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arXiv · 2204.07543

CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection

Quanfu Fan·Yilai Li·Yuguang Yao·John Cohn·Sijia Liu·Seychelle M. Vos·Michael A. Cianfrocco
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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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Quanfu Fan, Yilai Li, Yuguang Yao, John Cohn, Sijia Liu, Seychelle M. Vos, Michael A. Cianfrocco. 2022-04-15. CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection. https://arxiv.org/abs/2204.07543

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