CryoDiscovery: a machine learning platform for automated cryo-electron microscopy 2D class selection

Abstract

Structural Biology is an emerging critical area for disease research and drug discovery. This can be the basis for detecting novel biological threats and hence will help prepare the country's readiness. It should be noted that much of the SARS-CoV-2 virus that causes Covid-19 has been analyzed using structural biology. Cryogenic Microscopy (cryo-EM) is one of the most impactful and vital tools of biological structure analysis today. Single-particle cryo-EM produces images of individual particles, and has the potential to analyze the biological structures at the single-molecule level. However, the images generated by cryo-EM are highly noisy, and hence it takes a significant number of compute-intense steps [1] to process them to build recognizable 3D structures (proteins and others). Some of the steps must be repeated to filter out noisy data. Some of the steps require manual input, especially the 2D and 3D classes, which result in classification errors due to user bias, time waiting, and user fatigue. HTI proposes to address these problems with CryoDiscovery to eliminate or minimize the manual steps to improve productivity and accuracy. The role of CryoDiscovery is highlighted in the simplified workflow diagram (Figure 1). CryoDiscovery is designed by Health Technology Innovations Inc (https://hti.ai/), the recipient of the NSF grant [2]. CryoDiscovery uses AI/ML techniques for automated class selection. To achieve generation and the use of the AI/ML model and to be compatible with the workflow, it has: A training module to generate the AI/ML model. A module that uses that AI/ML model to infer whether images are good or bad during a workflow. A module that translates that inference into generating the data for the subsequent steps. In the figure here, the CryoDiscovery components are inside the dashed box, in the simplified CryoDiscovery diagram (Figure 2). CryoDiscovery uses Convolutional Neural Network (CNN) [3] implementation. CNN, a layered network, iteratively extracting features for classifying the class average images. After training with a few thousand images, the Accuracy and False Negative graph (Figure 3) showed very good results. The network is designed to be retrained, if needed, with few labeled images (often, less than a dozen). In this talk, we will discuss the results. The model was verified quantitatively with the Fourier Shell Correlation-Resolution graph (FSC graph) [4], and qualitatively by comparing against the published structures. We will look at the analysis of datasets from the EMPIAR site [5]. Relion [6] and CryoSPARC™ [7] were used in the flow, along with CryoDiscovery. We will use these examples to demonstrate the viability of CryoDiscovery. 1)Empiar 10204 dataset: beta-galactosidase: CryoDiscovery was used in the Relion example workflow for 2D Class Selection. The FSC graph shows the results from Relion's pre-calculated flow and with CryoDiscovery. As could be noted in Figure 4, it is very close. 2)Empiar 10256: TRPV5: As in the previous test, CryoDiscovery was used in the Relion ...

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

“…Multiple ML methods such as support vector machines, logistic regressions, K-means clustering, classification trees, or deep CNN attempted to automate this process. 183,192,[493][494][495][496] Interestingly, deep neural networks were successfully used to automatically guide the microscope in procedurally finding the carbon holes, single particles inside, and eventually acquiring the different image projections for their final projection matching. 178,189 It is great that this was achieved by directly applying the wide-spread YOLO network architecture, reinforcing the message we wanted to spread out in the previous section about the ease to deploy ML in microscopy despite the scientific background.…”

“…However, it is important to keep it in mind when trying to apply ML to materials science (or to any other field), as similar routines might have already been developed, for instance, within the mentioned cryo-TEM community. [177][178][179][180][181][182][183][184][185][186][187][188][189][190][191][192] The cross-fertilization with not only other microscopy techniques, but other scientific and technical disciplines is of capital importance and is deeply discussed in the fourth section of this review.…”