In silico analysis of the protein-protein interaction of the SARS-CoV-2 spike protein

Andressa Souza  Marques; Taiane de Macêdo  Gondim; Fulvia Soares Campos de  Sousa; Antônio Pedro Fróes de  Farias; Bruno Silva  Andrade; Soraya Castro  Trindade; José Tadeu Raynal da  Rocha Filho; Roberto  Meyer

doi:10.33448/rsd-v13i6.46139

Autores/as

Andressa Souza Marques Universidade Federal da Bahia https://orcid.org/0000-0003-4332-9079
Taiane de Macêdo Gondim Empresa Brasileira de Serviços Hospitalares – EBSERH; Universidade Federal da Bahia https://orcid.org/0000-0002-6802-4661
Fulvia Soares Campos de Sousa Universidade Federal da Bahia https://orcid.org/0000-0002-2779-5478
Antônio Pedro Fróes de Farias Universidade Federal da Bahia https://orcid.org/0000-0002-2625-9470
Bruno Silva Andrade Fundação Oswaldo Cruz; Universidade Estadual de Santa Cruz; Fundação Universidade Federal do Tocantins https://orcid.org/0000-0002-8031-9454
Soraya Castro Trindade Universidade Estadual de feira de Santana https://orcid.org/0000-0001-7125-9114
José Tadeu Raynal da Rocha Filho Universidade Federal da Bahia https://orcid.org/0000-0002-2771-0235
Roberto Meyer Universidade Federal da Bahia https://orcid.org/0000-0002-4727-4805

DOI:

https://doi.org/10.33448/rsd-v13i6.46139

Palabras clave:

SARS-CoV-2; Células T; Células B; Epítopos; Interacción proteína-proteína.

Resumen

La pandemia causada por el virus SARS-CoV-2 ha representado un desafío global con un impacto significativo en la salud pública desde su aparición en 2019. Comprender las interacciones entre este virus y el sistema inmunológico humano es esencial para el desarrollo de nuevas estrategias. terapias y diagnósticos más eficaces. Este estudio tuvo como objetivo predecir epítopos para las células T y B del SARS-CoV-2, así como evaluar la interacción de la proteína de pico con otras proteínas virales, utilizando métodos bioinformáticos. Métodos: Se recogieron secuencias de proteínas del SARS-CoV-2 de UniProt. Los epítopos de las células T se predijeron in silico utilizando alelos HLA específicos de la población de Bahía. Los epítopos de las células B se predijeron utilizando el servidor IEDB con múltiples métodos basados en las características de los aminoácidos. La interacción proteína-proteína se analizó utilizando la base de datos STRING. El resultado son 10.671 péptidos relacionados con diversas proteínas virales del SARS-CoV-2, incluida la espiga, esenciales para la infección y patogénesis del COVID-19. Además del pico, proteínas como ORF3a, ORF7a y ORF8 mostraron un potencial inmunogénico significativo. El análisis de la interacción proteína-proteína reveló que proteasas como TMPRSS2 y TMPRSS11D son cruciales para la entrada viral y son posibles objetivos terapéuticos. Este estudio amplía la comprensión de las interacciones moleculares del SARS-CoV-2, destacando nuevas dianas terapéuticas y complicaciones clínicas asociadas con el COVID-19. Los resultados proporcionan información valiosa para el desarrollo de estrategias terapéuticas específicas y diagnósticos mejorados, contribuyendo a la mitigación de la pandemia mundial.

Citas

Arabi-Jeshvaghani, F., Javadi‐Zarnaghi, F., & Ganjalikhany, M. R. (2023). Analysis of critical protein–protein interactions of SARS-CoV-2 capping and proofreading molecular machineries towards designing dual target inhibitory peptides. Scientific Reports, 13(1), 350.

Bar-On, Y M, Flamholz, A., Phillips, R., & Milo, R. (2020). SARS-CoV-2 (COVID-19) by the numbers. eLife, 9, e57309.

Bittmann, S., Weissenstein, A., Moschüring-Alieva, E., Bittmann, L., Luchter, E., & Villalon, G. (2020). The role of TMPRSS2 and TMPRSS2-inhibitors in cell entry mechanism of COVID-19. Journal of Regenerative Biology and Medicine, 2(3), 1-3.

Borza, R., Salgado-Polo, F., Moolenaar, W H., & Perrakis, A. (2022). Structure and function of the ecto-nucleotide pyrophosphatase/phosphodiesterase (ENPP) family: Tidying up diversity. The Journal of Biological Chemistry, 298(2), 101526.

Bowe, B., Xie, Y., & Al-Aly, Z. (2023). Postacute sequelae of COVID-19 at 2 years. Nature Medicine, 29, 2347–2357.

Bugge, T H, Antalis, T M, & Wu, Q. (2009). Type II transmembrane serine proteases. Journal of Biological Chemistry, 284(35), 23177-23181.

Bussani, R., Zentilin, L., Correa, R., Colliva, A., Silvestri, F., Zacchigna, S., & Giacca, M. (2023). Persistent SARS‐CoV‐2 infection in patients seemingly recovered from COVID‐19. The Journal of Pathology, 259(3), 254-263.

Chakraborty, C., Sharma, A R., Bhattacharya, M., Sharma, G., & Lee, S S. (2021). Immunoinformatics approach for the identification and characterization of T cell and B cell epitopes towards the peptide-based vaccine against SARS-CoV-2. Archives of Medical Research, 52(4), 362-370.

Chou, P. Y., & Fasman, G. D. (1978). Prediction of the secondary structure of proteins from their amino acid sequence. Advances in Enzymology and Related Areas of Molecular Biology, 47, 45–148.

Díaz-Castrillón, F. J., & Toro-Montoya, A I. (2020). SARS-CoV-2/COVID-19: el virus, la enfermedad y la pandemia. Medicina & Laboratorio, 24(3), 183-205.

Devi, Y D, Goswami, H B, Konwar, S., Doley, C., Dolley, A., Devi, A., & Namsa, N D. (2021). Immunoinformatics mapping of potential epitopes in SARS-CoV-2 structural proteins. PLOS ONE, 16(11), e0258645.

Disser, N P, De Micheli, A J, Schonk, M M, Konnaris, M A, Piacentini, A N, Edon, D L, Toresdahl, B G, Rodeo, S A, Casey, EK, & Mendias, CL. (2020). Musculoskeletal consequences of COVID-19. Journal of Bone and Joint Surgery, 102(14), 1197-1204.

Eckerle, I., & Meyer, B. (2020). SARS-CoV-2 seroprevalence in COVID-19 hotspots. The Lancet, 396(10250), 514-515.

Emini, E A, Hughes, J V, Perlow, D S, & Boger, J. (1985). Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of Virology, 55(3), 836–839.

Fang, Z., Marshall, C B, Yin, J C, Mazhab-Jafari, M T, Gasmi-Seabrook, G M, Smith, M J, Nishikawa, T., Xu, Y., Neel, B G, & Ikura, M. (2016). Biochemical classification of disease-associated mutants of RAS-like protein expressed in many tissues (RIT1). Journal of Biological Chemistry, 291(30), 15641-15652.

Fathollahi, M., Motamedi, H., Hossainpour, H., Abiri, R., Shahlaei, M., Moradi, S., & Alvandi, A. (2023). Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: In silico and immunoinformatics approach. Journal of Biomolecular Structure and Dynamics, 1-24.

Grifoni, A., Sidney, J., Vita, R., Peters, B., Crotty, S., Weiskopf, D., & Sette, A. (2021). SARS-CoV-2 human T cell epitopes: Adaptive immune response against COVID-19. Cell Host & Microbe, 29(7), 1076-1092.

Han, Q., Zheng, B., Daines, L., & Sheikh, A. (2022). Long-term sequelae of COVID-19: A systematic review and meta-analysis of one-year follow-up studies on post-COVID symptoms. Pathogens, 11(2), 269.

Hook, V., Funkelstein, L., Wegrzyn, J., Bark, S., Kindy, M., & Hook, G. (2012). Cysteine cathepsins in the secretory vesicle produce active peptides: Cathepsin L generates peptide neurotransmitters and cathepsin B produces beta-amyloid of Alzheimer's disease. Biochimica et Biophysica Acta, 1824(1), 89-104

Huseynov, A., Akin, I., Duerschmied, D., & Scharf, R E. (2023). Cardiac arrhythmias and COVID-19: A scientific statement from the German Cardiac Society (DGK) Working Group on arrhythmias. Heart, 109(5), 369-376.

Jespersen, M C, Peters, B., Nielsen, M., & Marcatili, P. (2017). BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Research, 45(W1), W24–W29.

Kampf, G., Brüggemann, Y., Kaba, H E J, Steinmann, J., Pfaender, S., Scheithauer, S., & Steinmann, E. (2020). Potential sources, modes of transmission and effectiveness of prevention measures against SARS-CoV-2. Journal of Hospital Infection, 106(4), 678-697.

Karplus, P. A., & Schulz, G. E. (1985). Predição da flexibilidade de cadeia em proteínas - Uma ferramenta para a seleção de antígenos peptídicos. Naturwissenschaften, 72, 212-213.

Karplus, P A., & Schulz, G E. (1985). Prediction of chain flexibility in proteins: A tool for the selection of peptide antigens. Naturwissenschaften, 72(4), 212-213.

Kishimoto, M., Uemura, K., Sanaki, T., Sato, A., Hall, W W, Kariwa, H., Orba, Y., Sawa, H., & Sasaki, M. (2021). TMPRSS11D and TMPRSS13 activate the SARS-CoV-2 spike protein. Viruses, 13(3), 384.

Kolaskar, A S, & Tongaonkar, P. C. (1990). A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Letters, 276(1-2), 172–174.

Kumar, P., Kumar, A., Garg, N., & Giri, R. (2023). An insight into SARS-CoV-2 membrane protein interaction with spike, envelope, and nucleocapsid proteins. Journal of Biomolecular Structure and Dynamics, 41(3), 1062-1071.

Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., & Moore, H. F. (2013). The genotype-tissue expression (GTEx) project. Nature Genetics, 45(6), 580-585.

Majra, D., Benson, J., Pitts, J., & Stebbing, J. (2021). SARS-CoV-2 (COVID-19) superspreader events. Journal of Infection, 82(1), 36-40.

Mehandru, S., & Merad, M. (2022). Pathological sequelae of long-haul COVID. Nature Immunology, 23, 194–202.

Miao, G., Zhao, H., Li, Y., ... & Zhong, Q. (2021). ORF3a of the COVID-19 SARS-CoV-2 virus blocks the HOPS complex-mediated assembly of the SNARE complex required for the formation of autolysosomes. Developmental Cell, 56(4), 427-442.e5.

Mukherjee, S., Tworowski, D., Detroja, R., & Frenkel-Morgenstern, M. (2020). Immunoinformatics and structural analysis for identification of immunodominant epitopes in SARS-CoV-2 as potential vaccine targets. Vaccines, 8(2), 290.

Navish, A A, & Uthayakumar, R. (2023). An exploration on the topologies of SARS-CoV-2/human protein-protein interaction network. Journal of Biomolecular Structure and Dynamics, 41(13), 6313-6325.

Ozger, Z B. (2023). A robust protein language model for SARS-CoV-2 protein–protein interaction network prediction. Artificial Intelligence in Medicine, 142, 102574.

Parker, J M, Guo, D., & Hodges, R S. (1986). New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry, 25(19), 5425–5432.

Pellequer, J L, Westhof, E., & Van Regenmortel, M H. (1993). Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunology Letters, 36(1), 83–99.

Saha, S A, Russo, A M, Chung, M K, Deering, T F, Lakkireddy, D., & Gopinathannair, R. (2022). COVID-19 and cardiac arrhythmias: A contemporary review. Current Treatment Options in Cardiovascular Medicine, 24(6), 87-107.

Shomuradova, A S, Vagida, M S, Sheetikov, S A, Zornikova, K V, Kiryukhin, D., Titov, A., & Efimov, G A. (2020). SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T cell receptors. Immunity, 53(6), 1245-1257.

Shulla, A., Heald-Sargent, T., Subramanya, G., Zhao, J., Perlman, S., & Gallagher, T. (2011). A transmembrane serine protease is linked to the severe acute respiratory syndrome coronavirus receptor and activates virus entry. Journal of Virology, 85(2), 873-882.

Szabo, R., & Bugge, T H. (2008). Type II transmembrane serine proteases in development and disease. The International Journal of Biochemistry & Cell Biology, 40(6-7), 1297-1316.

Tharappel, A M, Samrat, S K, Li, Z., & Li, H. (2020). Targeting crucial host factors of SARS-CoV-2. ACS Infectious Diseases, 6(11), 2844-2865.

Turk, B., Turk, D., & Turk, V. (2000). Lysosomal cysteine proteases: More than scavengers. Biochimica et Biophysica Acta, 1477(1-2), 98–111.

Weingarten-Gabbay, S., Chen, D Y, Sarkizova, S., Taylor, H B, Gentili, M., Hernandez, G M, & Sabeti, P C. (2024). The HLA-II immunopeptidome of SARS-CoV-2. Cell Reports, 43(1).

Yarmarkovich, M., Warrington, J. M., Farrel, A., & Maris, J. M. (2020). Identification of SARS-CoV-2 vaccine epitopes predicted to induce long-term population-scale immunity. Cell Reports Medicine, 1(3).

Zhou, Y., Liu, Y., Gupta, S., Paramo, M I, Hou, Y., Mao, C., & Yu, H. (2023). A comprehensive SARS-CoV-2–human protein–protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets. Nature Biotechnology, 41(1), 128-139.

Análisis in silico de la interacción proteína-proteína de la proteína espiga SARS-CoV-2

Autores/as

DOI:

Palabras clave:

Resumen

Citas

Descargas

Publicado

Cómo citar

Número

Sección

Licencia

JOURNAL METRICS

Idioma

Enviar un artículo