Predicting HLA CD4 Immunogenicity in Human Populations

Dhanda, Sandeep Kumar; Karosiene, Edita; Edwards, Lindy; Grifoni, Alba; Paul, Sinu; Andreatta, Massimo; Weiskopf, Daniela; Sidney, John; Nielsen, Morten; Peters, Bjoern; Sette, Alessandro

doi:10.3389/fimmu.2018.01369

Cited by 104 publications

(81 citation statements)

References 115 publications

(58 reference statements)

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“…Surprisingly, this was not the case and we found that 42% and 77·8% of MHCLEp clusters were also T‐cell assay +, for class I and class II, respectively. This is in agreement with the notion that MHC binding and ligand processing are necessary, but not sufficient, for T‐cell immunogenicity …”

Section: Resultssupporting

confidence: 91%

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Development of a novel clustering tool for linear peptide sequences

et al. 2018

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SummaryEpitopes identified in large‐scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope‐related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the ‘clique method’, in which all members of the cluster must share the same minimal level of identity with each other, and the ‘connected graph method’, in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel ‘cluster‐breaking algorithm’ for consensus sequence driven sub‐clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the ‘clique method’, (ii) sets of allergen‐derived peptides from related species for the ‘connected graph method’ and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T‐cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed ‘cluster‐breaking algorithm’. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/.

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Section: Resultssupporting

confidence: 91%

“…This is in agreement with the notion that MHC binding and ligand processing are necessary, but not sufficient, for T-cell immunogenicity. 45…”

Section: Further Analysis Of the Overlap Between Mhc Binding Mhcle Amentioning

confidence: 99%

Development of a novel clustering tool for linear peptide sequences

et al. 2018

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“…MHC-peptide binding predictions were performed using publicly available tools hosted by the Immune Epitope Database (IEDB) Analysis Resource [22]. Specifically, the prediction of peptides was established by the 7-allele HLA class II restricted method and by using peptides 15 residues in length and overlapping by 10 residues [23,24]. Additional filtering using an epitope cluster analysis tool [25] was performed.…”

Section: Study Approvalmentioning

confidence: 99%

Development and Validation of a Bordetella pertussis Whole-Genome Screening Strategy

Antunes

Quiambao

Sutherland

et al. 2020

Journal of Immunology Research

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The immune response elicited by the protective whole-cell pertussis (wP) versus the less-protective acellular pertussis (aP) vaccine has been well characterized; however, important clinical problems remain unsolved, as the inability of the currently administered aP vaccine is resulting in the reemergence of clinical disease (i.e., whooping cough). Strong evidence has shown that original, childhood aP and wP priming vaccines provide a long-lasting imprint on the CD4+ T cells that impacts protective immunity. However, aP vaccination might prevent disease but not infection, which might also affect the breadth of responses to Bordetella pertussis (BP) antigens. Thus, characterizing and defining novel targets associated with T cell reactivity are of considerable interest. Here, we compare the T cell reactivity of original aP and wP priming for different antigens contained or not contained in the aP vaccine and define the basis of a full-scale genomic map of memory T cell reactivity to BP antigens in humans. Our data show that the original priming after birth with aP vaccines has higher T cell reactivity than originally expected against a variety of BP antigens and that the genome-wide mapping of BP using an ex vivo screening methodology is feasible, unbiased, and reproducible. This could provide invaluable knowledge towards the direction of a new and improved pertussis vaccine design.

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“…The performance of the "seven-allele method" in predicting immunogenicity in patient populations was evaluated in a subsequent study (32). In the same study, we also considered an agnostic approach, where we used T-cell recognition data to directly train predictive algorithms.…”

Section: Predicting Immunogenicity In Vivo In Human Populationsmentioning

confidence: 99%

Major Histocompatibility Complex Binding, Eluted Ligands, and Immunogenicity: Benchmark Testing and Predictions

et al. 2020

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Antidrug antibody (ADA) responses impact drug safety, potency, and efficacy. It is generally assumed that ADA responses are associated with human leukocyte antigen (HLA) class II-restricted CD4+ T-cell reactivity. Although this review does not address ADA responses per se, the analysis presented here is relevant to the topic, because measuring or predicting CD4+ T-cell reactivity is a common strategy to address ADA and immunogenicity concerns. Because human CD4+ T-cell reactivity relies on the recognition of peptides bound to HLA class II, prediction, or measurement of the capacity of different peptides to bind or be natural ligands of HLA class II is used as a predictor of CD4+ T-cell reactivity and ADA development. Thus, three different interconnected variables are commonly utilized in predicting T-cell reactivity: major histocompatibility complex (MHC) binding, capacity to be generated as natural HLA ligands, and T-cell immunogenicity. To provide the scientific community with guidance in the relative merit of different approaches, it is necessary to clearly define what outcomes are being considered. Thus, the accuracy of HLA binding predictions varies as a function of what the outcome predicted is, whether it is binding itself, natural processing, or T-cell immunogenicity. Furthermore, it is necessary that the accuracy of prediction is based on rigorous benchmarking, grounded by fair, objective, transparent, and experimental criteria. In this review, we provide our perspective on how different variables and methodologies predict each of the various outcomes and point out knowledge gaps and areas to be addressed by further experimental work.

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Predicting HLA CD4 Immunogenicity in Human Populations

Cited by 104 publications

References 115 publications

Development of a novel clustering tool for linear peptide sequences

Development of a novel clustering tool for linear peptide sequences

Development and Validation of a Bordetella pertussis Whole-Genome Screening Strategy

Major Histocompatibility Complex Binding, Eluted Ligands, and Immunogenicity: Benchmark Testing and Predictions

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