Evaluación in silico de la interacción de la enzima Gamma-secretasa humana con sus sustratos, y su posible mecanismo de reconocimiento

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dc.contributor.advisorArango Isaza, Rafael Eduardo
dc.contributor.advisorSoto Ospina, Alejandro
dc.contributor.authorMuñoz Perdomo, Javier Felipe
dc.contributor.orcidMuñoz Perdomo, Javier Felipe [0009000713466685]
dc.contributor.orcidArango Isaza, Rafael Eduardo [0000000272766035]
dc.contributor.orcidSoto Ospina, Alejandro [0000000323827038]
dc.contributor.otherVillegas Lanau Carlos Andrés
dc.contributor.researchgroupBiotecnología Vegetal Unalmed Cib
dc.contributor.researchgroupGrupo de Neurociencias de Antioquia
dc.contributor.researchgroupGIPDTA
dc.date.accessioned2026-10-02T16:01:39Z
dc.date.available2026-10-02T16:01:39Z
dc.date.issued2026-09-28
dc.descriptionilustracionesspa
dc.description.abstractLa enfermedad de Alzheimer (EA) es la enfermedad neurodegenerativa más prevalente y se asocia con la producción de péptidos amiloide-β (Aβ) por γ-secretasa (GS). Aunque su inhibición farmacológica ha fracasado por toxicidad derivada del procesamiento de múltiples sustratos, el mecanismo de reconocimiento por GS sigue sin esclarecerse. Este estudio caracterizó in silico los determinantes de reconocimiento de GS humana. Se compiló una lista actualizada de 167 sustratos y 22 no sustratos y se analizaron sus regiones transmembrana (TM) y yuxtamembrana (JM) mediante alineamientos múltiples, descubrimiento de motivos con MEME y modelos ocultos de Markov. En un subconjunto Gold-estándar validado experimentalmente, se compararon estructuras predichas con AlphaFold3 y se modelaron las interacciones GS-sustrato mediante dinámica molecular dirigida (SMD). Los análisis de secuencia convergieron en el límite TM-C/JMD-C como la región más discriminante, con enriquecimiento en Arg/Lys. Los sustratos presentaron mayor carga neta, punto isoeléctrico e índice de Boman, y una hidrofobicidad C-terminal más negativa que los no sustratos (p<0,005). La hidrofobicidad global, el momento hidrofóbico y la conformación tridimensional no discriminaron entre grupos. En SMD, ningún descriptor dinámico aislado fue discriminante; la discriminación emergió de la coincidencia entre un registro recíproco específico del esqueleto peptídico y una geometría compatible con catálisis, asociada a un acoplamiento persistente mediante cadena principal de Gln112 de PS1 y a flexibilidad TM. En conjunto, el reconocimiento de GS depende de características integradas de secuencia, estructura y dinámica, concentradas mayoritariamente en TM-C/JMD-C, constituyendo candidatos mecanísticos para diseñar moduladores selectivos y reducir la toxicidad de las terapias anti-EA. (Texto tomado de la fuente)spa
dc.description.abstractAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease and is associated with the production of amyloid-β (Aβ) peptides by γ-secretase (GS). Although its pharmacological inhibition has failed because of toxicity resulting from the processing of multiple substrates, the mechanism underlying substrate recognition by GS remains unclear. This study characterized, in silico, the determinants of human GS substrate recognition. An updated list of 167 substrates and 22 non-substrates was compiled, and their transmembrane (TM) and juxtamembrane (JM) regions were analyzed using multiple sequence alignments, motif discovery with MEME, and hidden Markov models. In a Goldstandard subset experimentally validated in humans, predicted structures were compared using AlphaFold3, and GS–substrate interactions were modeled through steered molecular dynamics (SMD). Sequence analyses converged on the TM-C/JMD-C boundary as the most discriminative region, characterized by enrichment in Arg/Lys residues. Substrates exhibited higher net charge, isoelectric point, and Boman index, as well as more negative C-terminal hydrophobicity, than non-substrateseng
dc.description.curricularareaBiotecnología.Sede Medellín
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias - Biotecnología
dc.description.methodsEsta investigación es meramente computacional (in silico), con enfoque cuantitativo y alcance comparativo y exploratorio, orientada a generar hipótesis mecanísticas sobre el reconocimiento de sustratos por γ-secretasa; no incluye componente experimental de laboratorio. El diseño combinó tres niveles de análisis complementarios. Primero, se curó un conjunto de 167 sustratos y 21 no sustratos de γ-secretasa a partir de la literatura y UniProt, y se caracterizaron sus regiones transmembranal y yuxtamembranal mediante alineamientos múltiples, búsqueda de motivos lineales, modelos ocultos de Markov y perfiles fisicoquímicos. Segundo, sobre un subconjunto Gold-estándar validado experimentalmente (16 sustratos y 11 no sustratos), se compararon estructuras predichas con AlphaFold 3. Tercero, se realizaron simulaciones de dinámica molecular dirigida de dos sustratos (APP y NOTCH1) y dos no sustratos (ANPRA y un constructo artificial) con γ-secretasa. El fotograma más representativo de cada sistema se seleccionó con criterios geométricos de compatibilidad catalítica definidos a partir del análisis del poro catalítico de la estructura 6IYC, y en él se analizaron los puentes de hidrógeno con criterios de distancia y ángulo; la ocupación de los contactos se estimó sobre las trayectorias completas. Los resultados se interpretaron de forma descriptiva y convergente entre análisis independientes. Por su naturaleza computacional, las conclusiones se plantean como hipótesis que requieren validación experimental.
dc.description.notesAlzheimer’s disease (AD) is the most prevalent neurodegenerative disease and is associated with the production of amyloid-β (Aβ) peptides by γ-secretase (GS). Although its pharmacological inhibition has failed because of toxicity resulting from the processing of multiple substrates, the mechanism underlying substrate recognition by GS remains unclear. This study characterized, in silico, the determinants of human GS substrate recognition. An updated list of 167 substrates and 22 non-substrates was compiled, and their transmembrane (TM) and juxtamembrane (JM) regions were analyzed using multiple sequence alignments, motif discovery with MEME, and hidden Markov models. In a Gold-standard subset experimentally validated in humans, predicted structures were compared using AlphaFold3, and GS–substrate interactions were modeled through steered molecular dynamics (SMD). Sequence analyses converged on the TM-C/JMD-C boundary as the most discriminative region, characterized by enrichment in Arg/Lys residues. Substrates exhibited higher net charge, isoelectric point, and Boman index, as well as more negative C-terminal hydrophobicity, than non-substrates (p<0.005). Global hydrophobicity, hydrophobic moment, and predicted three-dimensional conformation did not discriminate between groups. In SMD, no single dynamic descriptor was discriminative; discrimination emerged from the coincidence of a specific reciprocal peptide-backbone register with a catalytically compatible geometry, associated with persistent backbone-mediated engagement of PS1 Gln112, and TM flexibility. Overall, GS substrate recognition depends on integrated sequence, structural, and dynamic features, concentrated predominantly in the TM-C/JMD-C region, providing mechanistic candidates for the rational design of selective modulators and for reducing the toxicity of AD-targeted therapies.eng
dc.description.researchareaBioinformática
dc.description.researchareaBiotecnología en salud
dc.description.researchareaEnfermedades neurodegenerativas
dc.description.researchareaBioquímica computacional
dc.description.technicalinfoEste trabajo se desarrolló con aproximaciones bioinformáticas y de simulación in silico, sin componente experimental de laboratorio. Las curaciones de secuencias se apoyaron en UniProt. Los alineamientos múltiples se realizaron con PSI/TM-Coffee (fragmentos TM-JMD completos) y MAFFT v7.526 (regiones JMD-N y JMD-C), y se recortaron con trimAl y visualizaron en Jalview. El descubrimiento de motivos se hizo con MEME, los modelos ocultos de Markov con HMMER v3.4 (Skylign para los logos) y el análisis de perfiles fisicoquímicos con AAanalysis v1.0.3. Las estructuras de los fragmentos Gold-estándar se predijeron con AlphaFold 3 (AlphaFold Server) y se compararon mediante UCSF Chimera v1.19 (MatchMaker y Match-Align). La dinámica molecular dirigida (SMD) de γ-secretasa (PDB 6IYC) con cada fragmento se ejecutó en el servidor en línea GS-SMD, que emplea el campo de fuerza CHARMM19 con membrana implícita IMM1, una velocidad de tracción de 1,0 Å/ns, una constante de resorte de 1,0 kcal/mol/Ų y 50 ns de simulación con 200 fotogramas. La geometría del poro catalítico se analizó con MOLEonline. Los datos se procesaron en Python 3.11.15 mediante scripts propios, que emplean Biopython, MDAnalysis, pandas, NumPy, Matplotlib y Seaborn para el análisis geométrico de puentes de hidrógeno y el cálculo de ocupaciones (HB_Eng_corrected_V3.py y hbond_occupancy_trajectory_v3.py, adjuntos). Las figuras estructurales se generaron con PyMOL 3.1.8. Los enlaces a los resultados de cada simulación, y los datos y scripts que permiten replicar el análisis, se indican en la Tabla 10 y en los anexos.spa
dc.format.extent222 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/90864
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.blaaEnfermedad de Alzheimerspa
dc.subject.blaaProteina beta amiloidespa
dc.subject.blaaAlzheimer’s diseaseeng
dc.subject.blaaEnfermedades neurodegenerativasspa
dc.subject.blaaRotación molecularspa
dc.subject.ddc570 - Biología::572 - Bioquímica
dc.subject.ddc610 - Medicina y salud::616 - Enfermedades
dc.subject.ddc000 - Ciencias de la computación, información y obras generales
dc.subject.proposalGamma-secretasaspa
dc.subject.proposalEnfermedad de Alzheimerspa
dc.subject.proposalDinámica molecularspa
dc.subject.proposalInteracciones de proteínas de membranaspa
dc.subject.proposalProteína precursora amiloidespa
dc.subject.proposalAlphaFold3spa
dc.subject.proposalModelado estructuralspa
dc.subject.proposalGamma-secretaseeng
dc.subject.proposalAlzheimer diseaseeng
dc.subject.proposalMolecular dynamicseng
dc.subject.proposalMembrane protein interactionseng
dc.subject.proposalAmyloid precursor proteineng
dc.subject.proposalAlfaFold3eng
dc.subject.proposalStructural modelingeng
dc.titleEvaluación in silico de la interacción de la enzima Gamma-secretasa humana con sus sustratos, y su posible mecanismo de reconocimientospa
dc.title.translatedIn silico evaluation of the Gamma-secretase enzyme interaction with its substrates, and its possible recognitioneng
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.versioninfo:eu-repo/semantics/acceptedVersion
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentEspecializada
dcterms.audience.professionaldevelopmentEstudiantes
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2

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