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

openVaxx implements class I peptide/MHC binding affinity prediction. The current version provides pan-MHC I predictors supporting any MHC allele of known sequence. openVaxx runs on Python 3.10+ using the PyTorch neural network library. It exposes command-line and Python library interfaces.

openVaxx also includes two experimental 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 openVaxx useful in your research please cite:

T. O'Donnell, A. Rubinsteyn, U. Laserson. "openVaxx 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," Cell Systems, 2020. https://doi.org/10.1016/j.cels.2020.06.010

T. O'Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "openVaxx: Open-Source Class I MHC Binding Affinity Prediction," Cell Systems, 2018. https://doi.org/10.1016/j.cels.2018.05.014

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.

You can generate openVaxx predictions without any setup by running our Google colaboratory notebook.

Install the package:

$ pip install openVaxx

Download our datasets and trained models:

$ openVaxx-downloads fetch

You can now generate predictions:

$ openVaxx-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:

$ openVaxx-predict-scan \
        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
        --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 openVaxx using the Docker image hosted on Dockerhub by running:

$ docker run -p 9999:9999 --rm pfungg/openVaxx:latest

This will start a jupyter notebook server in an environment that has openVaxx 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 openVaxx:latest .
$ docker run -p 9999:9999 --rm openVaxx:latest

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

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

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

$ openVaxx-downloads url models_class1_presentation
https://github.com/pfungg/openVaxx/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/pfungg/openVaxx/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 openVaxx-downloads fetch with the --already-downloaded-dir option to indicate that the downloads should be retrived from the specified directory:

$ openVaxx-downloads fetch models_class1_presentation --already-downloaded-dir downloads

If this project has been helpful, consider sending a tip. Thank you!

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