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Computational Prediction of MHC-Peptide Interaction

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45497

Siu,  Weng-In
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44023

Antes,  Iris
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Siu, W.-I. (2005). Computational Prediction of MHC-Peptide Interaction. Master Thesis, Universität des Saarlandes, Saarbrücken.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0027-F3C3-8
Zusammenfassung
T-cell recognition is a critical step in regulating immune response. Activation of Cytotoxic T-cell requires the MHC class I molecules in complex with specific peptides and present them on the surface of the cell. Identification of potential ligands to MHC is therefore important for understanding disease pathogenesis and aiding vaccine design. Despite years of effort in the field, reliable prediction of MHC ligands remains a difficult task. It is reported that only one out of 100 to 200 potential binders actually binds. Methods based on sequence data alone are fast but fail to capture all binding patterns, while the structure based methods are more promising but far too slow for large-scale screening of protein sequences. In this work, we propose a new method to the prediction problem. It is based on the assumption that peptide binding is an aggregrate effect of contributions from independent binding of residues. Compatibility of each amino acid in the MHC binding pockets is examined thoroughly by molecular dynamics simulation. Values of energy terms important for binding are collected from the generated ensembles, and are used to produce the allele-specific scoring matrix. Each entry in this matrix represents the favorableness in terms of a particular "feature" of an amino acid in a binding position. Prediction models based on machine learning techniques are then trained to discriminate binders from non-binders. Our method is compared to two other sequence-based methods using HLA-A*0201 9-mer sequences. Three publicly available data sets are used: the MHCPEP, SYFPEITHI data sets, and the HXB2 genome. In overall, our method successfully improves the prediction accuracy with higher specificity. Its robustness to different sizes and ratios of training data proves its ability to provide reliable prediction by less dependency on the sequence data. The method also shows better generalizability in cross-allele predictions. For predicting peptide bound conformations, our preliminary approach based on energy minimization gives the satisfactory result of a backbone RMSD at 1.7 to 1.88 A as compared to the crystal structures.