MHCPre

This model based on deep learning method can be used to predict peptide binding MHC class I molecules. The advantage of this model is to ignore the length of peptide. In other words, the same model can be used to predict different peptides with different length. We have trained different models for 67 alleles, including 49 human alleles, three mouse alleles, eight macaque alleles, six chimpanzee alleles and one rat allele.

Step1: Input peptide sequence
Enter peptide sequence(s)

or
Example
Step2: Select a species and Allele(s)
Species
Allele(s) or
Example
Step3: Set output
Output Value

All steps to use the model to predict peptide binding MHC class I are as follow:

  • To paste a peptide sequence into the text area, or to upload a file containing peptide sequences.
  • To select a species.
  • To select allele or to upload a file containing some alleles.
  • To select a output types including ic50 or score.
  • This dataset including 67 allele subdatasets, including 49 hunman alleles and 18 other species alleles. Every dataset contains four files, including training set, test set, validation set and independent test set. We also supply python code of our biLSTM models. Please download datasets by clicking the link below:

    Human
    Other species

    We appreciate your comments to make our website more complete in the future, please contact:

    Yan Guo, Ph.D, Yaguo@salud.unm.edu
    Limin Jiang, M.S., jianglm@tju.edu.cn