{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:05:09Z","timestamp":1761930309283,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA\u2013protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/www.crisprcasdocker.org\">www.crisprcasdocker.org<\/jats:ext-link> as a web server, and at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/hshimlab\/CRISPR-Cas-Docker\">https:\/\/github.com\/hshimlab\/CRISPR-Cas-Docker<\/jats:ext-link> as an open-source tool.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05296-y","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T10:03:30Z","timestamp":1682417010000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9937-8617","authenticated-orcid":false,"given":"Ho-min","family":"Park","sequence":"first","affiliation":[]},{"given":"Jongbum","family":"Won","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2005-1847","authenticated-orcid":false,"given":"Yunseol","family":"Park","sequence":"additional","affiliation":[]},{"given":"Esla Timothy","family":"Anzaku","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5813-5659","authenticated-orcid":false,"given":"Joris","family":"Vankerschaver","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8545-7437","authenticated-orcid":false,"given":"Arnout","family":"Van Messem","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8190-3839","authenticated-orcid":false,"given":"Wesley","family":"De Neve","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-0971","authenticated-orcid":false,"given":"Hyunjin","family":"Shim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"key":"5296_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1745-6150-1-7","volume":"1","author":"KS Makarova","year":"2006","unstructured":"Makarova KS, Grishin NV, Shabalina SA, Wolf YI, Koonin EV. 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