MHC Binding Predictor
Project description
mhcflurry
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. 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
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 \ --peptides SIINFEKL SIINFEKD SIINFEKQ \ --out /tmp/predictions.csv Wrote: /tmp/predictions.csv
Or scan protein sequences for potential epitopes:
$ mhcflurry-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 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 https://github.com/openvax/mhcflurry/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/openvax/mhcflurry/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 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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