Background: The binding of T-cell antigenic peptides to MHC molecules is a prerequisite for their immunogenicity. The ability to identify binding peptides based on the protein sequence is of great importance to the rational design of peptide vaccines. As the requirements for peptide binding cannot be fully explained by the peptide sequence per se, structural considerations should be taken into account and are expected to improve predictive algorithms. The first step in such an algorithm requires accurate and fast modeling of the peptide structure in the MHC-binding groove. Results: We have used 23 solved peptide-MHC class I complexes as a source of structural information in the development of a modeling algorithm. The peptide backbones and MHC structures were used as the templates for prediction. Sidechain conformations were built based on a rotamer library, using the 'dead end elimination' approach. A simple energy function selects the favorable combination of rotamers for a given sequence. It further selects the correct backbone structure from a limited library. The influence of different parameters on the prediction quality was assessed. With a specific rotamer library that incorporates information from the peptide sidechains in the solved complexes, the algorithm correctly identifies 85% (92%) of all (buried) sidechains and selects the correct backbones. Under cross- validation, 70% (78%) of all (buried) residues are correctly predicted and most of all backbones. The interaction between peptide sidechains has a negligible effect on the prediction quality. Conclusions: The structure of the peptide sidechains follows from the interactions with the MHC and the peptide backbone, as the prediction is hardly influenced by sidechain interactions. The proposed methodology was able to select the correct backbone from a limited set. The impairment in performance under cross- validation suggests that, currently, the specific rotamer library is not satisfactorily representative. The predictions might improve with an increase in the data.
Bibliographical noteFunding Information:
We thank Rakefet Rosenfeld and Yael Altuvia for helpful discussions. This study was supported by grants from the US–Israel Bi-national Science Foundation, The Ministry of Science and the Israel Cancer Research Fund granted to H.M., a grant from the Abisch–Fraenkel Foundation, granted to O.S.-F. and an NIH grant GM-41905 granted to R.E. The Fritz Haber Institute is supported by Minerva Fund.
- Antigenic peptides
- Peptide modeling
- Protein-protein interaction
- Rotamer library
- Structure prediction