Peptides that bind major histocompatibility complex (MHC) class I molecules serve as recognition targets for cytotoxic CD8⁺ T cells (CTLs). The major function of CTLs is recognition and destruction of infected (e.g. viruses, bacteria, parasites or fungi), mutated (e.g. cancer), or foreign (e.g. transplants) cells. CTLs recognize short antigenic peptides (T-cell epitopes) presented by MHC class I molecules that mainly originate from degradation of cytosolic proteins. Intracellular antigen processing pathways determine the selectivity of peptides which are available for binding to MHC class I molecules and are thereby important targets of CTL responses [2].

MHC class I antigen processing pathway steps include proteosomal cleavage of proteins into shorter peptides, translocation of peptides into the endoplasmic reticulum (ER) by TAP, optional ER trimming by aminopeptidases, insertion of peptides into the binding groove of MHC molecules, and transport of peptide/MHC complexes to the cell surface for presentation to CTLs [3]. TAP is a transmembrane protein responsible for the transport of antigenic peptides into the ER. TAP demonstrates peptide binding selectivity and the affinity of a particular peptide for TAP influences the probability of its presentation by MHC class I molecules. Peptides that are 8–16 amino acids long and have sufficient binding affinity are efficiently translocated by TAP into the ER, while longer peptides may be transported but with lower efficiency [4]. Human TAP (hTAP) is a heterodimer that has two subunits hTAP1 and hTAP2. TAP belongs to the ATP-binding cassette transporters and each subunit protein has one transmembrane domain and one ATP-binding binding domain. The genes for human TAP1 and TAP2 are located in the MHC II locus of chromosome 6 and comprise 10 kb each [5]. A more detailed description of function, structure, expression of TAP can be found in [6].

The efficiency of TAP-mediated translocation of a peptide is proportional to its TAP-binding affinity [7, 8]. Mutations, such as premature stop codons, or deletions of either hTAP1 or hTAP2 impair peptide transport into ER and result in a significant reduction of surface expression of peptide/MHC complexes [9]. TAP deficient cells have low cell-surface HLA class I expression shown to range from 10% (HLA-A2) to 3%, (HLA-B27 and -A3) [10]. The majority of the peptides presented by HLA class I on cell surface are thus dependent on TAP.

Identification of T-cell epitopes is a highly combinatorial problem. The diversity of human immune responses to T-cell epitopes originates from two sources – high allelic variation of the host (both HLA molecules and T-cell receptors) and high variation of target antigens, particularly those derived from viruses. Computational models are routinely used for pre-screening of potential T-cell epitopes and minimization of the number of necessary experiments. Most developments have focused on modeling and prediction of peptide binding to MHC molecules [see [11]]. Amongst computational models of peptide binding to hTAP that have been developed are binding motifs [7], quantitative matrices [12–14], artificial neural networks (ANN) [12, 15], and support vector machines (SVM) [16]. Combined computational methods that integrate multiple critical steps – proteasome cleavage, TAP transport, and MHC class I binding have been proposed as a supporting methodology for prediction of high probability targets for therapeutic peptides and vaccines [17]. Several combined computational applications of models of antigen processing and presentation have been reported [18–22]. Testing results indicate that these predictions produce a lower incidence of false positives and reduce the number of experiments required for identification of T-cell epitopes. However, these combined predictions need to be taken with a dose of caution. Alternative pathways for both proteolytic degradation [23] and TAP transport [24] have been reported. In some cases TAP-deficient individuals have normal immune responses [25], suggesting that TAP-independent immune responses are sufficient to provide effective protection from some intracellular pathogens. Nevertheless, the proteasome-TAP-MHC class I pathway is responsible for 90–97% of expression of peptide/MHC Class I complexes and therefore is critical for the identification of target epitopes for immunotherapies and vaccines.

We developed PRED^TAP, a computational system that predicts peptides binding to hTAP. It uses ANN and hidden Markov models (HMM) as predictive engines. Extensive testing was performed to validate the prediction models and ensure that PRED^TAP is both sensitive and specific. PRED^TAP is available for public use at [1].

The specificity vs. sensitivity plot of the ANN prediction model for prediction can be viewed at supplementary materials A [1]. The specificity/sensitivity plot of the HMM prediction model can be viewed at supplementary materials B [1].

Sensitivities and specificities of ANN and HMM models at various thresholds (based on normalized scores) in 10-fold cross-validation experiments are shown in Figures 1 and 2. We selected the normalized score of 6.0 as a reasonable selection threshold, with peptides with scores ≥ 6.0 predicted as TAP binders. In Table 3, the sensitivities and specificities of ANN and HMM models at the selection threshold 6.0 are shown. ANN model managed to correctly predict 88% of high binders at the cost of 11% of false positives (the 11% also includes moderate and low-affinity binders); 67% moderate and high binders with 3% false positives in the MH set, and 50% of all binders (low, moderate and high) with practically no false positives (Table 3A). The specificities of ANN model for all three sets (LMH, MH and H sets) are high (1.00, 0.97, 0.89 respectively), which indicates that 6.0 is a stringent selection threshold and the false positive rate is very low at this threshold. At threshold 6.0, HMM model managed to correctly predict 91% of high binders with 32% false positives, 81% moderate and high binders with 19% false positives, and 66% of all binders (low, moderate and high) with 14% false positives (Table 3B). The specificity of the HMM model for LMH set was 0.86, higher than that of MH set which was 0.81. The specificity of the HMM model for MH set is much higher than that of H set, which was 0.68. It implies that HMM model was able to select binders (low, moderate and high binders) with low false positive rate, but it failed to categorize them into subgroups – low, moderate or high binders.

We have earlier compared four prediction servers for prediction of H-2K^d binding peptides [34]. A 121-amino acid long sequence of the nuclear export protein NS2 from influenza A virus (GenPept accession NP_859033) was searched for 9-mer candidate binders to a mouse MHC molecule H-2K^d using four internet-accessible systems. Only three peptides were predicted within the top ten candidates as binders by all four methods. The performance comparison of PRED^TAP with SVMTAP and TAPPred (SVM) shows that consensus peptides can be selected by combining predictions. The examples suggested that individual predictions need to be taken with care and predictions may be improved by a consensus of multiple methods. A similar situation may be applicable to TAP predictions. Hence the combination of ANN and HMM predictions in PRED^TAP should result in higher specificity (fewer false positives) at the cost of slightly lower sensitivity. The predictions by TAPPred (cascade SVM) appear to be of a limited value.

The combinatorial properties of molecular mechanisms involved in antigen processing and adaptive learning nature of the immune responses limit our ability to fully predict immune responses. Combining experimental and computational techniques improves our ability to decipher complex interactions of the immune system. Computer models are used to complement laboratory experiments and thereby speed up knowledge discovery in immunology. In particular, the number of large-scale laboratory experiments for T-cell epitope mapping can be minimised by the judicious use of experiments aimed at developing and validating computer models. These models can then be used to perform large-scale computer simulations rapidly and inexpensively. The hypotheses generated from these experiments can then be retested in the laboratory to confirm their applicability to real-life immunology. Further work will include both the refinement of computational models and scanning disease-related antigens for peptide sequences that show high probability of processing and presentation. Those peptides that are most likely to be produced by proteasomal cleavage, transported by TAP, and bound by HLA class I molecules are likely to be promising candidates for peptide-based CTL vaccines. The PRED^TAP server provides for the prediction of peptide binding by TAP and can be used as a comparison method against other TAP-prediction servers.