Implementación de una estrategia in-silico para la identificación de péptidos candidatos a vacuna terapéutica individualizada en tumor de paciente con PEComa
Autores
Amaya Ramírez, Diego Alfredo
Director
Niño Vásquez, Luis Fernando
Parra López, Carlos Alberto
Tipo de contenido
Trabajo de grado - Maestría
Idioma del documento
EspañolFecha de publicación
2019-12-17
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Resumen
En el presente trabajo se expone la implementación de una estrategia in-silico para la identificación
y priorización de péptidos candidatos a vacuna personalizada en tumores de cáncer aplicado a un
caso de estudio de una paciente con PEComa que expresa el alelo HLA-A*24:02. Dicha estrategia se
compone de 2 grandes etapas. La primera etapa consiste en la identificación de péptidos candidatos
a vacuna personalizada a través de análisis de datos genéticos (ADN y ARN) que permiten la
identificación y cuantificación de expresión de mutaciones somáticas presentes en el tumor, a
partir de las cuales se obtienen todos los posibles péptidos mutantes (de entre 9 y 21 aminoácidos
de longitud) que podría expresar el tumor; estos péptidos son filtrados a través de algoritmos que
evalúan la afinidad y estabilidad con el haplotipo HLA del paciente para obtener una lista reducida
de péptidos candidatos a vacuna personaliza. En la segunda etapa se realizan simulaciones de
docking molecular entre los péptidos cortos previamente identificados (de entre 9 y l0 aminoácidos
de longitud) y la molécula HLA-A*24:02 con el fin de evaluar y caracterizar la interacción
péptido-HLA de tal manera que proporcione información que apoye el proceso de priorización de
péptidos a evaluar de manera in-vitro.
Para nuestro caso de estudio, se identificaron 12 péptidos candidatos a vacuna personalizada (6 de
los cuales son péptidos largos que enmarcan una epítope tanto para moléculas HLA clase I como
para clase II), de las cuales se simuló el d
ocking molecular de 5 péptidos cortos (tanto la versión
mutada como la nativa) con la molécula HLA-A*24:02. Dichas simulaciones permitieron identificar
los puentes de hidrógeno entre el péptido y la molécula HLA y realizar una estimación de la energía
del complejo, lo cual llevó a priorizar 2 de los 5 péptidos evaluados.
Aunque la estrategia permitió identificar y priorizar péptidos candidatos a vacunas personalizadas
en un tumor de una paciente con PEComa, queda como perspectiva extender la estrategia del
docking molecular a péptidos largos con moléculas HLA clase II y evaluar la estabilidad del
complejo péptido-HLA a través de dinámica molecular.
In the present work we present the implementation of an in-silico strategy for the identification and prioritization of peptide candidates for personalized vaccine in cancer tumors applied to a case study of a patient with PEComa expressing the allele HLA-A*24:02. This strategy consists of two major stages. The first stage consists of the identification of peptide candidates to personalized vaccine through the analysis of genetic data (DNA and RNA) that allow the identification and quantification of expression of somatic mutations present in the tumor, from which all possible mutant peptides (between 9 and 21 amino acids in length) that could express the tumor are obtained; these peptides are filtered through algorithms that evaluate the affinity and stability with the HLA haplotype of the patient to obtain a reduced list of peptide candidates for a personalized vaccine. In the second stage, molecular docking simulations are performed between the previously identified short peptides (between 9 and l0 amino acids in length) and the HLA-A*24:02 molecule in order to evaluate and characterize the HLA-peptide interaction in such a way as to provide information that supports the process of prioritization of peptides to be evaluated in-vitro. For our case study, 12 peptides were identified as candidates for personalized vaccine (6 of which are long peptides that frame an epitope for both HLA class I and class II molecules), of which the molecular docking of 5 short peptides (both the mutated and wildtype versions) were simulated with the HLA-A*24:02 molecule. These simulations allowed to identify the hydrogen bridges between the peptide and the HLA molecule and to estimate the energy of the complex, which led to prioritizing 2 of the 5 peptides evaluated. Although the strategy allowed to identify and prioritize peptide candidates for personalized vaccines in a tumor of a patient with PEComa, it is possible to extend the molecular docking strategy to long peptides with HLA class II molecules and to evaluate the stability of the HLA-peptide complex through molecular dynamics.
In the present work we present the implementation of an in-silico strategy for the identification and prioritization of peptide candidates for personalized vaccine in cancer tumors applied to a case study of a patient with PEComa expressing the allele HLA-A*24:02. This strategy consists of two major stages. The first stage consists of the identification of peptide candidates to personalized vaccine through the analysis of genetic data (DNA and RNA) that allow the identification and quantification of expression of somatic mutations present in the tumor, from which all possible mutant peptides (between 9 and 21 amino acids in length) that could express the tumor are obtained; these peptides are filtered through algorithms that evaluate the affinity and stability with the HLA haplotype of the patient to obtain a reduced list of peptide candidates for a personalized vaccine. In the second stage, molecular docking simulations are performed between the previously identified short peptides (between 9 and l0 amino acids in length) and the HLA-A*24:02 molecule in order to evaluate and characterize the HLA-peptide interaction in such a way as to provide information that supports the process of prioritization of peptides to be evaluated in-vitro. For our case study, 12 peptides were identified as candidates for personalized vaccine (6 of which are long peptides that frame an epitope for both HLA class I and class II molecules), of which the molecular docking of 5 short peptides (both the mutated and wildtype versions) were simulated with the HLA-A*24:02 molecule. These simulations allowed to identify the hydrogen bridges between the peptide and the HLA molecule and to estimate the energy of the complex, which led to prioritizing 2 of the 5 peptides evaluated. Although the strategy allowed to identify and prioritize peptide candidates for personalized vaccines in a tumor of a patient with PEComa, it is possible to extend the molecular docking strategy to long peptides with HLA class II molecules and to evaluate the stability of the HLA-peptide complex through molecular dynamics.