Modelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas

dc.contributorMera Banguero, Carlos Andres
dc.contributorBranch Bedoya, John Willian
dc.contributorOrduz Peralta, Sergio
dc.contributorBiología Funcional
dc.contributorGidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.contributor0000-0002-5143-0276
dc.contributorhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001737101
dc.contributorhttps://scholar.google.com/citations?user=K6Tz_4QAAAAJ&hl=es
dc.creatorOrrego Pérez, Andrés
dc.date.accessioned2023-05-19T19:12:22Z
dc.date.available2023-05-19T19:12:22Z
dc.date.created2023-05-19T19:12:22Z
dc.date.issued2023
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/83835
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractLa resistencia a los antibióticos se ha convertido en uno de los mayores problemas de salud a nivel mundial en los últimos años, provocando afectaciones directas contra la salud y la economía. Un tipo especial de proteínas cortas, denominadas péptidos antimicrobianos, está tomando gran relevancia en la investigación para combatir esta problemática, principalmente por sus bondades antibióticas. Existen diferentes métodos para la búsqueda de nuevos péptidos antimicrobianos, entre ellos está el uso de técnicas de aprendizaje automático que permiten reducir los costos y el tiempo de búsqueda, comparadas con las técnicas tradicionales de bioprospección. En esa línea, en este trabajo se propone un método para la generación de secuencias sintéticas de péptidos antimicrobianos con funcionalidades específicas utilizando una red neuronal con una arquitectura GAN condicional y celdas recurrentes. Este método es evaluado a partir de una estrategia de validación propuesta que se enfoca en medir la calidad y diversidad de las secuencias sintéticas generadas. Los modelos obtenidos fueron comparados con algunas referencias del estado del arte y los resultados mostraron que las secuencias generadas por los modelos propuestos tienen alto potencial antimicrobiano, son diversas, estructuralmente distintas a las secuencias de entrenamiento, pero similares a nivel de su composición de aminoácidos. Adicionalmente, los modelos propuestos pueden generar, a petición del usuario, secuencias con las siguientes funcionalidades específicas: antimicrobiano, antibacteriano, anti gramnegativo, anti grampositivo, antifúngico, antiviral, y anticáncer. (Tomado de la fuente)
dc.description.abstractAntibiotic resistance has become one of the biggest health problems worldwide in recent years, causing direct effects on health and the economy. A particular type of short protein, called antimicrobial peptides, is gaining great relevance in research to combat this problem, mainly due to its antibiotic benefits. There are different methods for searching for new antimicrobial peptide sequences, including machine learning techniques that reduce costs and search time compared to traditional bioprospecting techniques. In that line, this work proposes a method for generating synthetic sequences of antimicrobial peptides with specific functionalities using a neural network with a conditional GAN architecture and recurrent cells. This method is evaluated based on a proposed validation strategy that measures the quality and diversity of the generated synthetic sequences. The obtained models were compared with some state-of-the-art references. The results showed that the sequences generated by the proposed models have high antimicrobial potential and are diverse, structurally different from the training sequences, but similar at their amino acid composition level. Additionally, the proposed models can generate, at the user's request, sequences with the following specific functionalities: antimicrobial, antibacterial, anti-gram-negative, anti-gram-positive, antifungal, antiviral, and anticancer.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherMedellín - Minas - Maestría en Ingeniería - Analítica
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
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dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleModelo de aprendizaje profundo para la generación automática de péptidos antimicrobianos sintéticos con funcionalidades específicas
dc.typeTrabajo de grado - Maestría


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