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206 results for “epitope”
Trypanosoma Epitope Dataset: Valid Epitopes and Randomly Generated Peptides with Biochemical Metrics and AI-Generated Scores
<p>This dataset contains information about valid linear B-Cell epitopes from the Trypanosoma genus, as well as randomly generated peptides. It includes biochemical metrics generated by the EpiBuilder-1.0 tool and scores generated by the BepiPred-3.0 software. The data was originally collected from the IEDB and UniProtKB platforms and has been processed and enhanced with these informations for researchers interested in understanding the molecular interactions between Trypanosoma protozoans and the immune system.</p>
Supplementary data - Simultaneous polyclonal antibody sequencing and epitope mapping by cryo electron microscopy and mass spectrometry – a perspective
<p>Analysis files and scripts for <a href="https://doi.org/10.1101/2024.06.21.600107" target="_blank" rel="noopener">associated manuscript</a>. </p> <ul> <li>CR3022.zip: script (in Rust) and necessary data to run said script for CR3022 analysis with the results from running the script.</li> <li>MA-analysis-script.zip: script (in Rust) and necessary data to run said script for automated analysis of MA benchmark results.</li> <li>MA-analysis-data.zip: data from running the MA-analysis-script, containing all MA and Stitch output files.</li> <li>MA-analysis-data-EMPEM.zip: data from running MA and Stitch on the EMPEM benchmark.</li> </ul>
MOLECULAR MIMICRY BETWEEN TUMOR ASSOCIATED ANTIGENS AND MICROBIOTA-DERIVED EPITOPES.
<p><strong>Background: </strong>The gut microbiota profile is unique for each individual and are composed by different bacteria species according to individual birth-to-infant transitions. In the last years, the local and systemic effects of microbiota on cancer onset, progression and response to treatments, such as immunotherapies, have been extensively described. Here we offer a new perspective, proposing a role for the microbiota based on the molecular mimicry of tumor-associated antigens by microbiome-associated antigens.</p> <p><strong>Methods:</strong> In the present study we looked for homology between published TAAs and non-self microbiota-derived epitopes. Blast search for sequence homology was combined with extensive bioinformatics analyses.</p> <p><strong>Results:</strong> Several evidences for homology between TAAs and microbiota-derived antigens have been found. Strikingly, three cases of 100% homology between the paired sequences has been identified. The predicted average affinity to HLA molecules of microbiota-derived antigens is very high (<100nM). The structural conformation of the microbiota-derived epitopes is, in general, highly similar to the corresponding TAA. In some cases, it is identical and contact areas with both HLA and TCR chains are indistinguishable. Moreover, the spatial conformation of TCR-facing residues can be identical in paired TAA and microbiota-derived epitopes, with exactly the same values of planar as well as dihedral angles.</p> <p><strong>Conclusions:</strong> The data reported in the present study show for the first time the high homology in the linear sequence as well as in structure and conformation between TAAs and peptides derived from microbiota species of the Firmicutes and the Bacteroidetes phyla, which together account for 90% of gut microbiota. Cross-reacting CD8<sup>+</sup> T cell responses are very likely induced. Therefore, the anti-microbiota T cell memory may turn out to be an anti-cancer T cell memory, able to control the growth of cancer developed during the lifetime if the expressed TAA is similar to the microbiota epitope. This may ultimately represent a relevant selective advantage for cancer patients and may lead to a novel preventive anti-cancer vaccine strategy.</p>
Structural Homology of Epitope Pair Candidates for Molecular Mimicry Trigger of Type 1 Diabetes Mellitus
<p><strong><em><span>Background:</span></em></strong><span> </span><span>Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E<sub>INF</sub>) and T1DM epitopes (E<sub>T1D</sub>) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potential surfaces of the epitope pairs identified in previous work from our laboratory. </span></p> <p><strong><span> </span></strong><strong><em><span>Results:</span></em></strong><span> </span><span> For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold software program. </span><span>Of the 52 epitope pairs considered here only 10 do not exhibit any matching (i.e. less than 3 residues overlap). When considering all residues the RMSD ranges from 0.33 Å to 11.66 Å with an average of 2.68 Å. Twenty-two pairs (42%) have RMSD of less than 1.5 Å and 30 (58%) less than 3 Å. Even some of the matching pairs show some electrostatic similarities that need to be considered. In general there is good agreement between the folding predicted for the isolated </span><span>E<sub>INF</sub></span><span> and E<sub>T1D</sub> epitopes and the folding of the corresponding amino acid sequence in the parent antigen, but in some cases there are deviation that need to be considered, even when the RMDS is small.</span></p> <p><span> </span><strong><em><span>Conclusions:</span></em></strong><span> </span><span>Despite differences, most of the E<sub>INF</sub><span>/</span>E<sub>T1D </sub>pairs selected by sequence homology show similar structural and electrostatic distributions, indicating that the E<sub>INF</sub> may bind to the same protein targets, the major histocompatibility complex molecules, for T1DM, leading to molecular mimicry onset of the disease. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for molecular mimicry that should be considered for further study. Still structure and full docking calculations will be necessary to advance the in-silico molecular mimicry predictions. </span> Here we presnt the following files:</p> <p><span><span>·<span> </span></span></span>Fasta files of all epitopes studied.</p> <p><span><span>·<span> </span></span></span>Alphafold calculated Structures of all epitopes.</p> <p><span><span>·<span> </span></span></span>Antigen structures.</p> <p><span><span>·<span> </span></span></span>Epitope pair structure comparison and their electrostatics.</p> <p> </p> <p> </p>
INSPIRE-seq simultaneously selects nanobodies for immune epitopes in the complex tumor microenvironment
<p>scRNAseq of CD45 magnetic microbeads enriched cells were isolated form Py8119 bearing mice (three mice per pool/group) two hours after injection of either PBS, insertless phage display, CD45, DCs, or CD8 specific VHHs phage display libraries. </p>
Deep repertoire mining uncovers ultra-broad coronavirus neutralizing antibodies targeting multiple spike epitopes
<p><strong>Abstract:</strong> Development of vaccines and therapeutics that are broadly effective against known and emergent coronaviruses is an urgent priority. We screened the circulating B cell repertoires of COVID-19 survivors and vaccinees to isolate over 9,000 SARS-CoV-2-specific monoclonal Abs (<strong>mAbs</strong>), providing an expansive view of the SARS-CoV-2-specific Ab repertoire. Among the recovered antibodies was TXG-0078, an NTD-specific neutralizing mAb that recognizes diverse alpha- and beta-coronaviruses. TXG-0078 achieves its exceptional binding breadth while utilizing the same VH1-24 variable gene signature and heavy chain-dominant binding pattern seen in other NTD supersite-specific neutralizing Abs with much narrower specificity. We also report the discovery of CC24.2, a pan-sarbecovirus neutralizing antibody that targets a novel RBD epitope and shows similar neutralization potency against all tested SARS-CoV-2 variants, including BQ.1.1 and XBB.1.5. A cocktail of TXG-0078 and CC24.2 protects <i>in vivo</i>, suggesting potential use in variant-resistant therapeutic Ab cocktails and as templates for pan-coronavirus vaccine design.</p><p><strong>Datasets: </strong>This repository contains the 10x Genomic cellranger outputs (matrix and vdj contig files) as well as complied functional characterization dataset used to generate figures on the publication "Deep repertoire mining uncovers ultra-broad coronavirus neutralizing antibodies targeting multiple spike epitopes". </p><p>Post-vaccination samples for donors CC10, CC25, CC31, CC66 were processed in single 10x Genomic reactions. The timepoints samples consist of multiplexing donors CC10, CC25, CC31, CC66 into one 10x Genomic reaction. Similarly, donors CC26, CC42, CC62, CC67 were multiplexed into a single 10x Genomic reaction.</p><p><strong>Files:</strong></p><p>feature names.csv - csv file with sort bait/antigen barcode key </p><p>feature_reference.csv - csv file with cell hash and antigen barcode reference</p><p>filtered_contig<i>_</i>annotations.csv - High-level annotations of each high-confidence contigs from cell-associated barcodes. This is a subset of all_contig_annotations.csv.</p><p>filtered_contig.fasta - filtered antibody fasta</p><p>filtered_matrix.mtx.gz - 10x Genomic matrix file for filtered cells. Contains counts data for feature and gene expression library.</p><p>raw_matrix.mtx.gz - 10x Genomic matrix file for unfiltered cells. Contains counts data for feature and gene expression library.</p><p><strong>Code: </strong>All code used to generate analysis and figures is available under the MIT license on Github<br> </p>
Profiling of linear B-cell epitopes against human coronaviruses in pooled sera sampled early in the COVID-19 pandemic
<p>Background: Antibodies play a key role in the immune defence against infectious pathogens. Understanding the underlying process of B cell recognition is not only of fundamental interest; it supports important applications within diagnostics and therapeutics. Whereas the nature of conformational B cell epitope recognition is inherently complicated, linear B cell epitopes offer a straightforward approach that potentially can be reduced to one of peptide recognition.</p> <p>Methods: Using an overlapping peptide approach representing the entire proteomes of the seven main coronaviruses known to infect humans, we analysed sera pooled from eight PCR-confirmed COVID-19 convalescents and eight pre-pandemic controls. Using a high-density peptide microarray platform, 13-mer peptides overlapping by 11 amino acids were in situ synthesised and incubated with the pooled primary serum samples, followed by development with secondary fluorochrome-labelled anti-IgG and -IgA antibodies. Interactions were detected by fluorescence detection. Strong Ig interactions encompassing consecutive peptides were considered to represent "high-fidelity regions" (HFRs). These were mapped to the coronavirus proteomes using a 60% homology threshold for clustering.</p> <p>Results: We identified 333 human coronavirus derived HFRs. Among these, 98 (29%) mapped to SARS-CoV-2, 144 (44%) mapped to one or more of the four circulating common cold coronaviruses (CCC), and 54 (16%) cross-mapped to both SARS-CoV-2 and CCCs. The remaining 37 (11%) mapped to either SARS-CoV or MERS-CoV. Notably, the COVID-19 serum was skewed towards recognising SARS-CoV-2-mapped HFRs, whereas the pre-pandemic was skewed towards recognising CCC-mapped HFRs. In terms of absolute numbers of linear B cell epitopes, the primary targets are the ORF1ab protein (60%), the spike protein (21%), and the nucleoprotein (15%) in that order; however, in terms of epitope density, the order would be reversed.</p> <p>Conclusion: We identified linear B cell epitopes across coronaviruses, highlighting pan-, alpha-, beta-, or SARS-CoV-2-corona-specific B cell recognition patterns. These findings could be pivotal in deciphering past and current exposures to epidemic and endemic coronavirus. Moreover, our results suggest that pre-pandemic anti-CCC antibodies may cross-react against SARS-CoV-2, which could explain the highly variable outcome of COVID-19. Finally, the methodology used here offers a rapid and comprehensive approach to high-resolution linear B-cell epitope mapping, which could be vital for future studies of emerging infectious diseases.</p>
Supplementary Data and Figures for "Myomedin replicas of gp120 V3 loop glycan epitopes of PGT121 and PGT126 antibodies as non-cognate antigens for HIV-1 broadly neutralizing antibodies"
<p>Supplementary pymol sessions for "Myomedin replicas of gp120 V3 loop glycan epitopes of PGT121 and PGT126 antibodies as non-cognate antigens for HIV-1 broadly neutralizing antibodies". Pymol sessions contain the source data for Figure 3, panels A-K.</p>
Epitope Mapping of VZV-specific skin TRM
<p>T cell lines were generated from skin biopsies taken from rash site and contralateral (uninvolved) site skin of persons recovered from herpes zoster at days 45, 90, and 360 from rash onset. The cell lines were screened for reactivity to all VZV open-reading frame (ORF) products using high throughput assays designed to activate and measure responses of only CD8 T cells or only CD4 T cells. Artificial antigen presenting cells expressing a single HLA class I molecule from a study subject and a single VZV ORF was used to activate CD8 T cells. Secreted IFNgamma was measured (data represent OD450) in culture supernatants using ELISA. For CD4 T cells, crude proteins made by in vitro transcription translation (IVTT) was used as antigen. T cell proliferation was measured using 3H-thymidine incorporation assay (data represent CPM).</p> <p>Proteins that generated positive responses were mapped to epitope containing proteins using B-LCL as antigen presenting cells. Further studies using pan-specific HLA-DR, HLA-DP, or HLA-DQ blocking antibodies were done to identify the restricting HLA locus of the CD4 T cell response. More detailed studies confirmed the restricting HLA molecule through using artificial antigen presenting cells expressing single HLA class I (for CD8 T cells) or class II (for CD4 T cells). IFNg ELISA (OD450) was used to measure T cell activation.</p>
ESM-2 embeddings for TCR-Epitope Binding Affinity Prediction Task
<p>This is the accompanying dataset that was generated by the GitHub project: <a href="https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding">https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding</a>. In that repository I show how to create a machine learning models for predicting if a T-cell receptor (TCR) and protein epitope will bind to each other.</p> <p>A model that can predict how well a TCR bindings to an epitope can lead to more effective treatments that use immunotherapy. For example, in anti-cancer therapies it is important for the T-cell receptor to bind to the protein marker in the cancer cell so that the T-cell (actually the T-cell's friends in the immune system) can kill the cancer cell.</p> <div> <div>[HuggingFace](https://huggingface.co/facebook/esm2_t36_3B_UR50D) provides a "one-stop shop" to train and deploy AI models. In this case, we use Facebook's open-source [Evolutionary Scale Model (ESM-2)](https://github.com/facebookresearch/esm). These embeddings turn the protein sequences into a vector of numbers that the computer can use in a mathematical model.</div> <div> </div> To load them into Python use the Pandas library:</div> <pre><code>import pandas as pd train_data = pd.read_pickle("train_data.pkl") validation_data = pd.read_pickle("validation_data.pkl") test_data = pd.read_pickle("test_data.pkl")</code></pre> <p>The <strong>epitope_aa</strong> and the <strong>tcr_full</strong> columns are the protein (peptide) sequences for the epitope and the T-cell receptor, respectively. The letters correspond to the <a href="https://en.wikipedia.org/wiki/DNA_and_RNA_codon_tables">standard amino acid codes</a>.</p> <p>The <strong>epitope_smi</strong> column is the <a href="https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system">SMILES</a> notation for the chemical structure of the epitope. We won't use this information. Instead, the ESM-1b embedder should be sufficient for the input to our binary classification model.</p> <p>The <strong>tcr</strong> column is the CDR3 hyperloop. It's the part of the TCR that actually binds (assuming it binds) to the epitope.</p> <p>The <strong>label</strong> column is whether the two proteins bind. 0 = No. 1 = Yes.</p> <p>The <strong>tcr_vector</strong> and <strong>epitope_vector</strong> columns are the bio-embeddings of the TCR and epitope sequences generated by the Facebook ESM-1b model. These two vectors can be used to create a machine learning model that predicts whether the combination will produce a successful protein binding.</p> <p>From the TDC website:</p> <blockquote> <p>T-cells are an integral part of the adaptive immune system, whose survival, proliferation, activation and function are all governed by the interaction of their T-cell receptor (TCR) with immunogenic peptides (epitopes). A large repertoire of T-cell receptors with different specificity is needed to provide protection against a wide range of pathogens. This new task aims to predict the binding affinity given a pair of TCR sequence and epitope sequence.</p> <p>Weber et al.</p> </blockquote> <p>Dataset Description: The dataset is from Weber et al. who assemble a large and diverse data from the VDJ database and ImmuneCODE project. It uses human TCR-beta chain sequences. Since this dataset is highly imbalanced, the authors exclude epitopes with less than 15 associated TCR sequences and downsample to a limit of 400 TCRs per epitope. The dataset contains amino acid sequences either for the entire TCR or only for the hypervariable CDR3 loop. Epitopes are available as amino acid sequences. Since Weber et al. proposed to represent the peptides as SMILES strings (which reformulates the problem to protein-ligand binding prediction) the SMILES strings of the epitopes are also included. 50% negative samples were generated by shuffling the pairs, i.e. associating TCR sequences with epitopes they have not been shown to bind.</p> <blockquote> <p>Task Description: Binary classification. Given the epitope (a peptide, either represented as amino acid sequence or as SMILES) and a T-cell receptor (amino acid sequence, either of the full protein complex or only of the hypervariable CDR3 loop), predict whether the epitope binds to the TCR.</p> <p>Dataset Statistics: 47,182 TCR-Epitope pairs between 192 epitopes and 23,139 TCRs.</p> <p>References:</p> </blockquote> <ol> <li>Weber, Anna, Jannis Born, and María Rodriguez Martínez. “TITAN: T-cell receptor specificity prediction with bimodal attention networks.” Bioinformatics 37.Supplement_1 (2021): i237-i244.</li> <li>Bagaev, Dmitry V., et al. “VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium.” Nucleic Acids Research 48.D1 (2020): D1057-D1062.</li> <li>Dines, Jennifer N., et al. “The immunerace study: A prospective multicohort study of immune response action to covid-19 events with the immunecode™ open access database.” medRxiv (2020).</li> </ol> <blockquote> <p>Dataset License: CC BY 4.0.</p> <p>Contributed by: Anna Weber and Jannis Born.</p> </blockquote> <p> </p> <div>The Facebook ESM-2 model has the MIT license and was published in:</div> <div> </div> <div>* Zeming Lin et al, Evolutionary-scale prediction of atomic-level protein structure with a language model, Science (2023). DOI: 10.1126/science.ade2574 https://www.science.org/doi/10.1126/science.ade2574</div> <div> </div> <div>HuggingFace has several versions of the trained model.</div> <div> </div> <div> <table> <tbody> <tr> <td>Checkpoint name</td> <td>Number of layers</td> <td>Number of parameters</td> </tr> <tr> <td>esm2_t48_15B_UR50D</td> <td>48</td> <td>15B</td> </tr> <tr> <td>esm2_t36_3B_UR50D</td> <td>36</td> <td>3B</td> </tr> <tr> <td>esm2_t33_650M_UR50D</td> <td>33</td> <td>650M</td> </tr> <tr> <td>esm2_t30_150M_UR50D</td> <td>30</td> <td>150M</td> </tr> <tr> <td>esm2_t12_35M_UR50D</td> <td>12</td> <td>35M</td> </tr> <tr> <td>esm2_t6_8M_UR50D</td> <td>6</td> <td>8M</td> </tr> </tbody> </table> </div>
anti-phospho-SMAD1/5 antibody can recognize formaldehyde-fixed epitope
<p>Determining whether anti-phospho-SMAD1-5 and anti-phospho-SMAD2 antibodies can recognize formaldehyde-fixed epitopes</p>
NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
<p>Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. <em>Nature Communications</em>. Accepted (August 2019)</p> <p><strong>Abstract:</strong></p> <p>Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using <em>in vitro</em> co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.</p>
Monoclonal antibodies from humans with Mycobacterium tuberculosis exposure or latent infection recognize distinct arabinomannan epitopes
<p>The surface polysacharide arabinomannan (AM) and related glycolipid lipoarabinomannan (LAM) play critical roles in tuberculosis pathogenesis. Human antibody responses to AM/LAM are heterogenous and knowledge of reactivity to specific glycan epitopes at the monoclonal level is limited, especially in individuals who can control <i><span>M. tuberculosis</span></i> infection<span>. </span>We generated human IgG mAbs to AM/LAM from B cells of two asymptomatic individuals exposed to or latently infected with <i><span>M. tuberculosis</span></i>. We here show that two of these mAbs have high affinity to AM/LAM, are non-competing, and recognize different glycan epitopes <span>distinct from other anti-AM/LAM mAbs reported. Both mAbs recognize virulent </span><i><span>M. tuberculosis</span></i><span> and nontuberculous mycobacteria with marked differences, can be used for the detection of urinary LAM, and can detect </span><i><span>M. tuberculosis</span></i><span> and LAM in infected lungs. These mAbs enhance our understanding of the spectrum of antibodies to AM/LAM epitopes </span>in humans <span>and</span> are valuable for <span>tuberculosis</span> diagnostic and research applications.</p>
HLA epitopic mismatch and risk of developing Donor Specific Antibodies in pediatric kidney transplantation
<p>The aim of this study was to evaluate the correlation between epitopic HLA mismatches and the risk of developing DSA in pediatric kidney recipients. There are very few publications concerning epitope HLA mismatch in pediatric kidney transplantation although this topic has been growing in scientific articles in recent years and is promising in adult recipients. The majority of children with chronic renal failure will require multiple kidney transplants during their lifetime and it is therefore important that we can limit the risk of antibody development in order to give them the best chance of survival for their future kidney transplants.</p>
Supplementary Figures -B Cell Epitopes Mapping of the Vibrio cholera Toxins A, B, and P and an ELISA assay
<p>Figure S1- List of <em>Vibrio cholerae</em> [toxin A (P01555), B (P01556), and P (P29485)] synthetic peptides and position in the cellulose membrane of Spot synthesis.</p> <p>Figure S2- Purification of the peptide B (Vc/TxB-11) by HPLC using an XBridge BEH C18 (2.7 μ, 5 cm x 4.6 mm) column coupled to a Water HPLC system at a flow rate of 1.2 ml min<sup>-1 </sup>using mobile phases A [0.05% formic acid in water (18 MΩ × cm)] and B [(0.05% formic acid in ACN (acetonitrile acid)] (v/v) in water. Detection at 200-300 nm using a diode array.</p> <p>Figure S3- Mass spectrometry. The peptide B (Vc/TxB-11) was solubilized in deionized water to a final concentration of 10 µg/ml and then added formic acid to a final concentration of 0.1%. The mass spectrometer used was the Water UPLC model Acquity-I Class. The samples were electronically injected by the equipment at 1 µl/min. The range used for ion detection ranged from 1000-11500 m/z.</p>
Data from: Genome-wide epitope mapping reveals significant diversity in antibody responses to Coxiella burnetii vaccination and infection
<p><em>Coxiella burnetii</em> is an important zoonotic bacterial pathogen of global importance, causing the disease Q fever in a wide range of animal hosts. Ruminant livestock, in particular sheep and goats, are considered the main reservoir of infection. Vaccination is a key control measure and two commercial vaccines based on formalin-inactivated<em> C. burnetii </em>bacterins are currently available. However, their deployment is limited due to significant reactogenicity in individuals previously sensitized to <em>C. burnetii </em>antigens. Furthermore, these vaccines interfere with available serodiagnostic tests which are also based on <em>C. burnetii</em> bacterin preparations. Subunit vaccines based on recombinant proteins offer significant advantages, as they can be designed to reduce reactogenicity and can be co-designed with defined antigen serodiagnostic tests to allow discrimination between vaccinated and infected individuals. This study aimed to investigate the diversity of antibody responses to <em>C. burnetii </em>vaccination and/or infection in cattle, goats, humans, and sheep through genome-wide linear epitope mapping to identify candidate vaccine and diagnostic antigens within the predicted bacterial proteome. Using high-density peptide microarrays, we analyzed the seroreactivity in 156 serum samples from vaccinated and infected individuals to peptides derived from 2,092 ORFs in the <em>C. burnetii</em> genome. We found significant diversity in the antibody responses within and between species and across different <em>C. burnetii</em> exposure statuses. However, <em>C. burnetii</em> exposure did result in more uniform seroreactivity across species. Through the implementation of three different vaccine candidate methods, we identified 493 candidate protein antigens for protein subunit vaccine design or serodiagnostic, out of which 65 have been previously described. This is the first study to investigate seroreactivity against the entire <em>C. burnetii </em>genome presented as overlapping linear peptides and provides the basis for selection of antigen targets for next generation Q fever vaccines and diagnostic tests.</p>
Perioperative MVT-5873, a Fully Human Monoclonal Antibody Against a CA 19-9 Epitope, for Operable CA 19-9 Producing Pancreatic Cancers, Cholangiocarcinomas, and Metastatic Colorectal Cancers
ClinicalTrials.gov study NCT03801915. IPD Sharing: YES. Countries: 1. Publications: 1.
A Laboratory Evaluation of the Humoral Immune Response in Adults and Children to the H1 Hemagglutinin (HA) Stalk Domain and Other Influenza A Virus Protein Epitopes, After Administration of GlaxoSmith
ClinicalTrials.gov study NCT02415842. IPD Sharing: NO. Countries: 1. Publications: 1.
A Pilot Study of Vaccination With Epitope-Enhanced TARP Peptide and TARP Peptide-Pulsed Dendritic Cells in the Treatment of Stage D0 Prostate Cancer
ClinicalTrials.gov study NCT00972309. IPD Sharing: YES. Countries: 1. Publications: 4.
Data from: Genome-wide epitope mapping reveals significant diversity in antibody responses to Coxiella burnetii vaccination and infection
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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