Show simple item record

dc.contributor.authorLiberis, Edgar
dc.contributor.authorVelickovic, Petar
dc.contributor.authorSormanni, Pietro
dc.contributor.authorVendruscolo, Michele
dc.contributor.authorLiò, Pietro
dc.date.accessioned2018-08-01T14:20:35Z
dc.date.available2018-08-01T14:20:35Z
dc.date.issued2018-09-01
dc.identifier.issn1367-4803
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/278605
dc.description.abstractMotivation: Antibodies play essential roles in the immune system of vertebrates and are powerful tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). Results: In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method significantly improves on the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. Availability and implementation: The Parapred method is freely available as a webserver at http://www-mvsoftware.ch.cam.ac.uk/and for download at https://github.com/eliberis/parapred. Supplementary information: Supplementary information is available at Bioinformatics online.
dc.format.mediumPrint
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.subjectAntibodies
dc.subjectBinding Sites, Antibody
dc.subjectAmino Acid Sequence
dc.subjectAlgorithms
dc.subjectModels, Molecular
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectNeural Networks, Computer
dc.titleParapred: antibody paratope prediction using convolutional and recurrent neural networks.
dc.typeArticle
prism.endingPage2950
prism.issueIdentifier17
prism.publicationDate2018
prism.publicationNameBioinformatics
prism.startingPage2944
prism.volume34
dc.identifier.doi10.17863/CAM.25945
dcterms.dateAccepted2018-04-13
rioxxterms.versionofrecord10.1093/bioinformatics/bty305
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-09
dc.contributor.orcidSormanni, Pietro [0000-0002-6228-2221]
dc.contributor.orcidVendruscolo, Michele [0000-0002-3616-1610]
dc.contributor.orcidLio, Pietro [0000-0002-0540-5053]
dc.identifier.eissn1367-4811
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-04-16
rioxxterms.freetoread.startdate2019-04-16


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record