A Bayesian Nonparametric Approach to Single Molecule Förster Resonance Energy Transfer

Ioannis Sgouralis, Shreya Madaan, Franky Djutanta, Rachael Kha, Rizal Hariadi, Steve Presse

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    We develop a Bayesian nonparametric framework to analyze single molecule FRET (smFRET) data. This framework, a variation on infinite hidden Markov models, goes beyond traditional hidden Markov analysis, which already treats photon shot noise, in three critical ways: (1) it learns the number of molecular states present in a smFRET time trace (a hallmark of nonparametric approaches), (2) it accounts, simultaneously and self-consistently, for photophysical features of donor and acceptor fluorophores (blinking kinetics, spectral cross-talk, detector quantum efficiency), and (3) it treats background photons. Point 2 is essential in reducing the tendency of nonparametric approaches to overinterpret noisy single molecule time traces and so to estimate states and transition kinetics robust to photophysical artifacts. As a result, with the proposed framework, we obtain accurate estimates of single molecule properties even when the supplied traces are excessively noisy, subject to photoartifacts, and of short duration. We validate our method using synthetic data sets and demonstrate its applicability to real data sets from single molecule experiments on Holliday junctions labeled with conventional fluorescent dyes.

    Original languageEnglish (US)
    Pages (from-to)675-688
    Number of pages14
    JournalJournal of Physical Chemistry B
    Volume123
    Issue number3
    DOIs
    StatePublished - Jan 24 2019

    Fingerprint

    Bayes Theorem
    Energy Transfer
    Photons
    Energy transfer
    energy transfer
    Cruciform DNA
    Blinking
    Molecules
    Fluorescent Dyes
    Artifacts
    molecules
    blinking
    Shot noise
    Kinetics
    Fluorophores
    kinetics
    photons
    shot noise
    Hidden Markov models
    estimates

    ASJC Scopus subject areas

    • Physical and Theoretical Chemistry
    • Surfaces, Coatings and Films
    • Materials Chemistry

    Cite this

    A Bayesian Nonparametric Approach to Single Molecule Förster Resonance Energy Transfer. / Sgouralis, Ioannis; Madaan, Shreya; Djutanta, Franky; Kha, Rachael; Hariadi, Rizal; Presse, Steve.

    In: Journal of Physical Chemistry B, Vol. 123, No. 3, 24.01.2019, p. 675-688.

    Research output: Contribution to journalArticle

    Sgouralis, Ioannis ; Madaan, Shreya ; Djutanta, Franky ; Kha, Rachael ; Hariadi, Rizal ; Presse, Steve. / A Bayesian Nonparametric Approach to Single Molecule Förster Resonance Energy Transfer. In: Journal of Physical Chemistry B. 2019 ; Vol. 123, No. 3. pp. 675-688.
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    AU - Hariadi, Rizal

    AU - Presse, Steve

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