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dc.contributor.authorMervin, Lewisen
dc.contributor.authorAfzal, Avid Men
dc.contributor.authorBrive, Larsen
dc.contributor.authorEngkvist, Olaen
dc.contributor.authorBender, Andreasen
dc.date.accessioned2018-06-14T13:11:07Z
dc.date.available2018-06-14T13:11:07Z
dc.date.issued2018-01en
dc.identifier.issn1663-9812
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/277047
dc.description.abstractIn silico protein target deconvolution is frequently used for mechanism-of-action investigations; however existing protocols usually do not predict compound functional effects, such as activation or inhibition, upon binding to their protein counterparts. This study is hence concerned with including functional effects in target prediction. To this end, we assimilated a bioactivity training set for 332 targets, comprising 817,239 active data points with unknown functional effect (binding data) and 20,761,260 inactive compounds, along with 226,045 activating and 1,032,439 inhibiting data points from functional screens. Chemical space analysis of the data first showed some separation between compound sets (binding and inhibiting compounds were more similar to each other than both binding and activating or activating and inhibiting compounds), providing a rationale for implementing functional prediction models. We employed three different architectures to predict functional response, ranging from simplistic random forest models (‘Arch1’) to cascaded models which use separate binding and functional effect classification steps (‘Arch2’ and ‘Arch3’), differing in the way training sets were generated. Fivefold stratified cross-validation outlined cascading predictions provides superior precision and recall based on an internal test set. We next prospectively validated the architectures using a temporal set of 153,467 of in-house data points (after a 4-month interim from initial data extraction). Results outlined Arch3 performed with the highest target class averaged precision and recall scores of 71% and 53%, which we attribute to the use of inactive background sets. Distance-based applicability domain (AD) analysis outlined that Arch3 provides superior extrapolation into novel areas of chemical space, and thus based on the results presented here, propose as the most suitable architecture for the functional effect prediction of small molecules. We finally conclude including functional effects could provide vital insight in future studies, to annotate cases of unanticipated functional changeover, as outlined by our CHRM1 case study.
dc.description.sponsorshipLM thanks the Biotechnology and Biological Sciences Research Council (BBSRC) (BB/K011804/1); and AstraZeneca, grant number RG75821.
dc.format.mediumElectronic-eCollectionen
dc.languageengen
dc.publisherFrontiers Media
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleExtending in Silico Protein Target Prediction Models to Include Functional Effects.en
dc.typeArticle
prism.publicationDate2018en
prism.publicationNameFrontiers in pharmacologyen
prism.startingPage613
prism.volume9en
dc.identifier.doi10.17863/CAM.24347
dcterms.dateAccepted2018-05-22en
rioxxterms.versionofrecord10.3389/fphar.2018.00613en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-01en
dc.contributor.orcidMervin, Lewis [0000-0002-7271-0824]
dc.contributor.orcidEngkvist, Ola [0000-0003-4970-6461]
dc.contributor.orcidBender, Andreas [0000-0002-6683-7546]
dc.identifier.eissn1663-9812
rioxxterms.typeJournal Article/Reviewen


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International