Ligand-target prediction by structural network biology using nAnnoLyze

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• dc.contributor.author Martínez Jiménez, Franciscoca
• dc.contributor.author Martí Renom, Marc A.ca
• dc.date.accessioned 2015-11-18T19:07:09Z
• dc.date.available 2015-11-18T19:07:09Z
• dc.date.issued 2015
• dc.description.abstract Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for "anonymous" compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound-protein interactions and thus may benefit drug development.en
• dc.format.mimetype application/pdfca
• dc.identifier.citation Martínez-Jiménez F, Marti-Renom MA. Ligand-target prediction by structural network biology using nAnnoLyze. PLoS computational biology. 2015; 11(3): e1004157. DOI 10.1371/journal.pcbi.1004157ca
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1004157
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/25144
• dc.language.iso engca
• dc.publisher Public Library of Science (PLoS)ca
• dc.relation.ispartof PLoS computational biology. 2015; 11(3): e1004157
• dc.rights © 2015 Martínez-Jiménez, Marti-Renom. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/ca
• dc.subject.other Proteïnes
• dc.subject.other Biologia computacional
• dc.title Ligand-target prediction by structural network biology using nAnnoLyzeen
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/publishedVersionca