REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
11
04
2024
accepted:
10
10
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
1
11
2024
Statut:
epublish
Résumé
Cryo-EM particle identification from micrographs ("picking") is challenging due to the low signal-to-noise ratio and lack of ground truth for particle locations. State-of-the-art computational algorithms ("pickers") identify different particle sets, complicating the selection of the best-suited picker for a protein of interest. Here, we present REliable PIcking by Consensus (REPIC), a computational approach to identifying particles common to the output of multiple pickers. We frame consensus particle picking as a graph problem, which REPIC solves using integer linear programming. REPIC picks high-quality particles even when the best picker is not known a priori or a protein is difficult-to-pick (e.g., NOMPC ion channel). Reconstructions using consensus particles without particle filtering achieve resolutions comparable to those from particles picked by experts. Our results show that REPIC requires minimal (often no) manual intervention, and considerably reduces the burden on cryo-EM users for picker selection and particle picking. Availability: https://github.com/ccameron/REPIC .
Identifiants
pubmed: 39482410
doi: 10.1038/s42003-024-07045-0
pii: 10.1038/s42003-024-07045-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1421Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
ID : R35GM148072
Informations de copyright
© 2024. The Author(s).
Références
Knapek, E. & Dubochet, J. Beam damage to organic material is considerably reduced in cryo-electron microscopy. J. Mol. Biol. 141, 147–161 (1980).
doi: 10.1016/0022-2836(80)90382-4
pubmed: 7441748
Glaeser, R. M. Review: electron crystallography: present excitement, a nod to the past, anticipating the future. J. Struct. Biol. 128, 3–14 (1999).
doi: 10.1006/jsbi.1999.4172
pubmed: 10600552
Mishyna, M. et al. Effects of radiation damage in studies of protein-DNA complexes by cryo-EM. Micron 96, 57–64 (2017).
doi: 10.1016/j.micron.2017.02.004
pubmed: 28262565
Cheng, Y. Single-particle cryo-EM—how did it get here and where will it go. Science 361, 876–880 (2018).
doi: 10.1126/science.aat4346
pubmed: 30166484
pmcid: 6460916
Baldwin, P. R. et al. Big data in cryoEM: automated collection, processing and accessibility of EM data. Curr. Opin. Microbiol. 43, 1–8 (2018).
doi: 10.1016/j.mib.2017.10.005
pubmed: 29100109
Maruthi, K., Kopylov, M. & Carragher, B. Automating decision making in the cryo-EM pre-processing pipeline. Structure 28, 727–729 (2020).
doi: 10.1016/j.str.2020.06.004
pubmed: 32640251
Zhang, K. Index of /kzhang/Gautomatch ( http://www.mrc-lmb.cam.ac.uk/kzhang/ ).
Roseman, A. FindEM—a fast, efficient program for automatic selection of particles from electron micrographs. J. Struct. Biol. 145, 91–99 (2004).
doi: 10.1016/j.jsb.2003.11.007
pubmed: 15065677
Chen, J. Z. & Grigorieff, N. SIGNATURE: a single-particle selection system for molecular electron microscopy. J. Struct. Biol. 157, 168–173 (2007).
doi: 10.1016/j.jsb.2006.06.001
pubmed: 16870473
Tang, G. et al. EMAN2: an extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007).
doi: 10.1016/j.jsb.2006.05.009
pubmed: 16859925
Shaikh, T. R. et al. SPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs. Nat. Protoc. 3, 1941–1974 (2008).
doi: 10.1038/nprot.2008.156
pubmed: 19180078
pmcid: 2737740
Scheres, S. H. RELION: Implementation of a bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).
doi: 10.1016/j.jsb.2012.09.006
pubmed: 23000701
Hoang, T. V., Cavin, X., Schultz, P. & Ritchie, D. W. gEMpicker: a highly parallel GPU-accelerated particle picking tool for cryo-electron microscopy. BMC Struct. Biol. 13, 25 (2013).
doi: 10.1186/1472-6807-13-25
pubmed: 24144335
Liu, Y. & Sigworth, F. J. Automatic cryo-EM particle selection for membrane proteins in spherical liposomes. J. Struct. Biol. 185, 295–302 (2014).
doi: 10.1016/j.jsb.2014.01.004
pubmed: 24468290
pmcid: 3978669
Moriya, T. et al. High-resolution single particle analysis from electron cryo-microscopy images using SPHIRE. J. Vis. Exp. https://doi.org/10.3791/55448 (2017).
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
doi: 10.1038/nmeth.4169
pubmed: 28165473
Grant, T., Rohou, A. & Grigorieff, N. cisTEM, user-friendly software for single-particle image processing. eLife 7 https://doi.org/10.7554/elife.35383 (2018).
Marabini, R. et al. Xmipp: an image processing package for electron microscopy. J. Struct. Biol. 116, 237–240 (1996).
doi: 10.1006/jsbi.1996.0036
pubmed: 8812978
Wang, F. et al. DeepPicker: a deep learning approach for fully automated particle picking in cryo-EM. J. Struct. Biol. 195, 325–336 (2016).
doi: 10.1016/j.jsb.2016.07.006
pubmed: 27424268
Xiao, Y. & Yang, G. A fast method for particle picking in cryo-electron micrographs based on fast r-CNN. In: AIP Conference Proceedings https://doi.org/10.1063/1.4982020 (Author(s), 2017).
Zhu, Y., Ouyang, Q. & Mao, Y. A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy. BMC Bioinform. 18 https://doi.org/10.1186/s12859-017-1757-y (2017).
Da, T., Ding, J., Yang, L. & Chirikjian, G. A method for fully automated particle picking in cryo-electron microscopy based on a CNN. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics https://doi.org/10.1145/3233547.3233706 (ACM, 2018).
Heimowitz, A., Andén, J. & Singer, A. APPLE picker: automatic particle picking, a low-effort cryo-EM framework. J. Struct. Biol. 204, 215–227 (2018).
doi: 10.1016/j.jsb.2018.08.012
pubmed: 30134153
pmcid: 6183064
Sanchez-Garcia, R., Segura, J., Maluenda, D., Carazo, J. M. & Sorzano, C. O. S. Deep consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy. IUCrJ 5, 854–865 (2018).
doi: 10.1107/S2052252518014392
pubmed: 30443369
pmcid: 6211526
Al-Azzawi, A., Ouadou, A., Tanner, J. J. & Cheng, J. AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in cryo-EM images. BMC Bioinform. 20 https://doi.org/10.1186/s12859-019-2926-y (2019).
Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019).
doi: 10.1038/s41592-019-0575-8
pubmed: 31591578
Li, X., Lin, Y., Liu, Q., McSweeney, S. & Yoo, S. Picking particles in cryo-EM micrographs without knowing the particle size. In 2019 New York Scientific Data Summit (NYSDS) https://doi.org/10.1109/nysds.2019.8909792 (IEEE, 2019).
Tegunov, D. & Cramer, P. Real-time cryo-electron microscopy data preprocessing with warp. Nat. Methods 16, 1146–1152 (2019).
doi: 10.1038/s41592-019-0580-y
pubmed: 31591575
pmcid: 6858868
Wagner, T. et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun. Biol. 2 https://doi.org/10.1038/s42003-019-0437-z (2019).
Yao, R., Qian, J. & Huang, Q. Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules. Bioinformatics, https://doi.org/10.1093/bioinformatics/btz728 (2019).
Zhang, J. et al. PIXER: an automated particle-selection method based on segmentation using a deep neural network. BMC Bioinform. 20 https://doi.org/10.1186/s12859-019-2614-y (2019).
George, B. et al. CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy. Commun. Biol. 4 https://doi.org/10.1038/s42003-021-01721-1 (2021).
Nguyen, N. P., Ersoy, I., Gotberg, J., Bunyak, F. & White, T. A. DRPnet: automated particle picking in cryo-electron micrographs using deep regression. BMC Bioinform. 22 https://doi.org/10.1186/s12859-020-03948-x (2021).
Zhang, C. et al. TransPicker: a transformer-based framework for particle picking in cryoEM micrographs. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) https://doi.org/10.1109/bibm52615.2021.9669524 (IEEE, 2021).
Zhang, X., Zhao, T., Chen, J., Shen, Y. & Li, X. EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking. Nat. Commun. 13 https://doi.org/10.1038/s41467-022-29994-y (2022).
Bepler, T., Kelley, K., Noble, A. J. & Berger, B. Topaz-denoise: general deep denoising models for cryoEM and cryoET. Nat. Commun. 11 https://doi.org/10.1038/s41467-020-18952-1 (2020).
Scheres, S. H. Semi-automated selection of cryo-EM particles in RELION-1.3. J. Struct. Biol. 189, 114–122 (2015).
doi: 10.1016/j.jsb.2014.11.010
pubmed: 25486611
McSweeney, D. M., McSweeney, S. M. & Liu, Q. A self-supervised workflow for particle picking in cryo-EM. IUCrJ 7, 719–727 (2020).
doi: 10.1107/S2052252520007241
pubmed: 32695418
Dhakal, A., Gyawali, R., Wang, L. & Cheng, J. A large expert-curated cryo-em image dataset for machine learning protein particle picking. Sci. Data 10 https://doi.org/10.1038/s41597-023-02280-2 (2023).
Other pages—crYOLO documentation—cryolo.readthedocs.io. https://cryolo.readthedocs.io/en/stable/other/other.html#general-model-data-sets . [Accessed 16-Apr-2023].
Liao, M., Cao, E., Julius, D. & Cheng, Y. Structure of the TRPV1 ion channel determined by electron cryo-microscopy. Nature 504, 107–112 (2013).
doi: 10.1038/nature12822
pubmed: 24305160
pmcid: 4078027
Danev, R. & Baumeister, W. Cryo-EM single particle analysis with the volta phase plate. eLife 5 https://doi.org/10.7554/elife.13046 (2016).
Jin, P. et al. Electron cryo-microscopy structure of the mechanotransduction channel NOMPC. Nature 547, 118–122 (2017).
doi: 10.1038/nature22981
pubmed: 28658211
pmcid: 5669069
Singh, K. et al. Discovery of a regulatory subunit of the yeast fatty acid synthase. Cell 180, 1130–1143.e20 (2020).
doi: 10.1016/j.cell.2020.02.034
pubmed: 32160528
Noble, A. J. VirtualIce: Half-synthetic CryoEM Micrograph Generator. biorxiv https://www.biorxiv.org/content/10.1101/2024.09.28.615520v1 (2024).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
doi: 10.1038/nmeth.4193
pubmed: 28250466
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7 https://doi.org/10.7554/elife.42166 (2018).
Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
doi: 10.1016/j.jsb.2015.08.008
pubmed: 26278980
Bron, C. & Kerbosch, J. Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16, 575–577 (1973).
doi: 10.1145/362342.362367
Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using networkX. In: Varoquaux, G., Vaught, T. & Millman, J. (eds) Proceedings of the 7th Python in Science Conference, 11—15 (Pasadena, CA USA, 2008).
Gurobi Optimization, L.L.C. Gurobi optimizer reference manual. https://www.gurobi.com (2022).
Pettersen, E. F. et al. UCSF chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
doi: 10.1002/jcc.20084
pubmed: 15264254
Liao, M., Cao, E., Julius, D. & Cheng, Y. EMPIAR-10005: TRPV1 dataset taken on a K2 direct electron detector. EMPIAR https://www.ebi.ac.uk/empiar/EMPIAR-10005/ (2016).
Scheres, S. H. EMPIAR-10017: Beta-galactosidase Falcon-II micrographs plus manually selected coordinates by Richard Henderson. EMPIAR https://www.ebi.ac.uk/empiar/EMPIAR-10017/ (2014).
Danev, R. & Baumeister, W. EMPIAR-10057: volta phase plate in-focus dataset of T20S proteasome. EMPIAR https://empiar.pdbj.org/en/entry/10057/ (2016).
Jin, P. et al. EMPIAR-10093: Structure of an ion channel in nano disc. EMPIAR https://www.ebi.ac.uk/empiar/EMPIAR-10093 (2022).
Singh, K., Graf, B., Stark, H. & Chari, A. EMPIAR-10454: Saccharomyces cerevisiae fatty acid synthase complex with bound gamma subunitc. EMPIAR https://www.ebi.ac.uk/empiar/EMPIAR-10454/ (2020).
Noble, A. J. EMPIAR-12287: cryo-EM ice images and labels used for VirtualIce. EMPIAR https://www.ebi.ac.uk/empiar/EMPIAR-12287/ (2024).
Cameron, C. J., Seager, S. J., Sigworth, F. J., Tagare, H. D. & Gerstein, M. B. REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers. Source code, ccameron/REPIC: v1.0.0. Zenodo https://doi.org/10.5281/zenodo.13844192 (2024).