mhcflurry 2.3.0rc14

MHC Binding Predictor

MHCflurry

MHCflurry predicts which peptides are likely to be displayed by MHC class I molecules. It provides pretrained models for three related tasks:

• Binding affinity: how strongly a peptide binds an MHC allele.
• Antigen processing: whether cellular processing favors the peptide.
• Presentation: a combined score using binding and processing predictions.

You can use the released models from the command line or Python, scan proteins for candidate epitopes, or train models on your own data.

Quick start

Install MHCflurry and download the pretrained presentation models:

pip install mhcflurry
mhcflurry-downloads fetch models_class1_presentation

Predict a few peptides:

mhcflurry-predict \
    --alleles HLA-A0201 HLA-A0301 \
    --peptides SIINFEKL SIINFEKD SIINFEKQ \
    --out predictions.csv

Or scan a protein sequence for candidate ligands:

mhcflurry-predict-scan \
    --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
    --alleles 'HLA-A*02:01' \
    --out scan.csv

To try MHCflurry without installing anything, open the Colab notebook.

Important The current source tree documents the 2.3.0 release candidate (2.3.0rc14). Install it with pip install --pre mhcflurry or pip install mhcflurry==2.3.0rc14. A normal pip install mhcflurry stays on the latest stable 2.2.x release until 2.3.0 is final.

In 2.3.0, the same commands are also available through one parent command, for example mhcflurry predict and mhcflurry predict-scan. The historical mhcflurry-* commands remain supported. See the 2.3.0 release notes for training, performance, and evaluation changes.

Documentation

• Introduction and installation
• Command-line tutorial
• Python tutorial
• Command reference
• API reference

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

Citing MHCflurry

If you use MHCflurry 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. 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, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," Cell Systems, 2018. https://doi.org/10.1016/j.cels.2018.05.014

Development

Contributions are welcome. Start with CONTRIBUTING.md; the testing guide describes the fast local checks and full suite.

Docker

Run the latest image from Docker Hub:

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

Then open http://localhost:9999 to use the included Jupyter environment. To build the image from a checkout:

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

More resources

• Predicted binding motifs
• Manual download instructions