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. 2023 Dec 6:14:1278534.
doi: 10.3389/fimmu.2023.1278534. eCollection 2023.

Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies

Roc Farriol-Duran #  1 , Ruben López-Aladid #  2   3 , Eduard Porta-Pardo  1   4 , Antoni Torres  2   3 , Laia Fernández-Barat  2   3
Affiliations

Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies

Roc Farriol-Duran et al. Front Immunol. .

Abstract

The application of B-cell epitope identification to develop therapeutic antibodies and vaccine candidates is well established. However, the validation of epitopes is time-consuming and resource-intensive. To alleviate this, in recent years, multiple computational predictors have been developed in the immunoinformatics community. Brewpitopes is a pipeline that curates bioinformatic B-cell epitope predictions obtained by integrating different state-of-the-art tools. We used additional computational predictors to account for subcellular location, glycosylation status, and surface accessibility of the predicted epitopes. The implementation of these sets of rational filters optimizes in vivo antibody recognition properties of the candidate epitopes. To validate Brewpitopes, we performed a proteome-wide analysis of SARS-CoV-2 with a particular focus on S protein and its variants of concern. In the S protein, we obtained a fivefold enrichment in terms of predicted neutralization versus the epitopes identified by individual tools. We analyzed epitope landscape changes caused by mutations in the S protein of new viral variants that were linked to observed immune escape evidence in specific strains. In addition, we identified a set of epitopes with neutralizing potential in four SARS-CoV-2 proteins (R1AB, R1A, AP3A, and ORF9C). These epitopes and antigenic proteins are conserved targets for viral neutralization studies. In summary, Brewpitopes is a powerful pipeline that refines B-cell epitope bioinformatic predictions during public health emergencies in a high-throughput capacity to facilitate the optimization of experimental validation of therapeutic antibodies and candidate vaccines.

Keywords: antibody therapeutics; bioinformatics and computational biology; epitope prediction and antigenicity prediction; immunology and infectious diseases; vaccine development.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Biophysical constraints for in vivo antibody recognition. (A) Recognition of extracellular or extra-viral protein regions. Neutralizing antibodies only inspect the external surface of viral particles. Therefore, predicted epitopes located in intracellular or transmembrane epitopes will not be recognized. In Brewpitopes, we used protein topology-annotated information and topology predictors to assess the subcellular location of the target protein regions with predicted epitopes. Exclusively, candidates located on extracellular protein regions were selected. (B) Glycosylation coverage prevents in vivo antibody recognition of neutralizing epitopes. Predicted epitopes that contain glycosylation motifs are likely covered by glycans supporting the selection of predicted epitopes without glycosylated residues. In Brewpitopes, we predicted the glycosylation profiles of target proteins using Net-N-glyc and Net-O-glyc for N- and O-glycosylations, respectively. Only predicted epitopes without glycosylated residues pass this filter. (C) Epitope accessibility on parental protein surface. Predicted epitopes that contain buried residues will be less accessible for in vivo antibody recognition. Left: structure of S protein of SARS-CoV-2 highlighting a fully accessible predicted epitope. Right: structure of the S protein displaying a highly buried predicted epitope. In Brewpitopes, to assess epitope accessibility, we calculated the Residue Solvent Accessibility (RSA) of the predicted epitope sequences using crystal or structural models. Once predicted, fully accessible epitopes (all residues RSA ≥ 0.2) were selected and buried candidates were discarded (at least one residue RSA < 0.2).
Figure 2
Figure 2
Brewpitopes pipeline. Linear and conformational epitope predictions are performed using Bepipred2.0, ABCpred, and Discotope2.0. Epitope extraction is customized in each tool’s output using Epixtractor. Extracted epitopes are standardized using Epimerger. Subsequently, Brewpitopes implements three in silico predictors of biophysical constraints for in vivo antibody recognition: subcellular location, glycosylation coverage, and surface accessibility. Protein topology information to determine subcellular location can be uploaded into Brewpitopes using annotated data or via CCTOP predictions (.xml output) using Epitopology. Predicted epitopes located in extracellular regions are selected. Intracellular and transmembrane epitopes are discarded. Glycosylation patterns of target proteins are predicted with Net-N-Glyc and Net-O-Glyc and the output is used by Epiglycan to label all predicted epitopes containing one glycosylated residue as “glycosylated” and candidates not containing glycosylated positions as “non-glycosylated”. Epitope accessibility on the 3D surface of the parental protein structure is computed via compute_asa.icm (Molsoft - ICM Browser) and a PDB file obtained from a crystal structure or a computational model. Predicted RSA values are used by Epiaccess to label fully accessible epitopes as “accessible” (all residues RSA ≥ 0.2) and candidates containing at least one buried residue as “buried” (RSA < 0.2). The filtering of the candidate epitopes according to the predicted biophysical constraints (labeled as “extracellular”, “non-glycosylated”, and “accessible”) is performed by Epifilter. Curated candidates predicted by different tools will result in overlapping epitopes that are merged into epitope regions using Epiconsensus.
Figure 3
Figure 3
Epitope refinement for SARS-CoV-2 Wuhan S protein. The x-axis represents the filtering steps of the pipeline. The y-axis displays the number of epitopes refined by each filtering step of Brewpitopes.
Figure 4
Figure 4
Visualization of predicted epitope location on the 3D structure of SARS-CoV-2 S protein to compare the initially predicted epitopes versus the epitopes refined by Brewpitopes. This representation depicts the shrinkage of the region to be explored and experimentally validated since unrefined predictions represent a much larger surface than the epitopes refined by Brewpitopes. Left: Front view of the S protein 3D structure. Right: Top view. All the epitopes were only labeled on the chain A of the S protein for visualization purposes (blue). The epitope regions 6 and 7 were not displayed because they escaped the limits of the represented structure. Owing to the large number candidates predicted by ABCpred, only the best scored candidates of this software were included in the 3D representation.
Figure 5
Figure 5
Epitope refinement for the S protein of the Omicron variant. The x-axis represents the steps of the Brewpitopes pipeline and the y-axis denotes the number of epitopes selected by each filtering step of Brewpitopes ( Figure 2 ). Omicron’s epitope yield obtained with Brewpitopes (six epitope regions) is lower than Wuhan WT’s yield (seven epitope regions).
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References

    1. Cyster JG, Allen CDC. B cell responses: cell interaction dynamics and decisions. Cell (2019) 177:524–40. doi: 10.1016/j.cell.2019.03.016 - DOI - PMC - PubMed
    1. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res (2017) 45(W1):W24–9. doi: 10.1093/nar/gkx346 - DOI - PMC - PubMed
    1. Zhang Y, Banga Ndzouboukou JL, Gan M, Lin X, Fan X. Immune evasive effects of SARS-CoV-2 variants to COVID-19 emergency used vaccines. Front Immunol (2021) 12. doi: 10.3389/fimmu.2021.771242 - DOI - PMC - PubMed
    1. CoVariants. Available at: https://covariants.org/.
    1. Bahai A, Asgari E, Mofrad MRK, Kloetgen A, McHardy AC. EpitopeVec: Linear epitope prediction using deep protein sequence embeddings. Bioinformatics (2021) 37(23):4517–25. doi: 10.1093/bioinformatics/btab467 - DOI - PMC - PubMed

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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. RF-D received support by a La Caixa Junior Leader Fellowship (LCF/BQ/PI18/11630003) from Fundación La Caixa. EP-P received support by a La Caixa Junior Leader Fellowship (LCF/BQ/PI18/11630003) from Fundación La Caixa and a Ramon y Cajal fellowship from the Spanish Ministry of Science (RYC2019-026415-I). LF-B and RL-A received support by Direcció General de Recerca i Inovació en Salut (DGRIS) and BIOCAT (https://www.biocat.cat/ca) (Code: BIOCAT_DGRIS_COVID19) awarded to AT and LF-B; ISCIII-FOS (FI19/00090) grant awarded to RL-A, CB 06/06/0028/CIBER de enfermedades respiratorias (Ciberes), Ciberes is an initiative of ISCIII. ICREA Academy/Institució Catalana de Recerca i Estudis Avançats awarded to AT; 2.603/IDIBAPS, SGR/Generalitat de Catalunya awarded to AT. Funders did not play any role in project design, data collection, data analysis, interpretation, or writing of the paper.