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# Added by Tim:
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.. |Build Status| image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
:target: https://travis-ci.org/hammerlab/mhcflurry
.. |Coverage Status| image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
:target: https://coveralls.io/github/hammerlab/mhcflurry?branch=master
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
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.
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
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.. |Build Status| image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
:target: https://travis-ci.org/hammerlab/mhcflurry
.. |Coverage Status| image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
:target: https://coveralls.io/github/hammerlab/mhcflurry?branch=master
mhcflurry
=========
Open source neural network models for peptide-MHC binding affinity prediction
\ No newline at end of file
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