Bayesian Nonparametrics for FRET using Realistic Integrative Detectors

Förster resonance energy transfer (FRET) is a widely used tool to probe nanometer scale dynamics, projecting rich 3D biomolecular motion onto noisy 1D traces. However, interpretation of FRET traces remains challenging due to degeneracy—distinct structural states map to similar FRET efficiencies— and often suffers from under- and/or over-fitting due to the need to predefine the number of FRET states and noise characteristics. Here we provide a new software, Bayesian nonparametric FRET (BNP-FRET) for binned data obtained from integrative detectors, that eliminates user-dependent parameters and accurately incorporates all known noise sources, enabling the identification of distinct configurations from 1D traces in a plug-n-play manner. Using simulated and experimental data, we demonstrate that BNP-FRET eliminates logistical barrier of predetermining states for each FRET trace and permits high-throughput, simultaneous analysis of a large number of kinetically heterogeneous traces. Furthermore, working in the Bayesian paradigm, BNP-FRET naturally provides uncertainty estimates for all model parameters including the number of states, kinetic rates, and FRET efficiencies.