Jinbu Wang
ABSTRACT
The adaptive immune system is a defense system against repeated infection. In order to trigger the immune response, antigen peptides from the infecting agent must first be recognized by the Major Histocompatibility Complex (MHC) proteins. Identifying peptides that bind to MHC class II is thus a critical step in vaccine development. We hypothesize that comparing individual subsites of the peptide binding groove could predict the individual amino acids of possible antigens. This modularized approach to individual subsites could reduce the amount of training data needed for accurate classification while also reducing computing times associated with molecular simulation and docking. To test this hypothesis, we evaluated the capability of two classification techniques and multiple modular representations of the MHC subsites to correctly classify the binding preference categories of P1 subsites of MHC class II structures. Our results shows that the average accuracies are 0.87 for K-mean and 0.95 for SVM with all feature vector configurations. Our results demonstrate that accurate predictions on individual binding subsites is possible, pointing to larger scale applications predicting whole-peptide preferences.
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Index Terms
- MAPS: Analyzing Peptide Binding Subsites in Major Histocompatibility Complexes
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