DeepFRET: Rapid and automated single molecule FRET data classification using deep learning

Abstract

Single molecule Förster Resonance energy transfer (smFRET) is a mature and adaptable method for studying the structure of biomolecules and integrating their dynamics into structural biology. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and fully automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histogram of biomolecule behavior, is a user-adjustable quality threshold. Integrating all standard features of smFRET analysis, DeepFRET will consequently output common kinetic information metrics for biomolecules. We validated the utility of DeepFRET by performing quantitative analysis on simulated, ground truth, data and real smFRET data. The accuracy of classification by DeepFRET outperformed human operators and current commonly used hard threshold and reached >95% precision accuracy only requiring a fraction of the time (<1% as compared to human operators) on ground truth data. Its flawless and rapid operation on real data demonstrates its wide applicability. This level of classification was achieved without any preprocessing or parameter setting by human operators, demonstrating DeepFRET’s capacity to objectively quantify biomolecular dynamics. The provided a standalone executable based on open source code capitalises on the widespread adaptation of machine learning and may contribute to the effort of benchmarking smFRET for structural biology insights.

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Structural Dynamics of Glutamate Signaling Systems by smFRET

“…However, it is important to understand all the physical aspects and parameters of the dye to obtain accurate simulations. As machine learning continues to advance, it will become a great tool for smFRET data analysis (94,95).…”

FRETboard: semi-supervised classification of fret traces

“…As such different labeling schemes produce data of a different nature, it follows that widely applicable smFRET data analysis software should be flexible enough to adapt to this variety. Several software packages exist, many of which rely on some flavor of hidden Markov model (HMM) to extract characteristic FRET efficiencies and molecular kinetics for visited molecular conformations [8][9][10][11][12]. Some methods only analyze the energy transfer efficiency, measured as fraction of acceptor emission intensity over total intensity [8,10].…”

“…Some methods only analyze the energy transfer efficiency, measured as fraction of acceptor emission intensity over total intensity [8,10]. Others make use of the additional data contained in the individual donor and acceptor emission channels [9,11,12]. All aim to limit the influence of the user in setting parameters guiding the fitting process and thus adopt an unsupervised fitting procedure.…”