def test_netmhc2pan_too_small_epitope(self):
netmhc2pan_predictor = NetMhcIIPanPredictor(
runner=self.runner, configuration=self.configuration, mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner
)
# this is an epitope from IEDB of length 15
mutated = "ENPVVH"
combinations = netmhc2pan_predictor.represent_mhc2_isoforms(
netmhc2pan_predictor.generate_mhc2_alelle_combinations(self.test_mhc_two))
predictions = netmhc2pan_predictor.mhc2_prediction(sequence=mutated, mhc_alleles=combinations)
self.assertEqual(0, len(predictions))
def __init__(self, runner: Runner,
configuration: DependenciesConfiguration,
mhc_parser: MhcParser, blastp_runner: BlastpRunner):
self.runner = runner
self.configuration = configuration
self.mhc_parser = mhc_parser
self.organism = mhc_parser.mhc_database.organism
self.proteome_blastp_runner = blastp_runner
self.netmhc2pan = NetMhcIIPanPredictor(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
self._initialise()
def test_netmhc2pan_epitope_iedb(self):
netmhc2pan_predictor = NetMhcIIPanPredictor(
runner=self.runner, configuration=self.configuration, mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner
)
# this is an epitope from IEDB of length 15
mutated = "ENPVVHFFKNIVTPR"
combinations = netmhc2pan_predictor.represent_mhc2_isoforms(
netmhc2pan_predictor.generate_mhc2_alelle_combinations(self.test_mhc_two))
predictions = netmhc2pan_predictor.mhc2_prediction(sequence=mutated, mhc_alleles=combinations)
self.assertEqual(10, len(predictions))
for p in predictions:
self.assertIsNotNone(p.peptide)
self.assertIsNotNone(p.hla)
self.assertIsNotNone(p.affinity_score)
self.assertIsNotNone(p.pos)
self.assertIsNotNone(p.rank)
def extract_best_epitope_per_mhc2_alelle(
predictions: List[PredictedEpitope],
mhc_isoforms: List[Mhc2]) -> List[PredictedEpitope]:
"""
This function returns the predicted epitope with the lowest binding score for each patient allele,
considering homozygosity
"""
homozygous_alleles = BestAndMultipleBinderMhcII._get_homozygous_mhc2_alleles(
mhc_isoforms)
homozygous_alleles = NetMhcIIPanPredictor.generate_mhc2_alelle_combinations(
homozygous_alleles)
# removes duplicated homozygous combinations
homozygous_alleles = list({a.name: a
for a in homozygous_alleles}.values())
hetero_hemizygous_alleles = (
BestAndMultipleBinderMhcII.
_get_heterozygous_or_hemizygous_mhc2_alleles(mhc_isoforms))
hetero_hemizygous_alleles = NetMhcIIPanPredictor.generate_mhc2_alelle_combinations(
hetero_hemizygous_alleles)
return BestAndMultipleBinderMhcII._get_sorted_epitopes_mhc2(
hetero_hemizygous_alleles, homozygous_alleles, predictions)
def test_generator_rate_mhcII(self):
best_multiple = BestAndMultipleBinderMhcII(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
netmhc2pan = NetMhcIIPanPredictor(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
mutation = get_mutation(
mutated_xmer="RTNLLAALHRSVRWRAADQGHRSAFLV",
wild_type_xmer="RTNLLAALHRSVRRRAADQGHRSAFLV",
)
# all alleles = heterozygous
allele_combinations = netmhc2pan.generate_mhc2_alelle_combinations(
self.test_mhc_two)
patient_mhc2_isoforms = best_multiple._get_only_available_combinations(
allele_combinations, self.available_alleles_mhc2)
predictions = netmhc2pan.mhc2_prediction(patient_mhc2_isoforms,
mutation.mutated_xmer)
predictions_wt = netmhc2pan.mhc2_prediction(patient_mhc2_isoforms,
mutation.wild_type_xmer)
predicted_neoepitopes = netmhc2pan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
filtered_predictions_wt = netmhc2pan.filter_peptides_covering_snv(
position_of_mutation=mutation.position, predictions=predictions_wt)
generator_rate_ADN = best_multiple.determine_number_of_alternative_binders(
predictions=predicted_neoepitopes,
predictions_wt=filtered_predictions_wt)
generator_rate_CDN = best_multiple.determine_number_of_binders(
predictions=predicted_neoepitopes)
self.assertEqual(generator_rate_ADN, 0)
self.assertEqual(generator_rate_CDN, 0)
class BestAndMultipleBinderMhcII:
def __init__(self, runner: Runner,
configuration: DependenciesConfiguration,
mhc_parser: MhcParser, blastp_runner: BlastpRunner):
self.runner = runner
self.configuration = configuration
self.mhc_parser = mhc_parser
self.organism = mhc_parser.mhc_database.organism
self.proteome_blastp_runner = blastp_runner
self.netmhc2pan = NetMhcIIPanPredictor(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
self._initialise()
def _initialise(self):
self.phbr_ii = None
self.generator_rate = None
self.generator_rate_adn = None
self.generator_rate_cdn = None
self.best_predicted_epitope_rank = PredictedEpitope(
peptide=None,
pos=None,
hla=Mhc2Isoform(name=None),
affinity_score=None,
rank=None,
)
self.best_predicted_epitope_affinity = PredictedEpitope(
peptide=None,
pos=None,
hla=Mhc2Isoform(name=None),
affinity_score=None,
rank=None,
)
self.best_predicted_epitope_rank_wt = PredictedEpitope(
peptide=None,
pos=None,
hla=Mhc2Isoform(name=None),
affinity_score=None,
rank=None,
)
self.best_predicted_epitope_affinity_wt = PredictedEpitope(
peptide=None,
pos=None,
hla=Mhc2Isoform(name=None),
affinity_score=None,
rank=None,
)
def calculate_phbr_ii(
self, best_epitope_per_allele_mhc2: List[PredictedEpitope]):
"""
harmonic mean of best MHC II binding scores per MHC II allele
:param best_epitope_per_allele_mhc2: list of best MHC II epitopes per allele
:return: PHBR-II score, Marty et al
"""
best_epitope_per_allele_mhc2_new = list(best_epitope_per_allele_mhc2)
phbr_ii = None
for allele_with_score in best_epitope_per_allele_mhc2:
# add DRB1
if Mhc2GeneName.DRB1.name in allele_with_score.hla.name:
best_epitope_per_allele_mhc2_new.append(allele_with_score)
if len(best_epitope_per_allele_mhc2_new) == 12:
# 12 genes gene copies should be included into PHBR_II
best_mhc_ii_scores_per_allele = [
epitope.rank for epitope in best_epitope_per_allele_mhc2_new
]
phbr_ii = stats.hmean(best_mhc_ii_scores_per_allele)
return phbr_ii
@staticmethod
def determine_number_of_binders(predictions: List[PredictedEpitope],
threshold=1):
"""
Determines the number of HLA II binders per mutation based on a rank threshold. Default is set to 1, which is threshold used in generator rate.
"""
scores = [epitope.rank for epitope in predictions]
number_binders = 0
for score in scores:
if score < threshold:
number_binders += 1
return number_binders if not len(scores) == 0 else None
@staticmethod
def determine_number_of_alternative_binders(
predictions: List[PredictedEpitope],
predictions_wt: List[PredictedEpitope],
threshold=4):
"""
Determines the number of HLA II neoepitope candidates that bind stronger (4:1) to HLA in comparison to corresponding WT.
With the netMHCIIpan4.0 the rank score can get a value of 0.0. If this is the case, the next smaller possible
value is used as a measure for best possible binding.
This is 0.01 in case of netMHCIIpan4.0
TODO: this is not optimal. is there a better long-term solution?
"""
number_binders = 0
values = []
for epitope in predictions:
values.append(epitope.rank)
if epitope.rank < 4:
wt_peptide = AbstractNetMhcPanPredictor.select_best_by_rank(
predictions=AbstractNetMhcPanPredictor.
filter_wt_predictions_from_best_mutated(
predictions_wt, epitope))
rank_mutation = epitope.rank
if rank_mutation == 0:
rank_mutation = 0.01
if wt_peptide is not None:
dai = wt_peptide.rank / rank_mutation
if dai > threshold:
number_binders += 1
return number_binders if not len(values) == 0 else None
@staticmethod
def determine_number_of_alternative_binders_alternative(
predictions: List[PredictedEpitope],
predictions_wt: List[PredictedEpitope],
threshold=4):
"""
Determines the number of HLA I neoepitope candidates that bind stronger (10:1) to HLA in comparison to corresponding WT
"""
number_binders = 0
dai_values = []
for mut, wt in zip(predictions, predictions_wt):
dai_values.append(mut.rank)
if mut.rank < 4:
dai = mut.affinity_score / wt.affinity_score
if dai > threshold:
number_binders += 1
return number_binders if not len(dai_values) == 0 else None
def run(self, mutation: Mutation, mhc2_alleles_patient: List[Mhc2],
mhc2_alleles_available: Set, uniprot):
"""predicts MHC II epitopes; returns on one hand best binder and on the other hand multiple binder analysis is performed"""
# mutation
self._initialise()
allele_combinations = self.netmhc2pan.generate_mhc2_alelle_combinations(
mhc2_alleles_patient)
# TODO: migrate the available alleles into the model for alleles
patient_mhc2_isoforms = self._get_only_available_combinations(
allele_combinations, mhc2_alleles_available)
predictions = self.netmhc2pan.mhc2_prediction(patient_mhc2_isoforms,
mutation.mutated_xmer)
if len(mutation.mutated_xmer) >= LENGTH_MHC2_EPITOPE:
if mutation.wild_type_xmer:
# make sure that predicted epitopes cover mutation in case of SNVs
predictions = self.netmhc2pan.filter_peptides_covering_snv(
position_of_mutation=mutation.position,
predictions=predictions)
filtered_predictions = self.netmhc2pan.remove_peptides_in_proteome(
predictions, uniprot)
if len(filtered_predictions) > 0:
# multiple binding
best_predicted_epitopes_per_alelle = (
self.extract_best_epitope_per_mhc2_alelle(
filtered_predictions, mhc2_alleles_patient))
self.phbr_ii = self.calculate_phbr_ii(
best_predicted_epitopes_per_alelle)
# best prediction
self.best_predicted_epitope_rank = self.netmhc2pan.select_best_by_rank(
filtered_predictions)
self.best_predicted_epitope_affinity = self.netmhc2pan.select_best_by_affinity(
filtered_predictions)
self.generator_rate_cdn = self.determine_number_of_binders(
predictions=filtered_predictions)
# MHC binding predictions for WT pepti
if mutation.wild_type_xmer:
predictions = self.netmhc2pan.mhc2_prediction(
patient_mhc2_isoforms, mutation.wild_type_xmer)
if len(mutation.wild_type_xmer) >= LENGTH_MHC2_EPITOPE:
filtered_predictions_wt = self.netmhc2pan.filter_peptides_covering_snv(
mutation.position, predictions)
# best prediction
if self.best_predicted_epitope_rank:
self.best_predicted_epitope_rank_wt = self.netmhc2pan.select_best_by_rank(
self.netmhc2pan.
filter_wt_predictions_from_best_mutated(
filtered_predictions_wt,
self.best_predicted_epitope_rank))
if self.best_predicted_epitope_affinity:
self.best_predicted_epitope_affinity_wt = (
self.netmhc2pan.select_best_by_affinity(
self.netmhc2pan.
filter_wt_predictions_from_best_mutated(
filtered_predictions_wt,
self.best_predicted_epitope_affinity)))
if len(mutation.mutated_xmer) >= LENGTH_MHC2_EPITOPE:
self.generator_rate_adn = self.determine_number_of_alternative_binders(
predictions=filtered_predictions,
predictions_wt=filtered_predictions_wt)
if self.generator_rate_adn is not None:
if self.generator_rate_cdn is not None:
self.generator_rate = self.generator_rate_adn + self.generator_rate_cdn
else:
# alternative mutation classes
# do BLAST search for all predicted epitopes covering mutation to identify WT peptide and
# predict MHC binding for the identified peptide sequence
peptides_wt = self.netmhc2pan.find_wt_epitope_for_alternative_mutated_epitope(
filtered_predictions)
filtered_predictions_wt = []
for wt_peptide, mut_peptide in zip(peptides_wt,
filtered_predictions):
if wt_peptide is not None:
filtered_predictions_wt.extend(
self.netmhc2pan.mhc2_prediction_peptide(
mut_peptide.hla, wt_peptide))
if self.best_predicted_epitope_rank:
self.best_predicted_epitope_rank_wt = self.netmhc2pan.filter_wt_predictions_from_best_mutated_alernative(
mut_predictions=filtered_predictions,
wt_predictions=filtered_predictions_wt,
best_mutated_epitope=self.
best_predicted_epitope_rank)
if self.best_predicted_epitope_affinity:
self.best_predicted_epitope_affinity_wt = \
self.netmhc2pan.filter_wt_predictions_from_best_mutated_alernative(
mut_predictions=filtered_predictions, wt_predictions=filtered_predictions_wt,
best_mutated_epitope=self.best_predicted_epitope_affinity
)
if len(mutation.mutated_xmer) >= LENGTH_MHC2_EPITOPE:
# generator rate for MHC II
self.generator_rate_cdn = self.determine_number_of_binders(
predictions=filtered_predictions)
self.generator_rate_adn = self.determine_number_of_alternative_binders_alternative(
predictions=filtered_predictions,
predictions_wt=filtered_predictions_wt)
if self.generator_rate_adn is not None:
if self.generator_rate_cdn is not None:
self.generator_rate = self.generator_rate_adn + self.generator_rate_cdn
def _get_only_available_combinations(
self, allele_combinations: List[Mhc2Isoform],
set_available_mhc: List[str]) -> List[str]:
# parses isoforms into internal representation
parsed_allele_combinations = self.netmhc2pan.represent_mhc2_isoforms(
allele_combinations)
patients_available_alleles = list(
set(parsed_allele_combinations).intersection(
set(set_available_mhc)))
patients_not_available_alleles = list(
set(parsed_allele_combinations).difference(set(set_available_mhc)))
if len(patients_not_available_alleles) > 0:
logger.warning(
"MHC II alleles {} are not supported by NetMHC2pan and no binding or derived features will "
"include it".format(",".join(patients_not_available_alleles)))
return patients_available_alleles
def get_annotations(self) -> List[Annotation]:
annotations = []
if self.best_predicted_epitope_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank.rank,
name="Best_rank_MHCII_score",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank.peptide,
name="Best_rank_MHCII_score_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank.hla.name,
name="Best_rank_MHCII_score_allele",
)
])
if self.best_predicted_epitope_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity.affinity_score,
name="Best_affinity_MHCII_score",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity.peptide,
name="Best_affinity_MHCII_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity.hla.name,
name="Best_affinity_MHCII_allele",
)
])
if self.best_predicted_epitope_rank_wt:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank_wt.rank,
name="Best_rank_MHCII_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank_wt.peptide,
name="Best_rank_MHCII_score_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_rank_wt.hla.name,
name="Best_rank_MHCII_score_allele_WT",
)
])
if self.best_predicted_epitope_affinity_wt:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity_wt.
affinity_score,
name="Best_affinity_MHCII_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity_wt.peptide,
name="Best_affinity_MHCII_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_predicted_epitope_affinity_wt.hla.name,
name="Best_affinity_MHCII_allele_WT",
)
])
if self.organism == ORGANISM_HOMO_SAPIENS:
annotations.extend([
AnnotationFactory.build_annotation(value=self.phbr_ii,
name="PHBR_II")
])
annotations.extend([
# generator rate
AnnotationFactory.build_annotation(value=self.generator_rate,
name="Generator_rate_MHCII"),
AnnotationFactory.build_annotation(
value=self.generator_rate_cdn,
name="Generator_rate_CDN_MHCII"),
AnnotationFactory.build_annotation(
value=self.generator_rate_adn,
name="Generator_rate_ADN_MHCII"),
])
return annotations
@staticmethod
def _get_homozygous_mhc2_alleles(mhc_isoforms: List[Mhc2]) -> List[Mhc2]:
"""
Returns alleles that occur more than one time in list of patient alleles and hence are homozygous alleles.
Otherwise returns empty list
"""
return [
m for m in mhc_isoforms for g in m.genes
if g.zygosity == Zygosity.HOMOZYGOUS
]
@staticmethod
def _get_heterozygous_or_hemizygous_mhc2_alleles(
mhc_isoforms: List[Mhc2], ) -> List[Mhc2]:
"""
Returns alleles that occur more than one time in list of patient alleles and hence are homozygous alleles.
Otherwise returns empty list
"""
return [
m for m in mhc_isoforms for g in m.genes
if g.zygosity in [Zygosity.HETEROZYGOUS, Zygosity.HEMIZYGOUS]
]
@staticmethod
def _get_sorted_epitopes_mhc2(
hetero_hemizygous_alleles: List[Mhc2Isoform],
homozygous_alleles: List[Mhc2Isoform],
predictions: List[PredictedEpitope],
) -> List[PredictedEpitope]:
hetero_hemizygous_allele_names = [
a.name for a in hetero_hemizygous_alleles
]
homozygous_allele_names = [a.name for a in homozygous_alleles]
# groups epitopes by allele
epitopes_by_allele = {}
for p in predictions:
allele = p.hla.name
epitopes_by_allele.setdefault(allele, []).append(p)
# chooses the best epitope per allele and considers zygosity
best_epitopes_per_allele = []
for allele, epitopes in epitopes_by_allele.items():
# sort by rank to choose the best epitope, fixes ties with peptide by alphabetical order
epitopes.sort(key=lambda e: (e.rank, e.peptide))
best_epitope = epitopes[0]
num_repetitions = 0
if (best_epitope.hla.name in hetero_hemizygous_allele_names or
best_epitope.hla.name in hetero_hemizygous_allele_names):
# adds the epitope once if alleles heterozygous
num_repetitions = 1
if (best_epitope.hla in homozygous_allele_names):
# adds the epitope twice if one allele is homozygous
num_repetitions = 2
best_epitopes_per_allele.extend(
[best_epitope for _ in range(num_repetitions)])
return best_epitopes_per_allele
@staticmethod
def extract_best_epitope_per_mhc2_alelle(
predictions: List[PredictedEpitope],
mhc_isoforms: List[Mhc2]) -> List[PredictedEpitope]:
"""
This function returns the predicted epitope with the lowest binding score for each patient allele,
considering homozygosity
"""
homozygous_alleles = BestAndMultipleBinderMhcII._get_homozygous_mhc2_alleles(
mhc_isoforms)
homozygous_alleles = NetMhcIIPanPredictor.generate_mhc2_alelle_combinations(
homozygous_alleles)
# removes duplicated homozygous combinations
homozygous_alleles = list({a.name: a
for a in homozygous_alleles}.values())
hetero_hemizygous_alleles = (
BestAndMultipleBinderMhcII.
_get_heterozygous_or_hemizygous_mhc2_alleles(mhc_isoforms))
hetero_hemizygous_alleles = NetMhcIIPanPredictor.generate_mhc2_alelle_combinations(
hetero_hemizygous_alleles)
return BestAndMultipleBinderMhcII._get_sorted_epitopes_mhc2(
hetero_hemizygous_alleles, homozygous_alleles, predictions)
@staticmethod
def transform_mhc2allele(mhc2_allele):
if "DR" in mhc2_allele:
allele_out = mhc2_allele.rstrip("HLA-").replace("*", "_").replace(
":", "")
else:
allele_out = mhc2_allele.replace("*", "").replace(":", "")
return allele_out
def test_phbr2(self):
best_multiple = BestAndMultipleBinderMhcII(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
netmhc2pan = NetMhcIIPanPredictor(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
mutation = get_mutation(
mutated_xmer="DEVLGEPSQDILVTDQTRLEATISPET",
wild_type_xmer="DEVLGEPSQDILVIDQTRLEATISPET",
)
# all alleles = heterozygous
allele_combinations = netmhc2pan.generate_mhc2_alelle_combinations(
self.test_mhc_two)
patient_mhc2_isoforms = best_multiple._get_only_available_combinations(
allele_combinations, self.available_alleles_mhc2)
predictions = netmhc2pan.mhc2_prediction(patient_mhc2_isoforms,
mutation.mutated_xmer)
filtered_predictions = netmhc2pan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
logger.info(filtered_predictions)
logger.info(self.test_mhc_two)
best_predicted_epitopes_per_alelle = (
best_multiple.extract_best_epitope_per_mhc2_alelle(
predictions=filtered_predictions,
mhc_isoforms=self.test_mhc_two))
phbr_ii = best_multiple.calculate_phbr_ii(
best_predicted_epitopes_per_alelle)
self.assertIsNotNone(phbr_ii)
self.assertAlmostEqual(8.895757526065129, phbr_ii)
# mo info for one allele
mhc2_alleles = MhcFactory.build_mhc2_alleles([
"HLA-DRB1*04:02",
"HLA-DRB1*08:01",
"HLA-DQA1*03:01",
"HLA-DQA1*04:01",
"HLA-DQB1*03:02",
"HLA-DQB1*04:02",
"HLA-DPA1*01:03",
"HLA-DPA1*02:01",
"HLA-DPB1*13:01",
"HLA-DPB1*13:01",
], self.hla_database)
allele_combinations = netmhc2pan.generate_mhc2_alelle_combinations(
mhc2_alleles)
patient_mhc2_isoforms = best_multiple._get_only_available_combinations(
allele_combinations, self.available_alleles_mhc2)
predictions = netmhc2pan.mhc2_prediction(patient_mhc2_isoforms,
mutation.mutated_xmer)
filtered_predictions = netmhc2pan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
best_predicted_epitopes_per_alelle = (
best_multiple.extract_best_epitope_per_mhc2_alelle(
filtered_predictions, mhc2_alleles))
logger.info(best_predicted_epitopes_per_alelle)
logger.info(len(best_predicted_epitopes_per_alelle))
phbr_ii = best_multiple.calculate_phbr_ii(
best_predicted_epitopes_per_alelle)
self.assertIsNone(phbr_ii)
# one allele present
mhc2_alleles = MhcFactory.build_mhc2_alleles([
"HLA-DRB1*04:02",
"HLA-DRB1*08:01",
"HLA-DQA1*03:01",
"HLA-DQA1*04:01",
"HLA-DQB1*03:02",
"HLA-DQB1*04:02",
"HLA-DPA1*01:03",
"HLA-DPA1*02:01",
"HLA-DPB1*13:01",
], self.hla_database)
allele_combinations = netmhc2pan.generate_mhc2_alelle_combinations(
mhc2_alleles)
patient_mhc2_isoforms = best_multiple._get_only_available_combinations(
allele_combinations, self.available_alleles_mhc2)
predictions = netmhc2pan.mhc2_prediction(patient_mhc2_isoforms,
mutation.mutated_xmer)
filtered_predictions = netmhc2pan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
best_predicted_epitopes_per_alelle = (
best_multiple.extract_best_epitope_per_mhc2_alelle(
filtered_predictions, mhc2_alleles))
logger.info(best_predicted_epitopes_per_alelle)
logger.info(len(best_predicted_epitopes_per_alelle))
phbr_ii = best_multiple.calculate_phbr_ii(
best_predicted_epitopes_per_alelle)
self.assertIsNone(phbr_ii)