| Autor: |
Yilai Li, Quanfu Fan, Ziping Xu, Emma Rose Lee, John Cohn, Veronique Demers, Ja Young Lee, Lucy Yip, Michael A. Cianfrocco, Seychelle M. Vos |
| Rok vydání: |
2022 |
| DOI: |
10.1101/2022.06.17.496614 |
| Popis: |
Cryo-electron microscopy (cryo-EM) represents a powerful technology for determining atomic models of biological macromolecules(Kühlbrandt, 2014). Despite this promise, human-guided cryo-EM data collection practices limit the impact of cryo-EM because of a path planning problem: cryo-EM datasets typically represent 2-5% of the total sample area. Here, we address this fundamental problem by formalizing cryo-EM data collection as a path planning optimization from low signal data. Within this framework, we incorporate reinforcement learning (RL) and deep regression to design an algorithm that uses distributed surveying of cryo-EM samples at low magnification to learn optimal cryo-EM data collection policies. Our algorithm - cryoRL - solves the problem of path planning on cryo-EM grids, allowing the algorithm to maximize data quality in a limited time without human intervention. A head-to-head comparison of cryoRL versus human subjects shows that cryoRL performs in the top 10% of test subjects, surpassing the majority of users in collecting high-quality images from the same sample. CryoRL establishes a general framework that will enable human-free cryo-EM data collection to increase the impact of cryo-EM across life sciences research. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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