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MHC Binding Predictor

Project description

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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

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

Project details

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Files for mhcflurry, version 2.0.1
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Filename, size mhcflurry-2.0.1.tar.gz (130.5 kB) File type Source Python version None Upload date Hashes View

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