{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:50:35Z","timestamp":1775523035349,"version":"3.50.1"},"reference-count":42,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"crossref","award":["R15GM122013"],"award-info":[{"award-number":["R15GM122013"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Allostery is considered important in regulating protein\u2019s activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting and graph convolutional neural network, to predict allosteric sites. Our model can learn physical properties and topology without any\n                    <jats:italic>prior<\/jats:italic>\n                    information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/passer.smu.edu\" xlink:type=\"simple\">https:\/\/passer.smu.edu<\/jats:ext-link>\n                    ), along with a command line interface (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/smutaogroup\/passerCLI\" xlink:type=\"simple\">https:\/\/github.com\/smutaogroup\/passerCLI<\/jats:ext-link>\n                    ) provide insights for further analysis in drug discovery.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/abe6d6","type":"journal-article","created":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T17:34:08Z","timestamp":1613496848000},"page":"035015","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["PASSer: prediction of allosteric sites server"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0186-9811","authenticated-orcid":false,"given":"Hao","family":"Tian","sequence":"first","affiliation":[]},{"given":"Xi","family":"Jiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-0239","authenticated-orcid":false,"given":"Peng","family":"Tao","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"mlstabe6d6bib1","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1126\/science.1186121","article-title":"An ensemble view of allostery","volume":"327","author":"Hilser","year":"2010","journal-title":"Science"},{"key":"mlstabe6d6bib2","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.tips.2011.08.004","article-title":"Allo-network drugs: harnessing allostery in cellular networks","volume":"32","author":"Nussinov","year":"2011","journal-title":"Trends Pharmacol. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2020-12-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-02-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-05-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}