Accurate size-based protein localization from cryo-ET tomograms

• Published in: Journal of structural biology. X Vol. 10; p. 100104
• Main Authors: Jin, Weisheng, Zhou, Ye, Bartesaghi, Alberto
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2024; Elsevier
• Subjects: 3D particle picking; Cryo-electron tomography; Size-based object detection; Sub-tomogram averaging; 3D particle picking; Cryo-electron tomography; Size-based object detection; Sub-tomogram averaging
• DOI: 10.1016/j.yjsbx.2024.100104
• ISSN: 2590-1524

[Display omitted] •Size-based particle picking algorithm efficiently locates proteins within tomograms.•Does not require external templates, labeled data for training or access to GPUs.•Uses contamination mask to reduce the number of false-positives and improve accuracy.•Is faster and achieves higher accuracy than fully-supervised deep learning methods.•Validated on in vitro and in situ datasets and on complexes with 300 kDa to 3 MDa sizes. Cryo-electron tomography (cryo-ET) combined with sub-tomogram averaging (STA) allows the determination of protein structures imaged within the native context of the cell at near-atomic resolution. Particle picking is an essential step in the cryo-ET/STA image analysis pipeline that consists in locating the position of proteins within crowded cellular tomograms so that they can be aligned and averaged in 3D to improve resolution. While extensive work in 2D particle picking has been done in the context of single-particle cryo-EM, comparatively fewer strategies have been proposed to pick particles from 3D tomograms, in part due to the challenges associated with working with noisy 3D volumes affected by the missing wedge. While strategies based on 3D template-matching and deep learning are commonly used, these methods are computationally expensive and require either an external template or manual labelling which can bias the results and limit their applicability. Here, we propose a size-based method to pick particles from tomograms that is fast, accurate, and does not require external templates or user provided labels. We compare the performance of our approach against a commonly used algorithm based on deep learning, crYOLO, and show that our method: i) has higher detection accuracy, ii) does not require user input for labeling or time-consuming training, and iii) runs efficiently on non-specialized CPU hardware. We demonstrate the effectiveness of our approach by automatically detecting particles from tomograms representing different types of samples and using these particles to determine the high-resolution structures of ribosomes imaged in vitro and in situ.