MHC I ligand prediction package with competitive accuracy and a fast and documented implementation.
MHCflurry supports Class I peptide/MHC binding affinity prediction using ensembles of allele-specific models. It runs on Python 2.7 and 3.4+ using the keras neural network library. It exposes command-line and Python library interfaces.
If you find MHCflurry useful in your research please cite:
T. J. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," Cell Systems, 2018. Available at: https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30232-1.
Install the package:
$ pip install mhcflurry
Then download our datasets and trained models:
$ mhcflurry-downloads fetch
You can now generate predictions:
$ mhcflurry-predict \
--alleles HLA-A0201 HLA-A0301 \
--peptides SIINFEKL SIINFEKD SIINFEKQ \
--out /tmp/predictions.csv
Wrote: /tmp/predictions.csv
See the documentation for more details.
The default MHCflurry models are trained on affinity measurements. Mass spec datasets are incorporated only in the model selection step. We also release experimental predictors whose training data directly includes mass spec. To download these predictors, run:
$ mhcflurry-downloads fetch models_class1_trained_with_mass_spec
and then to make them used by default:
$ export MHCFLURRY_DEFAULT_CLASS1_MODELS="$(mhcflurry-downloads path models_class1_trained_with_mass_spec)/models"
We also release predictors that do not use mass spec datasets at all. To use these predictors, run:
$ mhcflurry-downloads fetch models_class1_selected_no_mass_spec
export MHCFLURRY_DEFAULT_CLASS1_MODELS="$(mhcflurry-downloads path models_class1_selected_no_mass_spec)/models"