Predicción funcional de péptidos antimicrobianos en metagenomas de suelo y mantillo de bosques de la Amazonia Noroccidental

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dc.contributor.advisorOrduz Peralta, Sergio
dc.contributor.advisorOrtiz Morea, Fausto Andrés
dc.contributor.authorToro-Ardila, Diego A.
dc.contributor.orcidToro-Ardila, Diego A. [0009000759708598]
dc.contributor.orcidOrdúz Peralta, Sergio [0000-000175873816]
dc.contributor.orcidOrtiz-Morea, Fausto Andrés [0000000309781256]
dc.contributor.researchgroupBiología Funcional
dc.date.accessioned2025-08-25T21:41:48Z
dc.date.available2025-08-25T21:41:48Z
dc.date.issued2025-08-20
dc.descriptionIlustraciones
dc.description.abstractLa resistencia a los antimicrobianos es un fenómeno natural que surge de la competencia entre microorganismos por espacio y nutrientes. Sin embargo, esta dinámica se ha intensificado desde el siglo XX debido al uso excesivo de antibióticos, lo que ha generado una fuerte presión selectiva que ha acelerado la aparición de microorganismos resistentes, convirtiéndose en un grave problema de salud pública. Ante esta situación, los péptidos antimicrobianos (AMPs) surgen como alternativas prometedoras a los antibióticos, debido a su destacada actividad de amplio espectro contra una variedad de patógenos. En este contexto, nuestro estudio emplea un enfoque bioinformático combinado con algoritmos de machine learning para identificar AMPs promisorios en metagenomas de suelo y mantillo de bosque de la Amazonia colombiana, una fuente rica e inexplorada de moléculas con potencial biotecnológico. A través del análisis de metaproteomas, identificamos 1.329.511 péptidos que cumplían con los criterios fisicoquímicos preestablecidos para ser considerados potenciales AMPs. Seleccionamos los 10 mejores péptidos y se modificaron buscando aumentar su potencial antimicrobiano. La evaluación in silico de las propiedades fisicoquímicas, estructurales y biológicas de los péptidos seleccionados y sus versiones modificadas, reveló que la mayoría presentan una baja toxicidad, alta probabilidad de unión a membranas microbianas y un alto potencial de actividad contra bacterias, hongos, virus, además de potenciales propiedades anticancerígenas. Los resultados obtenidos validan la eficacia del enfoque computacional y el diseño racional aplicados en la identificación de AMPs, abriendo nuevas perspectivas para el desarrollo de terapias antimicrobianas innovadoras. (Texto tomado de la fuente)spa
dc.description.abstractAntimicrobial resistance is a natural phenomenon that arises from competition between microorganisms for space and nutrients. However, this dynamic has been intensified since the 20th century due to the excessive use of antibiotics, which has generated a strong selective pressure that has accelerated the emergence of resistant microorganisms, becoming a serious public health problem. In this situation, antimicrobial peptides (AMPs) emerge as promising alternatives to antibiotics, due to their outstanding broad-spectrum activity against various pathogens. In this context, our study employs a bioinformatics approach combined with machine learning algorithms to identify promising AMPs in soil and leaf litter metagenomes from the Colombian Amazon, a rich and unexplored source of molecules with biotechnological potential. Through metaproteome analysis, we identified 1,329,511 peptides that met the pre-established physicochemical criteria to be considered potential AMPs. We selected the 10 best peptides and modified them to enhance their antimicrobial potential. In silico evaluation of the physicochemical, structural, and biological properties of the selected peptides and their modified versions have revealed that most of them present low toxicity, a high probability of binding to microbial membranes, and high potential activity against bacteria, fungi, viruses, as well as potential anticancer properties. The results obtained validate the efficacy of the computational approach and rational design applied in the identification of AMPs, opening new perspectives for the development of innovative antimicrobial therapies.eng
dc.description.curricularareaBiotecnología.Sede Medellín
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias - Biotecnología
dc.description.researchareaBiotecnología Microbiana: Sustancias bioactivas para el control de patógenos
dc.format.extent171 páginas
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/88458
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellín
dc.publisher.facultyFacultad de Ciencias
dc.publisher.placeMedellín, Colombia
dc.publisher.programMedellín - Ciencias - Maestría en Ciencias - Biotecnología
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseReconocimiento 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.jelAntibioticos
dc.subject.lembPeptidos
dc.subject.lembAprendizaje automático (inteligencia artificial)
dc.subject.proposalPéptidos antimicrobianosspa
dc.subject.proposalAntimicrobial peptideseng
dc.subject.proposalMetagenomasspa
dc.subject.proposalMetagenomeseng
dc.subject.proposalBioinformáticaspa
dc.subject.proposalBioinformaticseng
dc.subject.proposalMachine learning
dc.subject.proposalResistenciaspa
dc.subject.proposalResistanceeng
dc.subject.proposalSistema inmune innatospa
dc.subject.proposalInnate immune systemeng
dc.subject.proposalAntibióticosspa
dc.subject.proposalAntibioticseng
dc.titlePredicción funcional de péptidos antimicrobianos en metagenomas de suelo y mantillo de bosques de la Amazonia Noroccidentalspa
dc.title.translatedFunctional prediction of antimicrobial peptides in soil and leaf litter metagenomes from the Northwestern Amazoneng
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dcterms.audience.professionaldevelopmentPúblico general
dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2

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Maestría en Ciencias - Biotecnología