Brewpitopes : a pipeline to refine B-cell epitope predictions during public health emergencies
Farriol-Duran, Roc (Barcelona Supercomputing Center)
López-Aladid, Ruben (Pneumology Department. Hospital Clínic)
Porta-Pardo, Eduard (Institut Germans Trias i Pujol. Institut de Recerca contra la Leucèmia Josep Carreras)
Torres, Antoni (Hospital Clínic i Provincial de Barcelona)
Fernández-Barat, Laia (Hospital Clínic i Provincial de Barcelona)

Data:	2023
Resum:	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.
Ajuts:	Instituto de Salud Carlos III FOS/CB 06/06/0028/CIBER Instituto de Salud Carlos III FOS/FI19/00090 Ministerio de Ciencia e Innovación RYC2019-026415-I
Nota:	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.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Bioinformatics and computational biology ; Immunology and infectious diseases ; Vaccine development ; Antibody therapeutics ; Epitope prediction and antigenicity prediction
Publicat a:	Frontiers in immunology, Vol. 14 (2023) , p. 1278534, ISSN 1664-3224

DOI: 10.3389/fimmu.2023.1278534
PMID: 38124749

16 p, 2.1 MB

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