Class II MHC binding and antigen processing prediction
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Predicting presentation of peptides by Class II MHC molecules using convolutional neural networks.

This library allows you to train and predict with a collection of allele-specific MHC II peptide presentation models from categorical binding data (such as from a mass spec. assay).

Training Convolutional Predictors

Here's an example of a minimal invocation required to train a model on a new dataset:

mhc2-train-convolutional \
    --model-dir trained-models \
    --dataset dataset.csv

The file dataset.csv must contain the columns allele, peptide, and hit.

You can also specifiy training parameters and architectural hyperparameters:

mhc2-train-convolutional \
    --model-dir trained-models \
    --dataset dataset.csv \
    --conv-activation linear \
    --global-batch-normalization 0 \
    --embedding-dim 10 \
    --conv-dropout 0 \
    --batch-size 16 \
    --patience 2

A more complete list of parameters can be seen by running mhc2-train --help.


The following code will generate binding predictions for one or more alleles on a given set of peptides:

mhc2-predict \
    --model-dir trained-models \
    --allele DRB1*01:01 \
    --peptides-file peptides.txt

Additional alleles can be specified as part of the --allele flag, such as --allele DRB1*01:01 DRB1*11:01.

Peptides can also be given manually on the commandline using the --peptide option, such as --peptide SIINFEKLQQQQQQ.

If you want to make predictions for different peptides associated with each allele, you can provide a CSV file with columns "allele" and "peptide" using the --input-csv option.

Which alleles are available?

To see which alleles are available in a directory of trained models, run:

mhc2-list-alleles MODEL-DIR