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Multi-GPU-Based Detection of Protein Cavities using Critical Points

2017Journal article Restricted access
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dc.contributor.authorDias, Sérgio
dc.contributor.authorNguyen, Quoc
dc.contributor.authorJorge, Joaquim A
dc.contributor.authorGomes, Abel
dc.date.accessioned2020-01-15T11:45:25Z
dc.date.available2020-01-15T11:45:25Z
dc.date.issued2017
dc.description.abstractProtein cavities are specific regions on the protein surface where ligands (small molecules) may bind. Such cavities are putative binding sites of proteins for ligands. Usually, cavities correspond to voids, pockets, and depressions of molecular surfaces. The location of such cavities is important to better understand protein functions, as needed in, for example, structure-based drug design. This article introduces a geometric method to detecting cavities on the molecular surface based on the theory of critical points. The method, called CriticalFinder, differs from other surface-based methods found in the literature because it directly uses the curvature of the scalar field (or function) that represents the molecular surface, instead of evaluating the curvature of the Connolly function over the molecular surface. To evaluate the accuracy of CriticalFinder, we compare it to other seven geometric methods (i.e., LIGSITE-CS, GHECOM, ConCavity, POCASA, SURFNET, PASS, and Fpocket). The benchmark results show that CriticalFinder outperforms those methods in terms of accuracy. In addition, the performance analysis of the GPU implementation of CriticalFinder in terms of time consumption and memory space occupancy was carried out.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citation2017-DiasGomespt_PT
dc.identifier.doi10.1016/j.future.2016.07.009pt_PT
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/10400.6/8307
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X16302394pt_PT
dc.subjectProtein cavitypt_PT
dc.subjectProtein pocketpt_PT
dc.subjectGeometric detection of pocketspt_PT
dc.subjectProtein pocket detection algorithmpt_PT
dc.titleMulti-GPU-Based Detection of Protein Cavities using Critical Pointspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/UTAP-EXPL%2FQEQ-COM%2F0019%2F2014/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F50008%2F2013/PT
oaire.citation.endPage440pt_PT
oaire.citation.startPage430pt_PT
oaire.citation.titleFuture Generation Computer Systemspt_PT
oaire.citation.volume67pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream5876
person.familyNameDias
person.familyNameJorge
person.familyNamePadrão Gomes
person.givenNameSérgio
person.givenNameJoaquim
person.givenNameAbel João
person.identifierJ-8234-2017
person.identifierH-9602-2014
person.identifier.ciencia-id0512-AF85-D322
person.identifier.ciencia-id0912-2821-9E60
person.identifier.ciencia-idEC1D-4ACD-6A62
person.identifier.orcid0000-0002-9752-9386
person.identifier.orcid0000-0001-5441-4637
person.identifier.orcid0000-0002-5804-5717
person.identifier.ridC-5596-2008
person.identifier.scopus-author-id36668366300
person.identifier.scopus-author-id36882820600
person.identifier.scopus-author-id8325080300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.embargofctEste artigo foi publicado em regime de acesso fechado.pt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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