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  • Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population

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Original research
Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population
  1. Ronald Rodrigues de Moura1,
  2. Almerinda Agrelli2,
  3. Carlos André Santos-Silva3,
  4. Natália Silva2,
  5. Bruno Rodrigo Assunção2,
  6. Lucas Brandão2,
  7. Ana Maria Benko-Iseppon3,
  8. Sergio Crovella1
  1. 1 Department of Advanced Diagnostics, IRCCS Materno Infantile Burlo Garofolo, Trieste, Friuli Venezia Giulia, Italy
  2. 2 Department of Pathology, Federal University of Pernambuco, Recife, Brazil
  3. 3 Department of Genetics, Federal University of Pernambuco, Recife, Brazil
  1. Correspondence to Dr Ronald Rodrigues de Moura, Department of Advanced Diagnostics, IRCCS Materno Infantile Burlo Garofolo, 34137 Trieste, Friuli Venezia Giulia, Italy; ronaldmoura1989{at}gmail.com

Abstract

Aims Brazil is nowadays one of the epicentres of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and new therapies are needed to face it. In the context of specific immune response against the virus, a correlation between Major Histocompatibility Complex Class I (MHC-I) and the severity of the disease in patients with COVID-19 has been suggested. Aiming at better understanding the biology of the infection and the immune response against the virus in the Brazilian population, we analysed SARS-CoV-2 protein S peptides in order to identify epitopes able to elicit an immune response mediated by the most frequent MHC-I alleles using in silico methods.

Methods Our analyses consisted in searching for the most frequent Human Leukocyte Antigen (HLA)-A, HLA-B and HLA-C alleles in the Brazilian population, excluding the genetic isolates; then, we performed: molecular modelling for unsolved structures, MHC-I binding affinity and antigenicity prediction, peptide docking and molecular dynamics of the best fitted MHC-I/protein S complexes.

Results We identified 24 immunogenic epitopes in the SARS-CoV-2 protein S that could interact with 17 different MHC-I alleles (namely, HLA-A*01:01; HLA-A*02:01; HLA-A*11:01; HLA-A*24:02; HLA-A*68:01; HLA-A*23:01; HLA-A*26:01; HLA-A*30:02; HLA-A*31:01; HLA-B*07:02; HLA-B*51:01; HLA-B*35:01; HLA-B*44:02; HLA-B*35:03; HLA-C*05:01; HLA-C*07:01 and HLA-C*15:02) in the Brazilian population.

Conclusions Being aware of the intrinsic limitations of in silico analysis (mainly the differences between the real and the Protein Data Bank (PDB) structure; and accuracy of the methods for simulate proteasome cleavage), we identified 24 epitopes able to interact with 17 MHC-I more frequent alleles in the Brazilian population that could be useful for the development of strategic methods for vaccines against SARS-CoV-2.

  • immunogenetics
  • computers
  • molecular
  • HLA antigens
  • viruses

Data availability statement

Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information. Raw data generated in this study may be requested by sending an email to lpm.ccm@ufpe.br.

This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

https://bmj.com/coronavirus/usage

https://doi.org/10.1136/jclinpath-2020-206946

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    Data availability statement

    Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information. Raw data generated in this study may be requested by sending an email to lpm.ccm@ufpe.br.

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    Footnotes

    • Handling editor Runjan Chetty.

    • Contributors RRdM, CAS-S, BRA and NS made substantial contributions to the conception or design of the work, or the acquisition, analysis or interpretation of data. RRdM, AA, SC, CAS-S, LB and AMB-I drafted the work or revising it critically for important intellectual content. SC and LB made the final approval of the version published. The authors do agree that they have the accountability for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

    • Funding This study has been supported by the following grants: Italian-Slovenian Ecosystem for Electronic and Mobile Health from European Community (grant number: 07/2019) and BioHub—A High-throughput Platform For OMICs Data Analysis And Integration from the Italian Ministry of Health (grant number: RC03/20). LB is recipient of a senior fellowship from the Brazilian National Council for Scientific and Technological Development (CNPq) (grant number: 308540/2017). RRdM is recipient of a senior fellowship from RC03/20 project from IRCCS Burlo Garofolo, Trieste, Italy.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; internally peer reviewed.