CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection

• URL: http://arxiv.org/abs/2204.07543v1
• Date: Fri, 15 Apr 2022 17:00:06 GMT
• Title: CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection
• Authors: Quanfu Fan, Yilai Li, Yuguang Yao, John Cohn, Sijia Liu, Seychelle M. Vos, and Michael A. Cianfrocco
• Abstract summary: We show that reinforcement learning offers an effective way to plan cryo-EM data collection, successfully navigating heterogenous cryo-EM grids. The approach we developed, cryoRL, demonstrates better performance than average users for data collection under similar settings.
• Score: 13.499994658675638
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of dynamic bio-molecules. However, cryo-EM data acquisition remains expensive and labor-intensive, requiring substantial expertise. Structural biologists need a more efficient and objective method to collect the best data in a limited time frame. We formulate the cryo-EM data collection task as an optimization problem in this work. The goal is to maximize the total number of good images taken within a specified period. We show that reinforcement learning offers an effective way to plan cryo-EM data collection, successfully navigating heterogenous cryo-EM grids. The approach we developed, cryoRL, demonstrates better performance than average users for data collection under similar settings.

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