.. image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
    :target: https://travis-ci.org/hammerlab/mhcflurry

.. image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
    :target: https://coveralls.io/github/hammerlab/mhcflurry

mhcflurry
===================

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

MHCflurry is a Python package for peptide/MHC I binding affinity
prediction. It provides competitive accuracy with a fast, documented,
open source implementation.

You can download pre-trained MHCflurry models fit to affinity
measurements deposited in IEDB. See the
"downloads_generation/models_class1" directory in the repository for
the workflow used to train these predictors. Users with their own data
can also fit their own MHCflurry models.

Currently only allele-specific prediction is implemented, in which
separate models are trained for each allele. The released models
therefore support a fixed set of common class I alleles for which
sufficient published training data is available.

MHCflurry supports Python versions 2.7 and 3.4+. It uses the Keras
neural network library via either the Tensorflow or Theano backends.
GPUs may optionally be used for a generally modest speed improvement.

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.


Installation (pip)
******************

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:

   pip install nose
   nosetests .


Using conda
***********

You can alternatively get up and running with a conda environment as
follows. Some users have reported that this can avoid problems
installing tensorflow.

   conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.2'
   source activate mhcflurry-env

Then continue as above:

   pip install mhcflurry
   mhcflurry-downloads fetch


Command-line usage
==================


Downloading models
******************

Most users will use pre-trained MHCflurry models that we release.
These models are distributed separately from the source code and may
be downloaded with the following command:

We also release other "downloads," such as curated training data and
some experimental models. To see what you have downloaded, run:


mhcflurry-predict
*****************

The "mhcflurry-predict" command generates predictions from the
command-line. It defaults to using the pre-trained models you
downloaded above but this can be customized with the "--models"
argument. See "mhcflurry-predict -h" for details.

   $ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides SIINFEKL SIINFEKD SIINFEKQ
   2017-12-21 13:15:45.075649: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
   Using TensorFlow backend.
   allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high,mhcflurry_prediction_percentile
   HLA-A0201,SIINFEKL,4899.047843425702,2767.7636539507857,7269.683642935029,6.509787499999997
   HLA-A0201,SIINFEKD,21050.420242970613,16834.65859138968,24129.046091695887,34.297175
   HLA-A0201,SIINFEKQ,21048.47265780004,16736.561254929948,24111.013114442652,34.297175
   HLA-A0301,SIINFEKL,28227.298909150148,24826.30790978725,32714.28597399942,33.95121249999998
   HLA-A0301,SIINFEKD,30816.721218383507,27685.50847082019,36037.32590461623,41.22577499999998
   HLA-A0301,SIINFEKQ,24183.021046496786,19346.154182011513,32263.71247531383,24.81096249999999

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 1.0.0.

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.


Fitting your own models
***********************


Library usage
=============

The MHCflurry Python API exposes additional options and features
beyond those supported by the commandline tools. This tutorial gives a
basic overview of the most important functionality. See the API
Documentation for further details.

The "Class1AffinityPredictor" class is the primary user-facing
interface.


   >>> import mhcflurry
   >>> print("MHCflurry version: %s" % (mhcflurry.__version__))
   MHCflurry version: 1.0.0
   >>> 
   >>> # Load downloaded predictor
   >>> predictor = mhcflurry.Class1AffinityPredictor.load()
   >>> print(predictor.supported_alleles)
   ['BoLA-6*13:01', 'Eqca-1*01:01', 'H-2-Db', 'H-2-Dd', 'H-2-Kb', 'H-2-Kd', 'H-2-Kk', 'H-2-Ld', 'HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:05', 'HLA-A*02:06', 'HLA-A*02:07', 'HLA-A*02:11', 'HLA-A*02:12', 'HLA-A*02:16', 'HLA-A*02:17', 'HLA-A*02:19', 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', 'HLA-A*24:01', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-A*25:01', 'HLA-A*26:01', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*29:02', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01', 'HLA-A*32:07', 'HLA-A*33:01', 'HLA-A*66:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:23', 'HLA-A*69:01', 'HLA-A*80:01', 'HLA-B*07:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*14:02', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*15:42', 'HLA-B*18:01', 'HLA-B*27:01', 'HLA-B*27:03', 'HLA-B*27:04', 'HLA-B*27:05', 'HLA-B*27:06', 'HLA-B*27:20', 'HLA-B*35:01', 'HLA-B*35:03', 'HLA-B*35:08', 'HLA-B*37:01', 'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*42:01', 'HLA-B*44:01', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*45:06', 'HLA-B*46:01', 'HLA-B*48:01', 'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01', 'HLA-B*73:01', 'HLA-B*83:01', 'HLA-C*03:03', 'HLA-C*03:04', 'HLA-C*04:01', 'HLA-C*05:01', 'HLA-C*06:02', 'HLA-C*07:01', 'HLA-C*07:02', 'HLA-C*08:02', 'HLA-C*12:03', 'HLA-C*14:02', 'HLA-C*15:02', 'Mamu-A*01:01', 'Mamu-A*02:01', 'Mamu-A*02:0102', 'Mamu-A*07:01', 'Mamu-A*07:0103', 'Mamu-A*11:01', 'Mamu-A*22:01', 'Mamu-A*26:01', 'Mamu-B*01:01', 'Mamu-B*03:01', 'Mamu-B*08:01', 'Mamu-B*10:01', 'Mamu-B*17:01', 'Mamu-B*17:04', 'Mamu-B*39:01', 'Mamu-B*52:01', 'Mamu-B*66:01', 'Mamu-B*83:01', 'Mamu-B*87:01', 'Patr-A*01:01', 'Patr-A*03:01', 'Patr-A*04:01', 'Patr-A*07:01', 'Patr-A*09:01', 'Patr-B*01:01', 'Patr-B*13:01', 'Patr-B*24:01']

   # coding: utf-8

   # In[22]:

   import pandas
   import numpy
   import seaborn
   import logging
   from matplotlib import pyplot

   import mhcflurry



   # # Download data and models

   # In[2]:

   get_ipython().system('mhcflurry-downloads fetch')


   # # Making predictions with `Class1AffinityPredictor`

   # In[3]:

   help(mhcflurry.Class1AffinityPredictor)


   # In[4]:

   downloaded_predictor = mhcflurry.Class1AffinityPredictor.load()


   # In[5]:

   downloaded_predictor.predict(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])


   # In[6]:

   downloaded_predictor.predict_to_dataframe(allele="HLA-A0201", peptides=["SIINFEKL", "SIINFEQL"])


   # In[7]:

   downloaded_predictor.predict_to_dataframe(alleles=["HLA-A0201", "HLA-B*57:01"], peptides=["SIINFEKL", "SIINFEQL"])


   # In[8]:

   downloaded_predictor.predict_to_dataframe(
       allele="HLA-A0201",
       peptides=["SIINFEKL", "SIINFEQL"],
       include_individual_model_predictions=True)


   # In[9]:

   downloaded_predictor.predict_to_dataframe(
       allele="HLA-A0201",
       peptides=["SIINFEKL", "SIINFEQL", "TAAAALANGGGGGGGG"],
       throw=False)  # Without throw=False, you'll get a ValueError for invalid peptides or alleles


   # # Instantiating a `Class1AffinityPredictor`  from a saved model on disk

   # In[10]:

   models_dir = mhcflurry.downloads.get_path("models_class1", "models")
   models_dir


   # In[11]:

   # This will be the same predictor we instantiated above. We're just being explicit about what models to load.
   downloaded_predictor = mhcflurry.Class1AffinityPredictor.load(models_dir)
   downloaded_predictor.predict(["SIINFEKL", "SIQNPEKP", "SYNFPEPI"], allele="HLA-A0301")


   # # Fit a model: first load some data

   # In[12]:

   # This is the data the downloaded models were trained on
   data_path = mhcflurry.downloads.get_path("data_curated", "curated_training_data.csv.bz2")
   data_path


   # In[13]:

   data_df = pandas.read_csv(data_path)
   data_df


   # # Fit a model: Low level `Class1NeuralNetwork` interface

   # In[14]:

   # We'll use mostly the default hyperparameters here. Could also specify them as kwargs.
   new_model = mhcflurry.Class1NeuralNetwork(layer_sizes=[16])
   new_model.hyperparameters


   # In[16]:

   train_data = data_df.loc[
       (data_df.allele == "HLA-B*57:01") &
       (data_df.peptide.str.len() >= 8) &
       (data_df.peptide.str.len() <= 15)
   ]
   get_ipython().magic('time new_model.fit(train_data.peptide.values, train_data.measurement_value.values)')


   # In[17]:

   new_model.predict(["SYNPEPII"])


   # # Fit a model: high level `Class1AffinityPredictor` interface

   # In[18]:

   affinity_predictor = mhcflurry.Class1AffinityPredictor()

   # This can be called any number of times, for example on different alleles, to build up the ensembles.
   affinity_predictor.fit_allele_specific_predictors(
       n_models=1,
       architecture_hyperparameters={"layer_sizes": [16], "max_epochs": 10},
       peptides=train_data.peptide.values,
       affinities=train_data.measurement_value.values,
       allele="HLA-B*57:01",
   )


   # In[19]:

   affinity_predictor.predict(["SYNPEPII"], allele="HLA-B*57:01")


   # # Save and restore the fit model

   # In[20]:

   get_ipython().system('mkdir /tmp/saved-affinity-predictor')
   affinity_predictor.save("/tmp/saved-affinity-predictor")
   get_ipython().system('ls /tmp/saved-affinity-predictor')


   # In[21]:

   affinity_predictor2 = mhcflurry.Class1AffinityPredictor.load("/tmp/saved-affinity-predictor")
   affinity_predictor2.predict(["SYNPEPII"], allele="HLA-B*57:01")


Supported alleles and peptide lengths
=====================================

Models released with the current version of MHCflurry (1.0.0) support
peptides of length 8-15 and the following 124 alleles:

   BoLA-6*13:01, Eqca-1*01:01, H-2-Db, H-2-Dd, H-2-Kb, H-2-Kd, H-2-Kk,
   H-2-Ld, HLA-A*01:01, HLA-A*02:01, HLA-A*02:02, HLA-A*02:03,
   HLA-A*02:05, HLA-A*02:06, HLA-A*02:07, HLA-A*02:11, HLA-A*02:12,
   HLA-A*02:16, HLA-A*02:17, HLA-A*02:19, HLA-A*02:50, HLA-A*03:01,
   HLA-A*11:01, HLA-A*23:01, HLA-A*24:01, HLA-A*24:02, HLA-A*24:03,
   HLA-A*25:01, HLA-A*26:01, HLA-A*26:02, HLA-A*26:03, HLA-A*29:02,
   HLA-A*30:01, HLA-A*30:02, HLA-A*31:01, HLA-A*32:01, HLA-A*32:07,
   HLA-A*33:01, HLA-A*66:01, HLA-A*68:01, HLA-A*68:02, HLA-A*68:23,
   HLA-A*69:01, HLA-A*80:01, HLA-B*07:01, HLA-B*07:02, HLA-B*08:01,
   HLA-B*08:02, HLA-B*08:03, HLA-B*14:02, HLA-B*15:01, HLA-B*15:02,
   HLA-B*15:03, HLA-B*15:09, HLA-B*15:17, HLA-B*15:42, HLA-B*18:01,
   HLA-B*27:01, HLA-B*27:03, HLA-B*27:04, HLA-B*27:05, HLA-B*27:06,
   HLA-B*27:20, HLA-B*35:01, HLA-B*35:03, HLA-B*35:08, HLA-B*37:01,
   HLA-B*38:01, HLA-B*39:01, HLA-B*40:01, HLA-B*40:02, HLA-B*42:01,
   HLA-B*44:01, HLA-B*44:02, HLA-B*44:03, HLA-B*45:01, HLA-B*45:06,
   HLA-B*46:01, HLA-B*48:01, HLA-B*51:01, HLA-B*53:01, HLA-B*54:01,
   HLA-B*57:01, HLA-B*58:01, HLA-B*73:01, HLA-B*83:01, HLA-C*03:03,
   HLA-C*03:04, HLA-C*04:01, HLA-C*05:01, HLA-C*06:02, HLA-C*07:01,
   HLA-C*07:02, HLA-C*08:02, HLA-C*12:03, HLA-C*14:02, HLA-C*15:02,
   Mamu-A*01:01, Mamu-A*02:01, Mamu-A*02:0102, Mamu-A*07:01,
   Mamu-A*07:0103, Mamu-A*11:01, Mamu-A*22:01, Mamu-A*26:01,
   Mamu-B*01:01, Mamu-B*03:01, Mamu-B*08:01, Mamu-B*10:01, Mamu-B*17:01,
   Mamu-B*17:04, Mamu-B*39:01, Mamu-B*52:01, Mamu-B*66:01, Mamu-B*83:01,
   Mamu-B*87:01, Patr-A*01:01, Patr-A*03:01, Patr-A*04:01, Patr-A*07:01,
   Patr-A*09:01, Patr-B*01:01, Patr-B*13:01, Patr-B*24:01