- Tytuł:
- Identification of CD4plus T cell epitopes from Staphylococcus aureus secretome using immunoinformatic prediction and molecular docking
- Autorzy:
-
Francis, D.
Kumar, A.
Chittalakkottu, S. - Powiązania:
- https://bibliotekanauki.pl/articles/2096359.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
CD4+
T cell
epitope prediction
secretory proteins
Staphylococcus aureus
peptide vaccine
molecular docking - Opis:
- One major reason for the lack of clinical success of Staphylococcus aureus vaccine candidates is the inability of the antigens to develop a CD4+ T cell-mediated immune response. Hence, it is important to identify CD4+ T cell antigens from S. aureus. CD4+ T cells are activated following the presentation of epitopes derived from exogenous proteins on HLA class II molecules. Fifty-nine secretory proteins of S. aureus were analyzed computationally for the presence of HLA class II binding peptides. Fifteen-mer peptides were generated, and their binding to 26 HLA class II alleles was predicted. The structural feasibility of the peptides binding to HLA-II was studied using molecular docking. Of the 16,724 peptides generated, 6991 (41.8%) were predicted to bind to any one of the alleles with an IC50 value below 50 nM. Comparative sequence analysis revealed that only 545 of the strong binding peptides are non-self in the human system. Approximately 50% of the binding peptides were monoallele-specific. Moreover, approximately 95% of the predicted strong binding non-self peptides interacted with the binding groove of at least one HLA class II molecule with a glide score better than !10 kcal/mol. On the basis of the analysis of the strength of binding, non-self presentation in the human host, propensity to bind to a higher number of alleles, and energetically favorable interactions with HLA molecules, a set of 11 CD4+ T cell epitopes that can be used as vaccine candidates was identified.
- Źródło:
-
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2021, 102, 1; 43-54
0860-7796 - Pojawia się w:
- BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
- Dostawca treści:
- Biblioteka Nauki
Artykuł