Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands

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dc.citation.journalTitleMOLECULAR DIVERSITYeng
dc.contributor.authorPérez, G.M.
dc.contributor.authorSalomón, L.A.
dc.contributor.authorMontero-Cabrera, L.A.
dc.contributor.authorde la Vega, J.M.G.
dc.contributor.authorMascini, M.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.date.accessioned2021-04-12T14:07:10Z
dc.date.available2021-04-12T14:07:10Z
dc.date.issued2016-05-01
dc.description.abstractA novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1750
dc.identifier.doi10.1007/s11030-015-9648-5
dc.identifier.issn13811991
dc.identifier.issn1573501X
dc.identifier.otherWOS;000373857100006
dc.identifier.otherPUBMED;26553204
dc.identifier.otherSCOPUS;2-s2.0-84946762399
dc.identifier.urihttp://hdl.handle.net/10784/27759
dc.language.isoengeng
dc.publisherSpringer International Publishing
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84946762399&doi=10.1007%2fs11030-015-9648-5&partnerID=40&md5=b996309d07a67e34cfaab602320c59fc
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1381-1991
dc.sourceMOLECULAR DIVERSITY
dc.subject.keywordMolecular modelingeng
dc.subject.keywordInverse virtual screeningeng
dc.subject.keywordSampling techniqueseng
dc.subject.keywordMolecular dockingeng
dc.titleIntegrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligandseng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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