A Bayesian Nonparametric Approach to Single Molecule Forster Resonance Energy Transfer.

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 phot

SUBMITTER: Sgouralis I 

PROVIDER: S-EPMC6821439 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature