a decision must be made about what the 'best' model for the phenomenon being studied should be in order to inform upon other phenomena. In Section 3.5, we discussed how the evidence, ({ }| ), quantifies the predictive power of a model, and showed how Bayesian-inference based methods, such as vbFRET (2) and others (13,15,(27)(28)(29)(30)(31)(35)(36)(37), can utilize the maximum evidence approach to choose the 'best' model for the data. The maximum evidence approach, however, fails to account for the uncertainty from the limited amount of data collected during an experiment.…”

Section: Model Selection: Determining the Best Model Using Probabilitmentioning

confidence: 99%

Bayesian inference: The comprehensive approach to analyzing single-molecule experiments

Kinz-Thompson

Ray

Gonzalez

2020

Preprint

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Biophysics experiments performed at single-molecule resolution contain 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. Here, 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 manner to incorporate information from multiple experiments into a single analysis and to find 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.

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“…Note that at the conceptual level, FISIK is not limited to using indirect inference for stochastic model calibration. Other methods, such as Bayesian inference (28,29), including approximate Bayesian computation (30)(31)(32), are expected to be equally applicable.…”

Section: Overall Workflow Of Fisikmentioning

confidence: 99%

FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data

Oliveira

Jaqaman

2019

Biophysical Journal

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Recent experimental and computational developments have been pushing the limits of live-cell single-molecule imaging, enabling the monitoring of intermolecular interactions in their native environment with high spatiotemporal resolution. However, interactions are captured only for the labeled subset of molecules, which tends to be a small fraction. As a result, it has remained a challenge to calculate molecular interaction kinetics, in particular association rates, from live-cell single-molecule tracking data. To overcome this challenge, we developed a mathematical modeling-based Framework for the Inference of in Situ Interaction Kinetics (FISIK) from single-molecule imaging data with substoichiometric labeling. FISIK consists of (I) devising a mathematical model of molecular movement and interactions, mimicking the biological system and data-acquisition setup, and (II) estimating the unknown model parameters, including molecular association and dissociation rates, by fitting the model to experimental single-molecule data. Due to the stochastic nature of the model and data, we adapted the method of indirect inference for model calibration. We validated FISIK using a series of tests in which we simulated trajectories of diffusing molecules that interact with each other, considering a wide range of model parameters, and including resolution limitations, tracking errors, and mismatches between the model and the biological system it mimics. We found that FISIK has the sensitivity to determine association and dissociation rates, with accuracy and precision depending on the labeled fraction of molecules and the extent of molecule tracking errors. For cases where the labeled fraction is too low (e.g., to afford accurate tracking), combining dynamic but sparse single-molecule imaging data with almost-whole population oligomer distribution data improves FISIK's performance. All in all, FISIK is a promising approach for the derivation of molecular interaction kinetics in their native environment from singlemolecule imaging data with substoichiometric labeling.

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An Automated Bayesian Pipeline for Rapid Analysis of Single-Molecule Binding Data

Cited by 5 publications

References 44 publications

Argonaute bypasses cellular obstacles without hindrance during target search

Argonaute bypasses cellular obstacles without hindrance during target search

Bayesian inference: The comprehensive approach to analyzing single-molecule experiments

FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data

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