Substrate selection by transporters associated with antigen processing occurs during peptide binding to TAP

Gubler, Brigitte; Daniel, Soizic; Armandola, Elena; Hammer, Juergen; Caillat‐Zucman, Sophie; Endert, Peter Van; Inserm, U

doi:10.1016/s0161-5890(98)00059-5

Cited by 41 publications

(37 citation statements)

References 24 publications

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“…The experimental basis for previous attempts to predict TAP transports were the in vitro affinity assays (11,12), in which the affinity has been shown to correspond closely with the transport rate of TAP (12). Thus, in this study we took the correspondence of TAP transport and affinity to be exact, allowing us to equate predictions of TAP affinity with predictions of TAP transport.…”

Section: Identifying Mhc Class I Epitopes By Predicting the Tap Transmentioning

confidence: 99%

“…Two of the scoring matrices were taken from the literature and have been derived directly from experiments. The Ala-matrix was constructed by using the peptide AAASAAAAY as a reference and measuring IC 50 values for the peptides possessing an exchanged amino acid at one of the nine sequence positions (12,21). The mix-matrix was generated using libraries of 9-meric peptides X 1 X 2 .…”

Section: Prediction Of Tap Affinities For 9-mersmentioning

confidence: 99%

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Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors

Peters

Bulik

Tampé

et al. 2003

The Journal of Immunology

297

276

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We are able to make reliable predictions of the efficiency with which peptides of arbitrary lengths will be transported by TAP. The pressure exerted by TAP on Ag presentation thus can be assessed by checking to what extent MHC class I (MHC-I)-presented epitopes can be discriminated from random peptides on the basis of predicted TAP transport efficiencies alone. Best discriminations were obtained when N-terminally prolonged epitope precursor peptides were included and the contribution of the N-terminal residues to the score were down-weighted in comparison with the contribution of the C terminus. We provide evidence that two factors may account for this N-terminal down-weighting: 1) the uncertainty as to which precursors are used in vivo and 2) the coevolution in the C-terminal sequence specificities of TAP and other agents in the pathway, which may vary among the various MHC-I alleles. Combining predictions of MHC-I binding affinities with predictions of TAP transport efficiency led to an improved identification of epitopes, which was not the case when predictions of MHC-I binding affinities were combined with predictions of C-terminal cleavages made by the proteasome.

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Section: Identifying Mhc Class I Epitopes By Predicting the Tap Transmentioning

confidence: 99%

Section: Prediction Of Tap Affinities For 9-mersmentioning

confidence: 99%

Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors

Peters

Bulik

Tampé

et al. 2003

The Journal of Immunology

297

276

View full text Add to dashboard Cite

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“…Many studies have suggested that the peptide selectivity exhibited by TAP binding may contribute to epitope selection (18,(31)(32)(33). To assess quantitatively the impact of this preselection for alleles that are strongly or weakly TAP dependent, two ROC analyses were performed.…”

Section: Tap Preselection Of Mhc Bindersmentioning

confidence: 99%

“…Unsurprisingly, TAP preselection was more efficient for fully TAP-dependent alleles than for partially TAP-dependent alleles. Positions 1, 2, 3, and 9 were the most significant for binding to TAP (18,31,32). To test their contributions to MHC binding prediction, models considering the amino acid contributions at these positions were developed and applied to A*0201 binder/nonbinder test set (Fig.…”

Section: Figure 2 Roc Curves Of Tap Hla-a*0201 (A)mentioning

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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“…[16][17][18] Peptide transport rate by TAP is governed by the initial binding step. 19,20 Likewise, TAP preselection of peptides available for MHCI presentation is also controlled by their affinity to TAP. Selectivity of TAP has been studied from data generated using assays that determine peptide binding to TAP or peptide accumulation in the ER.…”

Section: Introductionmentioning

confidence: 99%

Quantitative modeling of peptide binding to TAP using support vector machine

et al. 2009

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The transport of peptides to the endoplasmic reticulum by the transporter associated with antigen processing (TAP) is a necessary step towards determining CD8 T cell epitopes. In this work, we have studied the predictive performance of support vector machine models trained on single residue positions and residue combinations drawn from a large dataset consisting of 613 nonamer peptides of known affinity to TAP. Predictive performance of these TAP affinity models was evaluated under 10‐fold cross‐validation experiments and measured using Pearson's correlation coefficients (Rp). Our results show that every peptide position (P1–P9) contributes to TAP binding (minimum Rp of 0.26 ± 0.11 was achieved by a model trained on the P6 residue), although the largest contributions to binding correspond to the C‐terminal end (Rp = 0.68 ± 0.06) and the P1 (Rp = 0.51 ± 0.09) and P2 (0.57 ± 0.08) residues of the peptide. Training the models on additional peptide residues generally improved their predictive performance and a maximum correlation (Rp = 0.89 ± 0.03) was achieved by a model trained on the full‐length sequences or a residue selection consisting of the first 5 N‐ and last 3 C‐terminal residues of the peptides included in the training set. A system for predicting the binding affinity of peptides to TAP using the methods described here is readily available for free public use at http://imed.med.ucm.es/Tools/tapreg/. Proteins 2010. © 2009 Wiley‐Liss, Inc.

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Substrate selection by transporters associated with antigen processing occurs during peptide binding to TAP

Cited by 41 publications

References 24 publications

Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors

Identifying MHC Class I Epitopes by Predicting the TAP Transport Efficiency of Epitope Precursors

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

Quantitative modeling of peptide binding to TAP using support vector machine

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