DeepFRET, a Software for Rapid and Automated Single-molecule FRET Data Classification Using Deep Learning

Overview

Journal Elife

Specialty Biology

Date 2020 Nov 3

PMID 33138911

Citations 32

Authors

Johannes Thomsen

Magnus Berg Sletfjerding

Simon Bo Jensen

Stefano Stella

Bijoya Paul

Mette Galsgaard Malle

Guillermo Montoya

Troels Christian Petersen

Nikos S Hatzakis

Affiliations

Soon will be listed here.

Abstract

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

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