文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期，新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

DeepFRET是一款利用深度学习对单分子荧光共振能量转移（FRET）数据进行快速自动分类的软件。

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

作者信息

Thomsen Johannes, Sletfjerding Magnus Berg, Jensen Simon Bo, Stella Stefano, Paul Bijoya, Malle Mette Galsgaard, Montoya Guillermo, Petersen Troels Christian, Hatzakis Nikos S

机构信息

Department of Chemistry and Nanoscience Centre, University of Copenhagen, Copenhagen, Denmark.

Structural Molecular Biology Group, Novo Nordisk Foundation Centre for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

出版信息

Elife. 2020 Nov 3;9:e60404. doi: 10.7554/eLife.60404.

DOI:10.7554/eLife.60404
PMID:33138911
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7609065/
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.

摘要

单分子荧光共振能量转移（smFRET）是一种用于研究生物分子结构和动力学的适应性方法。高通量方法的发展和商业仪器的增长超过了快速、标准化和自动化方法的发展，以便客观地分析所产生的大量数据。在这里，我们展示了DeepFRET，这是一种基于深度学习的自动化、开源独立解决方案，从原始显微镜图像到生物分子行为直方图的转换过程中，唯一关键的人工干预是用户可调整的质量阈值。DeepFRET整合了smFRET分析的标准特征，因此输出常见的动力学信息指标。它在真实数据上的分类准确率超过95%，优于人类操作员和常用阈值，且只需要大约1%的时间。它在实际数据上的精确快速操作证明了DeepFRET客观量化生物分子动力学的能力，以及为动态结构生物学的smFRET基准测试做出贡献的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/55677c0af46d/elife-60404-fig4-figsupp4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/dabaa9cf1510/elife-60404-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/0da39da99ca3/elife-60404-fig1-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/adeb018c3bfe/elife-60404-fig1-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/74101955f021/elife-60404-fig1-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/82f6c9750fff/elife-60404-fig1-figsupp4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/35e1d62f803b/elife-60404-fig1-figsupp5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/54e38404040f/elife-60404-fig1-figsupp6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/2c20efa1432b/elife-60404-fig1-figsupp7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/fcc698ff2d31/elife-60404-fig1-figsupp8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/e6d818c540d9/elife-60404-fig1-figsupp9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/08558853eb9d/elife-60404-fig1-figsupp10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/5f63b8caff5d/elife-60404-fig1-figsupp11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/919eb961484f/elife-60404-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/21b639fd0d44/elife-60404-fig2-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/36946a40a286/elife-60404-fig2-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/64e358c8c093/elife-60404-fig2-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/129c9dfa830c/elife-60404-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/aa5145a7bf31/elife-60404-fig3-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/85860a0e2830/elife-60404-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/885e286f6e37/elife-60404-fig4-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/e4a8fd953645/elife-60404-fig4-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/0495532066dc/elife-60404-fig4-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/55677c0af46d/elife-60404-fig4-figsupp4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/dabaa9cf1510/elife-60404-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/0da39da99ca3/elife-60404-fig1-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/adeb018c3bfe/elife-60404-fig1-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/74101955f021/elife-60404-fig1-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/82f6c9750fff/elife-60404-fig1-figsupp4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/35e1d62f803b/elife-60404-fig1-figsupp5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/54e38404040f/elife-60404-fig1-figsupp6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/2c20efa1432b/elife-60404-fig1-figsupp7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/fcc698ff2d31/elife-60404-fig1-figsupp8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/e6d818c540d9/elife-60404-fig1-figsupp9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/08558853eb9d/elife-60404-fig1-figsupp10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/5f63b8caff5d/elife-60404-fig1-figsupp11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/919eb961484f/elife-60404-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/21b639fd0d44/elife-60404-fig2-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/36946a40a286/elife-60404-fig2-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/64e358c8c093/elife-60404-fig2-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/129c9dfa830c/elife-60404-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/aa5145a7bf31/elife-60404-fig3-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/85860a0e2830/elife-60404-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/885e286f6e37/elife-60404-fig4-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/e4a8fd953645/elife-60404-fig4-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/0495532066dc/elife-60404-fig4-figsupp3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805a/7609065/55677c0af46d/elife-60404-fig4-figsupp4.jpg

相似文献

[1]
DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

Elife. 2020-11-3

[2]
Analyzing Single Molecule FRET Trajectories Using HMM.

Methods Mol Biol. 2017

[3]
Detecting molecular interactions in live-cell single-molecule imaging with proximity-assisted photoactivation (PAPA).

Elife. 2022-8-17

[4]
A Multicolor Single-Molecule FRET Approach to Study Protein Dynamics and Interactions Simultaneously.

Methods Enzymol. 2016

[5]
Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.

Nat Commun. 2023-10-17

[6]
Single-Molecule Kinetic Studies of Nucleic Acids by Förster Resonance Energy Transfer.

Methods Mol Biol. 2022

[7]
Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).

Phys Biol. 2013-8

[8]
Defining Dynamics of Membrane-Bound Pyrophosphatases by Experimental and Computational Single-Molecule FRET.

Methods Enzymol. 2018

[9]
FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices.

Elife. 2021-3-29

[10]
Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes.

J Vis Exp. 2021-11-23

引用本文的文献

[1]
Bayesian Nonparametrics for FRET using Realistic Integrative Detectors.

bioRxiv. 2025-8-27

[2]
Observation of SAM-VI Riboswitch Dynamics Using Single-Molecule FRET.

Biomolecules. 2025-6-9

[3]
Multiplexed single-molecule characterization at the library scale.

Nat Protoc. 2025-6-4

[4]
Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.

Nat Methods. 2025-5

[5]
The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.

J Mol Biol. 2025-3-4

[6]
Ligand response of guanidine-IV riboswitch at single-molecule level.

Elife. 2024-12-2

[7]
Foundation model for efficient biological discovery in single-molecule data.

Res Sq. 2024-10-17

[8]
Deep learning based local feature classification to automatically identify single molecule fluorescence events.

Commun Biol. 2024-10-28

[9]
Divergent molecular assembly and catalytic mechanisms between bacterial and archaeal RNase P in pre-tRNA cleavage.

Proc Natl Acad Sci U S A. 2024-10-22

[10]
Foundation model for efficient biological discovery in single-molecule data.

bioRxiv. 2024-8-27

本文引用的文献

[1]
Single-molecule study on conformational dynamics of M.HhaI.

RSC Adv. 2019-5-13

[2]
Direct Observation of Sophorolipid Micelle Docking in Model Membranes and Cells by Single Particle Studies Reveals Optimal Fusion Conditions.

Biomolecules. 2020-9-7

[3]
Single-molecule photoreaction quantitation through intraparticle-surface energy transfer (i-SET) spectroscopy.

Nat Commun. 2020-8-27

[4]
Fast three-color single-molecule FRET using statistical inference.

Nat Commun. 2020-7-3

[5]
Top-down machine learning approach for high-throughput single-molecule analysis.

Elife. 2020-4-8

[6]
A large size-selective DNA nanopore with sensing applications.

Nat Commun. 2019-12-11

[7]
Fast fit-free analysis of fluorescence lifetime imaging via deep learning.

Proc Natl Acad Sci U S A. 2019-11-12

[8]
Direct observation of Thermomyces lanuginosus lipase diffusional states by Single Particle Tracking and their remodeling by mutations and inhibition.

Sci Rep. 2019-11-7

[9]
ilastik: interactive machine learning for (bio)image analysis.

Nat Methods. 2019-9-30

[10]
Setting the standards for machine learning in biology.

Nat Rev Mol Cell Biol. 2019-11

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索