diff --git a/epytope/EpitopePrediction/ANN.py b/epytope/EpitopePrediction/ANN.py index cc8e403..4a78973 100644 --- a/epytope/EpitopePrediction/ANN.py +++ b/epytope/EpitopePrediction/ANN.py @@ -19,6 +19,7 @@ from abc import abstractmethod from enum import IntEnum +from enum import Enum import pandas from collections import defaultdict @@ -751,7 +752,7 @@ def predict(self, peptides, alleles=None, binary=False, **kwargs): # test mhcflurry models are available => download if not p = subprocess.call(['mhcflurry-downloads', 'path', 'models_class1'], - stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) if p != 0: logging.warn("mhcflurry models must be downloaded, as they were not found locally.") cp = subprocess.run(['mhcflurry-downloads', 'fetch', 'models_class1'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) @@ -818,8 +819,8 @@ class MHCFlurryPredictor_1_4_3(MHCFlurryPredictor_1_2_2): .. note:: T. J. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, - "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," Cell Systems, 2018. - Available at: https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30232-1. + "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," Cell Systems, 2018. + Available at: https://www.cell.com/cell-systems/fulltext/S2405-4712(18)30232-1. """ # retrieved with `mhcflurry-predict --list-supported-alleles` __alleles = frozenset(["HLA-A*01:01", "HLA-A*02:01", "HLA-A*02:02", "HLA-A*02:03", "HLA-A*02:05", @@ -888,6 +889,151 @@ def revert_allele_repr(self, name): except BadSignatureException: logging.warning("Class MHCFlurryPredictor_1_4_3 cannot be constructed, because of a bad method signature (predict)") +try: + class MHCFlurryPredictor_2_0_1(MHCFlurryPredictor_1_4_3): + """ + Implements MHCFlurry + + .. note:: + T. J. O’Donnell, A. Rubinsteyn, M. Bonsack, and U. Laserson, + "MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen + Processing," Cell Systems, 2020. + Available at: https://www.sciencedirect.com/science/article/pii/S2405471220302398. + """ + # retrieved with `mhcflurry-downloads fetch models_class1_presentation` + # `mhcflurry-predict --list-supported-alleles` + __alleles = 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+ # retrieved with `mhcflurry-predict --list-supported-peptide-lengths` + __supported_length = frozenset([5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) + __name = "mhcflurry" + __version = "2.0.1" + + # the interface defines three class properties + @property + def name(self): + # returns the name of the predictor + return self.__name + + @property + def supportedAlleles(self): + # returns the supported alleles as strings (without the HLA prefix) + return self.__alleles + + @property + def supportedLength(self): + # returns the supported epitope lengths as iterable + return self.__supported_length + + @property + def version(self): + # returns the version of the predictor + return self.__version + + def _represent(self, allele): + """ + Internal function transforming an allele object into its representative string + :param allele: The :class:`~epytope.Core.Allele.Allele` for which the internal predictor representation is + needed + :type alleles: :class:`~epytope.Core.Allele.Allele` + :return: str + """ + if isinstance(allele, MouseAllele): + return "%s-%s%s%s" % (allele.organism, allele.locus, allele.supertype, allele.subtype) + else: + return "%s-%s*%s:%s" % (allele.organism, allele.locus, allele.supertype, allele.subtype) + + # Converts the internal MHCFlurry representation back into a epytope representation + def revert_allele_repr(self, name): + if name.startswith("H-2-"): + return MouseAllele(name) + else: + return Allele(name) + + # predicts the binding affinity for a set of peptides and alleles + def predict(self, peptides, alleles=None, binary=False, **kwargs): + + # test whether one peptide or a list + if not isinstance(peptides, list): + peptides = [peptides] + + # if no alleles are specified do predictions for all supported alleles + if alleles is None: + alleles = self.supportedAlleles + else: + # filter for supported alleles + alleles = [a for a in alleles if a in self.supportedAlleles] + + # Create a dictionary with Allele Obj as key and the respective allele predictor representation as value + alleles_repr = {allele: self._represent(allele) for allele in alleles} + + # test mhcflurry models are available => download if not + p = subprocess.call(['mhcflurry-downloads', 'path', 'models_class1'], + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + if p != 0: + logging.warn("mhcflurry models must be downloaded, as they were not found locally.") + cp = subprocess.run(['mhcflurry-downloads', 'fetch', 'models_class1'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + if cp.returncode != 0: + for line in cp.stdout.decode().splitlines(): + logging.error(line) + raise RuntimeError("mhcflurry failed to download model file") + + # load model + predictor = Class1AffinityPredictor.load() + + # prepare results dictionary + scores = defaultdict(defaultdict) + ranks = defaultdict(defaultdict) + + # keep input peptide objects for later use + peptide_objects = {} + for peptide in peptides: + peptide_objects[str(peptide)] = peptide + + # group peptides by length + pep_groups = list(peptide_objects.keys()) + pep_groups.sort(key=len) + + for length, peps in itertools.groupby(peptides, key=len): + if length not in self.supportedLength: + logging.warning("Peptide length must be at least %i or at most %i for %s but is %i" % (min(self.supportedLength), max(self.supportedLength), + self.name, length)) + continue + peps = list(peps) + + # predict and assign binding affinities + for a in alleles: + for p in peps: + prediction_result = predictor.predict_to_dataframe(allele=alleles_repr[a], peptides=[str(p)]) + binding_affinity = prediction_result[ScoreKeys.MHCFLURRY_2_0_1] + rank = prediction_result[ScoreKeys.MHCFLURRY_2_0_1_RANK] + + if binary: + if binding_affinity <= 500: + scores[a][p] = 1.0 + else: + scores[a][p] = 0.0 + else: + # convert ic50 to raw prediction score + scores[a][p] = 1- math.log(binding_affinity, 50000) + ranks[a][p] = float(rank) + + if not scores: + raise ValueError("No predictions could be made with " + self.name + + " for given input. Check your epitope length and HLA allele combination.") + + # Create dictionary with hierarchy: {'Allele1': {'Score': {'Pep1': AffScore1, 'Pep2': AffScore2,..}, 'Allele2':...} + if binary: + result = {allele: {"Score":(list(scores.values())[j])} for j, allele in enumerate(alleles)} + else: + result = {allele: {metric:(list(scores.values())[j] if metric == "Score" else list(ranks.values())[j]) for metric in ["Score", "Rank"]} for j, allele in enumerate(alleles)} + + # create EpitopePredictionResult object. This is a multi-indexed DataFrame + # with Allele, Method and Score type as multi-columns and peptides as rows + df_result = EpitopePredictionResult.from_dict(result, peptide_objects.values(), self.name) + return df_result + +except BadSignatureException: + logging.warning("Class MHCFlurryPredictor_2_0_1 cannot be constructed, because of a bad method signature (predict)") class ScoreIndex(IntEnum): @@ -897,3 +1043,12 @@ class ScoreIndex(IntEnum): MHCNUGGETS_CLASS1_2_0 = 1 MHCNUGGETS_CLASS2_2_0 = 1 MHCFLURRY = 0 + + +class ScoreKeys(str, Enum): + """ + Specifies the score column key in the respective output format + """ + MHCFLURRY_2_0_1 = "prediction" + MHCFLURRY_2_0_1_RANK = "prediction_percentile" + diff --git a/epytope/EpitopePrediction/PSSM.py b/epytope/EpitopePrediction/PSSM.py index 24a2df1..b969d8c 100644 --- a/epytope/EpitopePrediction/PSSM.py +++ b/epytope/EpitopePrediction/PSSM.py @@ -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. @@ -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): @@ -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()) @@ -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 @@ -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): @@ -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"] @@ -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 @@ -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): @@ -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): @@ -979,13 +988,15 @@ 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)} scores = {} pep_groups = list(pep_seqs.keys()) @@ -993,7 +1004,8 @@ def __load_allele_model(allele, length): 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) @@ -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] @@ -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 diff --git a/epytope/EpitopePrediction/__init__.py b/epytope/EpitopePrediction/__init__.py index 267a148..91d69ab 100644 --- a/epytope/EpitopePrediction/__init__.py +++ b/epytope/EpitopePrediction/__init__.py @@ -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 * @@ -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. '''