Package: mhcflurry
mhcflurryMHC Iligand
prediction package with competitive accuracy and a fast anddocumentedimplementation.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 thetensorflowneural network library.
It exposescommand-lineandPython libraryinterfaces.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.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.2020.06.010T. 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.2018.05.014Please file an issue if you have questions or encounter problems.Have a bugfix or other contribution? We would love your help. See ourcontributing guidelines.Try it nowYou can generate MHCflurry predictions without any setup by running our Google colaboratorynotebook.Installation (pip)Install the package:$ pip install mhcflurryDownload our datasets and trained models:$ mhcflurry-downloads fetchYou can now generate predictions:$ mhcflurry-predict \
       --alleles HLA-A0201 HLA-A0301 \
       --peptides SIINFEKL SIINFEKD SIINFEKQ \
       --out /tmp/predictions.csv
       
Wrote: /tmp/predictions.csvOr scan protein sequences for potential epitopes:$ mhcflurry-predict-scan \
        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
        --alleles HLA-A*02:01 \
        --out /tmp/predictions.csv
        
Wrote: /tmp/predictions.csvSee thedocumentationfor more details.DockerYou can also try the latest (GitHub master) version of MHCflurry using the Docker
image hosted onDockerhubby
running:$ docker run -p 9999:9999 --rm openvax/mhcflurry:latestThis will start ajupyternotebook server in an
environment that has MHCflurry installed. Go tohttp://localhost:9999in 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:latestPredicted sequence motifsSequence logos for the binding motifs learned by MHCflurry BA are availablehere.Common issues and fixesProblems downloading data and modelsSome users have reported HTTP connection issues when usingmhcflurry-downloads fetch. As a workaround, you can download the data manually (e.g. usingwget) and then usemhcflurry-downloadsjust to copy the data to the right place.To do this, first get the URL(s) of the downloads you need usingmhcflurry-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 callmhcflurry-downloads fetchwith the--already-downloaded-diroption to indicate that the downloads should be retrived from the specified directory:$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads