OpenVaxx

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

[!IMPORTANT] Version 2.2.0 is the first release to use PyTorch as its neural network backend, replacing TensorFlow/Keras used in previous versions. It loads the same published weights and produces equivalent predictions, so existing workflows should continue to work with no changes.

Key changes in 2.2.0:

• Backend: TensorFlow/Keras replaced by PyTorch (>= 2.0)
• Python: Requires Python 3.10+ (previously 3.9+)
• Dependencies: pandas >= 2.0 is now required; tensorflow and keras are no longer needed
• Hardware: Automatic GPU detection; Apple Silicon (MPS) is now supported

If you are upgrading from 2.1.x, simply pip install --upgrade openVaxx. The published pre-trained models are unchanged and will be loaded and converted automatically.

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.

Try it now

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

Installation (pip)

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.

Docker

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

Predicted sequence motifs

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

Common issues and fixes

Problems downloading data and models

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

Support Us!

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

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