Evaluation of potential MHC-I allele-specific epitopes in Zika virus proteins and the effects of mutations on peptide-MHC-I interaction studied using in silico approaches

https://doi.org/10.1016/j.compbiolchem.2021.107459Get rights and content

Highlights

  • Potential epitopes are HLA allele specific.

  • Lys45 in B pock et of HLA B44 leads to selective binding of peptides with Glu at P2.

  • Asp at P1 of epitopes decrease the HLA B8 binding affinity and stability.

  • Mutation from Thr to Pro at P2 of the NS5 832 decrease the HLA A1 binding affinity.

  • The immunodominant peptide E 4 in mice has low HLA B 44 binding affinity.

Abstract

Zika virus (ZIKV) infection is a global health concern due to its association with microcephaly and neurological complications. The development of a T-cell vaccine is important to combat this disease. In this study, we propose ZIKV major histocompatibility complex I (MHC-I) epitopes based on in silico screening consensus followed by molecular docking, PRODIGY, and molecular dynamics (MD) simulation analyses. The effects of the reported mutations on peptide-MHC-I (pMHC-I) complexes were also evaluated. In general, our data indicate an allele-specific peptide-binding human leukocyte antigen (HLA) and potential epitopes. For HLA-B44, we showed that the absence of acidic residue Glu at P2, due to the loss of the electrostatic interaction with Lys45, has a negative impact on the pMHC-I complex stability and explains the low free energy estimated for the immunodominant peptide E-4 (IGVSNRDFV). Our MD data also suggest the deleterious effects of acidic residue Asp at P1 on the pMHC-I stability of HLA-B8 due to destabilization of the α-helix and β-strand. Free energy estimation further indicated that the mutation from Val to Ala at P9 of peptide E-247 (DAHAKRQTV), which was found exclusively in microcephaly samples, did not reduce HLA-B8 affinity. In contrast, the mutation from Thr to Pro at P2 of the peptide NS5−832 (VTKWTDIPY) decreased the interaction energy, number of intermolecular interactions, and adversely affected its binding mode with HLA-A1. Overall, our findings are important with regard to the design of T-cell peptide vaccines and for understanding how ZIKV escapes recognition by CD8 + T-cells.

Introduction

ZIKV was first isolated from a single rhesus monkeyin a forested area in Zika, Uganda in 1947 (Dick et al., 1952). ZIKV belongs to the family Flaviviridae and genus Flavivirus, which includes species such as the yellow fever virus (YFV) and dengue virus (DENV). Flaviviruses contain a single positive-strand genomic RNA that is translated into a viral polyprotein, which is cleaved into three structural proteins: the envelope (E), pre-membrane/membrane (prM/M) and capsid (C), and seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) (Shi and Gao, 2017).

In 2013, an outbreak of ZIKV occurred in French Polynesia, where the first case of a patient presenting with ZIKV infection complicated by Guillain–Barré syndrome was reported (Oehler et al., 2014). In 2015, an outbreak of ZIKV occurred in Brazil (Campos et al., 2015; Zanluca et al., 2015), and an unexpected increase in the prevalence of microcephaly was noticed in Pernambuco State, which was possibly related to ZIKV infection during pregnancy (Coelho et al., 2016). In 2016, the World Health Organization (WHO) declared ZIKV as a Public Health Emergency of International Concern (PHEIC) due to its potential association with microcephaly and other neurological disorders (World Health Organization, 2016).

Given the severity of the clinical manifestations, the study of transmission and pathogenesis of ZIKV has exponentially increased in recent years (Huang et al., 2019), highlighting the urgent need to develop drugs to treat and vaccines to prevent this infection. Classically, vaccines consist of live attenuated microorganisms or inactivated microorganisms (Skwarczynski and Toth, 2016); however, there is a concern about the humoral approach for ZIKV vaccines because of the possibility of antibody-dependent enhancement (ADE) of dengue severity in humans. Previous studies have shown that prior exposure to ZIKV significantly enhances peak dengue-2 viremia in Rhesus macaques and that the serum from ZIKV-infected animals is capable of mediating in vitro ADE of DENV serotypes (DENV-1, DENV-2, DENV-3, and DENV-4) (George et al., 2017). ADE occurs when preexisting antibodies facilitate viral entry into host cells, leading to enhanced infection (Takada and Kawaoka, 2003; Tirado and Yoon, 2003). Thus, a T-cell peptide vaccine for ZIKV may represent a safer and more effective approach since it would focus on the cellular immune response.

A T-cell-based vaccine induces antigen-specific memory of T-cells that remain capable of rapid proliferation and differentiation long after the antigen has been eliminated, conferring protection upon subsequent re-exposure to infectious agents (Pulendran and Ahmed, 2011). CD8 + T-cells (CTL) recognize protein-derived peptides in association with molecules of the major histocompatibility complex class I (MHC-I) (Blum et al., 2013). MHC-I molecules are extremely polymorphic, with most of the polymorphisms located in the peptide-binding region; therefore, each allele can bind to a specific repertoire of peptides. Groups of human leukocyte antigen (HLA) class I molecules that share largely overlapping peptide-binding specificity are designated as supertypes.

Epitopes bind with high affinity to the MHC-I molecule in a region called the binding groove, which presents six pockets denoted as A-F single bond(Sidney et al., 2008). This binding groove presents a size restriction of the bound peptides to approximately eight to ten residues with the C-terminal residue (e.g., 9-mer peptide (P1–P9), residue at position P9) in the F-pocket (Wieczorek et al., 2017). Optimal MHC-I peptide ligands are typically nine residues in length with defined residues at particular positions that dock into specialized pockets within the MHC-I peptide-binding groove (Falk et al., 1991; Fremont et al., 1992). The accommodation of peptides on the binding groove is based on the formation of a set of conserved hydrogen bonds between the side chains of the MHC-I molecule and the backbone of the peptide chain, and the occupation of the pockets by peptide side chains (anchor residues) (Wieczorek et al., 2017). This complex is specifically recognized by the T-cell receptor (TCR) through certain polymorphic contact sites on both peptides and HLA molecules (Robert, 2019). Thus, it is essential to identify MHC-I epitopes at the initial stage of a T-cell vaccine (Blum et al., 2013).

Previous studies characterized the peptide IGVSNRDFV (peptide E-4), derived from the E ZIKV protein (position 4–12 in the sequence), as immunodominant in mice, and it appears at position 1 as the MHC-I epitope of ZIKV in the Immune Epitope Database and Analysis Resource (IEDB) (Huang et al., 2017; Ngono et al., 2017; Pardy et al., 2017). The structural and stability characteristics of the peptide-MHC-I (pMHC-I) complex have not yet been determined.

The use of bioinformatics tools for the in silico prediction of MHC-I epitopes is the most useful and cost-effective initial step for selecting potential epitopes, and it has been employed in studies of T-cell vaccines (Jain and Baranwal, 2019; Panahi et al., 2018; Park et al., 2016). In silico studies have focused specifically on T-cell epitopes for the initial development stage of a T-cell peptide vaccine for ZIKV (Ashfaq and Ahmed, 2016; Dar et al., 2016; Janahi et al., 2017; Mirza et al., 2016; Pradhan et al., 2017). The main objective of predicting T-cell epitopes is to predict antigenic epitopes that bind with high affinity to MHC-I molecules (Patronov and Doytchinova, 2013).

The virus can prevent recognition by specific CTLs using various mechanisms, including amino acid substitutions in CTL epitopes or adjacent to them (Koup, 1994; Oldstone, 1997). The effects of mutations on MHC-I epitopes associated with viral escape from CTL recognition have been described for some viruses, including HIV (Bronke et al., 2013; Du et al., 2017), hepatitis C (Chang et al., 1997), and influenza (Rimmelzwaan et al., 2004). Sequence mutations in the ZIKV proteins E, NS1, NS3, and NS5 were verified in sequence samples globally by Baez et al. (2016). Studies have used deep mutational scanning to evaluate how mutations in the ZIKV E protein affect viral growth and escape from antibodies (Sourisseau et al., 2019). However, the effects of ZIKV mutations on pMHC-I complexes have not yet been identified.

In this study, we employed in silico prediction tools based on the primary sequence of ZIKV proteins, and molecular docking to select potential MHC-I allele-specific epitopes and investigate the effects of reported ZIKV mutations on the binding affinity to the MHC-I molecule. Additionally, we used molecular dynamics (MD) simulations to evaluate the stability of specific pMHC-I complexes, such as the immunodominant peptide E-4. Our findings may contribute to the design of a peptide vaccine based on T-cell epitopes, which can be a safer and faster alternative for the prevention of ZIKV and can advance the understanding of some mechanisms of viral escape.

Section snippets

ZIKV protein sequence

The ZIKV H/PF/2013 (UniProtKB: A0A024B7W1) human-derived sequence from French Polynesia was used as a reference to predict the E, NS1, NS3, and NS5 MHC-I epitopes, since ZIKV sequences from the Americas share a common ancestor with those circulating in French Polynesia in 2013 (Enfissi et al., 2016). The mutations analyzed in this study were previously identified by Baez et al. (2016).

Prediction of MHC-I epitopes

Predictions of MHC-I complexes were made using three sequence-based methods implemented as web servers: Propred

Prediction of MHC-I epitopes

Predictions of MHC-I (A1, A2, A3, B7, B8, and B44) epitopes in E, NS1, NS3, and NS5 proteins were conducted using the sequence-based methods Propred I, NetCtl 1.2, and Rankpep. A total of 461 HLA class I epitopes were predicted, of which 53 were HLA-A1, 114 HLA-A2, 87 HLA-A3, 88 HLA-B7, 68 HLA-B8, and 55 were HLA-B44. Since epitopes were not predicted for HLA-A1 by the Propred I and Rankpep predictors, we chose 30 HLA-A1 epitopes from the NetCtl prediction, using a score higher than 1 as the

Discussion

In this study, we used in silico prediction tools based on the primary sequence of ZIKV proteins, molecular docking, the PRODIGY server, and MD simulation to select and understand the structural molecular mechanisms involving potential ZIKV MHC-I epitopes, toward the initial development stage of a T-cell peptide vaccine for ZIKV. First, we used three independent MHC-I epitope predictors, and selected the superior epitopes based on the consensus criteria for each HLA allele. This consensus-based

Conclusions

A T-cell peptide vaccine-based approach may be a method to develop a ZIKV vaccine that prioritizes safety. Our data corroborate the theory of HLA allele-specific peptide binding and low promiscuity, due to the different potential epitopes selected for each allele, which reinforces the importance of vaccine design of T-cell multi-epitopes for optimal coverage in the global population. Specific contributions of individual residues of the peptides were energetically and structurally dissected,

Declaration of Competing Interest

The authors have no financial conflicts of interest with the contents of this article.

CRediT authorship contribution statement

Aline Silva da Costa: Conceptualization, Methodology, Investigation, Formal analysis, Validation, Visualization, Writing - original draft, Writing - review & editing. Tácio Vinício Amorim Fernandes: Methodology, Investigation, Formal analysis, Visualization, Writing - review & editing. Murilo Lamim Bello: Methodology, Writing - review & editing. Theo Luiz Ferraz de Souza: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Resources, Writing - review & editing,

Acknowledgments

This study was supported by the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), the Instituto Nacional de Ciência e Tecnologia de Biologia Estrutural e Bioimagem (INBEB), CCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) – Finance Code 001, and Programa Nacional de Apoio ao Desenvolvimento da Metrologia, Qualidade e Tecnologia (PRONAMETRO) from the Instituto

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