Command-line tutorial

Downloading models

Most users will use pre-trained MHCflurry models that we release. These models are distributed separately from the pip package and may be downloaded with the mhcflurry-downloads tool:

$ mhcflurry-downloads fetch models_class1_presentation

Files downloaded with mhcflurry-downloads are stored in a platform-specific directory. To get the path to downloaded data, you can use:

$ mhcflurry-downloads path models_class1_presentation
/Users/tim/Library/Application Support/mhcflurry/4/2.0.0/models_class1_presentation/

We also release a number of other “downloads,” such as curated training data and some experimental models. To see what’s available and what you have downloaded, run mhcflurry-downloads info.

Most users will only need models_class1_presentation, however, as the presentation predictor includes a peptide / MHC I binding affinity (BA) predictor as well as an antigen processing (AP) predictor.


The code we use for generating the downloads is in the downloads_generation directory in the repository (

Generating predictions

The mhcflurry-predict command generates predictions for individual peptides (see the next section for how to scan protein sequences for epitopes). By default it will use the pre-trained models you downloaded above. Other models can be used by specifying the --models argument.


$ mhcflurry-predict
    --alleles HLA-A0201 HLA-A0301
    --out /tmp/predictions.csv
Forcing tensorflow backend.
Predicting processing.
Predicting affinities.
Wrote: /tmp/predictions.csv

results in a file like this:

$ cat /tmp/predictions.csv

The binding affinity predictions are given as affinities (KD) in nM in the mhcflurry_affinity column. Lower values indicate stronger binders. A commonly-used threshold for peptides with a reasonable chance of being immunogenic is 500 nM.

The mhcflurry_affinity_percentile gives the percentile of the affinity prediction among a large number of random peptides tested on that allele (range 0 - 100). Lower is stronger. Two percent is a commonly-used threshold.

The last two columns give the antigen processing and presentation scores, respectively. These range from 0 to 1 with higher values indicating more favorable processing or presentation.


The processing predictor is experimental. It models allele-independent effects that influence whether a peptide will be detected in a mass spec experiment. The presentation score is a simple logistic regression model that combines the (log) binding affinity prediction with the processing score to give a composite prediction. The resulting prediction may be useful for prioritizing potential epitopes, but no thresholds have been established for what constitutes a “high enough” presentation score.

In most cases you’ll want to specify the input as a CSV file instead of passing peptides and alleles as commandline arguments. If you’re relying on the processing or presentation scores, you may also want to pass the upstream and downstream sequences of the peptides from their source proteins for potentially more accurate cleavage prediction. See the mhcflurry-predict docs.

Using the older, allele-specific models

Previous versions of MHCflurry (described in the 2018 paper) used models trained on affinity measurements, one allele per model (i.e. allele-specific). Mass spec datasets were incorporated in the model selection step.

These models are still available to use with the latest version of MHCflurry. To download these predictors, run:

$ mhcflurry-downloads fetch models_class1

and specify --models when you call mhcflurry-predict:

$ mhcflurry-predict \
    --alleles HLA-A0201 HLA-A0301 \
    --models "$(mhcflurry-downloads path models_class1)/models"
    --out /tmp/predictions.csv

Scanning protein sequences for predicted MHC I ligands

Starting in version 1.6.0, MHCflurry supports scanning proteins for MHC-binding peptides using the mhcflurry-predict-scan command.

We’ll generate predictions across example.fasta, a FASTA file with two short sequences:


Here’s the mhcflurry-predict-scan invocation to scan the proteins for binders to either of two MHC I genotypes (using a 100 nM threshold):

$ mhcflurry-predict-scan
    --results-filtered affinity
    --threshold-affinity 100
Forcing tensorflow backend.
Guessed input file format: fasta
Read input fasta with 2 sequences
  sequence_id                sequence
0    protein1      MSSSSTPVCPNGPGNCQV
Predicting processing.
Predicting affinities.

See the mhcflurry-predict-scan docs for more options.

Fitting your own models

If you have your own data and want to fit your own MHCflurry models, you have a few options. If you have data for only one or a few MHC I alleles, the best approach is to use the mhcflurry-class1-train-allele-specific-models command to fit an “allele-specific” predictor, in which separate neural networks are used for each allele.

To call mhcflurry-class1-train-allele-specific-models you’ll need some training data. The data we use for our released predictors can be downloaded with mhcflurry-downloads:

$ mhcflurry-downloads fetch data_curated

It looks like this:

$ bzcat "$(mhcflurry-downloads path data_curated)/curated_training_data.csv.bz2" | head -n 3
BoLA-1*21:01,AENDTLVVSV,7817.0,=,quantitative,affinity,Barlow - purified MHC/competitive/fluorescence,BoLA-1*02101
BoLA-1*21:01,NQFNGGCLLV,1086.0,=,quantitative,affinity,Barlow - purified MHC/direct/fluorescence,BoLA-1*02101

Here’s an example invocation to fit a predictor:

$ mhcflurry-class1-train-allele-specific-models \
    --data curated_training_data.csv.bz2 \
    --hyperparameters hyperparameters.yaml \
    --min-measurements-per-allele 75 \
    --out-models-dir models

The hyperparameters.yaml file gives the list of neural network architectures to train models for. Here’s an example specifying a single architecture:

- activation: tanh
  dense_layer_l1_regularization: 0.0
  dropout_probability: 0.0
  early_stopping: true
  layer_sizes: [8]
  locally_connected_layers: []
  loss: custom:mse_with_inequalities
  max_epochs: 500
  minibatch_size: 128
  n_models: 4
  output_activation: sigmoid
  patience: 20
  peptide_amino_acid_encoding: BLOSUM62
  random_negative_affinity_max: 50000.0
  random_negative_affinity_min: 20000.0
  random_negative_constant: 25
  random_negative_rate: 0.0
  validation_split: 0.1

The available hyperparameters for binding predictors are defined in Class1NeuralNetwork. To see exactly how these are used you will need to read the source code.


MHCflurry predictors are serialized to disk as many files in a directory. The model training command above will write the models to the output directory specified by the --out-models-dir argument. This directory has files like:


The manifest.csv file gives metadata for all the models used in the predictor. There will be a weights_... file for each model giving its weights (the parameters for the neural network). The percent_ranks.csv stores a histogram of model predictions for each allele over a large number of random peptides. It is used for generating the percent ranks at prediction time.

To fit pan-allele models like the ones released with MHCflurry, you can use a similar tool, mhcflurry-class1-train-pan-allele-models. You’ll probably also want to take a look at the scripts used to generate the production models, which are available in the downloads-generation directory in the MHCflurry repository. See the scripts in the models_class1_pan subdirectory to see how the fitting and model selection was done for models currently distributed with MHCflurry.


The production MHCflurry models were fit using a cluster with several dozen GPUs over a period of about two days. If you model select over fewer architectures, however, it should be possible to fit a predictor using less resources.

Environment variables

MHCflurry behavior can be modified using these environment variables:


Path to models directory. If you call Class1AffinityPredictor.load() with no arguments, the models specified in this environment variable will be used. If this environment variable is undefined, the downloaded models for the current MHCflurry release are used.


The pan-allele models can be somewhat slow. As an optimization, when this variable is greater than 0 (default is 1), we “stitch” the pan-allele models in the ensemble into one large tensorflow graph. In our experiments it gives about a 30% speed improvement. It has no effect on allele-specific models. Set this variable to 0 to disable this behavior. This may be helpful if you are running out of memory using the pan-allele models.


For large prediction tasks, it can be helpful to increase the prediction batch size, which is set by this environment variable (default is 4096). This affects both allele-specific and pan-allele predictors. It can have large effects on performance. Alternatively, if you are running out of memory, you can try decreasing the batch size.