Structural in silico analysis of cross-genotype-reactivity among naturally occurring HCV NS3-1073-variants in the context of HLA-A*02:01 allele

NBLI - Núcleo de Bioinformática do Laboratório de Imunogenética, Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
Molecular Immunology (Impact Factor: 2.97). 07/2011; 48(12-13):1461-7. DOI: 10.1016/j.molimm.2011.03.019
Source: PubMed


Cellular immune response plays a central role in outcome of Hepatitis C Virus (HCV) infection. While specific T-cell responses are related to viral clearance, impaired responses can lead to chronic infection, turning HCV variability into a major obstacle for vaccine development. In a recent work, Fytili et al. (2008) studied the cross reactive potential of HCV specific CD8+ T-cells and observed a large variation in immunogenicity among 28 naturally occurring NS3(1073) variants. In this work, we intend to evaluate this immunogenic variation at molecular level, through bioinformatics approaches. The D1-EM-D2 strategy was used to build in silico MHC:peptide complexes (pMHC) of these HCV-derived peptides in the context of HLA-A*02:01 allele. The TCR-interacting surface of these complexes were evaluated using the GRASP2 program. Structural analysis indicated a sharing of topological and electrostatic features among complexes that induced strong response in vitro. Besides, complexes that induced low response presented an important positively charged spot in the center of TCR-interacting area. This spot was seen even in complexes with conservative amino acid changes and is consistent with the impairment of recognition by wild-type-specific T-cells, observed in vitro. Furthermore, the most remarkable difference in electrostatic potential was seen precisely in the only complex unable to induce in vitro stimulation. All these observations were confirmed by Principal Component Analysis (PCA) and this approach was also applied to a set of 45 non-related immunogenic viral epitopes, indicating possible new targets for cross-reactivity studies. Our results suggest structural in silico analysis of pMHC complexes as a reliable tool for vaccine development, affording to predict the impact of viral escape mutations and selection of epitopes with potential to induce cross-reactive immune responses.

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Available from: José Artur Bogo Chies, Apr 27, 2015
    • "CD8+ T cells expanded with the HCV-genotype 1-derived prototype peptide responded to HCV genotype 4, 5 or 6-derived peptides while peptides from HCV genotype 2 and 3 were not recognized [48]. Structural in silico information about peptide-MHC complexes, especially regarding the electrostatic potential over the TCR-interacting area, could predict the level of HCV NS3-1073 cross-reactivity seen in vitro [49]. Another study demonstrated substantial cross-reactivity between epitope variants of HCV NS3-1406 within genotype 1b but lack of cross-reactivity between HCV subtypes 1a and 1b [47] . "
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    • "The visual inspection revealed a striking similarity between them, evidencing the reliability of this kind of investigation. Here, we extended a previous study (20) and analysed 60 unrelated pMHC-I complexes presenting virus-derived peptides, in the context of the most frequent human MHC allele (HLA-A*02:01). These complexes, 5 crystal structures and 55 in silico predicted structures, were obtained from the CrossTope. "
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Questions & Answers about this publication

  • Dinler Antunes added an answer in Bioinformatic Tools:
    How can I visualise the structures of HLA alleles?

    I have a couple of peptides (epitopes) with corresponding HLA alleles. Any possibility that I can visualise the structure of HLA alleles along with the bound epitope and to see how the epitope is accommodated in side the pocket utilising some bioinformatic tools? 

    Dinler Antunes

    As discussed above by Maurício, you cannot just "align" your desired peptide in the cleft of a given MHC allele, the structure would not be reliable. There is one webserver to provide initial structures of desired pMHC complexes, MHCSim, but these structures are obtained by mutating available crystal structures and need to be further refined after modeling. 

    There are other prediction methods already described, such as D1-EM-D2 and pDOCK. The D1-EM-D2 was developed by our group and was used to make structure-based predictions on T cell cross-reactivity. It was also used to model several complexes presenting virus-derived epitopes, which are avaliable through the CrossTope Data Bank. Maybe your complexes of interest were already predicted using our approach. 

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