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Wan, Ji; Liu, Wen; Xu, Qiqi; Ren, Yongliang; Flower, Darren R; Li, Tongbin (2006)
Publisher: BioMed Central
Journal: BMC Bioinformatics
Languages: English
Types: Article
Subjects: R858-859.7, Computer applications to medicine. Medical informatics, Software, Biology (General), QH301-705.5



The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort.


Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods.


SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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  • Discovered through pilot similarity algorithms. Send us your feedback.

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