tetano
Editor, Senior Moderator
J Clin Pathol
. 2020 Aug 5;jclinpath-2020-206946.
doi: 10.1136/jclinpath-2020-206946. Online ahead of print.
Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population
Ronald Rodrigues de Moura[SUP] 1 [/SUP], Almerinda Agrelli[SUP] 2 [/SUP], Carlos Andr? Santos-Silva[SUP] 3 [/SUP], Nat?lia Silva[SUP] 2 [/SUP], Bruno Rodrigo Assun??o[SUP] 2 [/SUP], Lucas Brand?o[SUP] 2 [/SUP], Ana Maria Benko-Iseppon[SUP] 3 [/SUP], Sergio Crovella[SUP] 4 [/SUP]
Affiliations
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.
Keywords: HLA antigens; computers; immunogenetics; molecular; viruses.
. 2020 Aug 5;jclinpath-2020-206946.
doi: 10.1136/jclinpath-2020-206946. Online ahead of print.
Immunoinformatic approach to assess SARS-CoV-2 protein S epitopes recognised by the most frequent MHC-I alleles in the Brazilian population
Ronald Rodrigues de Moura[SUP] 1 [/SUP], Almerinda Agrelli[SUP] 2 [/SUP], Carlos Andr? Santos-Silva[SUP] 3 [/SUP], Nat?lia Silva[SUP] 2 [/SUP], Bruno Rodrigo Assun??o[SUP] 2 [/SUP], Lucas Brand?o[SUP] 2 [/SUP], Ana Maria Benko-Iseppon[SUP] 3 [/SUP], Sergio Crovella[SUP] 4 [/SUP]
Affiliations
- PMID: 32759312
- DOI: 10.1136/jclinpath-2020-206946
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.
Keywords: HLA antigens; computers; immunogenetics; molecular; viruses.