Optimizing vaccine design for cellular processing, MHC binding and TCR recognition

Sette, Alessandro; Newman, Mark J.; Livingston, Brian; McKinney, Denise M.; Sidney, John; Ishioka, Glenn; Tangri, Shabnam; Alexander, Jeff; Fikes, John; Chesnut, Robert W.

doi:10.1034/j.1399-0039.2002.590601.x

Cited by 101 publications

(86 citation statements)

References 42 publications

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“…Latter, the logical framework of this approach has been inverted. Instead of developing a single epitope for each of the common HLA alleles, attempts were made to identify epitopes capable of binding multiple HLA types (12)(13)(14)16). The grouping of alleles into supertypes, based on common structural and functional features, is useful in addressing such attempts.…”

Section: Discussionmentioning

confidence: 99%

“…It has been found that certain class I alleles can recognize similar motifs (11)(12)(13)(14) and thus be grouped into HLA "supertypes", binding common "supermotifs". The classification of MHC molecules into supertypes, based on structural features and/or peptide specificity, is of prime importance in the development of epitope-based vaccines (15,16). The experimental determination of motifs for every allele is prohibitively expensive in terms of labor, time, and resources.…”

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confidence: 99%

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In Silico Identification of Supertypes for Class II MHCs

Doytchinova

Flower

2005

The Journal of Immunology

177

192

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The development of epitope-based vaccines, which have wide population coverage, is greatly complicated by MHC polymorphism. The grouping of alleles into supertypes, on the basis of common structural and functional features, addresses this problem directly. In the present study we applied a combined bioinformatics approach, based on analysis of both protein sequence and structure, to identify similarities in the peptide binding sites of 2225 human class II MHC molecules, and thus define supertypes and supertype fingerprints. Two chemometric techniques were used: hierarchical clustering using three-dimensional Comparative Similarity Indices Analysis fields and nonhierarchical k-means clustering using sequence-based z-descriptors. An average consensus of 84% was achieved, i.e., 1872 of 2225 class II molecules were classified in the same supertype by both techniques. Twelve class II supertypes were defined: five DRs, three DQs, and four DPs. The HLA class II supertypes and their fingerprints given in parenthesis are DR1 (Trp9β), DR3 (Glu9β, Gln70β, and Gln/Arg74β), DR4 (Glu9β, Gln/Arg70β, and Glu/Ala74β), DR5 (Glu9β, Asp70β), and DR9 (Lys/Gln9β); DQ1 (Ala/Gly86β), DQ2 (Glu86β, Lys71β), and DQ3 (Glu86β, Thr/Asp71β); DPw1 (Asp84β and Lys69β), DPw2 (Gly/Val84β and Glu69β), DPw4 (Gly/Val84β and Lys69β), and DPw6 (Asp84β and Glu69β). Apart from the good agreement between known binding motifs and our classification, several new supertypes, and corresponding thematic binding motifs, were also defined.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

In Silico Identification of Supertypes for Class II MHCs

Doytchinova

Flower

2005

The Journal of Immunology

177

192

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“…can induce more potent immune responses and they appear to be safer to use and easier to produce [21,20,22,13].…”

Section: Introductionmentioning

confidence: 99%

Identifying HLA supertypes by learning distance functions

Hertz

Yanover²

2007

Bioinformatics

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The development of epitope-based vaccines crucially relies on the ability to classify Human Leukocyte Antigen (HLA) molecules into sets that have similar peptide binding specificities, termed supertypes. In their seminal work, Sette and Sidney [21] defined 9 HLA class I supertypes, and claimed that these provide an almost perfect coverage of the entire repertoire of HLA class I molecules.HLA alleles are highly polymorphic and polygenic and therefore experimentally classifying each of these molecules to supertypes is at present an impossible task. Recently, a number of computational methods have been proposed for this task. These methods are based on defining protein similarity measures, derived from analysis of binding peptides or from analysis of the proteins themselves [13,7]. In this paper we define both peptide derived and protein derived similarity measures, which are based on learning distance functions. The peptide driven measure is defined using a peptide-peptide distance function, which is learnt using information about known binding and non-binding peptides [28]. The protein similarity measure is defined using a protein-protein distance function, which is learnt using information about alleles previously classified into supertypes by [21]. We compare the classification obtained by these two complimentary methods to previously suggested classification methods. In general, our results are in excellent agreement with the classifications suggested by Sette and Sidney [21] and with those reported in [13].There are two important advantages of our proposed distancebased approach. First, it makes use of two different and important immunological sources of information -HLA alleles and peptides that are known to bind or not bind to these alleles. Second, since each of our distance measures is trained using a different source of information, their combination can provide a more confident classification of alleles into supertypes.

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“…E7-LLMGTLGIV82-90 was known to induce the cellular response in HLA A2.1 rabbit model [39] and reduced tumor burden in aged mice [40]. E6-KLPDLCTEL13-22 [41] and E7-TLQDIVLHL [7][8][9][10][11][12][13][14][15] One of the predicted peptides, QLFVTVVDT [42] were proved to be CTL epitopes for HPV 18.…”

Section: Discussionmentioning

confidence: 99%

“…The accurate identification of CTL epitopes is a critical step towards the development of peptide vaccine [12]. The identification of CTL epitopes could be accelerated using in silico prediction methods [13].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Prediction of Human Papilloma Virus Specific T-Cell Epitopes Using a Combination of Matrix Based Computational Tools

Mohan

Sundar

2017

Int J Pharm Pharm Sci

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Objective: To predict the immunogenic epitopes from human papillomavirus (HPV) virus using matrix based computational tools. Methods:In the present study, three matrix based algorithms, SYFPETHI, BIMAS and RANKPEP were used to predict the cytotoxic T lymphocyte (CTL) epitopes of HPV 16 and 18. The ability of the peptides to bind HLA A_0201, a most common allele, was evaluated using these algorithms. High scoring peptides were considered as potential binders.Results: Evaluation of HPV 16 proteome resulted in the prediction of 249 peptides as potential binders. Out of these only 25 peptides were predicted as binders by all three algorithms. Analysis of HPV 18 predicted 215 peptides, as potential binders. Among the 215 peptides only 20 peptides were predicted as binders by all three algorithms. Conclusion:The efficacy of these peptides in inducing a stronger immune response needs to be tested using in vitro and in vivo assays. The identified epitopes could be used in designing a novel epitope vaccine for HPV.

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Optimizing vaccine design for cellular processing, MHC binding and TCR recognition

Cited by 101 publications

References 42 publications

In Silico Identification of Supertypes for Class II MHCs

In Silico Identification of Supertypes for Class II MHCs

Identifying HLA supertypes by learning distance functions

Genome-Wide Prediction of Human Papilloma Virus Specific T-Cell Epitopes Using a Combination of Matrix Based Computational Tools

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