mhcflurry 1.4.3

MHC Binding Predictor

mhcflurry

MHC I ligand prediction package with competitive accuracy and a fast and documented implementation.

MHCflurry implements class I peptide/MHC binding affinity prediction. By default it supports 112 MHC alleles using ensembles of allele-specific models. Pan-allele predictors supporting virtually any MHC allele of known sequence are available for testing (see below). MHCflurry 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.

Have a bugfix or other contribution? We would love your help. See our contributing guidelines for more information.

Installation (pip)

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.

Pan-allele models (experimental)

We are testing new models that support prediction for any MHC I allele of known sequence (as opposed to the 112 alleles supported by the allele-specific predictors). These models are trained on both affinity measurements and mass spec.

To try the pan-allele models, first download them:

$ mhcflurry-downloads fetch models_class1_pan

then set this environment variable to use them by default:

$ export MHCFLURRY_DEFAULT_CLASS1_MODELS="$(mhcflurry-downloads path models_class1_pan)/models.with_mass_spec"

You can now generate predictions for about 14,000 MHC I alleles. For example:

$ mhcflurry-predict --alleles HLA-A*02:04 --peptides SIINFEKL

If you use these models please let us know how it goes.

Other allele-specific models

The default MHCflurry models are trained on affinity measurements, one allele per model (i.e. allele-specific). Mass spec datasets are incorporated in the model selection step.

We also release experimental allele-specific 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"

Common issues and fixes

Problems downloading data and models

Some users have reported HTTP connection issues when using mhcflurry-downloads fetch. As a workaround, you can download the data manually (e.g. using wget) and then use mhcflurry-downloads just to copy the data to the right place.

To do this, first get the URL(s) of the downloads you need using mhcflurry-downloads url:

$ mhcflurry-downloads url models_class1
http://github.com/openvax/mhcflurry/releases/download/pre-1.2/models_class1.20180225.tar.bz2

Then make a directory and download the needed files to this directory:

$ mkdir downloads
$ wget  --directory-prefix downloads http://github.com/openvax/mhcflurry/releases/download/pre-1.2/models_class1.20180225.tar.bz2
HTTP request sent, awaiting response... 200 OK
Length: 72616448 (69M) [application/octet-stream]
Saving to: 'downloads/models_class1.20180225.tar.bz2'

Now call mhcflurry-downloads fetch with the --already-downloaded-dir option to indicate that the downloads should be retrived from the specified directory:

$ mhcflurry-downloads fetch models_class1 --already-downloaded-dir downloads

Problems deserializing models

If you encounter errors loading the MHCflurry models, such as:

...
  File "/usr/local/lib/python3.6/site-packages/keras/engine/topology.py", line 293, in __init__
    raise TypeError('Keyword argument not understood:', kwarg)
TypeError: ('Keyword argument not understood:', 'data_format')

You may need to upgrade Keras:

pip install --upgrade Keras