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CryoDiscovery™: Public Data Based AI/ML Model Enhancements for Cryo-Electron Microscopy Image Analysis

This year, we present our development of a tool to reduce the need for human intervention in Cryo-EM single-particle analysis. Our tool automatically distinguishes “good” from “bad” candidates from among 2D class-average images that are produced by the 2D classification step typically performed during single-particle reconstruction [2]. Ordinarily, human operators can select from among these 2D classaverage particles as a way to remove noisy or artifactual data that is not easily removed during prior data processing steps, and in our experience represents a significant labor cost and potential for human bias. Our model is a Convolutional Neural Network and is trained using annotated data comprising three different proteins collected in partnership with the Oregon Health & Science University Cryo-EM center. Our model can distinguish good from bad images with receiver-operating-characteristic and average-precision scores exceeding 0.95, and further analysis of our model’s mistakes reveals potential human annotation errors, suggesting the true model performance could be as good or better than humanlevel. The key learning from the current work is that for the structural analysis of proteins (and other particles), the model must be trained continually with current and diverse data to make it more efficient in detection.