Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm

The useful structural features of class II MHC molecules are rarely integrated into T‐cell epitope predictions. We propose an approach that applies a novel expectation‐maximization algorithm to align the naturally processed peptides selected by the class II MHC I‐Ag7 molecule – focusing on the five...

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Bibliographic Details
Published in:Proteomics (Weinheim) 2007-02, Vol.7 (3), p.367-377
Main Authors: Chang, Kuan Y., Suri, Anish, Unanue, Emil R.
Format: Article
Language:English
Subjects:
Algorithms
Amino Acid Sequence
Analytical, structural and metabolic biochemistry
Animals
Biological and medical sciences
Cell Line
Class II MHC molecules
EM algorithm
Epitope prediction
Epitopes - immunology
Fundamental and applied biological sciences. Psychology
Hindering residues
Histocompatibility Antigens Class II - genetics
Histocompatibility Antigens Class II - immunology
Histocompatibility Antigens Class II - metabolism
Lod Score
Log of odds
Mice
Mice, Inbred NOD
Miscellaneous
Peptides - genetics
Peptides - metabolism
Predictive Value of Tests
Protein Binding - physiology
Proteins
Sequence Alignment
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Summary:The useful structural features of class II MHC molecules are rarely integrated into T‐cell epitope predictions. We propose an approach that applies a novel expectation‐maximization algorithm to align the naturally processed peptides selected by the class II MHC I‐Ag7 molecule – focusing on the five MHC‐specific anchor positions. Based on the alignment profile, log of odds (LOD) scores supplemented with the Laplace plus‐one pseudocounts method are applied to identify the potential T‐cell epitopes. In addition, an innovative computational concept of hindering residues using statistical and structural information is developed to refine the prediction. Performance analysis by receiver operating characteristics statistics and the experimental validation of the LOD scores demonstrate the accuracy of our predictive model. Furthermore, our model successfully predicts T‐cell epitopes of hen egg‐white lysozyme protein antigen. Our study provides a framework for predicting T‐cell epitopes in class II MHC molecules.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.200600584