SVRMHCdb

A SVR-based prediction server for MHC-binding peptides

About SVRMHC server

The SVRMHC server predicts peptide-MHC binding affinities using SVRMHC models. Currently, 36 class I SVRMHC models and 6 class II SVRMHC models are hosted here.
Model configurations and statistics can be found here.

About SVRMHC method

SVRMHC is a quantitative method of modeling the interaction between a peptide and a MHC molecule, based on the support vector machine regression (SVR) method.

References

Liu W, Meng X, Xu Q, Flower DR, Li T: Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics 2006, 7: 182. [PubMed] [Full-text]

Wan J, Liu W, Xu Q, Ren Y, Flower DR, Li T: SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 2006, 7: 463. [PubMed] [Full-text]

Liu, W., Wan, J., Meng, X., Flower, D.R. and Li, T. (2007) In silico prediction of peptide-MHC binding affinity using SVRMHC. Methods Mol Biol , 409 , 283-291. [PubMed]

Related Links

Input a protein sequence (length<2048) Example

Input a protein sequence in plain or FASTA format:
Or select a FASTA or plain file:

Select a class I or class II MHC molecule

Select Class I or Class II:

Select a MHC molecule:

Set the threshold value of pIC50 or percentile score

Threshold value of pIC50 : Threshold value of Percentile Score: %

Select the order for display

Display Order:

In order of occurrence in the protein
Descendingly according to pIC50 values or percentile scores