Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos

ilustraciones, tablas, gráficas

Autores:: Victoria Muñoz, Daniel Felipe

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
dc.title.translated.eng.fl_str_mv	Contribution to the identification of potential antagonists associated with quorum sensing in Pseudomonas aeruginosa through chemoinformatic methods
title	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
spellingShingle	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos 540 - Química y ciencias afines Pseudomonas aeruginosa Farmacología Preparaciones Farmacéuticas Pharmaceutical Preparations Pharmacology Pharmaceutical technology Tecnología farmacéutica Quorum sensing P. aeruginosa LasR / PqsR / RhlR Computer-aided drug design Protein-ligand interaction fingerprints Transcriptional factors Cheminformatics Molecular dynamics simulation Machine learning Factores de transcripción Quimioinformatica Simulaciones de dinámica molecular Diseño de fármacos asistido por computadora Aprendizaje de maquina
title_short	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
title_full	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
title_fullStr	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
title_full_unstemmed	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
title_sort	Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos
dc.creator.fl_str_mv	Victoria Muñoz, Daniel Felipe
dc.contributor.advisor.none.fl_str_mv	López Vallejo, Fabián
dc.contributor.author.none.fl_str_mv	Victoria Muñoz, Daniel Felipe
dc.contributor.researchgroup.spa.fl_str_mv	Productos Naturales Vegetales Bioactivos y Quimica EcoIogica
dc.contributor.supervisor.none.fl_str_mv	Medina-Franco Jose Luis Sánchez-Cruz Norberto
dc.subject.ddc.spa.fl_str_mv	540 - Química y ciencias afines
topic	540 - Química y ciencias afines Pseudomonas aeruginosa Farmacología Preparaciones Farmacéuticas Pharmaceutical Preparations Pharmacology Pharmaceutical technology Tecnología farmacéutica Quorum sensing P. aeruginosa LasR / PqsR / RhlR Computer-aided drug design Protein-ligand interaction fingerprints Transcriptional factors Cheminformatics Molecular dynamics simulation Machine learning Factores de transcripción Quimioinformatica Simulaciones de dinámica molecular Diseño de fármacos asistido por computadora Aprendizaje de maquina
dc.subject.decs.spa.fl_str_mv	Pseudomonas aeruginosa Farmacología Preparaciones Farmacéuticas
dc.subject.decs.eng.fl_str_mv	Pharmaceutical Preparations
dc.subject.lemb.eng.fl_str_mv	Pharmacology Pharmaceutical technology
dc.subject.lemb.spa.fl_str_mv	Tecnología farmacéutica
dc.subject.proposal.eng.fl_str_mv	Quorum sensing P. aeruginosa LasR / PqsR / RhlR Computer-aided drug design Protein-ligand interaction fingerprints Transcriptional factors Cheminformatics Molecular dynamics simulation Machine learning
dc.subject.proposal.spa.fl_str_mv	Factores de transcripción Quimioinformatica Simulaciones de dinámica molecular Diseño de fármacos asistido por computadora Aprendizaje de maquina
description	ilustraciones, tablas, gráficas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-04T17:36:16Z
dc.date.available.none.fl_str_mv	2021-10-04T17:36:16Z
dc.date.issued.none.fl_str_mv	2021-09-28
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80376
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80376 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
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dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
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spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2López Vallejo, Fabiáne19897795d8276b679189b9984410785Victoria Muñoz, Daniel Felipeada54702cb66291cd3ea3e3f58365acaProductos Naturales Vegetales Bioactivos y Quimica EcoIogicaMedina-Franco Jose LuisSánchez-Cruz Norberto2021-10-04T17:36:16Z2021-10-04T17:36:16Z2021-09-28https://repositorio.unal.edu.co/handle/unal/80376Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, tablas, gráficasDe acuerdo con el centro de control de enfermedades de los Estados Unidos (CDC’s) en su reporte de resistencia a antibióticos, las infecciones causadas por P. aeruginosa multi-fármaco resistente comprometieron alrededor de 32,600 pacientes y generaron 2,700 muertes durante el 2017. En Colombia el Sistema Nacional de Vigilancia de Resistencia Antimicrobiana (IAAS) en su informe del 2018 mostró que la P. aeruginosa es el patógeno con mayor recurrencia de la métalo β-lactamasa VIM en el territorio colombiano, alcanzando 840 casos contabilizados y dificultando el tratamiento de las infecciones de este patógeno. Como parte de la búsqueda de nuevas soluciones contra este patógeno, el mecanismo de comunicación intercelular denominado quorum sensing y los factores de transcripción que intervienen en él resultan ser blancos promisorios como adyuvantes en el tratamiento de infecciones por este patógeno. Por esta razón, en la presente investigación se seleccionaron los factores de transcripción LasR, PqsR y RhlR como blancos de estudio en el control de esta vía de comunicación. A través de la construcción de bases de datos con moléculas a las que se les ha demostrado su actividad biológica y su análisis mediante diversas herramientas del diseño de fármacos basado en el ligando, se encontraron múltiples características estructurales involucradas con la actividad contra estos factores de transcripción. Para LasR, anillos de 5 y 6 miembros (lactónicos y fenílicos), cadenas carbonadas de 10 o más carbonos, dos aceptores y un donador de puente de hidrogeno y el tamaño molecular superior a los 15 átomos fueron las características estructurales mínimas asociadas a moléculas con actividad sobre este factor. Adicionalmente se identificaron las interacciones con los residuos Tyr 47, ala 105, Thr 80, Leu 36, Ile 36, Ile 52 y Tyr 56 como posibles responsables de la actividad antagonista utilizando herramientas como fingerprints de interacción proteína-ligando y simulación de dinámicas molecular; siendo estos asociados al diseño de fármacos basado en la estructura. Los compuestos con actividad sobre PqsR exhibieron las siguientes características estructurales: biciclos nitrogenados de 6 miembros (quinoleína y quinazolina), cadenas alifáticas de un tamaño mínimo de 10 carbonos, mínimo un aceptor y donador de puente de hidrogeno y se debe igualar o superar a los 15 átomos; las interacciones con los aminoácidos que podrían estar involucrados en la actividad antagonista sobre este factor son: Tyr 258, Phe 221, Ala 102, Leu 207, Ile 149, siendo estas caracterizadas con los instrumentos del diseño e fármacos basado en la estructura. A partir de la base de datos construida para RhlR y los análisis quimioinformáticos se detectó que las moléculas con un anillo de 5 o 6 miembros (lactonas y fenilos), cadenas carbonadas de 3 a 6 carbonos; al menos 2 aceptores de puente de hidrogeno, 1 donador de puente de hidrogeno y que el conteo de átomos oscile entre 8 y 12. Como en las bases de datos públicas al momento no se encuentra elucidada la estructura terciaria de este factor de transcripción, en este trabajo se planteó y validó un modelo de estructura: con la anterior se procedió a hacer los análisis de fingerprints interacción proteína-ligando y la simulación de dinámica molecular para encontrar los residuos involucrados con la actividad antagonista, encontrado a las interacciones con la Val 60, Leu 69, Tyr 64, Tyr 72 y Ala 83 como las posibles responsables. Con el fin de complementar las características estructurales encontradas anteriormente, se crearon los farmacóforos a través del alineamiento estructural de las conformaciones obtenidas con los acoplamientos de las 10 moléculas con mejor actividad antagonista sobre cada uno de los factores de transcripción. Adicionalmente, se construyeron modelos de aprendizaje de maquina supervisados, donde los compuestos de la base de datos fueron clasificados como activos e inactivos y con el molecular fingerprint ECFP4 se caracterizó cada una de las moléculas para el entrenamiento posterior de 14 modelos; los cuales fueron clasificados según 6 métricas diferentes. De lo anterior se encontró que los mejores clasificadores fueron extreme gradient boosting y k-nearest neighbors classifier para todos los factores de transcripción y se propone el uso conjunto para la clasificación de los compuestos con posible actividad. Para integrar la información anterior, se realizó un cribado virtual sobre una base de datos molecular de productos naturales; encontrando a los compuestos: litseanolido C2, 1-hidroxi-3-metoxi-N-metil acridona y hematomato de metilo como los mejores clasificados para ser antagonistas contra LasR, PqsR y RhlR; además se identificó a las familias Rutaceae, Lauraceae y Piperaceae como una fuente potencial de compuestos con actividad contra estos factores de transcripción. (Texto tomado de la fuente).According to the United States Center for Disease Control (CDC's) in its report of antibiotic resistance, infections caused by multi-drug resistant P. aeruginosa affected around 32,600 patients and generated 2,700 deaths during 2017. In Colombia the National Antimicrobial Resistance Surveillance System (IAAS) in its 2018 report showed that P. aeruginosa is the pathogen with the highest recurrence of Metallo β-lactamase VIM in the Colombian territory, reaching 840 counted cases and making it difficult to treat the infections of this pathogen. As part of the search for new solutions against this pathogen, the intercellular communication mechanism called quorum sensing and the transcription factors involved in it turn out to be promising targets as adjuvants in the treatment of infections caused by this pathogen. For this reason, in this research, the transcription factors LasR, PqsR, and RhlR were selected as study targets in the control of this communication pathway. Through the construction of databases with active molecules. In this thesis using various tools of drug design based on the ligand, multiple structural characteristics involved with the activity against these transcription factors were found. For LasR, 5 and 6-membered rings (lactonic and phenyl), carbon chains of 10 or more carbons, two acceptors and a hydrogen bridge donor, and molecular size greater than 15 atoms were the minimum structural characteristics associated with molecules with activity on this factor. Additionally, interactions with residues Tyr 47, ala 105, Thr 80, Leu 36, Ile 36, Ile 52, and Tyr 56 were identified as possibly responsible for the antagonist activity using tools such as protein-ligand interaction fingerprints and molecular dynamics simulation; these being associated with structure-based drug design. Compounds with activity on PqsR exhibited the following structural characteristics: 6-membered nitrogenous bicycles (quinoline and quinazoline), aliphatic chains of a minimum size of 10 carbons, at least one hydrogen bridge acceptor and donor, and should equal or exceed those 15 atoms; the interactions with the amino acids that could be involved in the antagonist activity on this factor are: Tyr 258, Phe 221, Ala 102, Leu 207, Ile 149, being these characterized with the instruments of design and drugs based on the structure. From the database built for RhlR and the chemoinformatics analyzes, it was detected that molecules with a 5 or 6-membered ring (lactones and phenyl), carbon chains of 3 to 6 carbons; at least 2 hydrogen bridge acceptors, 1 hydrogen bridge donor, and that the atom count ranges between 8 and 12. As in the public databases at the moment the tertiary structure of this transcription factor is not elucidated. This work proposed and validated a structure model: with the previous one, we proceeded to do the analysis of protein-ligand interaction fingerprints and the simulation of molecular dynamics to find the residues involved with the antagonist activity, found in the interactions with Val 60, Leu 69, Tyr 64, Tyr 72 and Ala 83 as the possible culprits. In order to complement the structural characteristics, found previously, the pharmacophores were created through the structural alignment of the conformations obtained with the couplings of the 10 molecules with the best antagonist activity on each of the transcription factors. Additionally, supervised machine learning models were built, where the compounds in the database were classified as active and inactive, and with the molecular fingerprint ECFP4, each of the molecules was characterized for the subsequent training of 14 models; which were classified according to 6 different metrics. From the above, it was found that the best classifiers were extreme gradient boosting and k-nearest neighbor’s classifier for all transcription factors, and joint use is proposed for the classification of compounds with possible activity. To integrate the above information, virtual screening was performed on a molecular database of natural products; finding the compounds: C2 litseanolide, 1-hydroxy-3-methoxy-N-methyl acridone, and methyl hematomate as the best classified to be antagonists against LasR, PqsR, and RhlR; Furthermore, the Rutaceae, Lauraceae, and Piperaceae families were identified as a potential source of compounds with activity against these transcription factors.MinCienciasFPIT - Fundación para la promoción de la investigación y la tecnologíaMaestríaMagíster en Ciencias FarmacéuticasQuímica medicinal y modelamiento molecularxxii, 147 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias FarmacéuticasDepartamento de FarmaciaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá540 - Química y ciencias afinesPseudomonas aeruginosaFarmacologíaPreparaciones FarmacéuticasPharmaceutical PreparationsPharmacologyPharmaceutical technologyTecnología farmacéuticaQuorum sensingP. aeruginosaLasR / PqsR / RhlRComputer-aided drug designProtein-ligand interaction fingerprintsTranscriptional factorsCheminformaticsMolecular dynamics simulationMachine learningFactores de transcripciónQuimioinformaticaSimulaciones de dinámica molecularDiseño de fármacos asistido por computadoraAprendizaje de maquinaContribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticosContribution to the identification of potential antagonists associated with quorum sensing in Pseudomonas aeruginosa through chemoinformatic methodsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMSchopf, J. 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Composición química y actividad antibacteriana del aceite esencial de los frutos de una nueva especie del género. 158–174 (2017).Identificación de moléculas lideres de origen natural con acción multidiana como inhibidores de quorum sensing en Pseudomonas aeruginosa multirresistenteAnálisis quimioinformático de moléculas activas contra los factores de transcripción asociados al quorum sensing en Pseudomonas aeruginosaUniversidad Nacional de ColombiaInvestigadoresPúblico generalORIGINAL1022389620.2021.pdf1022389620.2021.pdfTesis de Maestría en Ciencias Farmacéuticasapplication/pdf7160716https://repositorio.unal.edu.co/bitstream/unal/80376/6/1022389620.2021.pdf02c345ae0208444025f1d27316a55f75MD56LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80376/5/license.txtcccfe52f796b7c63423298c2d3365fc6MD55THUMBNAIL1022389620.2021.pdf.jpg1022389620.2021.pdf.jpgGenerated 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Contribución a la identificación de potenciales antagonistas asociados al quorum sensing en Pseudomonas aeruginosa a través de métodos quimioinformáticos

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