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Zika Virus Specific Diagnostic Epitope Discovery.

ABSTRACT: High-density peptide microarrays allow screening of more than six thousand peptides on a single standard microscopy slide. This method can be applied for drug discovery, therapeutic target identification, and developing of diagnostics. Here, we present a protocol to discover specific Zika virus (ZIKV) diagnostic peptides using a high-density peptide microarray. A human serum sample validated for ZIKV infection was incubated with a high-density peptide microarray containing the entire ZIKV protein translated into 3,423 unique 15 linear amino acid (aa) residues with a 14-aa residue overlap printed in duplicate. Staining with different secondary antibodies within the same array, we detected peptides that bind to Immunoglobulin M (IgM) and Immunoglobulin G (IgG) antibodies present in serum. These peptides were selected for further validation experiments. In this protocol, we describe the strategy followed to design, process, and analyze a high-density peptide microarray.

SUBMITTER: Sabalza M

PROVIDER: S-EPMC5755560 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Zika Virus Specific Diagnostic Epitope Discovery.

Sabalza Maite M Barber Cheryl A CA Abrams William R WR Montagna Richard R Malamud Daniel D

Journal of visualized experiments : JoVE 20171212 130

High-density peptide microarrays allow screening of more than six thousand peptides on a single standard microscopy slide. This method can be applied for drug discovery, therapeutic target identification, and developing of diagnostics. Here, we present a protocol to discover specific Zika virus (ZIKV) diagnostic peptides using a high-density peptide microarray. A human serum sample validated for ZIKV infection was incubated with a high-density peptide microarray containing the entire ZIKV protei ...[more]

PMID: 29286404

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Project description:Zika virus (ZIKV) glycoproteins are the primary target of the humoral immune response. In this study, we explored the capacity of these glycoproteins to tolerate insertion of linear epitope sequences and the potential of antibodies that bind these epitopes to inhibit infection. We first created a panel of ZIKV mutants with the FLAG epitope inserted in the premembrane (prM) and envelope (E) glycoprotein regions. The insertion locations were based on the results of our recent transposon insertional mutagenesis screen. Although FLAG insertions in prM greatly impaired viral fitness, this sequence was tolerated in numerous surface-exposed E protein sites. We observed that mutants bearing FLAG epitopes in E domains I and II and the E domain I-II hinge region were all neutralized by FLAG antibody; however, the neutralization sensitivity varied highly. We measured the antibody binding efficiency and found that this closely matched the pattern of neutralization sensitivity. We determined that E glycosylation did not affect antibody binding to a nearby epitope or its capacity to serve as a neutralization target. Although we could not generate infectious viruses with FLAG epitope insertions in a buried region of E protein domain III, we found that the V5 epitope could be inserted at this site without greatly impacting fitness. Furthermore, this virus was efficiently neutralized by V5 antibodies, highlighting that even buried epitopes can function as neutralization targets. Finally, we analyzed the timing of antibody neutralization activity during cell entry and found that all antibodies blocked a step after cell attachment.IMPORTANCE Zika virus (ZIKV) infections are associated with severe birth defects and neurological disease. The structure of the mature ZIKV particle reveals a virion surface covered by the envelope glycoprotein, which is the dominant target of the humoral immune response. It is unclear if all regions of the envelope protein surface or even buried epitopes can function as neutralization targets. To test this, we created a panel of ZIKV mutants with epitope insertions in different regions of the envelope protein. In characterizing these viruses, we found that the strength of antibody binding to an epitope is the major determinant of the neutralization potential of an antibody, that even a buried region of the envelope protein can be efficiently targeted, and that the sole potential envelope glycan does not impact nearby epitope antibody binding and neutralization. Furthermore, this work provides important insights into our understanding of how antibodies neutralize ZIKV.

Project description:Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

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Zika Virus Specific Diagnostic Epitope Discovery.

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Zika Virus Specific Diagnostic Epitope Discovery.

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