Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2
ilustraciones a color, diagramas, fotografías
- Autores:
-
Hernandez Nieto, Holman Alexander
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85609
- Palabra clave:
- 570 - Biología
610 - Medicina y salud::615 - Farmacología y terapéutica
Biología computacional
Computational biology
Pipeline bioinformático
Péptido
Epítopo
SARS-CoV-2
Espícula
Vacuna
HLA
Pipeline
Peptide
Epitope
Spike
Vaccine
Vacuna SARS-CoV-2
Antígeno de SARS-CoV-2
Vacunas peptídicas
Antígenos HLA
COVID-19 Vaccines
Antigen of SARS-CoV-2
Vaccines, peptide
HLA Antigens
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Universidad Nacional de Colombia |
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|
| dc.title.spa.fl_str_mv |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| dc.title.translated.eng.fl_str_mv |
In-silico model for the prediction of HLA-I restricted peptides vaccine candidates in SARS-CoV-2 |
| title |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| spellingShingle |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 570 - Biología 610 - Medicina y salud::615 - Farmacología y terapéutica Biología computacional Computational biology Pipeline bioinformático Péptido Epítopo SARS-CoV-2 Espícula Vacuna HLA Pipeline Peptide Epitope Spike Vaccine Vacuna SARS-CoV-2 Antígeno de SARS-CoV-2 Vacunas peptídicas Antígenos HLA COVID-19 Vaccines Antigen of SARS-CoV-2 Vaccines, peptide HLA Antigens |
| title_short |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| title_full |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| title_fullStr |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| title_full_unstemmed |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| title_sort |
Modelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2 |
| dc.creator.fl_str_mv |
Hernandez Nieto, Holman Alexander |
| dc.contributor.advisor.spa.fl_str_mv |
Niño Vásquez, Luis Fernando Parra López, Carlos Alberto |
| dc.contributor.author.spa.fl_str_mv |
Hernandez Nieto, Holman Alexander |
| dc.contributor.researchgroup.spa.fl_str_mv |
laboratorio de Investigación en Sistemas Inteligentes Lisi Inmunología y Medicina Traslacional |
| dc.subject.ddc.spa.fl_str_mv |
570 - Biología 610 - Medicina y salud::615 - Farmacología y terapéutica |
| topic |
570 - Biología 610 - Medicina y salud::615 - Farmacología y terapéutica Biología computacional Computational biology Pipeline bioinformático Péptido Epítopo SARS-CoV-2 Espícula Vacuna HLA Pipeline Peptide Epitope Spike Vaccine Vacuna SARS-CoV-2 Antígeno de SARS-CoV-2 Vacunas peptídicas Antígenos HLA COVID-19 Vaccines Antigen of SARS-CoV-2 Vaccines, peptide HLA Antigens |
| dc.subject.decs.spa.fl_str_mv |
Biología computacional |
| dc.subject.decs.eng.fl_str_mv |
Computational biology |
| dc.subject.proposal.spa.fl_str_mv |
Pipeline bioinformático Péptido Epítopo SARS-CoV-2 Espícula Vacuna HLA |
| dc.subject.proposal.eng.fl_str_mv |
Pipeline Peptide Epitope Spike Vaccine |
| dc.subject.umls.spa.fl_str_mv |
Vacuna SARS-CoV-2 Antígeno de SARS-CoV-2 Vacunas peptídicas Antígenos HLA |
| dc.subject.umls.eng.fl_str_mv |
COVID-19 Vaccines Antigen of SARS-CoV-2 Vaccines, peptide HLA Antigens |
| description |
ilustraciones a color, diagramas, fotografías |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-08-01 |
| dc.date.accessioned.none.fl_str_mv |
2024-02-05T15:18:16Z |
| dc.date.available.none.fl_str_mv |
2024-02-05T15:18:16Z |
| 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/85609 |
| 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/85609 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 |
| dc.relation.references.spa.fl_str_mv |
[1] Zhu, N. et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382, 727–733 (2020). [2] Riou, J. & Althaus, C. L. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance 25, 2000058 (2020). [3] Coronavirus disease (COVID-19). (n.d.). Retrieved December 11, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019 [4] SARS-CoV-2 Variant Classifications and Definitions. (n.d.). Retrieved December 11, 2021, from https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html?C DC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%2F2019-ncov% 2Fvariants%2Fvariant-info.html [5] Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern. (n.d.). Retrieved December 11, 2021, from https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sa rs-cov-2-variant-of-concern [6] Esquemas posológicos para el tratamiento de la infección de tuberculosis latente | Tratamiento | TB | CDC. (n.d.). Retrieved July 30, 2023, from https://www.cdc.gov/tb/esp/topic/treatment/ltbi.htm [7] Hundal, J., Carreno, B. M., Petti, A. A., Linette, G. P., Griffith, O. L., Mardis, E. R., & Griffith, M. (2016). pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Medicine, 8(1), 1–11. https://doi.org/10.1186/S13073-016-0264-5/FIGURES/3 [8] V’kovski, P., Kratzel, A., Steiner, S., Stalder, H. & Thiel, V. Coronavirus biology and replication: implications for SARS-CoV-2. Nat. Rev. Microbiol. 19, 1–16 (2020). [9] Zhang, Y., Park, C., Bennett, C., Thornton, M., & Kim, D. (2021). Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N. Genome Research, 31(7), 1290–1295. https://doi.org/10.1101/GR.275193.120 [10] Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., & Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/BIOINFORMATICS/BTP352 [11] Scholak, T., Schucher, N., & Bahdanau, D. (2021). PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings, 9895–9901. https://doi.org/10.18653/v1/2021.emnlp-main.779 [12] Craik, DJ , Fairlie, DP , Liras, S. y Price, D. ( 2013 ). El futuro de los fármacos basados en péptidos . Chemical Biology & Drug Design , 81 ( 1 ), 136 - 147 . https://doi.org/10.1111/cbdd.12055 [13] Uhlig, T. , Kyprianou, T. , Martinelli, FG , Oppici, CA , Heiligers, D. , Hills, D. ,... Verhaert, P. ( 2014 ). La aparición de péptidos en el negocio farmacéutico: de la exploración a la explotación . Proteómica Abierta EuPA , 4 , 58 - 69 . https://doi.org/10.1016/j.euprot.2014.05.003 [14] Amaya Ramirez - Niño - Parra Lopez. (n.d.). Implementación de una estrategia in-silico para la predicción de epítopes potencialmente inmunogénicas en tumores de pacientes con cáncer (mama).https://extension.unal.edu.co/fileadmin/recursos/proyectos-importancia-in stitucional/medicina-traslacional/docs/Una_estrategia_in-silico_para_prediccion_d e_neoantigenos.pdf [15] Rezaei, S., Sefidbakht, Y., & Uskoković, V. (2021). Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design. Briefings in Bioinformatics, 22(6), 1–20. https://doi.org/10.1093/bib/bbab241 [16] Fosgerau, K. y Hoffmann, T. ( 2015 ). Terapéutica de péptidos: estado actual y direcciones futuras . Drug Discovery Today , 20 ( 1 ), 122 - 128 . https://doi.org/10.1016/j.drudis.2014.10.003 [17] bgzip(1) manual page. (n.d.). Retrieved July 28, 2023, from http://www.htslib.org/doc/bgzip.html [18] Troubleshooting GATK-SV – GATK. (n.d.). Retrieved July 28, 2023, from https://gatk.broadinstitute.org/hc/en-us/articles/5334566940699-Troubleshooting- GATK-SV [19] bgzip(1) manual page. (n.d.). Retrieved July 28, 2023, from http://www.htslib.org/doc/bgzip.html [20] tabix(1) manual page. (n.d.). Retrieved July 28, 2023, from http://www.htslib.org/doc/tabix.html [21] bcftools(1). (n.d.). Retrieved July 28, 2023, from https://samtools.github.io/bcftools/bcftools.html [22] Zhang, C., Bickis, M. G., Wu, F. X., & Kusalik, A. J. (2006). Optimally-connected hidden markov models for predicting MHC-binding peptides. Journal of Bioinformatics and Computational Biology, 4(5), 959–980. https://doi.org/10.1142/S0219720006002314 [23] Doytchinova, I. A., & Flower, D. R. (2001). Toward the Quantitative Prediction of T-Cell Epitopes: CoMFA and CoMSIA Studies of Peptides with Affinity for the Class I MHC Molecule HLA-A*0201. Journal of Medicinal Chemistry, 44(22), 3572–3581. https://doi.org/10.1021/JM010021J [24] Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica, 31, 249–268. [25] Sette, A., Buus, S., Appella, E., Smith, J. A., Chesnut, R., Miles, C., Colon, S. M., & Grey, H. M. (1989). Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proceedings of the National Academy of Sciences of the United States of America, 86(9), 3296–3300. https://doi.org/10.1073/PNAS.86.9.3296 [26] Agerer, B., Koblischke, M., Gudipati, V., Montaño-Gutierrez, L. F., Smyth, M., Popa, A., Genger, J.-W., Endler, L., Florian, D. M., Mühlgrabner, V., Graninger, M., Aberle, S. W., Husa, A.-M., Shaw, L. E., Lercher, A., Gattinger, P., Torralba-Gombau, R., Trapin, D., Penz, T., ... Bergthaler, A. (2021). SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8 + T cell responses. Science Immunology, 6(57). https://doi.org/10.1126/sciimmunol.abg6461 [27] Prachar, M., Justesen, S., Steen-Jensen, D.B. et al. Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools. Sci Rep 10, 20465 (2020). https://doi.org/10.1038/s41598-020-77466-4 [28] Harndahl, M. et al. Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays. J. Biomol. Screen. 14, 173–180 (2009). [29] Polyiam, K., Phoolcharoen, W., Butkhot, N. et al. Immunodominant linear B cell epitopes in the spike and membrane proteins of SARS-CoV-2 identified by immunoinformatics prediction and immunoassay. Sci Rep 11, 20383 (2021). https://doi.org/10.1038/s41598-021-99642-w [30] Orsburn, B., Jenkins, C., Miller, S. M., Neely, B. A., & Bumpus, N. M. (2020). In silico - Approach Toward the Identification of Unique Peptides from Viral Protein Infection: Application to COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3589835 [31] Sitthiyotha, T., & Chunsrivirot, S. (2020). Computational Design of 25-mer Peptide Binders of SARS-CoV-2. Journal of Physical Chemistry B, 124(48), 10930–10942. https://doi.org/10.1021/ACS.JPCB.0C07890/SUPPL_FILE/JP0C07890_SI_001.P DF [32] Peng, Y., Mentzer, A. J., Liu, G., Yao, X., Yin, Z., Dong, D., Dejnirattisai, W., Rostron, T., Supasa, P., Liu, C., López-Camacho, C., Slon-Campos, J., Zhao, Y., Stuart, D. I., Paesen, G. C., Grimes, J. M., Antson, A. A., Bayfield, O. W., Hawkins, D. E. D. P., ... Dong, T. (2020). Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19. Nature Immunology, 21(11), 1336–1345. https://doi.org/10.1038/s41590-020-0782-6 [33] Machuca, I., Vidal, E., de la Torre-Cisneros, J., & Rivero-Román, A. (2018). Tuberculosis in immunosuppressed patients. Enfermedades Infecciosas y Microbiologia Clinica (English Ed.), 36(6), 366–374. https://doi.org/10.1016/J.EIMC.2017.10.009 [34] Peters, B., Nielsen, M. & Sette, A. T cell epitope predictions. Annu. Rev. Immunol. https://doi.org/10.1146/annurev-immunol-082119 (2019). [35] Mei, S. et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 21, 1119–1135 (2020). [36] Saethang, T. et al. EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information. BMC Bioinform. 13, 313 (2012). [37] The Variant Call Format (VCF) Version 4.2 Specification. (2022). [38] Szolek, A, Schubert, B, Mohr, C, Sturm, M, Feldhahn, M, and Kohlbacher, O (2014). OptiType: precision HLA typing from next-generation sequencing data Bioinformatics, 30(23):3310-6. [39] McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome Biology Jun 6;17(1):122. (2016) https://doi:10.1186/s13059-016-0974-4 [40] Lank, S. M., Golbach, B. A., Creager, H. M., Wiseman, R. W., Keskin, D. B., Reinherz, E. L., Brusic, V., & O’Connor, D. H. (2012). Ultra-high resolution HLA genotyping and allele discovery by highly multiplexed cDNA amplicon pyrosequencing. BMC Genomics, 13(1). https://doi.org/10.1186/1471-2164-13-378 [41] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17(1), 1–14. https://doi.org/10.1186/S13059-016-0974-4/TABLES/8 [42] NetMHC - 4.0 - Services - DTU Health Tech. (n.d.). Retrieved April 27, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHC-4.0 [43] NetMHCpan - 4.0 - Services - DTU Health Tech. (n.d.). Retrieved April 28, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.0 [44] O’Donnell, T. J., Rubinsteyn, A., & Laserson, U. (2020). MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing. Cell Systems, 11(1), 42-48.e7. https://doi.org/10.1016/J.CELS.2020.06.010 [45] NetMHCstabpan - 1.0 - Services - DTU Health Tech. (n.d.). Retrieved April 28, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHCstabpan-1.0 [46] Karchin Lab Johns Hopkins University SCHISM. (n.d.). Retrieved April 28, 2022, from https://karchinlab.org/apps/appMHCnuggets.html [47] NetMHCstabpan - 1.0 - Services - DTU Health Tech. (n.d.). Retrieved April 28, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHCstabpan-1.0 [48] Vacunas y fármacos biotecnológicos (uab.cat) [49] [Reverse vaccinology: strategy against emerging pathogens] - PubMed (nih.gov) [50] Componentes celulares del sistema inmunitario - Inmunología y trastornos alérgicos - Manual MSD versión para profesionales (msdmanuals.com) [51] (2021-12-11) Representación tridimensional en la que se muestran las cuatro proteínas de superficie del virus: E, S, M, HE. tomado de: https://www.scientificanimations.com [52] Imagen tomada de https://ambientech.org/mycobacterium-tuberculosis [53] Gorbalenya, A. E., Baker, S. C., Baric, R. S., de Groot, R. J., Drosten, C., Gulyaeva, A. A., Haagmans, B. L., Lauber, C., Leontovich, A. M., Neuman, B. W., Penzar, D., Perlman, S., Poon, L. L. M., Samborskiy, D. v., Sidorov, I. A., Sola, I., & Ziebuhr, J. (2020). The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nature Microbiology, 5(4), 536. https://doi.org/10.1038/S41564-020-0695-Z [54] Tracking SARS-CoV-2 variants. (n.d.). Retrieved July 30, 2023, from https://www.who.int/activities/tracking-SARS-CoV-2-variants/ [55] Síntomas del COVID-19 | CDC. (n.d.). Retrieved July 30, 2023, from https://espanol.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html [56] Cómo se propaga el coronavirus | CDC. (n.d.). Retrieved July 30, 2023, from https://espanol.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.ht ml [57] Huang, Y., Yang, C., Xu, X. feng, Xu, W., & Liu, S. wen. (2020). Structural and functional properties of SARS-CoV-2 spike protein: potential antivirus drug development for COVID-19. Acta Pharmacologica Sinica 2020 41:9, 41(9), 1141–1149. https://doi.org/10.1038/s41401-020-0485-4 [58] Walls, A. C., Park, Y. J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler, D. (2020). Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell, 181(2), 281-292.e6. https://doi.org/10.1016/J.CELL.2020.02.058 [59] Tuberculosis. (n.d.). Retrieved July 30, 2023, from https://www.who.int/news-room/fact-sheets/detail/tuberculosis [60] Basic TB Facts | TB | CDC. (n.d.). Retrieved July 30, 2023, from https://www.cdc.gov/tb/topic/basics/default.htm [61] Junttila, I. S. (2018). Tuning the cytokine responses: An update on interleukin (IL)-4 and IL-13 receptor complexes. Frontiers in Immunology, 9(JUN). https://doi.org/10.3389/FIMMU.2018.00888/FULL [62] Yarmohammadi, H., & Cunningham-Rundles, C. (2017). Idiopathic CD4 lymphocytopenia: Pathogenesis, etiologies, clinical presentations and treatment strategies. Annals of Allergy, Asthma and Immunology, 119(4), 374–378. https://doi.org/10.1016/j.anai.2017.07.021 [63] Generalidades sobre el sistema inmunitario - Inmunología y trastornos alérgicos - Manual Merck versión para profesionales. (n.d.). Retrieved July 30, 2023, from https://www.merckmanuals.com/es-us/professional/inmunolog%C3%ADa-y-trastor nos-al%C3%A9rgicos/biolog%C3%ADa-del-sistema-inmunitario/generalidades-so bre-el-sistema-inmunitario [64] Grifoni, A., Weiskopf, D., Ramirez, S. I., Mateus, J., Dan, J. M., Moderbacher, C. R., Rawlings, S. A., Sutherland, A., Premkumar, L., Jadi, R. S., Marrama, D., de Silva, A. M., Frazier, A., Carlin, A. F., Greenbaum, J. A., Peters, B., Krammer, F., Smith, D. M., Crotty, S., & Sette, A. (2020). Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell, 181(7), 1489-1501.e15. https://doi.org/10.1016/J.CELL.2020.05.015 [65] Mateus, J., Grifoni, A., Tarke, A., Sidney, J., Ramirez, S. I., Dan, J. M., Burger, Z. C., Rawlings, S. A., Smith, D. M., Phillips, E., Mallal, S., Lammers, M., Rubiro, P., Quiambao, L., Sutherland, A., Yu, E. D., da Silva Antunes, R., Greenbaum, J., Frazier, A., ... Weiskopf, D. (2020). Selective and cross-reactive SARS-CoV-2 T cell epitopes in unexposed humans. Science (New York, N.Y.), 370(6512). https://doi.org/10.1126/SCIENCE.ABD3871 [66] Zhang, H., Deng, S., Ren, L., Zheng, P., Hu, X., Jin, T., & Tan, X. (2021). Profiling CD8 + T cell epitopes of COVID-19 convalescents reveals reduced cellular immune responses to SARS-CoV-2 variants. Cell Reports, 36(11). https://doi.org/10.1016/J.CELREP.2021.109708 [67] Moise, L., Gutierrez, A., Kibria, F., Martin, R., Tassone, R., Liu, R., Terry, F., Martin, B., & de Groot, A. S. (2015). iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines. Human Vaccines & Immunotherapeutics, 11(9), 2312–2321. https://doi.org/10.1080/21645515.2015.1061159 [68] Liu, G., Carter, B., Bricken, T., Jain, S., Viard, M., Carrington, M., & Gifford, D. K. (2020). Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions. Cell Systems, 11(2), 131. https://doi.org/10.1016/J.CELS.2020.06.009 [68] Liu, G., Carter, B., Bricken, T., Jain, S., Viard, M., Carrington, M., & Gifford, D. K. (2020). Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions. Cell Systems, 11(2), 131. https://doi.org/10.1016/J.CELS.2020.06.009 [69] Kared, H., Redd, A. D., Bloch, E. M., Bonny, T. S., Sumatoh, H., Kairi, F., Carbajo, D., Abel, B., Newell, E. W., Bettinotti, M. P., Benner, S. E., Patel, E. U., Littlefield, K., Laeyendecker, O., Shoham, S., Sullivan, D., Casadevall, A., Pekosz, A., Nardin, A., ... Quinn, T. C. (2020). CD8+ T cell responses in convalescent COVID-19 individuals target epitopes from the entire SARS-CoV-2 proteome and show kinetics of early differentiation. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.10.08.330688 [70] Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A. (2020). A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host & Microbe, 27(4), 671-680.e2. https://doi.org/10.1016/J.CHOM.2020.03.002 [71] Finkel, Y., Mizrahi, O., Nachshon, A., Weingarten-Gabbay, S., Morgenstern, D., Yahalom-Ronen, Y., Tamir, H., Achdout, H., Stein, D., Israeli, O., Beth-Din, A., Melamed, S., Weiss, S., Israely, T., Paran, N., Schwartz, M., & Stern-Ginossar, N. (2020). The coding capacity of SARS-CoV-2. Nature 2020 589:7840, 589(7840), 125–130. https://doi.org/10.1038/s41586-020-2739-1 [72] Agerer, B., Koblischke, M., Gudipati, V., Montaño-Gutierrez, L. F., Smyth, M., Popa, A., Genger, J.-W., Endler, L., Florian, D. M., Mühlgrabner, V., Graninger, M., Aberle, S. W., Husa, A.-M., Shaw, L. E., Lercher, A., Gattinger, P., Torralba-Gombau, R., Trapin, D., Penz, T., ... Bergthaler, A. (2021). SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8 + T cell responses. Science Immunology, 6(57). https://doi.org/10.1126/sciimmunol.abg6461 [73] Campbell, K. M., Steiner, G., Wells, D. K., Ribas, A., & Kalbasi, A. (2020). Prioritization of SARS-CoV-2 epitopes using a pan-HLA and global population inference approach. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.03.30.016931 [74] Daouda, T., Dumont-Lagacé, M., Feghaly, A., & Villani, A.-C. (2021). Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine. BioRxiv, 2021.02.04.429819. https://doi.org/10.1101/2021.02.04.429819 [75] Kared, H., Redd, A. D., Bloch, E. M., Bonny, T. S., Sumatoh, H., Kairi, F., Carbajo, D., Abel, B., Newell, E. W., Bettinotti, M. P., Benner, S. E., Patel, E. U., Littlefield, K., Laeyendecker, O., Shoham, S., Sullivan, D., Casadevall, A., Pekosz, A., Nardin, A., ... Quinn, T. C. (2020). CD8+ T cell responses in convalescent COVID-19 individuals target epitopes from the entire SARS-CoV-2 proteome and show kinetics of early differentiation. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.10.08.330688 [76] Mallajosyula, V., Ganjavi, C., Chakraborty, S., McSween, A. M., Pavlovitch-Bedzyk, A. J., Wilhelmy, J., Nau, A., Manohar, M., Nadeau, K. C., & Davis, M. M. (2021). CD8+ T cells specific for conserved coronavirus epitopes correlate with milder disease in COVID-19 patients. Science Immunology, 6(61). https://doi.org/10.1126/sciimmunol.abg5669 [77] Nathan, A., Rossin, E. J., Kaseke, C., Park, R. J., Khatri, A., Koundakjian, D., Urbach, J. M., Singh, N. K., Bashirova, A., Tano-Menka, R., Senjobe, F., Waring, M. T., Piechocka-Trocha, A., Garcia-Beltran, W. F., Iafrate, A. J., Naranbhai, V., Carrington, M., Walker, B. D., & Gaiha, G. D. (2021). Structure-guided T cell vaccine design for SARS-CoV-2 variants and sarbecoviruses. Cell, 184(17), 4401-4413.e10. https://doi.org/10.1016/j.cell.2021.06.029 [78] Peng, Y., Mentzer, A. J., Liu, G., Yao, X., Yin, Z., Dong, D., Dejnirattisai, W., Rostron, T., Supasa, P., Liu, C., López-Camacho, C., Slon-Campos, J., Zhao, Y., Stuart, D. I., Paesen, G. C., Grimes, J. M., Antson, A. A., Bayfield, O. W., Hawkins, D. E. D. P., ... Dong, T. (2020). Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19. Nature Immunology, 21(11), 1336–1345. https://doi.org/10.1038/s41590-020-0782-6 [79] Prachar, M., Justesen, S., Steen-Jensen, D. B., Thorgrimsen, S., Jurgons, E., Winther, O., & Bagger, F. O. (2020). Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools. Scientific Reports, 10(1), 20465. https://doi.org/10.1038/s41598-020-77466-4 [80] Quadeer, A. A., Ahmed, S. F., & McKay, M. R. (2021). Landscape of epitopes targeted by T cells in 852 individuals recovered from COVID-19: Meta-analysis, immunoprevalence, and web platform. Cell Reports Medicine, 2(6), 100312. https://doi.org/10.1016/j.xcrm.2021.100312 [81] Schulien, I., Kemming, J., Oberhardt, V., Wild, K., Seidel, L. M., Killmer, S., Sagar, Daul, F., Salvat Lago, M., Decker, A., Luxenburger, H., Binder, B., Bettinger, D., Sogukpinar, O., Rieg, S., Panning, M., Huzly, D., Schwemmle, M., Kochs, G., ... Neumann-Haefelin, C. (2021). Characterization of pre-existing and induced SARS-CoV-2-specific CD8+ T cells. Nature Medicine, 27(1), 78–85. https://doi.org/10.1038/s41591-020-01143-2 [82] Sohail, M. S., Ahmed, S. F., Quadeer, A. A., & McKay, M. R. (2021). In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives. Advanced Drug Delivery Reviews, 171, 29–47. https://doi.org/10.1016/j.addr.2021.01.007 [83] Weingarten-Gabbay, S., Klaeger, S., Sarkizova, S., Pearlman, L. R., Chen, D.-Y., Bauer, M. R., Taylor, H. B., Conway, H. L., Tomkins-Tinch, C. H., Finkel, Y., Nachshon, A., Gentili, M., Rivera, K. D., Keskin, D. B., Rice, C. M., Clauser, K. R., Hacohen, N., Carr, S. A., Abelin, J. G., ... Sabeti, P. C. (2020). SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.10.02.324145 [84] The web framework for perfectionists with deadlines | Django. (n.d.). Retrieved October 31, 2023, from https://www.djangoproject.com/ [85] Conda — conda documentation. (n.d.). Retrieved October 31, 2023, from https://docs.conda.io/en/latest/ [86] Docker: Accelerated Container Application Development. (n.d.). Retrieved October 31, 2023, from https://www.docker.com/ [87] Scrum y las metodologías ágiles en construcción - miguelgarcia.me. (n.d.). Retrieved November 1, 2023, from https://miguelgarcia.me/scrum-y-las-metodologias-agiles-en-construccion/ |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbe600Parra López, Carlos Alberto72ac583cfa47cd3a2fb760ecf10befccHernandez Nieto, Holman Alexanderb3f7a7d22a9bbb17afbece83de269fdflaboratorio de Investigación en Sistemas Inteligentes LisiInmunología y Medicina Traslacional2024-02-05T15:18:16Z2024-02-05T15:18:16Z2023-08-01https://repositorio.unal.edu.co/handle/unal/85609Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones a color, diagramas, fotografíasEn este trabajo se resalta la importancia de las herramientas computacionales en el diseño de vacunas contra el SARS-CoV-2, virus que desde su descubrimiento en Wuhan China en diciembre de 2019, entre los años 2020 y 2022, generó una pandemia a nivel mundial que causó cerca de siete millones de muertes y cerca de ochocientos millones de casos. El SARS-CoV-2 pertenece a la familia Coronaviridae y consta de múltiples proteínas, siendo la proteína de espícula (S) importante motivo de estudio para el desarrollo de vacunas. Las células presentadoras desempeñan un papel vital en este proceso, ya que son responsables de procesar y presentar los antígenos a las células T, lo cual desencadena la activación y regulación de la respuesta inmunitaria adaptativa mediada por las células T. Este mecanismo de presentación de antígenos es esencial para el funcionamiento adecuado del sistema inmunológico contra patógenos y el cáncer. En este trabajo, tiene que ver con los procesos de procesamiento y presentación de antígenos en la superficie de células presentadoras de antígeno en el contexto de moléculas del MHC clase I o II necesario para su reconocimiento por parte de los Receptores para el Antígenos de las Células T (TCR) requisito fundamental para la generación de una respuesta inmune eficaz de las células T contra el antígeno. Se destaca la imperiosa necesidad de impulsar constantemente el desarrollo y mejora de herramientas bioinformáticas con el objetivo de identificar el universo de péptidos que se unen de manera altamente estable a las moléculas MHC I y II, como herramienta útil para para el rápido diseño de nuevas vacunas contra patógenos emergentes como el SARS-CoV-2. En este contexto es necesario avanzar en el refinamiento de herramientas bioinformáticas en la identificación de fragmentos de proteínas de los patógenos que presentados en moléculas MHC, estimulan eficientemente a los linfocitos T, resulta de vital importancia para el ámbito clínico, ya que estas tienen un impacto significativo en la 7 rápida identificación de fragmentos procesados de los patógenos o de los tumores importante para el diseño de nuevas vacunas. El avance de las ciencias ómicas y métodos de secuenciación de última generación, han permitido no solo un análisis más detallado y completo de la información genética y proteómica relacionada con los péptidos y el MHC sino mejorar el desempeño de herramientas bioinformáticas para la predicción de epítopos inmunogénicos (fragmentos de los patógenos o de los tumores presentados en moléculas MHC eficientemente reconocidos por los linfocitos T). Esto, a su vez, facilita la identificación de antígenos específicos presentados por el MHC, lo que es fundamental para comprender cómo el sistema inmunológico detecta y responde a distintos tipos de amenaza, como lo son las infecciones el cáncer y las enfermedades autoinmunes. El perfeccionamiento continuo de las herramientas bioinformáticas para seleccionar de forma más precisas posibles antígenos útiles como vacuna, fortalece la posibilidad de diseñar vacunas sintéticas basadas en péptidos que por su inmunogenicidad y simplicidad de producción son una importante alternativa para el diseño racional de vacunas contra patógenos emergentes. La identificación de péptidos presentados eficientemente por moléculas MHC va a contribuir al desarrollo de nuevas vacunas más efectivas y a refinar estrategias de inmunoterapia dirigidas contra el cáncer, agentes infecciosos, y enfermedades autoinmunes, estrategias en las que los linfocitos T juegan un papel fundamental. En este trabajo, haciendo uso de herramientas de predicción se desarrolló un pipeline bioinformático para la predicción de epítopos candidatos a vacuna contra el SARS-CoV-2 teniendo en cuenta las moléculas MHC-I expresadas por la población colombiana. Cuando se comparó la inmunogenicidad para el sistema inmune de pacientes con SARS-CoV-2 del universo de péptidos identificados en el proteoma del virus utilizando la herramienta diseñada, con la inmunogenicidad de estos péptidos reportados en la literatura científica por otros autores revela que los péptidos predichos por nosotros merecen ser considerados como nuevos candidatos a vacuna contra el SARS-CoV-2 para ser utilizada en la población colombiana. (Texto tomado de la fuente)In this work, the importance of computational tools in the design of vaccines against SARS-CoV-2 is emphasized. Since its discovery in Wuhan, China, in December 2019, the virus caused a global pandemic between 2020 and 2022, resulting in nearly seven million deaths and around eight hundred million cases. SARS-CoV-2 belongs to the Coronaviridae family and consists of multiple proteins, with the spike protein (S) being a significant focus of study for vaccine development. Crucial to this process are antigen-presenting cells, responsible for processing and presenting antigens to T cells, triggering the activation and regulation of adaptive immune responses mediated by T cells. This mechanism of antigen presentation is essential for the proper functioning of the immune system against pathogens and cancer. This work primarily deals with the antigen processing and presentation on the surface of antigen-presenting cells in the context of MHC class I or II molecules, which is necessary for recognition by T Cell Receptor (TCR) and is a fundamental requirement for generating an effective T cell immune response against the antigen. It highlights the urgent need to continually advance and improve bioinformatic tools to identify the universe of peptides that bind highly stably to MHC I and II molecules as a useful resource for the rapid design of new vaccines against emerging pathogens such as SARS-CoV-2. In this context, the refinement of bioinformatic tools in the identification of protein fragments from pathogens presented on MHC molecules, effectively stimulating T lymphocytes, becomes of vital importance in the clinical setting, as they significantly impact the rapid identification of processed pathogen or tumor fragments, crucial for vaccine design. The progress in omics sciences and next-generation sequencing methods has not only allowed for a more detailed and comprehensive analysis of genetic and proteomic information related to peptides and MHC but also improved the performance of bioinformatic tools for predicting immunogenic epitopes (fragments of pathogens or tumors presented on MHC molecules and efficiently recognized by T cells). This, in turn, 9 facilitates the identification of specific antigens presented by MHC, which is essential for understanding how the immune system detects and responds to various threats such as infections, cancer, and autoimmune diseases. The continuous improvement of bioinformatic tools to more accurately select potential vaccine antigens strengthens the possibility of designing synthetic peptide-based vaccines, which are immunogenic and easy to produce, making them a crucial alternative for the rational design of vaccines against emerging pathogens. The identification of efficiently presented peptides by MHC molecules will contribute to the development of more effective vaccines and refined immunotherapy strategies targeting cancer, infectious agents, and autoimmune diseases, where T cells play a fundamental role. In this work, a bioinformatic pipeline for predicting vaccine candidate epitopes against SARS-CoV-2 was developed using prediction tools, considering MHCI molecules expressed in the Colombian population. When comparing the immunogenicity for the immune system of SARS-CoV-2 patients from the universe of peptides identified in the virus's proteome using the designed tool with the immunogenicity of these peptides reported in the scientific literature by other authors, it reveals that the peptides predicted by us deserve consideration as new vaccine candidates against SARS-CoV-2 for use in the Colombian population.MaestríaMagíster en BioinformáticaTecnologías computacionales en Bioinformática89 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en BioinformáticaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá570 - Biología610 - Medicina y salud::615 - Farmacología y terapéuticaBiología computacionalComputational biologyPipeline bioinformáticoPéptidoEpítopoSARS-CoV-2EspículaVacunaHLAPipelinePeptideEpitopeSpikeVaccineVacuna SARS-CoV-2Antígeno de SARS-CoV-2Vacunas peptídicasAntígenos HLACOVID-19 VaccinesAntigen of SARS-CoV-2Vaccines, peptideHLA AntigensModelo in-silico para la predicción de péptidos con restricción HLA-l candidatos a vacuna en SARS-CoV-2In-silico model for the prediction of HLA-I restricted peptides vaccine candidates in SARS-CoV-2Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[1] Zhu, N. et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382, 727–733 (2020).[2] Riou, J. & Althaus, C. L. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance 25, 2000058 (2020).[3] Coronavirus disease (COVID-19). (n.d.). Retrieved December 11, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019[4] SARS-CoV-2 Variant Classifications and Definitions. (n.d.). Retrieved December 11, 2021, from https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html?C DC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%2F2019-ncov% 2Fvariants%2Fvariant-info.html[5] Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern. (n.d.). Retrieved December 11, 2021, from https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sa rs-cov-2-variant-of-concern[6] Esquemas posológicos para el tratamiento de la infección de tuberculosis latente | Tratamiento | TB | CDC. (n.d.). Retrieved July 30, 2023, from https://www.cdc.gov/tb/esp/topic/treatment/ltbi.htm[7] Hundal, J., Carreno, B. M., Petti, A. A., Linette, G. P., Griffith, O. L., Mardis, E. R., & Griffith, M. (2016). pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Medicine, 8(1), 1–11. https://doi.org/10.1186/S13073-016-0264-5/FIGURES/3[8] V’kovski, P., Kratzel, A., Steiner, S., Stalder, H. & Thiel, V. Coronavirus biology and replication: implications for SARS-CoV-2. Nat. Rev. Microbiol. 19, 1–16 (2020).[9] Zhang, Y., Park, C., Bennett, C., Thornton, M., & Kim, D. (2021). Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N. Genome Research, 31(7), 1290–1295. https://doi.org/10.1101/GR.275193.120[10] Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., & Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/BIOINFORMATICS/BTP352[11] Scholak, T., Schucher, N., & Bahdanau, D. (2021). PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings, 9895–9901. https://doi.org/10.18653/v1/2021.emnlp-main.779[12] Craik, DJ , Fairlie, DP , Liras, S. y Price, D. ( 2013 ). El futuro de los fármacos basados en péptidos . Chemical Biology & Drug Design , 81 ( 1 ), 136 - 147 . https://doi.org/10.1111/cbdd.12055[13] Uhlig, T. , Kyprianou, T. , Martinelli, FG , Oppici, CA , Heiligers, D. , Hills, D. ,... Verhaert, P. ( 2014 ). La aparición de péptidos en el negocio farmacéutico: de la exploración a la explotación . Proteómica Abierta EuPA , 4 , 58 - 69 . https://doi.org/10.1016/j.euprot.2014.05.003[14] Amaya Ramirez - Niño - Parra Lopez. (n.d.). Implementación de una estrategia in-silico para la predicción de epítopes potencialmente inmunogénicas en tumores de pacientes con cáncer (mama).https://extension.unal.edu.co/fileadmin/recursos/proyectos-importancia-in stitucional/medicina-traslacional/docs/Una_estrategia_in-silico_para_prediccion_d e_neoantigenos.pdf[15] Rezaei, S., Sefidbakht, Y., & Uskoković, V. (2021). Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design. Briefings in Bioinformatics, 22(6), 1–20. https://doi.org/10.1093/bib/bbab241[16] Fosgerau, K. y Hoffmann, T. ( 2015 ). Terapéutica de péptidos: estado actual y direcciones futuras . Drug Discovery Today , 20 ( 1 ), 122 - 128 . https://doi.org/10.1016/j.drudis.2014.10.003[17] bgzip(1) manual page. (n.d.). Retrieved July 28, 2023, from http://www.htslib.org/doc/bgzip.html[18] Troubleshooting GATK-SV – GATK. (n.d.). Retrieved July 28, 2023, from https://gatk.broadinstitute.org/hc/en-us/articles/5334566940699-Troubleshooting- GATK-SV[19] bgzip(1) manual page. (n.d.). Retrieved July 28, 2023, from http://www.htslib.org/doc/bgzip.html[20] tabix(1) manual page. (n.d.). Retrieved July 28, 2023, from http://www.htslib.org/doc/tabix.html[21] bcftools(1). (n.d.). Retrieved July 28, 2023, from https://samtools.github.io/bcftools/bcftools.html[22] Zhang, C., Bickis, M. G., Wu, F. X., & Kusalik, A. J. (2006). Optimally-connected hidden markov models for predicting MHC-binding peptides. Journal of Bioinformatics and Computational Biology, 4(5), 959–980. https://doi.org/10.1142/S0219720006002314[23] Doytchinova, I. A., & Flower, D. R. (2001). Toward the Quantitative Prediction of T-Cell Epitopes: CoMFA and CoMSIA Studies of Peptides with Affinity for the Class I MHC Molecule HLA-A*0201. Journal of Medicinal Chemistry, 44(22), 3572–3581. https://doi.org/10.1021/JM010021J[24] Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica, 31, 249–268.[25] Sette, A., Buus, S., Appella, E., Smith, J. A., Chesnut, R., Miles, C., Colon, S. M., & Grey, H. M. (1989). Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proceedings of the National Academy of Sciences of the United States of America, 86(9), 3296–3300. https://doi.org/10.1073/PNAS.86.9.3296[26] Agerer, B., Koblischke, M., Gudipati, V., Montaño-Gutierrez, L. F., Smyth, M., Popa, A., Genger, J.-W., Endler, L., Florian, D. M., Mühlgrabner, V., Graninger, M., Aberle, S. W., Husa, A.-M., Shaw, L. E., Lercher, A., Gattinger, P., Torralba-Gombau, R., Trapin, D., Penz, T., ... Bergthaler, A. (2021). SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8 + T cell responses. Science Immunology, 6(57). https://doi.org/10.1126/sciimmunol.abg6461[27] Prachar, M., Justesen, S., Steen-Jensen, D.B. et al. Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools. Sci Rep 10, 20465 (2020). https://doi.org/10.1038/s41598-020-77466-4[28] Harndahl, M. et al. Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays. J. Biomol. Screen. 14, 173–180 (2009).[29] Polyiam, K., Phoolcharoen, W., Butkhot, N. et al. Immunodominant linear B cell epitopes in the spike and membrane proteins of SARS-CoV-2 identified by immunoinformatics prediction and immunoassay. Sci Rep 11, 20383 (2021). https://doi.org/10.1038/s41598-021-99642-w[30] Orsburn, B., Jenkins, C., Miller, S. M., Neely, B. A., & Bumpus, N. M. (2020). In silico - Approach Toward the Identification of Unique Peptides from Viral Protein Infection: Application to COVID-19. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3589835[31] Sitthiyotha, T., & Chunsrivirot, S. (2020). Computational Design of 25-mer Peptide Binders of SARS-CoV-2. Journal of Physical Chemistry B, 124(48), 10930–10942. https://doi.org/10.1021/ACS.JPCB.0C07890/SUPPL_FILE/JP0C07890_SI_001.P DF[32] Peng, Y., Mentzer, A. J., Liu, G., Yao, X., Yin, Z., Dong, D., Dejnirattisai, W., Rostron, T., Supasa, P., Liu, C., López-Camacho, C., Slon-Campos, J., Zhao, Y., Stuart, D. I., Paesen, G. C., Grimes, J. M., Antson, A. A., Bayfield, O. W., Hawkins, D. E. D. P., ... Dong, T. (2020). Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19. Nature Immunology, 21(11), 1336–1345. https://doi.org/10.1038/s41590-020-0782-6[33] Machuca, I., Vidal, E., de la Torre-Cisneros, J., & Rivero-Román, A. (2018). Tuberculosis in immunosuppressed patients. Enfermedades Infecciosas y Microbiologia Clinica (English Ed.), 36(6), 366–374. https://doi.org/10.1016/J.EIMC.2017.10.009[34] Peters, B., Nielsen, M. & Sette, A. T cell epitope predictions. Annu. Rev. Immunol. https://doi.org/10.1146/annurev-immunol-082119 (2019).[35] Mei, S. et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 21, 1119–1135 (2020).[36] Saethang, T. et al. EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information. BMC Bioinform. 13, 313 (2012).[37] The Variant Call Format (VCF) Version 4.2 Specification. (2022).[38] Szolek, A, Schubert, B, Mohr, C, Sturm, M, Feldhahn, M, and Kohlbacher, O (2014). OptiType: precision HLA typing from next-generation sequencing data Bioinformatics, 30(23):3310-6.[39] McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome Biology Jun 6;17(1):122. (2016) https://doi:10.1186/s13059-016-0974-4[40] Lank, S. M., Golbach, B. A., Creager, H. M., Wiseman, R. W., Keskin, D. B., Reinherz, E. L., Brusic, V., & O’Connor, D. H. (2012). Ultra-high resolution HLA genotyping and allele discovery by highly multiplexed cDNA amplicon pyrosequencing. BMC Genomics, 13(1). https://doi.org/10.1186/1471-2164-13-378[41] McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016). The Ensembl Variant Effect Predictor. Genome Biology, 17(1), 1–14. https://doi.org/10.1186/S13059-016-0974-4/TABLES/8[42] NetMHC - 4.0 - Services - DTU Health Tech. (n.d.). Retrieved April 27, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHC-4.0[43] NetMHCpan - 4.0 - Services - DTU Health Tech. (n.d.). Retrieved April 28, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHCpan-4.0[44] O’Donnell, T. J., Rubinsteyn, A., & Laserson, U. (2020). MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing. Cell Systems, 11(1), 42-48.e7. https://doi.org/10.1016/J.CELS.2020.06.010[45] NetMHCstabpan - 1.0 - Services - DTU Health Tech. (n.d.). Retrieved April 28, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHCstabpan-1.0[46] Karchin Lab Johns Hopkins University SCHISM. (n.d.). Retrieved April 28, 2022, from https://karchinlab.org/apps/appMHCnuggets.html[47] NetMHCstabpan - 1.0 - Services - DTU Health Tech. (n.d.). Retrieved April 28, 2022, from https://services.healthtech.dtu.dk/service.php?NetMHCstabpan-1.0[48] Vacunas y fármacos biotecnológicos (uab.cat)[49] [Reverse vaccinology: strategy against emerging pathogens] - PubMed (nih.gov)[50] Componentes celulares del sistema inmunitario - Inmunología y trastornos alérgicos - Manual MSD versión para profesionales (msdmanuals.com)[51] (2021-12-11) Representación tridimensional en la que se muestran las cuatro proteínas de superficie del virus: E, S, M, HE. tomado de: https://www.scientificanimations.com[52] Imagen tomada de https://ambientech.org/mycobacterium-tuberculosis[53] Gorbalenya, A. E., Baker, S. C., Baric, R. S., de Groot, R. J., Drosten, C., Gulyaeva, A. A., Haagmans, B. L., Lauber, C., Leontovich, A. M., Neuman, B. W., Penzar, D., Perlman, S., Poon, L. L. M., Samborskiy, D. v., Sidorov, I. A., Sola, I., & Ziebuhr, J. (2020). The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nature Microbiology, 5(4), 536. https://doi.org/10.1038/S41564-020-0695-Z[54] Tracking SARS-CoV-2 variants. (n.d.). Retrieved July 30, 2023, from https://www.who.int/activities/tracking-SARS-CoV-2-variants/[55] Síntomas del COVID-19 | CDC. (n.d.). Retrieved July 30, 2023, from https://espanol.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html[56] Cómo se propaga el coronavirus | CDC. (n.d.). Retrieved July 30, 2023, from https://espanol.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.ht ml[57] Huang, Y., Yang, C., Xu, X. feng, Xu, W., & Liu, S. wen. (2020). Structural and functional properties of SARS-CoV-2 spike protein: potential antivirus drug development for COVID-19. Acta Pharmacologica Sinica 2020 41:9, 41(9), 1141–1149. https://doi.org/10.1038/s41401-020-0485-4[58] Walls, A. C., Park, Y. J., Tortorici, M. A., Wall, A., McGuire, A. T., & Veesler, D. (2020). Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell, 181(2), 281-292.e6. https://doi.org/10.1016/J.CELL.2020.02.058[59] Tuberculosis. (n.d.). Retrieved July 30, 2023, from https://www.who.int/news-room/fact-sheets/detail/tuberculosis[60] Basic TB Facts | TB | CDC. (n.d.). Retrieved July 30, 2023, from https://www.cdc.gov/tb/topic/basics/default.htm[61] Junttila, I. S. (2018). Tuning the cytokine responses: An update on interleukin (IL)-4 and IL-13 receptor complexes. Frontiers in Immunology, 9(JUN). https://doi.org/10.3389/FIMMU.2018.00888/FULL[62] Yarmohammadi, H., & Cunningham-Rundles, C. (2017). Idiopathic CD4 lymphocytopenia: Pathogenesis, etiologies, clinical presentations and treatment strategies. Annals of Allergy, Asthma and Immunology, 119(4), 374–378. https://doi.org/10.1016/j.anai.2017.07.021[63] Generalidades sobre el sistema inmunitario - Inmunología y trastornos alérgicos - Manual Merck versión para profesionales. (n.d.). Retrieved July 30, 2023, from https://www.merckmanuals.com/es-us/professional/inmunolog%C3%ADa-y-trastor nos-al%C3%A9rgicos/biolog%C3%ADa-del-sistema-inmunitario/generalidades-so bre-el-sistema-inmunitario[64] Grifoni, A., Weiskopf, D., Ramirez, S. I., Mateus, J., Dan, J. M., Moderbacher, C. R., Rawlings, S. A., Sutherland, A., Premkumar, L., Jadi, R. S., Marrama, D., de Silva, A. M., Frazier, A., Carlin, A. F., Greenbaum, J. A., Peters, B., Krammer, F., Smith, D. M., Crotty, S., & Sette, A. (2020). Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals. Cell, 181(7), 1489-1501.e15. https://doi.org/10.1016/J.CELL.2020.05.015[65] Mateus, J., Grifoni, A., Tarke, A., Sidney, J., Ramirez, S. I., Dan, J. M., Burger, Z. C., Rawlings, S. A., Smith, D. M., Phillips, E., Mallal, S., Lammers, M., Rubiro, P., Quiambao, L., Sutherland, A., Yu, E. D., da Silva Antunes, R., Greenbaum, J., Frazier, A., ... Weiskopf, D. (2020). Selective and cross-reactive SARS-CoV-2 T cell epitopes in unexposed humans. Science (New York, N.Y.), 370(6512). https://doi.org/10.1126/SCIENCE.ABD3871[66] Zhang, H., Deng, S., Ren, L., Zheng, P., Hu, X., Jin, T., & Tan, X. (2021). Profiling CD8 + T cell epitopes of COVID-19 convalescents reveals reduced cellular immune responses to SARS-CoV-2 variants. Cell Reports, 36(11). https://doi.org/10.1016/J.CELREP.2021.109708[67] Moise, L., Gutierrez, A., Kibria, F., Martin, R., Tassone, R., Liu, R., Terry, F., Martin, B., & de Groot, A. S. (2015). iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines. Human Vaccines & Immunotherapeutics, 11(9), 2312–2321. https://doi.org/10.1080/21645515.2015.1061159 [68] Liu, G., Carter, B., Bricken, T., Jain, S., Viard, M., Carrington, M., & Gifford, D. K. (2020). Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions. Cell Systems, 11(2), 131. https://doi.org/10.1016/J.CELS.2020.06.009[68] Liu, G., Carter, B., Bricken, T., Jain, S., Viard, M., Carrington, M., & Gifford, D. K. (2020). Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions. Cell Systems, 11(2), 131. https://doi.org/10.1016/J.CELS.2020.06.009[69] Kared, H., Redd, A. D., Bloch, E. M., Bonny, T. S., Sumatoh, H., Kairi, F., Carbajo, D., Abel, B., Newell, E. W., Bettinotti, M. P., Benner, S. E., Patel, E. U., Littlefield, K., Laeyendecker, O., Shoham, S., Sullivan, D., Casadevall, A., Pekosz, A., Nardin, A., ... Quinn, T. C. (2020). CD8+ T cell responses in convalescent COVID-19 individuals target epitopes from the entire SARS-CoV-2 proteome and show kinetics of early differentiation. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.10.08.330688[70] Grifoni, A., Sidney, J., Zhang, Y., Scheuermann, R. H., Peters, B., & Sette, A. (2020). A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host & Microbe, 27(4), 671-680.e2. https://doi.org/10.1016/J.CHOM.2020.03.002[71] Finkel, Y., Mizrahi, O., Nachshon, A., Weingarten-Gabbay, S., Morgenstern, D., Yahalom-Ronen, Y., Tamir, H., Achdout, H., Stein, D., Israeli, O., Beth-Din, A., Melamed, S., Weiss, S., Israely, T., Paran, N., Schwartz, M., & Stern-Ginossar, N. (2020). The coding capacity of SARS-CoV-2. Nature 2020 589:7840, 589(7840), 125–130. https://doi.org/10.1038/s41586-020-2739-1[72] Agerer, B., Koblischke, M., Gudipati, V., Montaño-Gutierrez, L. F., Smyth, M., Popa, A., Genger, J.-W., Endler, L., Florian, D. M., Mühlgrabner, V., Graninger, M., Aberle, S. W., Husa, A.-M., Shaw, L. E., Lercher, A., Gattinger, P., Torralba-Gombau, R., Trapin, D., Penz, T., ... Bergthaler, A. (2021). SARS-CoV-2 mutations in MHC-I-restricted epitopes evade CD8 + T cell responses. Science Immunology, 6(57). https://doi.org/10.1126/sciimmunol.abg6461[73] Campbell, K. M., Steiner, G., Wells, D. K., Ribas, A., & Kalbasi, A. (2020). Prioritization of SARS-CoV-2 epitopes using a pan-HLA and global population inference approach. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.03.30.016931[74] Daouda, T., Dumont-Lagacé, M., Feghaly, A., & Villani, A.-C. (2021). Codon arrangement modulates MHC-I peptides presentation: implications for a SARS-CoV-2 peptide-based vaccine. BioRxiv, 2021.02.04.429819. https://doi.org/10.1101/2021.02.04.429819[75] Kared, H., Redd, A. D., Bloch, E. M., Bonny, T. S., Sumatoh, H., Kairi, F., Carbajo, D., Abel, B., Newell, E. W., Bettinotti, M. P., Benner, S. E., Patel, E. U., Littlefield, K., Laeyendecker, O., Shoham, S., Sullivan, D., Casadevall, A., Pekosz, A., Nardin, A., ... Quinn, T. C. (2020). CD8+ T cell responses in convalescent COVID-19 individuals target epitopes from the entire SARS-CoV-2 proteome and show kinetics of early differentiation. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.10.08.330688[76] Mallajosyula, V., Ganjavi, C., Chakraborty, S., McSween, A. M., Pavlovitch-Bedzyk, A. J., Wilhelmy, J., Nau, A., Manohar, M., Nadeau, K. C., & Davis, M. M. (2021). CD8+ T cells specific for conserved coronavirus epitopes correlate with milder disease in COVID-19 patients. Science Immunology, 6(61). https://doi.org/10.1126/sciimmunol.abg5669[77] Nathan, A., Rossin, E. J., Kaseke, C., Park, R. J., Khatri, A., Koundakjian, D., Urbach, J. M., Singh, N. K., Bashirova, A., Tano-Menka, R., Senjobe, F., Waring, M. T., Piechocka-Trocha, A., Garcia-Beltran, W. F., Iafrate, A. J., Naranbhai, V., Carrington, M., Walker, B. D., & Gaiha, G. D. (2021). Structure-guided T cell vaccine design for SARS-CoV-2 variants and sarbecoviruses. Cell, 184(17), 4401-4413.e10. https://doi.org/10.1016/j.cell.2021.06.029[78] Peng, Y., Mentzer, A. J., Liu, G., Yao, X., Yin, Z., Dong, D., Dejnirattisai, W., Rostron, T., Supasa, P., Liu, C., López-Camacho, C., Slon-Campos, J., Zhao, Y., Stuart, D. I., Paesen, G. C., Grimes, J. M., Antson, A. A., Bayfield, O. W., Hawkins, D. E. D. P., ... Dong, T. (2020). Broad and strong memory CD4+ and CD8+ T cells induced by SARS-CoV-2 in UK convalescent individuals following COVID-19. Nature Immunology, 21(11), 1336–1345. https://doi.org/10.1038/s41590-020-0782-6[79] Prachar, M., Justesen, S., Steen-Jensen, D. B., Thorgrimsen, S., Jurgons, E., Winther, O., & Bagger, F. O. (2020). Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools. Scientific Reports, 10(1), 20465. https://doi.org/10.1038/s41598-020-77466-4[80] Quadeer, A. A., Ahmed, S. F., & McKay, M. R. (2021). Landscape of epitopes targeted by T cells in 852 individuals recovered from COVID-19: Meta-analysis, immunoprevalence, and web platform. Cell Reports Medicine, 2(6), 100312. https://doi.org/10.1016/j.xcrm.2021.100312[81] Schulien, I., Kemming, J., Oberhardt, V., Wild, K., Seidel, L. M., Killmer, S., Sagar, Daul, F., Salvat Lago, M., Decker, A., Luxenburger, H., Binder, B., Bettinger, D., Sogukpinar, O., Rieg, S., Panning, M., Huzly, D., Schwemmle, M., Kochs, G., ... Neumann-Haefelin, C. (2021). Characterization of pre-existing and induced SARS-CoV-2-specific CD8+ T cells. Nature Medicine, 27(1), 78–85. https://doi.org/10.1038/s41591-020-01143-2[82] Sohail, M. S., Ahmed, S. F., Quadeer, A. A., & McKay, M. R. (2021). In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives. Advanced Drug Delivery Reviews, 171, 29–47. https://doi.org/10.1016/j.addr.2021.01.007[83] Weingarten-Gabbay, S., Klaeger, S., Sarkizova, S., Pearlman, L. R., Chen, D.-Y., Bauer, M. R., Taylor, H. B., Conway, H. L., Tomkins-Tinch, C. H., Finkel, Y., Nachshon, A., Gentili, M., Rivera, K. D., Keskin, D. B., Rice, C. M., Clauser, K. R., Hacohen, N., Carr, S. A., Abelin, J. G., ... Sabeti, P. C. (2020). SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs. BioRxiv : The Preprint Server for Biology. https://doi.org/10.1101/2020.10.02.324145[84] The web framework for perfectionists with deadlines | Django. (n.d.). Retrieved October 31, 2023, from https://www.djangoproject.com/[85] Conda — conda documentation. (n.d.). Retrieved October 31, 2023, from https://docs.conda.io/en/latest/[86] Docker: Accelerated Container Application Development. (n.d.). Retrieved October 31, 2023, from https://www.docker.com/[87] Scrum y las metodologías ágiles en construcción - miguelgarcia.me. (n.d.). Retrieved November 1, 2023, from https://miguelgarcia.me/scrum-y-las-metodologias-agiles-en-construccion/EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85609/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1072719322.2023.pdf1072719322.2023.pdfTesis de Maestría en Bioinformaticaapplication/pdf6721338https://repositorio.unal.edu.co/bitstream/unal/85609/2/1072719322.2023.pdf4a10db0c5166b3755ad97d187f0960dbMD52THUMBNAIL1072719322.2023.pdf.jpg1072719322.2023.pdf.jpgGenerated Thumbnailimage/jpeg5068https://repositorio.unal.edu.co/bitstream/unal/85609/3/1072719322.2023.pdf.jpgb06b92250c0730b79cec6807e82702e3MD53unal/85609oai:repositorio.unal.edu.co:unal/856092024-02-05 23:03:41.776Repositorio Institucional Universidad Nacional de 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