Major histocompatibility complex class I binding predictions as a tool in epitope discovery

Lundegaard, Claus; Lund, Ole; Buus, Søren; Nielsen, Morten

doi:10.1111/j.1365-2567.2010.03300.x

Cited by 114 publications

(85 citation statements)

References 103 publications

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“…In fact, whether proteasomal cleavage and TAP transport predictions should be incorporated into neoepitope prediction pipelines is another controversially discussed topic. 5,45,48 The fact that we did not see an increase in performance when combining binding and proteasomal cleavage and/or TAP transport predictions should however not be taken to signify that these processes are not relevant. Proteasomal cleavage prediction and TAP transport prediction alone achieved reasonable AUC values, showing that these tools indeed have predictive value, but this value gets lost when combined with binding predictions.…”

Section: Discussionmentioning

confidence: 71%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4

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Section: Discussionmentioning

confidence: 71%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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“…A total of 76 (38%) of the 200 positive HLA-restricted ELISpot responses corresponded to at least one predicted nonamer epitope with a concordant predicted HLA restriction (see Table S1). This relatively low extent of correspondence among the discovered HLA-restricted immunogenic peptides and predicted HLA binding motifs is comparable to those reported for other pathogens (27) and can be attributed, at least in part, to the fact that HLA binding prediction does not account for the pivotal role of the TCR in the immunogenicity of a peptide (67).…”

Section: Identification Of Hla-restricted T-cell Ligand Sequences Of mentioning

confidence: 63%

West Nile Virus T-Cell Ligand Sequences Shared with Other Flaviviruses: a Multitude of Variant Sequences as Potential Altered Peptide Ligands

Jung

Khan

Tan

et al. 2012

J Virol

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West Nile virus (WNV), a member of the genus Flavivirus, is a mosquito-borne RNA pathogen closely related to numerous other flaviviruses of the Japanese encephalitis (JE) group, and to a lesser degree to Dengue virus (DENV), Yellow fever virus (YFV), and several other flaviviruses of about 70 different species (5, 18). The genome is a positive-sense, single-stranded RNA of approximately 10,293 nucleotides encoding a polyprotein of approximately 3,430 amino acids (aa) that is cleaved to produce three structural proteins, capsid (C), precursor membrane (prM), and envelope (E), and seven nonstructural (NS) proteins, NS1, 2a, 2b,3, 4a, 4b, and 5 (24,37). Originally isolated in Africa in 1937 (62), WNV has become an increasingly important human pathogen widely endemic in Africa, Asia, Europe, and North America and a significant agent of viral encephalitis (28,38,45). WNV and several of the major human pathogen flaviviruses are known to cocirculate (28, 71); for example, WNV and St. Louis encephalitis virus (LEV) in North America; WNV, DENV, and Japanese encephalitis virus (JEV) around the Indian subcontinent and portions of Southeast Asia (SEA); and WNV, DENV, JEV, and Murray Valley encephalitis virus (MVE) in neighboring pockets of SEA and Australasia.The widespread colocalization of WNV with other flaviviruses calls for a greater understanding of the phylogenetic relatedness and possible pathophysiologic associations of flaviviruses. We previously reported a large-scale analysis of the conservation and variability of overlapping nonamers, the typical length of human leukocyte antigen (HLA) class I or class II binding cores of T-cell epitopes (47), of the 2,746 WNV protein sequences collected from the NCBI Entrez Protein Database (17). Notably, of the 88 completely conserved sequence fragments identified, representing 34% of the WNV proteome, 67 were also present in many other flaviviruses with identities of nine or more amino acids. These sequence homologies called attention to a broad inter-Flavivirus risk of altered peptide ligands (APL) (58), T-cell epitopes with one or more amino acid differences, that possibly would result in modified immune responses in the event of exposure to multiple Flavivirus pathogens.This study focused on analyses of the diversity and presence in

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“…For example, it is known that HLA-A*0201 and HLA A*0301 share 93% sequence identity but exhibit completely different peptide binding specificities [19]. Thus, clustering on function rather than sequence of MHC molecules is a more important parameter in CTL biology and immunology.…”

Section: Discussionmentioning

confidence: 99%

Use of “one-pot, mix-and-read” peptide-MHC class I tetramers and predictive algorithms to improve detection of cytotoxic T lymphocyte responses in cattle

Svitek

Hansen

Steinaa

et al. 2014

Vet Res

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Peptide-major histocompatibility complex (p-MHC) class I tetramer complexes have facilitated the early detection and functional characterisation of epitope specific CD8+ cytotoxic T lymphocytes (CTL). Here, we report on the generation of seven recombinant bovine leukocyte antigens (BoLA) and recombinant bovine β2-microglobulin from which p-MHC class I tetramers can be derived in ~48 h. We validated a set of p-MHC class I tetramers against a panel of CTL lines specific to seven epitopes on five different antigens of Theileria parva, a protozoan pathogen causing the lethal bovine disease East Coast fever. One of the p-MHC class I tetramers was tested in ex vivo assays and we detected T. parva specific CTL in peripheral blood of cattle at day 15-17 post-immunization with a live parasite vaccine. The algorithm NetMHCpan predicted alternative epitope sequences for some of the T. parva CTL epitopes. Using an ELISA assay to measure peptide-BoLA monomer formation and p-MHC class I tetramers of new specificity, we demonstrate that a predicted alternative epitope Tp229-37 rather than the previously reported Tp227-37 epitope is the correct Tp2 epitope presented by BoLA-6*04101. We also verified the prediction by NetMHCpan that the Tp587-95 epitope reported as BoLA-T5 restricted can also be presented by BoLA-1*02301, a molecule similar in sequence to BoLA-T5. In addition, Tp587-95 specific bovine CTL were simultaneously stained by Tp5-BoLA-1*02301 and Tp5-BoLA-T5 tetramers suggesting that one T cell receptor can bind to two different BoLA MHC class I molecules presenting the Tp587-95 epitope and that these BoLA molecules fall into a single functional supertype.

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Major histocompatibility complex class I binding predictions as a tool in epitope discovery

Cited by 114 publications

References 103 publications

Predicting T cell recognition of MHC class I restricted neoepitopes

Predicting T cell recognition of MHC class I restricted neoepitopes

West Nile Virus T-Cell Ligand Sequences Shared with Other Flaviviruses: a Multitude of Variant Sequences as Potential Altered Peptide Ligands

Use of “one-pot, mix-and-read” peptide-MHC class I tetramers and predictive algorithms to improve detection of cytotoxic T lymphocyte responses in cattle

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