mhcflurry 0.9.2

MHC Binding Predictor

[](https://travis-ci.org/hammerlab/mhcflurry) [](https://coveralls.io/github/hammerlab/mhcflurry?branch=master)

# mhcflurry
Open source neural network models for peptide-MHC binding affinity prediction

The [adaptive immune system](https://en.wikipedia.org/wiki/Adaptive_immune_system)
depends on the presentation of protein fragments by [MHC](https://en.wikipedia.org/wiki/Major_histocompatibility_complex)
molecules. Machine learning models of this interaction are used in studies of
infectious diseases, autoimmune diseases, vaccine development, and cancer
immunotherapy.

MHCflurry supports Class I peptide/MHC binding affinity prediction using
ensembles of allele-specific models. You can fit MHCflurry models to your own data or download models that we fit to data from
[IEDB](http://www.iedb.org/home_v3.php) and [Kim 2014](http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241).
Our combined dataset is available for download [here](https://github.com/hammerlab/mhcflurry/releases/download/pre-1.0.0-alpha/data_curated.tar.bz2).

Pan-allelic prediction is supported in principle but is not yet performing
accurately. Infrastructure for modeling other aspects of antigen
processing is also implemented but experimental.

If you find MHCflurry useful in your research please cite:

> O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC binding affinity prediction. bioRxiv. Available at: http://www.biorxiv.org/content/early/2017/08/09/174243.

## Setup

Install the package:

```
pip install mhcflurry
```

Then download our datasets and trained models:

```
mhcflurry-downloads fetch
```

From a checkout you can run the unit tests with:

```
nosetests .
```

The MHCflurry predictors are implemented in Python using [keras](https://keras.io).

MHCflurry works with both the tensorflow and theano keras backends. The
tensorflow backend gives faster model-loading time but is undergoing more
rapid development and sometimes hits issues. If you encounter tensorflow errors
running MHCflurry, try setting this environment variable to switch to the theano
backend:

```
export KERAS_BACKEND=theano
```

You may also needs to `pip install theano`.


## Making predictions from the command-line

```shell
$ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides SIINFEKL SIINFEKD SIINFEKQ
allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high
HLA-A0201,SIINFEKL,5326.541919062165,3757.86675352994,7461.37693353508
HLA-A0201,SIINFEKD,18763.70298522213,13140.82000240037,23269.82139560844
HLA-A0201,SIINFEKQ,18620.10057358322,13096.425874678192,23223.148184869413
HLA-A0301,SIINFEKL,24481.726678691946,21035.52779725433,27245.371837497867
HLA-A0301,SIINFEKD,24687.529360239587,21582.590014592537,27749.39869616437
HLA-A0301,SIINFEKQ,25923.062203902562,23522.5793450799,28079.456657427705
```

The predictions returned are affinities (KD) in nM. The `prediction_low` and
`prediction_high` fields give the 5-95 percentile predictions across the models
in the ensemble. The predictions above were generated with MHCflurry 0.9.2.
Your exact predictions may vary slightly from these (up to about 1 nM)
depending on the Keras backend in use and other numerical details.
Different versions of MHCflurry can of course give results considerably
different from these.

You can also specify the input and output as CSV files.
Run `mhcflurry-predict -h` for details.


## Making predictions from Python

```python
>>> from mhcflurry import Class1AffinityPredictor
>>> predictor = Class1AffinityPredictor.load()
>>> predictor.predict_to_dataframe(peptides=['SIINFEKL'], allele='A0201')


allele peptide prediction prediction_low prediction_high
A0201 SIINFEKL 6029.084473 4474.103253 7771.297702
```

See the [class1_allele_specific_models.ipynb](https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb)
notebook for an overview of the Python API, including fitting your own predictors.


## Details on the downloadable models

An ensemble of eight single-allele models was trained for each allele with at least
100 measurements in the training set (118 alleles). The models were trained on a
random 80% sample of the data for the allele and the remaining 20% was used for
early stopping. All models use the same [architecture](downloads-generation/models_class1/hyperparameters.json). The
predictions are taken to be the geometric mean of the nM binding affinity
predictions of the individual models. The training script is [here](downloads-generation/models_class1/GENERATE.sh).

## Environment variables

The path where MHCflurry looks for model weights and data can be set with the `MHCFLURRY_DOWNLOADS_DIR` environment variable. This directory should contain subdirectories like "models_class1".