bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.11.28.518301

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

Abstract

The application of B-cell epitope identification for the development of therapeutic antibodies is well established but consuming in terms of time and resources. For this reason, in the last few years, the immunoinformatic community has developed several computational predictive tools. While relatively successful, most of these tools only use a few properties of the candidate region to determine their likelihood of being a true B-cell epitope. However, this likelihood is influenced by a wide variety of protein features, including the presence of glycosylated residues in the neighbourhood of the candidate epitope, the subcellular location of the protein region or the three-dimensional information about their surface accessibility in the parental protein. In this study we created Brewpitopes, an integrative pipeline to curate computational predictions of B-cell epitopes by accounting for all the aforementioned features. To this end, we implemented a set of rational filters to mimic the conditions for the in vivo antibody recognition to enrich the B-cell epitope predictions in actionable candidates. To validate Brewpitopes, we analyzed the SARS-CoV-2 proteome. In the S protein, Brewpitopes enriched the initial predictions in 5-fold on epitopes with neutralizing potential (p-value < 2e-4). Other than S protein, 4 out of 16 proteins in the proteome contain curated B-cell epitopes and hence, have also potential interest for viral neutralization, since mutational escape mainly affects the S protein. Our results demonstrate that Brewpitopes is a powerful pipeline for the rapid prediction of refined B-cell epitopes during public health emergencies. Statement of significanceWe have created Brewpitopes, a new pipeline that integrates additional important features such as glycosylation or structural accessibility, to curate B-cell epitope more likely to be functional in vivo. We have also validated Brewpitopes against SARS-CoV-2 not only for S protein but also for the entire viral proteome demonstrating that is a rapid and reliable epitope predictive tool to be implemented in present or future public health emergencies. Brewpitopes has identified 7 SARS-CoV-2 epitopes in S and epitopes allocated in 4 other proteins. Overall, offering an accurate selection of epitopes that might be scaled up to the production of new antibodies.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Farriol-Duran, R., Lopez-Aladid, R., Porta-Pardo, E., Torres, A., Fernandez-Barat, L.. 2022-11-29. Brewpitopes: a pipeline to refine B-cell epitope predictions during public health emergencies. https://doi.org/10.1101/2022.11.28.518301

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗