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Add interface for MHCflurry 2.0.x #54

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Add interface for MHCflurry 2.0.x #54

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161 changes: 158 additions & 3 deletions epytope/EpitopePrediction/ANN.py
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78 changes: 46 additions & 32 deletions epytope/EpitopePrediction/PSSM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,10 +22,6 @@
from epytope.Core.Result import EpitopePredictionResult
from epytope.Core.Base import AEpitopePrediction

from mhcflurry import Class1AffinityPredictor
from mhcnuggets.src.predict import predict


class APSSMEpitopePrediction(AEpitopePrediction):
"""
Abstract base class for PSSM predictions.
Expand All @@ -49,7 +45,8 @@ def predict(self, peptides, alleles=None, **kwargs):
def __load_allele_model(allele, length):
allele_model = "%s_%i" % (allele, length)
return getattr(
__import__("epytope.Data.pssms." + self.name + ".mat." + allele_model, fromlist=[allele_model]),
__import__("epytope.Data.pssms." + self.name +
".mat." + allele_model, fromlist=[allele_model]),
allele_model)

if isinstance(peptides, Peptide):
Expand All @@ -63,13 +60,15 @@ def __load_allele_model(allele, length):

if alleles is None:
al = [Allele(a) for a in self.supportedAlleles]
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(al), al)}
alleles_string = {conv_a: a for conv_a,
a in zip(self.convert_alleles(al), al)}
else:
if isinstance(alleles, Allele):
alleles = [alleles]
if any(not isinstance(p, Allele) for p in alleles):
raise ValueError("Input is not of type Allele")
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(alleles), alleles)}
alleles_string = {conv_a: a for conv_a, a in zip(
self.convert_alleles(alleles), alleles)}

result = {}
pep_groups = list(pep_seqs.keys())
Expand All @@ -78,29 +77,33 @@ def __load_allele_model(allele, length):
peps = list(peps)
# dynamicaly import prediction PSSMS for alleles and predict
if self.supportedLength is not None and length not in self.supportedLength:
warnings.warn("Peptide length of %i is not supported by %s" % (length, self.name))
warnings.warn(
"Peptide length of %i is not supported by %s" % (length, self.name))
continue

for a in alleles_string.keys():
try:
pssm = __load_allele_model(a, length)
except ImportError:
warnings.warn("No model found for %s with length %i" % (alleles_string[a], length))
warnings.warn("No model found for %s with length %i" % (
alleles_string[a], length))
continue

if alleles_string[a] not in result:
result[alleles_string[a]] = {'Score': {}}

for p in peps:
score = sum(pssm[i].get(p[i], 0.0) for i in range(length)) + pssm.get(-1, {}).get("con", 0)
score = sum(pssm[i].get(p[i], 0.0) for i in range(
length)) + pssm.get(-1, {}).get("con", 0)
result[alleles_string[a]]['Score'][pep_seqs[p]] = score

if not result:
raise ValueError("No predictions could be made with "
+ self.name + " for given input. Check your epitope length and HLA allele combination.")

df_result = EpitopePredictionResult.from_dict(result, pep_seqs.values(), self.name)


df_result = EpitopePredictionResult.from_dict(
result, pep_seqs.values(), self.name)

return df_result


Expand Down Expand Up @@ -587,7 +590,8 @@ def predict(self, peptides, alleles=None, **kwargs):
def __load_allele_model(allele, length):
allele_model = "%s_%i" % (allele, length)
return getattr(
__import__("epytope.Data.pssms." + self.name + ".mat." + allele_model, fromlist=[allele_model]),
__import__("epytope.Data.pssms." + self.name +
".mat." + allele_model, fromlist=[allele_model]),
allele_model)

if isinstance(peptides, Peptide):
Expand All @@ -601,33 +605,38 @@ def __load_allele_model(allele, length):

if alleles is None:
al = [Allele(a) for a in self.supportedAlleles]
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(al), al)}
alleles_string = {conv_a: a for conv_a,
a in zip(self.convert_alleles(al), al)}
else:
if isinstance(alleles, Allele):
alleles = [alleles]
if any(not isinstance(p, Allele) for p in alleles):
raise ValueError("Input is not of type Allele")
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(alleles), alleles)}
alleles_string = {conv_a: a for conv_a, a in zip(
self.convert_alleles(alleles), alleles)}

scores = {}
for length, peps in itertools.groupby(pep_seqs.keys(), key=lambda x: len(x)):
peps = list(peps)
# dynamicaly import prediction PSSMS for alleles and predict
if length not in self.supportedLength:
warnings.warn("Peptide length of %i is not supported by %s" % (length, self.name))
warnings.warn(
"Peptide length of %i is not supported by %s" % (length, self.name))
continue

for a in alleles_string.keys():
try:
pssm = __load_allele_model(a, length)
except ImportError:
warnings.warn("No model found for %s with length %i" % (alleles_string[a], length))
warnings.warn("No model found for %s with length %i" % (
alleles_string[a], length))
continue

scores[alleles_string[a]] = {}
##here is the prediction and result object missing##
for p in peps:
score = sum(pssm[i].get(p[i], 0.0) for i in range(length)) + pssm.get(-1, {}).get("con", 0)
score = sum(pssm[i].get(p[i], 0.0) for i in range(
length)) + pssm.get(-1, {}).get("con", 0)
score /= -length
score -= pssm[-1]["intercept"]
score /= pssm[-1]["slope"]
Expand All @@ -641,8 +650,9 @@ def __load_allele_model(allele, length):
if not scores:
raise ValueError("No predictions could be made with " + self.name + " for given input. Check your"
"epitope length and HLA allele combination.")

result = {allele: {"Score":(list(scores.values())[j])} for j, allele in enumerate(alleles)}

result = {allele: {
"Score": (list(scores.values())[j])} for j, allele in enumerate(alleles)}

df_result = EpitopePredictionResult.from_dict(result, peps, self.name)
return df_result
Expand Down Expand Up @@ -717,9 +727,7 @@ def predict(self, peptides, alleles=None, **kwargs):
:rtype: :class:`~epytope.Core.Result.EpitopePredictionResult`
"""
return EpitopePredictionResult(
super(ComblibSidney2008, self).predict(peptides,
alleles=alleles,
**kwargs).applymap(lambda x: math.pow(10, x)))
super(ComblibSidney2008, self).predict(peptides, alleles=alleles, **kwargs).applymap(lambda x: math.pow(10, x)))


class TEPITOPEpan(APSSMEpitopePrediction):
Expand Down Expand Up @@ -965,7 +973,8 @@ def predict(self, peptides, alleles=None, **kwargs):
def __load_allele_model(allele, length):
allele_model = "%s" % allele
return getattr(
__import__("epytope.Data.pssms." + self.name + ".mat." + allele_model, fromlist=[allele_model]),
__import__("epytope.Data.pssms." + self.name +
".mat." + allele_model, fromlist=[allele_model]),
allele_model)

if isinstance(peptides, Peptide):
Expand All @@ -979,21 +988,24 @@ def __load_allele_model(allele, length):

if alleles is None:
al = [Allele(a) for a in self.supportedAlleles]
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(al), al)}
alleles_string = {conv_a: a for conv_a,
a in zip(self.convert_alleles(al), al)}
else:
if isinstance(alleles, Allele):
alleles = [alleles]
if any(not isinstance(p, Allele) for p in alleles):
raise ValueError("Input is not of type Allele")
alleles_string = {conv_a: a for conv_a, a in zip(self.convert_alleles(alleles), alleles)}
alleles_string = {conv_a: a for conv_a, a in zip(
self.convert_alleles(alleles), alleles)}

scores = {}
pep_groups = list(pep_seqs.keys())
pep_groups.sort(key=len)
for length, peps in itertools.groupby(pep_groups, key=len):

if self.supportedLength is not None and length not in self.supportedLength:
warnings.warn("Peptide length of %i is not supported by %s" % (length, self.name))
warnings.warn(
"Peptide length of %i is not supported by %s" % (length, self.name))
continue

peps = list(peps)
Expand All @@ -1009,7 +1021,8 @@ def __load_allele_model(allele, length):
pssm = []

importance = self.__importance if length <= 9 else \
self.__importance[:5] + ((length - 9) * [0.30]) + self.__importance[5:]
self.__importance[:5] + \
((length - 9) * [0.30]) + self.__importance[5:]

for p in peps:
score = sum(self.__log_enrichment.get(p[i], 0.0) * importance[i]
Expand All @@ -1020,7 +1033,8 @@ def __load_allele_model(allele, length):
raise ValueError("No predictions could be made with " + self.name + " for given input. Check your"
"epitope length and HLA allele combination.")

result = {allele: {"Score":(list(scores.values())[j])} for j, allele in enumerate(alleles)}
result = {allele: {
"Score": (list(scores.values())[j])} for j, allele in enumerate(alleles)}

df_result = EpitopePredictionResult.from_dict(result, peps, self.name)
return df_result
Expand Down
3 changes: 1 addition & 2 deletions epytope/EpitopePrediction/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@
from epytope.Core.Base import AEpitopePrediction
from epytope.EpitopePrediction.External import *
from epytope.EpitopePrediction.PSSM import *
# from epytope.EpitopePrediction.SVM import *
from epytope.EpitopePrediction.ANN import *
try:
from fred_plugin import *
Expand All @@ -26,7 +25,7 @@ def __init__(cls, name, bases, nmspc):

def __call__(self, _predictor, *args, **kwargs):
'''
If a third person wants to write a new Epitope Predictior. He/She has to name the file fred_plugin and
If a third person wants to write a new Epitope Predictor. He/She has to name the file fred_plugin and
inherit from AEpitopePrediction. That's it nothing more.
'''

Expand Down