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

Abstract

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.

Quantitative modeling of peptide binding to TAP using support vector machine

“…Previously, only the positions P1, P2, P3, and the C-terminal end of the peptide were thought to be clearly relevant for binding to TAP. 12,22,26,28,29 We have confirmed that the C-terminal end of the peptide has the largest quantitative input to TAP binding; a model trained on this residue alone reached an R p 5 0.68 AE 0.06. Nonetheless, we have shown that the N-terminal half of the peptide has a larger contribution to TAP binding than the C-terminal half of the peptide, as judged by the predictive performance of SMVs trained on peptide fragments encompassing a varying number of N-terminal and C-terminal residues of the peptides in the DS 613 dataset (Fig.…”

“…[26][27][28][29] It is worth noting that, unlike any of the related studies, we have not only evaluated the predictive performance of our models in cross-validation experiments but have also repeated the experiments 10 times and provided confidence values (standard deviations). Moreover, we have also shown that the enhanced predictive performance obtained with the model trained on the DS 613 dataset is not related to sequence similarity redundancy (Fig.…”

“…methods of peptide binding affinity to TAP, which are readily available from the relevant publications (those by Peters et al 28 and Doytchinova et al 29 ) or from dedicated Web services (TAPPRED 26 and SVMTAP 27 ). The method developed by Doytchinova et al 29 consists of a matrix generated from 163 poly-Alanine 9-mer peptides of known affinity to TAP using an additive method 30 ; hence, we will refer to this method as ADM. The ADM method achieved a reported R p between 0.72 and 0.83, depending of the testing set.…”

“…The ADM method achieved a reported R p between 0.72 and 0.83, depending of the testing set. 29 The remaining methods have been trained on the PVE 435 dataset. 28 Briefly, Peters' et al 28 method is based on a consensus matrix (CM) that was obtained from three scoring matrices, which included a poly-Alanine derived matrix and a SMM-matrix (generated using the Stabilized Matrix Method) trained on the PVE 435 dataset.…”

“…29,30 The majority of these methods were trained on the same training set of $435 nonamer (9-mer) peptides of known affinity to TAP made available by Dr. van Endert, and until now their performance has not been compared in an independent testing set. In contrast, here we have used a much larger training set, encompassing 178 new peptides, to analyze TAP binding preferences using SVMs.…”