def test_netmhcpan_too_small_epitope(self):
netmhcpan_predictor = NetMhcPanPredictor(
runner=self.runner, configuration=self.configuration, mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner
)
mutated = "NLVP"
predictions = netmhcpan_predictor.mhc_prediction(
sequence=mutated,
mhc_alleles=self.test_mhc_one,
set_available_mhc=self.available_alleles.get_available_mhc_i(),
)
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.blastp_runner = blastp_runner
self._initialise()
self.netmhcpan = NetMhcPanPredictor(runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.blastp_runner)
def test_netmhcpan_rare_aminoacid(self):
netmhcpan_predictor = NetMhcPanPredictor(
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 9
mutated = "XTTDSWGKF"
predictions = netmhcpan_predictor.mhc_prediction(
sequence=mutated,
mhc_alleles=self.test_mhc_one,
set_available_mhc=self.available_alleles.get_available_mhc_i(),
)
self.assertEqual(9, len(predictions))
def test_generator_rate(self):
best_multiple = BestAndMultipleBinder(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
netmhcpan = NetMhcPanPredictor(
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
predictions = netmhcpan.mhc_prediction(self.test_mhc_one,
self.available_alleles_mhc1,
mutation.mutated_xmer)
predictions_wt = netmhcpan.mhc_prediction(self.test_mhc_one,
self.available_alleles_mhc1,
mutation.wild_type_xmer)
predicted_neoepitopes = netmhcpan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
filtered_predictions_wt = netmhcpan.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, threshold=50)
logger.info(generator_rate_ADN)
logger.info(generator_rate_CDN)
self.assertEqual(generator_rate_ADN, 0)
self.assertEqual(generator_rate_CDN, 0)
class BestAndMultipleBinder:
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.blastp_runner = blastp_runner
self._initialise()
self.netmhcpan = NetMhcPanPredictor(runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.blastp_runner)
def _initialise(self):
self.phbr_i = None
self.epitope_affinities = None
self.generator_rate = None
self.mutation_in_anchor_9mer = None
self.generator_rate = None
self.generator_rate_adn = None
self.generator_rate_cdn = None
self.best_epitope_by_rank = self._get_empty_epitope()
self.best_epitope_by_affinity = self._get_empty_epitope()
self.best_ninemer_epitope_by_affinity = self._get_empty_epitope()
self.best_ninemer_epitope_by_rank = self._get_empty_epitope()
self.best_wt_epitope_by_rank = self._get_empty_epitope()
self.best_wt_epitope_by_affinity = self._get_empty_epitope()
self.best_ninemer_wt_epitope_by_rank = self._get_empty_epitope()
self.best_ninemer_wt_epitope_by_affinity = self._get_empty_epitope()
@staticmethod
def _get_empty_epitope():
return PredictedEpitope(
peptide=None,
pos=None,
hla=MhcAllele(name=None),
affinity_score=None,
rank=None,
)
def calculate_phbr_i(self, predictions: List[PredictedEpitope],
mhc1_alleles: List[Mhc1]):
"""returns list of multiple binding scores for mhcII considering best epitope per allele, applying different types of means (harmonic ==> PHRB-II, Marty et al).
2 copies of DRA - DRB1 --> consider this gene 2x when averaging mhcii binding scores
"""
best_epitopes_per_allele = (
BestAndMultipleBinder.extract_best_epitope_per_alelle(
predictions, mhc1_alleles))
phbr_i = None
if len(best_epitopes_per_allele) == 6:
phbr_i = stats.hmean(
list(map(lambda e: e.rank, best_epitopes_per_allele)))
return phbr_i
@staticmethod
def extract_best_epitope_per_alelle(
epitopes: List[PredictedEpitope],
mhc_isoforms: List[Mhc1]) -> List[PredictedEpitope]:
"""
This function returns the predicted epitope with the lowest binding score for each patient allele,
considering homozyogosity
"""
homozygous_alleles = BestAndMultipleBinder._get_homozygous_mhc1_alleles(
mhc_isoforms)
hetero_hemizygous_alleles = (
BestAndMultipleBinder._get_heterozygous_or_hemizygous_mhc1_alleles(
mhc_isoforms))
return BestAndMultipleBinder._get_sorted_epitopes(
hetero_hemizygous_alleles, homozygous_alleles, epitopes)
@staticmethod
def _get_homozygous_mhc1_alleles(mhc_isoforms: List[Mhc1]) -> List[str]:
"""
Returns alleles that occur more than one time in list of patient alleles and hence are homozygous alleles.
Otherwise retunrs empty list
"""
return [
a.name for m in mhc_isoforms for a in m.alleles
if m.zygosity == Zygosity.HOMOZYGOUS
]
@staticmethod
def _get_heterozygous_or_hemizygous_mhc1_alleles(
mhc_isoforms: List[Mhc1], ) -> List[str]:
"""
Returns alleles that occur more than one time in list of patient alleles and hence are homozygous alleles.
Otherwise retunrs empty list
"""
return [
a.name for m in mhc_isoforms for a in m.alleles
if m.zygosity in [Zygosity.HETEROZYGOUS, Zygosity.HEMIZYGOUS]
]
@staticmethod
def _get_sorted_epitopes(
hetero_hemizygous_alleles,
homozygous_alleles,
predictions: List[PredictedEpitope],
) -> List[PredictedEpitope]:
# groups epitopes by allele
epitopes_by_allele = {}
for p in predictions:
epitopes_by_allele.setdefault(p.hla.name, []).append(p)
# chooses the best epitope per allele while considering zygosity
best_epis_per_allele = []
for list_alleles in epitopes_by_allele.values():
# sort by rank to choose the best epitope, ties are solved choosing the first peptide in alphabetcial order
list_alleles.sort(key=lambda x: (x.rank, x.peptide))
best_epitope = list_alleles[0]
if best_epitope.hla.name in hetero_hemizygous_alleles:
best_epis_per_allele.append(
best_epitope) # adds the epitope once
if best_epitope.hla.name in homozygous_alleles:
best_epis_per_allele.append(best_epitope)
best_epis_per_allele.append(
best_epitope) # adds the epitope twice
return best_epis_per_allele
@staticmethod
def determine_number_of_binders(predictions: List[PredictedEpitope],
threshold=50):
"""
Determines the number of HLA I binders per mutation based on an affinity threshold. Default is set to 50, which is threshold used in generator rate.
"""
scores = [epitope.affinity_score 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=10):
"""
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 epitope in predictions:
dai_values.append(epitope.affinity_score)
if epitope.affinity_score < 5000:
wt_peptide = AbstractNetMhcPanPredictor.select_best_by_affinity(
AbstractNetMhcPanPredictor.
filter_wt_predictions_from_best_mutated(
predictions=predictions_wt,
mutated_prediction=epitope),
none_value=BestAndMultipleBinder._get_empty_epitope())
if wt_peptide is not None and wt_peptide.affinity_score is not None:
dai = wt_peptide.affinity_score / epitope.affinity_score
if dai > threshold:
number_binders += 1
if len(dai_values) == 0:
number_binders = None
return number_binders
@staticmethod
def determine_number_of_alternative_binders_alternative(
predictions: List[PredictedEpitope],
predictions_wt: List[PredictedEpitope],
threshold=10):
"""
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.affinity_score)
if mut.affinity_score < 5000 and wt.affinity_score:
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,
mhc1_alleles_patient: List[Mhc1],
mhc1_alleles_available: Set,
uniprot,
):
"""
predicts MHC epitopes; returns on one hand best binder and on the other hand multiple binder analysis is performed
"""
self._initialise()
predictions = self.netmhcpan.mhc_prediction(mhc1_alleles_patient,
mhc1_alleles_available,
mutation.mutated_xmer)
if mutation.wild_type_xmer:
# make sure that predicted epitopes cover mutation in case of SNVs
predictions = self.netmhcpan.filter_peptides_covering_snv(
position_of_mutation=mutation.position,
predictions=predictions)
# make sure that predicted neoepitopes are part of the WT proteome
filtered_predictions = self.netmhcpan.remove_peptides_in_proteome(
predictions=predictions, uniprot=uniprot)
if len(filtered_predictions) > 0:
# multiple binding
self.epitope_affinities = "/".join([
str(epitope.affinity_score) for epitope in filtered_predictions
])
# best prediction
self.best_epitope_by_rank = self.netmhcpan.select_best_by_rank(
filtered_predictions, none_value=self._get_empty_epitope())
self.best_epitope_by_affinity = self.netmhcpan.select_best_by_affinity(
filtered_predictions, none_value=self._get_empty_epitope())
logger.info(self.best_epitope_by_rank)
# best predicted epitope of length 9
ninemer_predictions = self.netmhcpan.filter_for_9mers(
filtered_predictions)
self.best_ninemer_epitope_by_rank = self.netmhcpan.select_best_by_rank(
ninemer_predictions, none_value=self._get_empty_epitope())
self.best_ninemer_epitope_by_affinity = self.netmhcpan.select_best_by_affinity(
ninemer_predictions, none_value=self._get_empty_epitope())
# multiple binding based on affinity
self.generator_rate_cdn = self.determine_number_of_binders(
predictions=filtered_predictions, threshold=50)
# PHBR-I
self.phbr_i = self.calculate_phbr_i(
predictions=filtered_predictions,
mhc1_alleles=mhc1_alleles_patient)
self.best_wt_epitope_by_rank = None
self.best_wt_epitope_by_affinity = None
self.best_ninemer_wt_epitope_by_rank = None
self.best_ninemer_wt_epitope_by_affinity = None
# MHC binding predictions for WT peptides
if mutation.wild_type_xmer:
# SNVs
predictions_wt = self.netmhcpan.mhc_prediction(
mhc1_alleles_patient, mhc1_alleles_available,
mutation.wild_type_xmer)
filtered_predictions_wt = self.netmhcpan.filter_peptides_covering_snv(
position_of_mutation=mutation.position,
predictions=predictions_wt)
self.best_wt_epitope_by_rank = self.netmhcpan.select_best_by_rank(
self.netmhcpan.filter_wt_predictions_from_best_mutated(
filtered_predictions_wt, self.best_epitope_by_rank),
none_value=BestAndMultipleBinder._get_empty_epitope())
self.best_wt_epitope_by_affinity = self.netmhcpan.select_best_by_affinity(
self.netmhcpan.filter_wt_predictions_from_best_mutated(
filtered_predictions_wt,
self.best_epitope_by_affinity),
none_value=BestAndMultipleBinder._get_empty_epitope())
# best predicted epitope of length 9
ninemer_predictions_wt = self.netmhcpan.filter_for_9mers(
filtered_predictions_wt)
self.best_ninemer_wt_epitope_by_rank = self.netmhcpan.select_best_by_rank(
self.netmhcpan.filter_wt_predictions_from_best_mutated(
ninemer_predictions_wt,
self.best_ninemer_epitope_by_rank),
none_value=BestAndMultipleBinder._get_empty_epitope())
self.best_ninemer_wt_epitope_by_affinity = self.netmhcpan.select_best_by_affinity(
self.netmhcpan.filter_wt_predictions_from_best_mutated(
ninemer_predictions_wt,
self.best_ninemer_epitope_by_affinity),
none_value=BestAndMultipleBinder._get_empty_epitope())
# multiple binding based on affinity
self.generator_rate_adn = self.determine_number_of_alternative_binders(
predictions=filtered_predictions,
predictions_wt=filtered_predictions_wt)
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.netmhcpan.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:
hla = Mhc1(name=mut_peptide.hla.gene,
zygosity=Zygosity.HOMOZYGOUS,
alleles=[mut_peptide.hla])
filtered_predictions_wt.extend(
self.netmhcpan.mhc_prediction_peptide(
[hla], mhc1_alleles_available, wt_peptide))
if self.best_epitope_by_rank:
self.best_wt_epitope_by_rank = self.netmhcpan.filter_wt_predictions_from_best_mutated_alernative(
mut_predictions=filtered_predictions,
wt_predictions=filtered_predictions_wt,
best_mutated_epitope=self.best_epitope_by_rank)
if self.best_epitope_by_affinity:
self.best_wt_epitope_by_affinity = self.netmhcpan.filter_wt_predictions_from_best_mutated_alernative(
mut_predictions=filtered_predictions,
wt_predictions=filtered_predictions_wt,
best_mutated_epitope=self.best_epitope_by_affinity)
if self.best_ninemer_epitope_by_rank:
self.best_ninemer_wt_epitope_by_rank = self.netmhcpan.filter_wt_predictions_from_best_mutated_alernative(
mut_predictions=filtered_predictions,
wt_predictions=filtered_predictions_wt,
best_mutated_epitope=self.best_ninemer_epitope_by_rank)
if self.best_ninemer_epitope_by_affinity:
self.best_ninemer_wt_epitope_by_affinity = self.netmhcpan.filter_wt_predictions_from_best_mutated_alernative(
mut_predictions=filtered_predictions,
wt_predictions=filtered_predictions_wt,
best_mutated_epitope=self.
best_ninemer_epitope_by_affinity)
# multiple binding based on affinity
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_annotations(self, mutation) -> List[Annotation]:
annotations = []
if self.best_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_epitope_by_rank.rank,
name="Best_rank_MHCI_score"),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_rank.peptide,
name="Best_rank_MHCI_score_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_rank.hla.name,
name="Best_rank_MHCI_score_allele")
])
if self.best_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_epitope_by_affinity.affinity_score,
name="Best_affinity_MHCI_score",
),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_allele",
)
])
if self.best_ninemer_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_rank.rank,
name="Best_rank_MHCI_9mer_score",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_rank.peptide,
name="Best_rank_MHCI_9mer_epitope",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_rank.hla.name,
name="Best_rank_MHCI_9mer_allele",
)
])
if self.best_ninemer_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_affinity.affinity_score,
name="Best_affinity_MHCI_9mer_score",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_9mer_allele",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_9mer_epitope",
)
])
# wt
if self.best_wt_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_affinity.affinity_score,
name="Best_affinity_MHCI_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_allele_WT",
)
])
if self.best_wt_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_rank.rank,
name="Best_rank_MHCI_score_WT"),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_rank.peptide,
name="Best_rank_MHCI_score_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_wt_epitope_by_rank.hla.name,
name="Best_rank_MHCI_score_allele_WT",
)
])
if self.best_ninemer_wt_epitope_by_rank:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_rank.rank,
name="Best_rank_MHCI_9mer_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_rank.peptide,
name="Best_rank_MHCI_9mer_epitope_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_rank.hla.name,
name="Best_rank_MHCI_9mer_allele_WT",
)
])
if self.best_ninemer_wt_epitope_by_affinity:
annotations.extend([
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_affinity.
affinity_score,
name="Best_affinity_MHCI_9mer_score_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_affinity.hla.name,
name="Best_affinity_MHCI_9mer_allele_WT",
),
AnnotationFactory.build_annotation(
value=self.best_ninemer_wt_epitope_by_affinity.peptide,
name="Best_affinity_MHCI_9mer_epitope_WT",
)
])
if self.organism == ORGANISM_HOMO_SAPIENS:
annotations.extend([
AnnotationFactory.build_annotation(value=self.phbr_i,
name="PHBR_I")
])
annotations.extend([
# generator rate
AnnotationFactory.build_annotation(value=self.generator_rate,
name="Generator_rate_MHCI"),
AnnotationFactory.build_annotation(value=self.generator_rate_cdn,
name="Generator_rate_CDN_MHCI"),
AnnotationFactory.build_annotation(value=self.generator_rate_adn,
name="Generator_rate_ADN_MHCI")
])
annotations.extend(
self._get_positions_and_mutation_in_anchor(mutation))
return annotations
def _get_positions_and_mutation_in_anchor(self, mutation):
"""
returns if mutation is in anchor position for best affinity epitope over all lengths and best 9mer affinity
"""
position_9mer = None
mutation_in_anchor_9mer = None
if self.best_ninemer_epitope_by_affinity.peptide and mutation.wild_type_xmer:
position_9mer = EpitopeHelper.position_of_mutation_epitope(
wild_type=self.best_ninemer_wt_epitope_by_affinity.peptide,
mutation=self.best_ninemer_epitope_by_affinity.peptide,
)
mutation_in_anchor_9mer = EpitopeHelper.position_in_anchor_position(
position_mhci=position_9mer,
peptide_length=len(
self.best_ninemer_epitope_by_affinity.peptide),
)
annotations = [
AnnotationFactory.build_annotation(
value=position_9mer,
name="Best_affinity_MHCI_9mer_position_mutation"),
AnnotationFactory.build_annotation(
value=mutation_in_anchor_9mer,
name="Best_affinity_MHCI_9mer_anchor_mutated",
),
]
return annotations
def test_phbr1(self):
best_multiple = BestAndMultipleBinder(
runner=self.runner,
configuration=self.configuration,
mhc_parser=self.mhc_parser,
blastp_runner=self.proteome_blastp_runner)
netmhcpan = NetMhcPanPredictor(
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
predictions = netmhcpan.mhc_prediction(self.test_mhc_one,
self.available_alleles_mhc1,
mutation.mutated_xmer)
predicted_neoepitopes = netmhcpan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
best_epitopes_per_allele = (
BestAndMultipleBinder.extract_best_epitope_per_alelle(
predicted_neoepitopes, self.test_mhc_one))
phbr_i = best_multiple.calculate_phbr_i(best_epitopes_per_allele,
self.test_mhc_one)
self.assertIsNotNone(phbr_i)
self.assertAlmostEqual(1.359324592015038, phbr_i)
# one homozygous allele present
mhc_alleles = MhcFactory.build_mhc1_alleles([
"HLA-A*24:02",
"HLA-A*02:01",
"HLA-B*15:01",
"HLA-B*44:02",
"HLA-C*05:01",
"HLA-C*05:01",
], self.hla_database)
predictions = netmhcpan.mhc_prediction(self.test_mhc_one,
self.available_alleles_mhc1,
mutation.mutated_xmer)
predicted_neoepitopes = netmhcpan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
best_epitopes_per_allele = (
BestAndMultipleBinder.extract_best_epitope_per_alelle(
predicted_neoepitopes, mhc_alleles))
phbr_i = best_multiple.calculate_phbr_i(best_epitopes_per_allele,
mhc_alleles)
self.assertIsNotNone(phbr_i)
self.assertAlmostEqual(1.0036998409510969, phbr_i)
# mo info for one allele
mhc_alleles = MhcFactory.build_mhc1_alleles([
"HLA-A*24:02", "HLA-A*02:01", "HLA-B*15:01", "HLA-B*44:02",
"HLA-C*05:01"
], self.hla_database)
predictions = netmhcpan.mhc_prediction(self.test_mhc_one,
self.available_alleles_mhc1,
mutation.mutated_xmer)
predicted_neoepitopes = netmhcpan.remove_peptides_in_proteome(
predictions=predictions, uniprot=self.uniprot)
best_epitopes_per_allele = (
BestAndMultipleBinder.extract_best_epitope_per_alelle(
predicted_neoepitopes, mhc_alleles))
phbr_i = best_multiple.calculate_phbr_i(best_epitopes_per_allele,
mhc_alleles)
self.assertIsNone(phbr_i)