MHCPre

Major Histocompatibility Complex (MHC) has an important research value in treatment of complex human disease. Here, the BVLSTM-MHC model based on Bilateral and Variable Long Short-Term Memory (BVLSTM) was designed to overcome limitation of dependenting on the length of peptides. In other words, the same model can be used to predict different peptides with different length. We have trained four models for four species covering a total of 77 alleles, including 62 human alleles, three mouse alleles, ten Macaque alleles and two chimpanzee alleles.

Input peptide sequence
Enter peptide sequence(s)

or
Example
Select a species and Allele(s)
Species
Allele(s) or
Example
Set output format
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 four species subdatasets, including hunman, mouse, Macaque and chimpanzee. Every dataset contains three files, including training set, test set, validation set. In addition, human dataset also includes an independent test set. We also supply python code of our BVLSTM models. Please download datasets and python code by clicking the link below:

    Human and Other species and BVdLSTM

    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