{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T08:47:31Z","timestamp":1780735651552,"version":"3.54.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM146340"],"award-info":[{"award-number":["R01GM146340"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset\u2014CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code and data for CryoTransformer are openly available at: https:\/\/github.com\/jianlin-cheng\/CryoTransformer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae109","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T17:21:15Z","timestamp":1708622475000},"source":"Crossref","is-referenced-by-count":37,"title":["CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4047-9947","authenticated-orcid":false,"given":"Ashwin","family":"Dhakal","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Missouri , Columbia, MO 65211, United States"},{"name":"NextGen Precision Health, University of Missouri , Columbia, MO 65211, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajan","family":"Gyawali","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Missouri , Columbia, MO 65211, United States"},{"name":"NextGen Precision Health, University of Missouri , Columbia, MO 65211, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"Laboratory for BioMolecular Structure (LBMS), Brookhaven National Laboratory , Upton, NY 11973, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-2853","authenticated-orcid":false,"given":"Jianlin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Missouri , Columbia, MO 65211, United States"},{"name":"NextGen Precision Health, University of Missouri , Columbia, MO 65211, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,24]]},"reference":[{"key":"2024031405370399300_btae109-B1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1186\/s12859-019-2926-y","article-title":"AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images","volume":"20","author":"Al-Azzawi","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2024031405370399300_btae109-B2","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1186\/s12859-020-03809-7","article-title":"DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM","volume":"21","author":"Al-Azzawi","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"2024031405370399300_btae109-B3","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1038\/s41586-022-04845-4","article-title":"Structure of the bile acid transporter and HBV receptor NTCP","volume":"606","author":"Asami","year":"2022","journal-title":"Nature"},{"key":"2024031405370399300_btae109-B4","first-page":"3285","author":"Bello","year":"2019"},{"key":"2024031405370399300_btae109-B5","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1038\/s41592-019-0575-8","article-title":"Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs","volume":"16","author":"Bepler","year":"2019","journal-title":"Nat Methods"},{"key":"2024031405370399300_btae109-B6","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.cell.2019.12.030","article-title":"Cryo-EM reveals integrin-mediated TGF-b activation without release from latent TGF-b article Cryo-EM reveals integrin-mediated TGF-b activation without release from latent TGF-b","volume":"180","author":"Campbell","year":"2020","journal-title":"Cell"},{"key":"2024031405370399300_btae109-B7","first-page":"213","article-title":"End to end object detection using transformers","volume":"11900","author":"Carion","year":"2020","journal-title":"ECCV"},{"key":"2024031405370399300_btae109-B8","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1038\/s41597-023-02280-2","article-title":"A large expert-curated cryo-EM image dataset for machine learning protein particle picking","volume":"10","author":"Dhakal","year":"2023","journal-title":"Sci Data"},{"key":"2024031405370399300_btae109-B9","author":"Dhakal","year":"2023"},{"key":"2024031405370399300_btae109-B10","author":"Dhakal","year":"2023"},{"key":"2024031405370399300_btae109-B11","doi-asserted-by":"crossref","first-page":"bbab476","DOI":"10.1093\/bib\/bbab476","article-title":"Artificial intelligence in the prediction of protein\u2013ligand interactions: recent advances and future directions","volume":"23","author":"Dhakal","year":"2022","journal-title":"Brief. Bioinform"},{"key":"2024031405370399300_btae109-B12","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3390\/biom13010132","article-title":"Improving protein\u2013ligand interaction modeling with cryo-EM data, templates, and deep learning in 2021 ligand model challenge","volume":"13","author":"Giri","year":"2023","journal-title":"Biomolecules"},{"key":"2024031405370399300_btae109-B13","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1038\/nmeth.2486","article-title":"Stroboscopic imaging of macromolecular complexes","volume":"10","author":"Glaeser","year":"2013","journal-title":"Nat Methods"},{"key":"2024031405370399300_btae109-B14","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010","journal-title":"J Mach Learn Res"},{"key":"2024031405370399300_btae109-B15","author":"Gyawali","year":"2023"},{"key":"2024031405370399300_btae109-B16","first-page":"770","author":"He","year":"2016"},{"key":"2024031405370399300_btae109-B17","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.jsb.2018.08.012","article-title":"APPLE picker: automatic particle picking, a low-effort cryo-EM framework","volume":"204","author":"Heimowitz","year":"2018","journal-title":"J Struct Biol"},{"key":"2024031405370399300_btae109-B18","doi-asserted-by":"crossref","first-page":"D1503","DOI":"10.1093\/nar\/gkac1062","article-title":"EMPIAR: the electron microscopy public image archive","volume":"51","author":"Iudin","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2024031405370399300_btae109-B19","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1038\/nature22981","article-title":"Electron cryo-microscopy structure of the mechanotransduction channel NOMPC","volume":"547","author":"Jin","year":"2017","journal-title":"Nature"},{"key":"2024031405370399300_btae109-B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jsb.2014.03.001","article-title":"Automated particle picking for low-contrast macromolecules in cryo-electron microscopy","volume":"186","author":"Langlois","year":"2014","journal-title":"J Struct Biol"},{"key":"2024031405370399300_btae109-B21","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cell.2016.12.023","article-title":"Structures of the human HCN1 hyperpolarization-activated channel","volume":"168","author":"Lee","year":"2017","journal-title":"Cell"},{"key":"2024031405370399300_btae109-B22","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","article-title":"Microsoft COCO: common objects in context","volume":"8693","author":"Lin","year":"2014","journal-title":"Lect Notes Comput Sci"},{"key":"2024031405370399300_btae109-B23","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jsb.2003.11.005","article-title":"Detecting particles in cryo-EM micrographs using learned features","volume":"145","author":"Mallick","year":"2004","journal-title":"J Struct Biol"},{"key":"2024031405370399300_btae109-B24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1006\/jsbi.1996.0036","article-title":"Xmipp: an image processing package for electron microscopy","volume":"116","author":"Marabini","year":"1996","journal-title":"J Struct Biol"},{"key":"2024031405370399300_btae109-B25","volume-title":"30th Br. Mach. Vis. Conf. 2019, BMVC. 2019","author":"Masoumzadeh","year":"2020"},{"key":"2024031405370399300_btae109-B26","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1107\/S2052252520007241","article-title":"A self-supervised workflow for particle picking in cryo-EM","volume":"7","author":"McSweeney","year":"2020","journal-title":"IUCrJ"},{"key":"2024031405370399300_btae109-B27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s12859-020-03948-x","article-title":"DRPnet: automated particle picking in cryo-electron micrographs using deep regression","volume":"22","author":"Nguyen","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2024031405370399300_btae109-B28","first-page":"4055","volume-title":"35th International Conference on Machine Learning. ICML 2018","author":"Parmar","year":"2018"},{"key":"2024031405370399300_btae109-B29","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/nmeth.4169","article-title":"CryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination","volume":"14","author":"Punjani","year":"2017","journal-title":"Nat Methods"},{"key":"2024031405370399300_btae109-B30","first-page":"658","author":"Rezatofighi","year":"2019"},{"key":"2024031405370399300_btae109-B31","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.jsb.2014.11.010","article-title":"Semi-automated selection of cryo-EM particles in RELION-1.3","volume":"189","author":"Scheres","year":"2015","journal-title":"J Struct Biol"},{"key":"2024031405370399300_btae109-B32","first-page":"2325","author":"Stewart","year":"2016"},{"key":"2024031405370399300_btae109-B33","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1107\/S2059798320012474","article-title":"Through-grid wicking enables high-speed cryoEM specimen preparation","volume":"76","author":"Tan","year":"2020","journal-title":"Acta Crystallogr D Struct Biol"},{"key":"2024031405370399300_btae109-B34","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1038\/s41592-019-0580-y","article-title":"Real-time cryo-electron microscopy data preprocessing with Warp","volume":"16","author":"Tegunov","year":"2019","journal-title":"Nat Methods"},{"key":"2024031405370399300_btae109-B35","first-page":"5999","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024031405370399300_btae109-B36","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s42003-019-0437-z","article-title":"SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM","volume":"2","author":"Wagner","year":"2019","journal-title":"Commun Biol"},{"key":"2024031405370399300_btae109-B37","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.jsb.2016.07.006","article-title":"DeepPicker: a deep learning approach for fully automated particle picking in cryo-EM","volume":"195","author":"Wang","year":"2016","journal-title":"J Struct Biol"},{"key":"2024031405370399300_btae109-B38","author":"Xiao","year":"2017"},{"key":"2024031405370399300_btae109-B39","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1186\/s12859-017-1757-y","article-title":"A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy","volume":"18","author":"Zhu","year":"2017","journal-title":"BMC Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae109\/56756164\/btae109.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/3\/btae109\/56965062\/btae109.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/3\/btae109\/56965062\/btae109.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T01:37:30Z","timestamp":1710380250000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae109\/7614090"}},"subtitle":[],"editor":[{"given":"Arne","family":"Elofsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,2,24]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae109","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.10.19.563155","asserted-by":"object"}]},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2024,2,24]]},"article-number":"btae109"}}