def test_netmhc_pan():
alleles = [normalize_allele_name(DEFAULT_ALLELE)]
pan_predictor = NetMHCpan(
alleles=alleles,
epitope_lengths=[9])
fasta_dictionary = {
"SMAD4-001": "ASIINFKELA",
"TP53-001": "ASILLLVFYW"
}
epitope_collection = pan_predictor.predict(
fasta_dictionary=fasta_dictionary)
assert len(epitope_collection) == 4, \
"Expected 4 epitopes from %s" % (epitope_collection,)
# Downloaded from https://github.com/hammerlab/mhctools#Example
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"
}
epitope_collection = predictor.predict(protein_sequences)
# flatten binding predictions into a Pandas DataFrame
df = epitope_collection.dataframe()
# epitope collection is sorted by percentile rank
# of binding predictions
strongest_predicted_binder = epitope_collection[0]
# should be NLYIQWLKDGGPSSGRPPPS
print strongest_predicted_binder.source_sequence
