DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.
Journal:
eLife
Published Date:
Nov 3, 2020
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.
Authors
Keywords
Algorithms
Deep Learning
False Positive Reactions
Fluorescence Resonance Energy Transfer
Fluorescent Dyes
Kinetics
Markov Chains
Molecular Dynamics Simulation
Nanotechnology
Normal Distribution
Reproducibility of Results
Signal Processing, Computer-Assisted
Single Molecule Imaging
Software
User-Computer Interface