mhcflurry 1.6.0

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. The current version provides pan-MHC I predictors supporting any MHC allele of known sequence. MHCflurry runs on Python 3.4+ using the keras neural network library. It exposes command-line and Python library interfaces.

Starting in version 1.6.0, MHCflurry also includes two expermental predictors, an “antigen processing” predictor that attempts to model MHC allele-independent effects such as proteosomal cleavage and a “presentation” predictor that integrates processing predictions with binding affinity predictions to give a composite “presentation score.” Both models are trained on mass spec-identified MHC ligands.

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. https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30232-1.

Please file an issue if you have questions or encounter problems.

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

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

Or scan protein sequences for potential epitopes:

$ mhcflurry-predict-scan \
        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
        --alleles HLA-A*02:01 \
        --out /tmp/predictions.csv

Wrote: /tmp/predictions.csv

See the documentation for more details.

Older allele-specific models

Previous versions of MHCflurry used models trained on affinity measurements, one allele per model (i.e. allele-specific). Mass spec datasets were incorporated in the model selection step.

These models are still available to use with the latest version of MHCflurry. To download these predictors, run:

$ mhcflurry-downloads fetch models_class1

and specify --models when you call mhcflurry-predict:

$ mhcflurry-predict \
       --alleles HLA-A0201 HLA-A0301 \
       --peptides SIINFEKL SIINFEKD SIINFEKQ \
       --models "$(mhcflurry-downloads path models_class1)/models"
       --out /tmp/predictions.csv

Wrote: /tmp/predictions.csv

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_presentation
https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```

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

$ mkdir downloads
$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```

HTTP request sent, awaiting response... 200 OK
Length: 72616448 (69M) [application/octet-stream]
Saving to: 'downloads/models_class1_presentation.20200205.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_presentation --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