You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning}
Article:
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.
Introduction:
Single-molecule Förster resonance energy transfer( smFRET) combined with TIRFm( total internal reflection fluorescence microscopy) is a key powerful method to study the structure of biomolecules and provide a dynamic perspective in structural biology( Lerner et al., 2018). Capturing the real-time readouts of nanometer-scale distances of individual biomolecules by smFRET allows the direct observations of dynamics, interactions, and intermediates of stochastic non-accumulating events, as well as dynamic equilibria between unsynchronized molecules, all of which are obscured in ensemble averaging techniques( Dimura et al., 2016; Hellenkamp et al., 2018; Holmstrom et al., 2019; Juette et al., 2016; Newton et al., 2019; Preus et al., 2015; Roy et al., 2008; Schuler and Eaton, 2008; Stella et al., 2018). The high fidelity and proficiency of smFRET established it as a key toolbox for the accurate characterization of mechanisms, biomolecular interactions function, and even structures of biomolecules( Craggs and Kapanidis, 2012; Dulin et al., 2018; Kalinin et al., 2012; Kilic et al., 2018; Ratzke et al., 2014), under both in vitro( Schluesche et al., 2007; Sharma et al., 2008; Stein et al., 2011) and in vivo( Okamoto et al., 2020; Sakon and Weninger, 2010) conditions. Despite its great quantitative utility and profound impact on structural biology, smFRET is not a direct imaging modality and data treatment for extracting quantitative dynamic information relies on multiple layers of preprocessing: raw image treatment, trace selection, and data analysis. Raw image treatment( Greenfeld et al., 2012; Hon and Gonzalez, 2019; Juette et al., 2016; Preus et al., 2015; Stella et al., 2018) and data analysis of the selected smFRET traces is in general well-standardized and relies on well-defined methodologies with strong theoretical backing( Hon and Gonzalez, 2019; Schmid et al., 2016). The actual trace selection can be time-consuming but crucial due to the presence of undesired phenomena at the single-molecule scale, such as sample aggregation, fluorescent contaminants, incomplete or incorrect sample labeling, complex photophysical behaviors, and high noise, to mention a few( Algar et al., 2019; Hellenkamp et al., 2018; Roy et al., 2008). Existing software( Greenfeld et al., 2012; Hellenkamp et al., 2018; Hon and Gonzalez, 2019; Juette et al., 2016; Preus et al., 2015; Stella et al., 2018) by single-molecule labs can simplify the tedious and time-consuming selection of traces and were recently expanded to large datasets( Juette et al., 2016) albeit requiring some form of manual supervision and hyper-parameter tuning selecting the proper thresholds by an expert user. This need for human intervention could potentially be subjected to cognitive biases especially by less experienced users and could limit the expansion of smFRET to classic biology labs. The increasing expansion of smFRET to structural biology labs would benefit from rapid and benchmarked methodologies, reproducible across laboratories, with minimal human intervention. This is highlighted by several initiatives to standardize the smFRET field( Greenfeld et al., 2012; Hellenkamp et al., 2018; Lerner et al., 2018; Sali et al., 2015). Recent advances in machine learning( ML) and specifically deep learning( DL)( LeCun et al., 2015), have radically improved our capacity to access and extract information from abstract and noisy inputs independently of human interventions as we( ATLAS collaboration, 2014) and others have shown( Berg et al., 2019; Christiansen et al., 2018; Falk et al., 2019; Gómez-García et al., 2018; Jones, 2019; Ouyang et al., 2018; Smith et al., 2019; Zhang et al., 2018). DL implementations are providing high-level robust performances and have been successfully used to analyze and augment a wide range of the fluorescence microscopy analysis pipeline including assessing microscope image quality( Yang et al., 2018), in-silico cell labeling( Christiansen et al., 2018), single-cell morphology analysis( Berg et al., 2019; Falk et al., 2019), detecting single molecules( White et al., 2020; Wu and Rifkin, 2015) and linking smFRET experiments with molecular dynamics simulations( Matsunaga and Sugita, 2018), amongst others( Berg et al., 2019; Christiansen et al., 2018; Falk et al., 2019; Gómez-García et al., 2018; Jones, 2019; Ouyang et al., 2018; Smith et al., 2019; Zhang et al., 2018). Deep learning-based analysis has several advantages over other approaches: It recognizes abstract patterns and learns useful features directly from the raw input data, which allows the implementation of analysis routines that do not require extensive data preprocessing or empirically defined rules, and thus offer reproducible and less opinionated evaluation of single-molecule data; It is significantly faster than human annotation for large single-molecule datasets; it comes close to, or outperforms human performance; and its performance is increased when increasing dataset size constituting an ideal case for evaluating the large datasets obtained from single-molecule data( Berg et al., 2019; Christiansen et al., 2018; Falk et al., 2019; Gómez-García et al., 2018; Jones, 2019; Ouyang et al., 2018; Smith et al., 2019; Zhang et al., 2018). Especially important are convolutional deep neural networks( DNN), artificial neural networks that learn to approximate the underlying function that transforms input to associated output through multiple rounds of optimization. The strength of DNN is the ability to learn arbitrarily complex functions to best recognize particular aspects of the given input data and model complex nonlinear relationships. The DNN is then able to classify data into predefined classes based on the provided training labels. While the training of a DNN is generally a computationally intensive process, once trained the final model can easily be shared and used for making predictions at almost no computational cost to end-users. Here we provide DeepFRET, an all-inclusive analysis software with a pre-trained DNN at its core, for a rapid, objective, and accurate assessment of smFRET data for quantifying biomolecular dynamics. The fully automated analysis software operates with minimal crucial human intervention and requires only a threshold on the data quality confidence, as an initial step, to output detailed quantification of structural dynamic from raw images. This is attained by an intuitive and user-friendly interface that integrates and automates common smFRET analysis procedures( Greenfeld et al., 2012; Hellenkamp et al., 2018; Hon and Gonzalez, 2019; Juette et al., 2016; Preus et al., 2015; Stella et al., 2018) from raw image analysis and background-corrected intensity trace extraction( Thomsen et al., 2019), to sophisticated trace classification, statistical analysis of single-molecule data and production of publication-quality figures of dynamic structural biology insights( see Materials and methods). DeepFRET comes as a free-to-use standalone executable for both Windows and Mac users allowing end-users with limited programming skills to easily operate it. A script-based version implemented entirely in Python enables experts to adjust features pipelining the analysis specified for their needs. We anticipate DeepFRET to take full advantage of the widespread digitization and open repository of smFRET data and form a reference point setting a bar for the data quality and data classification performance metrics, offering additional benchmarking the field for dynamic structural biology.
Discussion:
smFRET is a powerful toolkit, key for exploring dynamic structural biology, but to meet its full potential, automated standardized and user-independent analysis of data is essential. Because the experimental conditions, instruments, and biological systems drastically vary across laboratories, the treatment of data based on semi-automatic methods and simplified assumptions could yield different conclusions. DeepFRET is designed to fill this void and analyze data independently of any assumptions and reproducibly across laboratories. Our experiments show that a neural network classifier trained on purely simulated smFRET time-series accurately and efficiently recognizes and classifies smFRET both in simulated ground truth data and in a real-world dataset. DeepFRET classification accuracy consistently outperformed trace selection using commonly published thresholds. Similarly, it supersedes the selection accuracy of human operators and importantly, only requiring a fraction of the time( minutes versus weeks if traces are manually selected). Such a drastic reduction of analysis times will allow the acquisition of even larger datasets expanding the field for high throughput analysis and improving the robustness of conclusion. The fact that DeepFRET does so only requiring a score threshold, as a sole human intervention, demonstrates its strength as a novel smFRET analysis method and its potential to form a reference against which the quality of the data and the structural biology insights are benchmarked. DeepFRET was found to perform accurately for both ALEX and non-ALEX smFRET data( Figure 4, Figure 4—figure supplements 3–4) highlighting its precise classification and applicability across laboratories and methods. The limited effect of human operators on data selection on the other hand illustrates its potential to contribute to the standardization of the field, increasing reproducibility across laboratories. We anticipate that DeepFRET, combined with the advent of commercial single-molecule instruments, will contribute in materializing the smFRET as a robust mainstream toolkit for structural biology labs. DeepFRET is currently trained and thoroughly tested to operate on 2-color ALEX and non-ALEX smFRET data. Other experimental techniques such as 3- and 4-color FRET( Stein et al., 2011; Yoo et al., 2020), protein-induced fluorescence enhancement( PIFE; Hwang and Myong, 2014), metal-induced energy transfer( MIET; Chizhik et al., 2014) or intraparticle surface energy transfer( i-SET; Zhou et al., 2020) could be implemented in the future through additional simulation of training data with subsequent DNN re-training and software optimization( see Materials and methods and data availability for instructions). The simulation of data in DeepFRET is based on the assumption that protein dynamics as observed by smFRET follows a Markov process. Based on this assumption, practically any kind of experimental data can be simulated, using the implemented visual trace simulator, to meet specific requirements. This may include additional noise levels above 0. 30, transition probabilities larger than 0. 2, traces with more than 4 FRET states, slower or faster lifetimes that may be classified as noisy, etc. In its current version, DeepFRET operates on surface-immobilized particles only, however implementing single-particle tracking as described in Bohr et al., 2019 would allow simultaneous tracking and extraction of dynamics through smFRET on freely diffusing particles open up exciting possibilities, for example, live cell imaging with single-particletracking( Singh et al., 2020) and in vivo high throughput smFRET studies( White et al., 2020). DeepFRET’s neural network is trained to operate for smFRET data but our approach of time-series classification and sequence annotation can conveniently be extended to consider a spectrum of stochastic single-molecule trajectories of individual turnovers including tracking( Bohr et al., 2019; Ferro et al., 2019; Lu et al., 1998; Persson et al., 2013), constant force measurements( Goldman et al., 2015) and blinking of individual molecules( Durisic et al., 2014; Wang et al., 2019) using either simulated or high-quality annotated experimental data for training. Consequently, we expect the neural network of DeepFRET or similar approaches to be a paradigm shift and enable new fully automated analysis methodologies related to biomolecular recognition, protein folding and dynamics, and super resolution. Such advances are paramount for increasing the breadth and impact of single-molecule studies to be fully exploited in 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 .