Accurate size-based protein localization from cryo-ET tomograms
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 locat...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Journal of Structural Biology: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590152424000096 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846127255089053696 |
|---|---|
| author | Weisheng Jin Ye Zhou Alberto Bartesaghi |
| author_facet | Weisheng Jin Ye Zhou Alberto Bartesaghi |
| author_sort | Weisheng Jin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5992e91f9a94cd3a5343d71fcce38c3 |
| institution | Kabale University |
| issn | 2590-1524 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Structural Biology: X |
| spelling | doaj-art-b5992e91f9a94cd3a5343d71fcce38c32024-12-12T05:22:34ZengElsevierJournal of Structural Biology: X2590-15242024-12-0110100104Accurate size-based protein localization from cryo-ET tomogramsWeisheng Jin0Ye Zhou1Alberto Bartesaghi2Department of Computer Science, Duke University, Durham, USADepartment of Computer Science, Duke University, Durham, USADepartment of Computer Science, Duke University, Durham, USA; Department of Biochemistry, Duke University School of Medicine, Durham, USA; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, USA; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2590152424000096Cryo-electron tomography3D particle pickingSize-based object detectionSub-tomogram averaging |
| spellingShingle | Weisheng Jin Ye Zhou Alberto Bartesaghi Accurate size-based protein localization from cryo-ET tomograms Journal of Structural Biology: X Cryo-electron tomography 3D particle picking Size-based object detection Sub-tomogram averaging |
| title | Accurate size-based protein localization from cryo-ET tomograms |
| title_full | Accurate size-based protein localization from cryo-ET tomograms |
| title_fullStr | Accurate size-based protein localization from cryo-ET tomograms |
| title_full_unstemmed | Accurate size-based protein localization from cryo-ET tomograms |
| title_short | Accurate size-based protein localization from cryo-ET tomograms |
| title_sort | accurate size based protein localization from cryo et tomograms |
| topic | Cryo-electron tomography 3D particle picking Size-based object detection Sub-tomogram averaging |
| url | http://www.sciencedirect.com/science/article/pii/S2590152424000096 |
| work_keys_str_mv | AT weishengjin accuratesizebasedproteinlocalizationfromcryoettomograms AT yezhou accuratesizebasedproteinlocalizationfromcryoettomograms AT albertobartesaghi accuratesizebasedproteinlocalizationfromcryoettomograms |