CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection

TL;DR

CryoRL applies reinforcement learning to optimize cryo-EM data collection, significantly improving efficiency and image quality in a complex, resource-limited setting, thus advancing structural biology research.

Contribution

This work introduces cryoRL, a novel reinforcement learning framework that formulates cryo-EM data collection as an optimization problem, outperforming average users in data acquisition efficiency.

Findings

CryoRL outperforms average users in data collection efficiency.

Reinforcement learning effectively navigates heterogenous cryo-EM grids.

The approach maximizes the number of high-quality images within limited time.

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.