IEDB-AR: immune epitope database—analysis resource in 2019

Dhanda, Sandeep Kumar; Mahajan, Swapnil; Paul, Sinu; Zhen, Yan; Kim, Haeuk; Jespersen, Martin Closter; Jurtz, Vanessa Isabell; Andreatta, Massimo; Greenbaum, Jason A.; Marcatili, Paolo; Sette, Alessandro; Nielsen, Morten; Peters, Bjoern

doi:10.1093/nar/gkz452

Cited by 276 publications

(249 citation statements)

References 35 publications

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“…Both the AP and the IP epitope predictions are "pan" HLA or HLA-agnostic and can be carried out for any allele in the human population, however for the purpose of this study we limited the analysis to the 100 of the most frequent HLA-A, HLA-B and HLA-DR alleles in the human population. Class II HLA binding predictions were also incorporated into the large scale epitope screen from the IEDB consensus of tools [33], and B cell epitope predictions were performed using BepiPred [33].…”

Section: The Immunogenic Landscape Of Sars-cov-2 Reveals Diversity Ammentioning

confidence: 99%

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Malone

Simovski²,

Moliné³

et al. 2020

Preprint

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The global population is at present suffering from a pandemic of Coronavirus disease 2019 , caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

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Section: The Immunogenic Landscape Of Sars-cov-2 Reveals Diversity Ammentioning

confidence: 99%

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Malone

Simovski²,

Moliné³

et al. 2020

Preprint

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“…For identification of CD4 T cell epitopes, MHC class II binding predictions were performed on 15mer peptides found within positive peptide pools identified by IFN-γ Enzyme-Linked Immunospot assay (ELISPOT). Using the MHC Class II Binding, T Cell Epitope Prediction resource of the Immune Epitope Database (IEDB, https://www.iedb.org/), peptides restricted to mouse MHC class II allele H2-IAb were analyzed using "IEDB Recommended" prediction method [58]. The most promising candidates were then chosen for further experimental epitope prediction studies.…”

Section: Generation Of Synthetic Peptides Design Of Peptide Pools Amentioning

confidence: 99%

A Soluble Version of Nipah Virus Glycoprotein G Delivered by Vaccinia Virus MVA Activates Specific CD8 and CD4 T Cells in Mice

Kalodimou

Veit²,

Jany³

et al. 2019

Viruses

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Nipah virus (NiV) is an emerging zoonotic virus that is transmitted by bats to humans and to pigs, causing severe respiratory disease and often fatal encephalitis. Antibodies directed against the NiV-glycoprotein (G) protein are known to play a major role in clearing NiV infection and in providing vaccine-induced protective immunity. More recently, T cells have been also shown to be involved in recovery from NiV infection. So far, relatively little is known about the role of T cell responses and the antigenic targets of NiV-G that are recognized by CD8 T cells. In this study, NiV-G protein served as the target immunogen to activate NiV-specific cellular immune responses. Modified Vaccinia virus Ankara (MVA), a safety-tested strain of vaccinia virus for preclinical and clinical vaccine research, was used for the generation of MVA-NiV-G candidate vaccines expressing different versions of recombinant NiV-G. Overlapping peptides covering the entire NiV-G protein were used to identify major histocompatibility complex class I/II-restricted T cell responses in type I interferon receptor-deficient (IFNAR−/−) mice after vaccination with the MVA-NiV-G candidate vaccines. We have identified an H2-b-restricted nonamer peptide epitope with CD8 T cell antigenicity and a H2-b 15mer with CD4 T cell antigenicity in the NiV-G protein. The identification of this epitope and the availability of the MVA-NiV-G candidate vaccines will help to evaluate NiV-G-specific immune responses and the potential immune correlates of vaccine-mediated protection in the appropriate murine models of NiV-G infection. Of note, a soluble version of NiV-G was advantageous in activating NiV-G-specific cellular immune responses using these peptides. the first time NiV emerged in southern India [2]. Two strains of NiV have been identified, Malaysia and Bangladesh strains, which share 91.8% sequence homology [3]. NiV causes severe respiratory disease and encephalitis [4][5][6], with average case fatality rates (CFR) of 40-75% [7]. Moreover, long-term neurological sequelae and even relapse of encephalitis are observed in many survivors of infections with both strains of NiV [8][9][10].The natural reservoir of NiV and Hendra virus (HeV) are the fruit bats of the genus Pteropus, which are widely distributed in Asia, Australasia, and parts of Africa [11,12]. Moreover, NiV has a broad species tropism and can cause disease in a many animal species [13][14][15]. NiV can infect humans via several routes, which include the consumption of food contaminated with bat secretions [16], transmission from amplification hosts such as pigs [4], and direct human-to-human transmission between very close contacts [17]. Currently there are no licensed treatments or preventative vaccines available for use in humans, which make the development of effective prophylactic measures imperative.Evidence to date indicates that the Henipavirus glycoprotein G is a highly promising target of virus-neutralizing antibodies to counteract infections with highly pathogenic henipaviruses. The G gly...

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“…We also plan to extend the use of ChEBI within UniProtKB to describe all small molecule chemical structure data, including functionally important ligands and post-translational modifications (see https://www.uniprot.org/docs/ptmlist). This will further improve links and interoperability between UniProtKB and other resources that use ChEBI such as the metabolomics data repository MetaboLights (Kale, et al, 2016), the IMEx molecular interaction databases (Orchard, et al, 2012) and the Complex Portal (Meldal, et al, 2015), the literature annotation services of Europe PubMed Central (Europe PMC Consortium, 2015), the BioModels repository (Glont, et al, 2018), and the Immune Epitope Database (IEDB) (Dhanda, et al, 2019).…”

Section: Conclusion and Futures Directionsmentioning

confidence: 99%

Enzyme annotation in UniProtKB using Rhea

Morgat

Lombardot

Coudert

et al. 2019

Preprint

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Motivation: To provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ontology ChEBI (Chemical Entities of Biological Interest). Results:We replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API, and SPARQL endpoint that leverage the chemical structure data, nomenclature, and classification that Rhea and ChEBI provide.Availability and Implementation: UniProtKB at https://www.uniprot.org/; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/sparql; Rhea at https://www.rhea-db.org/.Contact: anne.morgat@sib.swiss

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IEDB-AR: immune epitope database—analysis resource in 2019

Cited by 276 publications

References 35 publications

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

A Soluble Version of Nipah Virus Glycoprotein G Delivered by Vaccinia Virus MVA Activates Specific CD8 and CD4 T Cells in Mice

Enzyme annotation in UniProtKB using Rhea

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