A simulated annealing approach for resolution guided homogeneous cryo-electron microscopy image selection

Abstract

Background: Cryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal-to-noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher-quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed.

Methods: We propose a simulated annealing-based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo-ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets.

Results: Experimented on simulated and experimental 2D Cryo-EM images of Ribosome complex datasets respectively with SNR=10 and SNR=0.5, our method achieved better accuracy in terms of F-measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases.

Conclusions: SA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo-EM and 3D Cryo-ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo-EM and Cryo-ET.

Graphical abstract

Keywords

simulated annealing / image averaging / cryo-electron microscopy / cryo-electron tomography

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ACKNOWLEDGEMENTS

We thank Dr. Ming Sun for suggestions and Mr. Shan Zhou for initial exploratory studies. We thank Ms. Xindi Wu for helping with manuscript editing. This work was supported in part by U.S. National Institutes of Health (NIH) grant (P41 GM103712). MX acknowledges support from Samuel and Emma Winters Foundation. XZ was supported by a fellowship from Carnegie Mellon University’s Center for Machine Learning and Health. RJ is a RONG professor at the Institute for Data Science, Tsinghua University.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Jie Shi, Xiangrui Zeng, Rui Jiang, Tao Jiang and Min Xu declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

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