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dc.contributor.authorMéndez-Lucio, Oscaren
dc.contributor.authorKooistra, Albert Jen
dc.contributor.authorde, Graaf Chrisen
dc.contributor.authorBender, Andreasen
dc.contributor.authorMedina-Franco, José Len
dc.date.accessioned2015-04-17T11:25:18Z
dc.date.available2015-04-17T11:25:18Z
dc.date.issued2015-01-23en
dc.identifier.citationJournal of Chemical Information and Modeling 2015, 55 (2), pp 251–262 DOI: 10.1021/ci500721xen
dc.identifier.issn1549-9596
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/247359
dc.description.abstractActivity landscape modelling is mostly a descriptive technique that allows rationalising continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modelling of 507 ligand-kinase complexes (from KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information of ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity, but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provide a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.
dc.description.sponsorshipM-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government.
dc.languageEnglishen
dc.language.isoenen
dc.publisherAmerican Chemical Society
dc.subjectActivity landscapeen
dc.subjectActivity cliffsen
dc.subjectInteraction cliffsen
dc.subjectProtein-ligand interaction fingerprintsen
dc.subjectKinase inhibitorsen
dc.subjectSAS mapsen
dc.titleAnalysing Multitarget Activity Landscapes using Protein-Ligand Interaction Fingerprints: Interaction cliffsen
dc.typeArticle
dc.description.versionThis is the accepted manuscript. The final version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.en
prism.endingPage262
prism.publicationDate2015en
prism.publicationNameJournal of Chemical Information and Modelingen
prism.startingPage251
prism.volume55en
dc.rioxxterms.funderERC
dc.rioxxterms.projectidERC-2013-StG-336159 MIXTURE
rioxxterms.versionofrecord10.1021/ci500721xen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-01-23en
dc.contributor.orcidBender, Andreas [0000-0002-6683-7546]
dc.identifier.eissn1549-960X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEuropean Research Council (336159)
rioxxterms.freetoread.startdate2016-01-23


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