Skip to content

Latest commit

 

History

History
77 lines (57 loc) · 3.38 KB

README.md

File metadata and controls

77 lines (57 loc) · 3.38 KB
Raw

Tests PyPI

mhctools

Python interface to running command-line and web-based MHC binding predictors.

Commandline examples

Prediction for user-supplied peptide sequences

mhctools --sequence SIINFEKL SIINFEKLQ --mhc-predictor netmhc --mhc-alleles A0201

Automatically extract peptides as subsequences of specified length

mhctools --sequence AAAQQQSIINFEKL --extract-subsequences --mhc-peptide-lengths 8-10 --mhc-predictor mhcflurry --mhc-alleles A0201

Python usage

from mhctools import NetMHCpan
# Run NetMHCpan for alleles HLA-A*01:01 and HLA-A*02:01
predictor = NetMHCpan(alleles=["A*02:01", "hla-a0101"])

# scan the short proteins 1L2Y and 1L3Y for epitopes
protein_sequences = {
  "1L2Y": "NLYIQWLKDGGPSSGRPPPS",
  "1L3Y": "ECDTINCERYNGQVCGGPGRGLCFCGKCRCHPGFEGSACQA"
}

binding_predictions = predictor.predict_subsequences(protein_sequences, peptide_lengths=[9])

# flatten binding predictions into a Pandas DataFrame
df = binding_predictions.to_dataframe()

# epitope collection is sorted by percentile rank
# of binding predictions
for binding_prediction in binding_predictions:
    if binding_prediction.affinity < 100:
        print("Strong binder: %s" % (binding_prediction,))

API

The following MHC binding predictors are available in mhctools:

  • MHCflurry: open source predictor installed by default with mhctools, requires the user run mhcflurry-downloads fetch first to download MHCflurry models
  • NetMHC3: requires locally installed version of NetMHC 3.x
  • NetMHC4: requires locally installed version of NetMHC 4.x
  • NetMHC: a wrapper function to automatically use NetMHC3 or NetMHC4 depending on what's installed.
  • NetMHCpan: requires locally installed version of NetMHCpan
  • NetMHCIIpan: requires locally installed version of NetMHCIIpan
  • NetMHCcons: requires locally installed version of NetMHCcons
  • IedbMhcClass1: Uses IEDB's REST API for class I binding predictions.
  • IedbMhcClass2: Uses IEDB's REST API for class II binding predictions.
  • RandomBindingPredictor: Creates binding predictions with random IC50 and percentile rank values.

Every binding predictor is constructed with an alleles argument specifying the HLA type for which to make predictions. Predictions are generated by calling the predict method with a dictionary mapping sequence IDs or names to amino acid sequences.

Additionally there is a module for running the NetChop proteosomal cleavage predictor:

  • NetChop: requires locally installed version of NetChop-3.1