Methods for prediction of peptide binding to MHC molecules: A comparative study

Yu, K, Petrovsky, N, Schonbach, C, Koh, JLY and Brusic, V (2002) Methods for prediction of peptide binding to MHC molecules: A comparative study. Molecular Medicine, 8 3: 137-148.

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Author Yu, K
Petrovsky, N
Schonbach, C
Koh, JLY
Brusic, V
Title Methods for prediction of peptide binding to MHC molecules: A comparative study
Journal name Molecular Medicine   Check publisher's open access policy
ISSN 1076-1551
Publication date 2002
Sub-type Article (original research)
Open Access Status File (Publisher version)
Volume 8
Issue 3
Start page 137
End page 148
Total pages 12
Language eng
Abstract Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.
Keyword Biochemistry & Molecular Biology
Cell Biology
Medicine, Research & Experimental
T-cell Epitopes
Hidden Markov-models
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Agriculture and Food Sciences
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Citation counts: TR Web of Science Citation Count  Cited 88 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 13 Aug 2007, 13:04:34 EST