Coupling In Silico and In Vitro Analysis of Peptide-MHC Binding: A Bioinformatic Approach Enabling Prediction of Superbinding Peptides and Anchorless Epitopes

Doytchinova, Irini; Walshe, Valerie; Jones, Nicola; Gloster, Simone; Borrow, Persephone; Flower, Darren R.

doi:10.4049/jimmunol.172.12.7495

Cited by 71 publications

(80 citation statements)

References 39 publications

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“…The veracity of this analysis is confirmed, as far as possible, by reference to known peptide binding motifs. Although such motifs are an imperfect, or at least incomplete, representation of binding (102,103), they have clear utility as an approximation to peptide specificity. All supertypes are theoretically derived.…”

Section: ␤mentioning

confidence: 99%

In Silico Identification of Supertypes for Class II MHCs

Doytchinova

Flower

2005

The Journal of Immunology

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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: ␤mentioning

confidence: 99%

In Silico Identification of Supertypes for Class II MHCs

Doytchinova

Flower

2005

The Journal of Immunology

Self Cite

177

192

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“…The additive method is universal, being equally applicable to any peptide-protein interaction. We have subsequently used this method in the cyclical optimization of high affinity peptides binding to HLA-A*0201, generating superbinders and anchorless epitopes (25). In the present study we used the additive method to develop a TAP binding prediction model, analyzing and extending the TAP binding motif.…”

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confidence: 99%

Transporter Associated with Antigen Processing Preselection of Peptides Binding to the MHC: A Bioinformatic Evaluation

Doytchinova

Hemsley

Flower

2004

The Journal of Immunology

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TAP is responsible for the transit of peptides from the cytosol to the lumen of the endoplasmic reticulum. In an immunological context, this event is followed by the binding of peptides to MHC molecules before export to the cell surface and recognition by T cells. Because TAP transport precedes MHC binding, TAP preferences may make a significant contribution to epitope selection. To assess the impact of this preselection, we have developed a scoring function for TAP affinity prediction using the additive method, have used it to analyze and extend the TAP binding motif, and have evaluated how well this model acts as a preselection step in predicting MHC binding peptides. To distinguish between MHC alleles that are exclusively dependent on TAP and those exhibiting only a partial dependence on TAP, two sets of MHC binding peptides were examined: HLA-A*0201 was selected as a representative of partially TAP-dependent HLA alleles, and HLA-A*0301 represented fully TAP-dependent HLA alleles. TAP preselection has a greater impact on TAP-dependent alleles than on TAP-independent alleles. The reduction in the number of nonbinders varied from 10% (TAP-independent) to 33% (TAP-dependent), suggesting that TAP preselection is an important component in the successful in silico prediction of T cell epitopes.

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“…Experimental characterization of thousands of MHC-binding ligands and T-cell epitopes and the crystal structural resolution of a variety of pMHC complexes provide a solid information base for the development of various algorithms for computational prediction of MHC-binding peptides. Many APL with substitution at MHC anchor residues had been successfully generated by using in silico techniques such as Monte Carlo, molecular dynamics simulations, and free energy simulation [16,17]. When referring to designing new APL with amino acid substitutions at the TCR contact site, a highperformance in silico screening method to predict and guide such a modification is still lacking, as there is still a big bottle neck for using in silico techniques to calculate the interaction strength between two proteins, such as the TCR and pMHC.…”

Section: Discussionmentioning

confidence: 99%

“…In the past, only a few APL were identified by methods such as eluting naturally occurring mutant peptides from tumor cells, highthroughput screening of synthetic combinatorial peptides libraries, and random phage displayed peptides libraries; however, these methods are costly and time consuming [14,15]. It is thus necessary to develop a novel rational approach to guide such substitution.There have been many successful studies on evaluating the design of APL for increased MHC-binding affinity through the calculation of peptide and MHC interaction energies using in silico techniques [16,17]. We reasoned that calculation of the interaction energy between TCR and pMHC using computer-aided methods could be applied in the design of APL for enhanced TCR engagement.…”

mentioning

confidence: 99%

“…There have been many successful studies on evaluating the design of APL for increased MHC-binding affinity through the calculation of peptide and MHC interaction energies using in silico techniques [16,17]. We reasoned that calculation of the interaction energy between TCR and pMHC using computer-aided methods could be applied in the design of APL for enhanced TCR engagement.…”

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confidence: 99%

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Rational optimization of tumor epitopes using in silico analysis‐assisted substitution of TCR contact residues

et al. 2009

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Altered peptide ligands with increased affinity of the peptide-MHC complex for the TCR provide an alternative strategy to natural T-cell epitopes for cancer immunotherapy, as they can recruit and stimulate stronger T-cell repertoires. However, it remains unclear how alterations of the TCR contact residues improve the interaction between the peptide-MHC complex and the TCR molecule. In this study, we introduced a molecular simulation strategy to optimize a tumor immunodominant epitope NY-ESO-1 157-165 by the substitution of the potential TCR contact residues. We correlated molecule simulation with T-cell activation capacity assay and detected the effect of modifications of TCR contact residues on T-cell recognition. An agonist peptide W5F with substitution at Trp5 with Phe was identified and it exhibits a stronger ability to induce a cross-reactive CTL response with the WT peptide. Additionally, the W5F-induced CTL could be maintained with the WT peptide and possess higher capacity in lysing native NY-ESO-1-expressing tumor cells. These results provide important insights into the enhanced immunogenicity of epitopes through substitution at the TCR contact sites and revealed a novel molecular simulation approach for rational therapeutic peptide vaccine design.Key words: Cancer immunotherapy . Epitope . Molecular simulation . TCR contact residue IntroductionInduction of antigen-specific CTL by therapeutic peptide vaccination is a promising approach for cancer immunotherapy [1]. The specific cellular immune response starts from recognition by TCR of an immunogenic epitope presented in the context of the class I MHC molecules. Thus, modulation of CTL response by manipulating T-cell epitopes is a particularly attractive approach for cancer immunotherapy, because peptides from cancer cells are usually poorly immunogenic and often induce immune-tolerance [2,3]. Vaccination with altered peptide ligands (APL), which can be generated by appropriate amino acid substitutions at certain T-cell epitopes, has become an attractive strategy to enhance specic T-cell responses to tumors. This strategy can be achieved by two general approaches: (i) by increasing the affinity between the epitope and the MHC through substitution in the MHC anchor residues or (ii) by enhancing the interactions between the TCR and peptide-MHC (pMHC) complex through alteration at the TCR contact residues [4,5].Although APL with altered MHC contact residues can efficiently activate tumor-specific T cells in vitro, vaccination with this kind of APL has generally failed to elicit an effective anti-tumor CTL response 2248that can lead to clinical tumor regression [6,7]. APL with increased pMHC complex affinity for the TCR molecule, which are designed by modifications at the TCR contact sites rather than the MHC anchor residues, have unexpected potency to induce stronger T-cell responses and may even covert cross-reactive T cells from a tolerance state [8][9][10]. According to the structure of the TCR/pMHC complex established by X-ray crystallography, recogni...

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Coupling In Silico and In Vitro Analysis of Peptide-MHC Binding: A Bioinformatic Approach Enabling Prediction of Superbinding Peptides and Anchorless Epitopes

Cited by 71 publications

References 39 publications

In Silico Identification of Supertypes for Class II MHCs

In Silico Identification of Supertypes for Class II MHCs

Transporter Associated with Antigen Processing Preselection of Peptides Binding to the MHC: A Bioinformatic Evaluation

Rational optimization of tumor epitopes using in silico analysis‐assisted substitution of TCR contact residues

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