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. 2008 Mar 16:9:8.
doi: 10.1186/1471-2172-9-8.

Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research

Hong Huang Lin  1 , Surajit Ray, Songsak Tongchusak, Ellis L Reinherz, Vladimir Brusic
Affiliations

Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research

Hong Huang Lin et al. BMC Immunol. .

Abstract

Background: Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules.

Results: Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201).

Conclusion: Non-linear predictors outperform matrix-based predictors. Most predictors can be improved by non-linear transformations of their raw prediction scores. The best predictors of peptide binding are also best in prediction of T-cell epitopes. We propose a new standard for MHC-I binding prediction - a common scale for normalization of prediction scores, applicable to both experimental and predicted data. The results of this study provide assistance to researchers in selection of most adequate prediction tools and selection criteria that suit the needs of their projects.

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Figures

Figure 1
Figure 1
AROC values of predictions using the combined test set for the 27 servers. Black bars designate predictors showing the best performance. Vertical axes show the value of AROC while horizontal axes show numbers designating individual servers, as shown in Table 4.
Figure 2
Figure 2
AROC values of predictions using survivin test set for the 27 servers. Black bars designate predictors showing the best performance. Vertical axes show the value of AROC while horizontal axes show numbers designating individual servers, as shown in Table 4.
Figure 3
Figure 3
AROC values of predictions using CMV construct test set for the 27 servers. Black bars designate predictors showing the best performance. Vertical axes show the value of AROC while horizontal axes show numbers designating individual servers, as shown in Table 4.
Figure 4
Figure 4
The correlation coefficients of 27 servers for three datasets. Black bars for survivin, gray bars for the CMV construct, and white bars for the combined set of peptides. Vertical axis shows the value of correlation coefficients while horizontal axis shows numbers designating individual servers, as shown in Table 4.
Figure 5
Figure 5
Results of non-linear transformations of the prediction scores for HLA-A*0201. The letters indicate type of transformation that provided the best results: O for original, L for logarithmic, E for exponential, S for square, and R for square root. Vertical axis shows the value of AROC while horizontal axis shows numbers designating individual servers, as shown in Table 4.
Figure 6
Figure 6
Representative graphs for A*0201 binding predictions on T-cell epitopes and the test peptide. The thresholds marked by broken lines predict approximately 90% of T-cell epitopes and are used for the assessment of false positives and false negatives in binding predictions. Representative examples of predictor groups are shown. The x-axis in the left figure represents experimental scores of test peptides while y-axis represented their scaled predicted scores. The x-axis in the right figure indicates index of sorted list T-cell epitopes while the y-axis represented their scaled predicted binding scores.
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