A Bayesian Nonparametric Approach to Single Molecule Förster Resonance Energy Transfer

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.