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dc.contributor.authorAuthorOchoa, Rodrigo
dc.contributor.authorAuthorSantiago, Ángel
dc.contributor.authorAuthorAlegría-Arcos, Melissa.
dc.date.accessionedDate Accessioned2024-09-03T19:17:47Z
dc.date.availableDate Available2024-09-03T19:17:47Z
dc.date.issuedDate Issued2022
dc.identifier.citationReferencia BibliográficaArtificial Intelligence in the Life Sciences, 2, 8 p.
dc.identifier.issnISSN2667-3185
dc.identifier.uriURIhttp://repositorio.udla.cl/xmlui/handle/udla/1362
dc.identifier.uriURIhttps://www.sciencedirect.com/journal/artificial-intelligence-in-the-life-sciences/publish/open-access-options
dc.description.abstractAbstractThe study of protein-peptide interactions is an active research field from an experimental and computational perspective, with the latest presenting challenges to model and simulate the peptides' intrinsic flexibility. Predicting affinities towards protein systems of interest, such as proteases, is crucial to understand the specificity of the interactions and support the discovery of novel substrates. Here we provide a set of computational protocols to run structural and dynamical analysis of protein-peptide complexes from a binding perspective. The protocols are based on state-of-the-art methods, but the code is open and can be customized depending on the user needs. These include a fragment-growing peptide docking protocol to predict bound conformations of flexible peptides, a protocol to extract descriptors from protein-peptide molecular dynamics trajectories, and a workflow to build and test machine learning regression models. As a toy example, we applied the protocols to a serine protease structure with a set of known peptide substrates and random sequences to illustrate the use of the code, which is publicly available at: https://github.com/rochoa85/Protocols-Peptide-Binding
dc.format.extentdc.format.extent8 páginas
dc.format.extentdc.format.extent2.126Mb
dc.format.mimetypedc.format.mimetypePDF
dc.language.isoLanguage ISOeng
dc.publisherPublisherElsevier
dc.rightsRightsCreative Commons Attribution License (CC BY)
dc.sourceSourcesArtificial Intelligence in the Life Sciences
dc.subjectSubjectPeptide
dc.subjectSubjectDocking
dc.subject.lcshdc.subject.lcshDinámica molecular
dc.subject.lcshdc.subject.lcshAprendizaje de máquina
dc.titleTitleOpen protocols for docking and MD-based scoring of peptide substrates
dc.typeDocument TypeArtículo
dc.udla.catalogadordc.udla.catalogadorCBM
dc.udla.indexdc.udla.indexWoS
dc.udla.indexdc.udla.indexScopus
dc.identifier.doidc.identifier.doi10.1016/j.ailsci.2022.100044
dc.facultaddc.facultadFacultad de Ingeniería y Negocios


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