DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.
DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.
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DOI：
10.7554/elife.60404
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发表时间：
2020-11-03
期刊：
影响因子：
7.7
通讯作者：
Hatzakis NS
中科院分区：
文献类型：
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作者：
Thomsen J;Sletfjerding MB;Jensen SB;Stella S;Paul B;Malle MG;Montoya G;Petersen TC;Hatzakis NS
单分子Förster共振能量转移（smFRET）是研究生物分子结构和动力学的一种适应性强的方法。高通量方法的发展和商业仪器的增长已经超过了快速、标准化和自动化方法的发展，以客观地分析产生的数据的财富。在这里，我们提出了DeepFRET，一个基于深度学习的自动化，开源的独立解决方案，其中从原始显微镜图像转换到生物分子行为直方图的唯一关键人为干预是用户可调的质量阈值。整合smFRET分析的标准特征，DeepFRET输出共同的动力学信息指标。其在地面真值数据上的分类准确率达到了bb0 95%，优于人类操作员和常用阈值，只需要~1%的时间。它在真实数据上的精确和快速操作证明了DeepFRET客观量化生物分子动力学的能力，并有可能为动态结构生物学的smFRET基准做出贡献。蛋白质被折叠成特定的形状，以便在细胞中发挥它们的作用。然而，它们的结构并不是刚性的：蛋白质会随着环境的变化而弯曲和旋转。识别这些运动是理解蛋白质如何工作和相互作用的重要组成部分。不幸的是，当研究人员研究蛋白质的结构时，他们通常只关注蛋白质的“平均”形状，而忽略了蛋白质可能只是暂时存在的其他构象。研究蛋白质柔韧性的一项重要技术被称为单分子Förster共振能量转移（FRET）。在这项技术中，两个光敏标签附着在同一个蛋白质分子上，当它们近距离接触时就会发出信号。这种纳米级传感器允许结构生物学家从单个蛋白质运动中获得信息，这些信息在观察蛋白质的平均构象时可能会丢失。用于执行FRET的仪器的进步使得观察单个蛋白质的运动更广泛地适用于非专业人员，但这些仪器产生的数据的分析仍然需要高水平的专业知识。为了降低非专业人员使用该技术的门槛，并确保实验可以在不同的仪器上由不同的研究人员重现，Thomsen等人开发了一种自动化数据分析的新方法。他们使用机器学习技术来识别、过滤和表征数据，从而产生可靠的结果，用户只需要执行几个步骤。这种新的分析方法可以帮助将单分子FRET的应用扩展到不同的领域，使研究人员能够研究蛋白质灵活性对某些疾病的重要性，或者更好地理解蛋白质在细胞中的作用。
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. Proteins are folded into particular shapes in order to carry out their roles in the cell. However, their structures are not rigid: proteins bend and rotate in response to their environment. Identifying these movements is an important part of understanding how proteins work and interact with each other. Unfortunately, when researchers study the structures of proteins, they often look at the ‘average’ shape a protein takes, missing out on other conformations the protein might only be in temporarily. An important technique for studying protein flexibility is known as single molecule Förster resonance energy transfer (FRET). In this technique, two light-sensitive tags are attached to the same protein molecule and give off a signal when they come into close contact. This nano-scale sensor allows structural biologists to get information from individual protein movements that can be lost when looking at the average conformations of proteins. Advances in the instruments used to perform FRET have made observing the motion of individual proteins more widely accessible to non-specialists, but the analysis of the data that these instruments produce still requires a high level of expertise. To lower the barrier for non-specialists to use the technology, and to ensure that experiments can be reproduced on different instruments and by different researchers, Thomsen et al. have developed a new way to automate the data analysis. They used machine learning technology to recognize, filter and characterize data so as to produce reliable results, with the user only needing to perform a couple of steps. This new analysis approach could help expand the use of single-molecule FRET to different fields , allowing researchers to investigate the importance of protein flexibility for certain diseases, or to better understand the roles that proteins have in a cell.
影响因子：
3.9
作者：
通讯作者：
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