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A modular concept of HLA for comprehensive peptide binding prediction

Immunogenetics volume 59, pages 25–35 (2007)Cite this article

Abstract

A variety of algorithms have been successful in predicting human leukocyte antigen (HLA)-peptide binding for HLA variants for which plentiful experimental binding data exist. Although predicting binding for only the most common HLA variants may provide sufficient population coverage for vaccine design, successful prediction for as many HLA variants as possible is necessary to understand the immune response in transplantation and immunotherapy. However, the high cost of obtaining peptide binding data limits the acquisition of binding data. Therefore, a prediction algorithm, which applies the binding information from well-studied HLA variants to HLA variants, for which no peptide data exist, is necessary. To this end, a modular concept of class I HLA-peptide binding prediction was developed. Accurate predictions were made for several alleles without using experimental peptide binding data specific to those alleles. We include a comparison of module-based prediction and supertype-based prediction. The modular concept increased the number of predictable alleles from 15 to 75 of HLA-A and 12 to 36 of HLA-B proteins. Under the modular concept, binding data of certain HLA alleles can make prediction possible for numerous additional alleles. We report here a ranking of HLA alleles, which have been identified to be the most informative. Modular peptide binding prediction is freely available to researchers on the web at http://www.peptidecheck.org.

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Fig. 1
Fig. 2

Abbreviations

MHCBN:

major histocompatibility complex binding database

A ROC :

area under the receiver operating characteristic curve

SE:

sensitivity

SP:

specificity

TP:

true positive

TN:

true negative

FP:

false positive

FN:

false negative

P1, P2, ..., P9:

portions of the HLA binding groove responsible for binding positions 1, 2, ..., 9 of the peptide

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Acknowledgement

The MHCBN database was kindly and conveniently provided by GPS Raghava and Manoj Bhasin of the Institute of Microbial Technology, Chandigarh, India. The NetMHC algorithm and technical support were provided by Søren Brunak and Claus Lundegaard of the Center for Biological Sequence Analysis, BioCentrum-DTU, Denmark. The authors are very grateful to Peter Horn of the Institute for Transfusion Medicine at Hanover Medical School for bringing important perspectives to the analysis of these results.

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Affiliations

  1. Institute for Transfusion Medicine, Hanover Medical School, Carl-Neuberg-Str 1, 30625, Hanover, Germany

    David S. DeLuca, Barbara Khattab & Rainer Blasczyk

Authors
  1. David S. DeLuca
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  2. Barbara Khattab
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  3. Rainer Blasczyk
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Corresponding author

Correspondence to Rainer Blasczyk.

Electronic supplementary material

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Supplemental figure

Shannon’s entropy is a measure variance in a system, and is applied here to the positions in-cluded in HLA pocket definitions. Average entropy was determined by calculating the en-tropy for each position in the pocket definition, and dividing by the number of positions in the pocket definition. Num. High Entropy Positions refers to the number of positions in the pocket definition that had entropy above the threshold of 2.4. Entropy values above 2.0 are considered variable (Reche et al. 2004) (PDF 105 kb)

Supplementary Table 1

Ranking of the alleles which would provide the most new module data (PDF 69 kb)

Supplementary Table 2

Ranking of the alleles which would provide the most new anchor module data (PDF 24 kb)

Supplementary Table 3

Ranking of the alleles which would enable the prediction of the most (PDF 24 kb)

Supplementary Table 4

Peptide sequences derived from random positions in a random selection of human proteins (PDF 115 kb)

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DeLuca, D.S., Khattab, B. & Blasczyk, R. A modular concept of HLA for comprehensive peptide binding prediction. Immunogenetics 59, 25–35 (2007). https://doi.org/10.1007/s00251-006-0176-4

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Keywords

  • Histocompatibility Antigens class I
  • Variation (genetics)/immunology