{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T03:04:22Z","timestamp":1772679862641,"version":"3.50.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Protein\u2013protein interactions (PPIs) are key elements in numerous biological pathways and the subject of a growing number of drug discovery projects including against infectious diseases. Designing drugs on PPI targets remains a difficult task and requires extensive efforts to qualify a given interaction as an eligible target. To this end, besides the evident need to determine the role of PPIs in disease-associated pathways and their experimental characterization as therapeutics targets, prediction of their capacity to be bound by other protein partners or modulated by future drugs is of primary importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present InDeep, a tool for predicting functional binding sites within proteins that could either host protein epitopes or future drugs. Leveraging deep learning on a curated dataset of PPIs, this tool can proceed to enhanced functional binding site predictions either on experimental structures or along molecular dynamics trajectories. The benchmark of InDeep demonstrates that our tool outperforms state-of-the-art ligandable binding sites predictors when assessing PPI targets but also conventional targets. This offers new opportunities to assist drug design projects on PPIs by identifying pertinent binding pockets at or in the vicinity of PPI interfaces.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The tool is available on GitLab at https:\/\/gitlab.pasteur.fr\/InDeep\/InDeep.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab849","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T07:11:09Z","timestamp":1639379469000},"page":"1261-1268","source":"Crossref","is-referenced-by-count":18,"title":["InDeep: 3D fully convolutional neural networks to assist\n                    <i>in silico<\/i>\n                    drug design on protein\u2013protein interactions"],"prefix":"10.1093","volume":"38","author":[{"given":"Vincent","family":"Mallet","sequence":"first","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"},{"name":"Center for Computational Biology, Mines ParisTech, Paris-Sciences-et-Lettres Research University , Paris 75272, France"}]},{"given":"Luis","family":"Checa Ruano","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"},{"name":"Coll\u00e8ge Doctoral, Sorbonne Universit\u00e9 , Paris F-75005, France"}]},{"given":"Alexandra","family":"Moine Franel","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"},{"name":"Coll\u00e8ge Doctoral, Sorbonne Universit\u00e9 , Paris F-75005, France"}]},{"given":"Michael","family":"Nilges","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"}]},{"given":"Karen","family":"Druart","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2792-6084","authenticated-orcid":false,"given":"Guillaume","family":"Bouvier","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6610-2729","authenticated-orcid":false,"given":"Olivier","family":"Sperandio","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, Institut Pasteur, Universit\u00e9 de Paris, CNRS UMR3528 , Paris F-75015, France"}]}],"member":"286","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"2024022703512053700_btab849-B1","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1016\/j.bpj.2018.02.038","article-title":"Ensemble docking in drug discovery","volume":"114","author":"Amaro","year":"2018","journal-title":"Biophys. 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