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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 tensorflow 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. These models were updated to incorporate minor improvements for the MHCflurry 2.0 release.

If you find MHCflurry useful in your research please cite:

T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," Cell Systems, 2020.

T. 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.

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 \
       --out /tmp/predictions.csv
Wrote: /tmp/predictions.csv

Or scan protein sequences for potential epitopes:

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

See the documentation for more details.


You can also try the latest (GitHub master) version of MHCflurry using the Docker image hosted on Dockerhub by running:

$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest

This will start a jupyter notebook server in an environment that has MHCflurry installed. Go to http://localhost:9999 in a browser to use it.

To build the Docker image yourself, from a checkout run:

$ docker build -t mhcflurry:latest .
$ docker run -p 9999:9999 --rm mhcflurry:latest

Predicted sequence motifs

Sequence logos for the binding motifs learned by MHCflurry BA are available here.

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```

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

$ mkdir downloads
$ wget  --directory-prefix downloads```

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
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