diff --git a/Fred2/EpitopePrediction/ANN.py b/Fred2/EpitopePrediction/ANN.py index f6936408..f59aa932 100644 --- a/Fred2/EpitopePrediction/ANN.py +++ b/Fred2/EpitopePrediction/ANN.py @@ -80,24 +80,24 @@ class MHCNuggetsPredictor_1(AANNEpitopePrediction): """ __metaclass__ = SignatureCheckerMeta __alleles = frozenset( - ["HLA-A01:01", "HLA-A02:01", "HLA-A02:02", "HLA-A02:03", "HLA-A02:04", "HLA-A02:05", "HLA-A02:06", - "HLA-A02:07", "HLA-A02:08", "HLA-A02:09", "HLA-A02:10", "HLA-A02:11", "HLA-A02:12", "HLA-A02:14", - "HLA-A02:16", "HLA-A02:17", "HLA-A02:19", "HLA-A02:50", "HLA-A03:01", "HLA-A03:02", "HLA-A03:19", - "HLA-A11:01", "HLA-A11:02", "HLA-A23:01", "HLA-A24:01", "HLA-A24:02", "HLA-A24:03", "HLA-A25:01", - "HLA-A26:01", "HLA-A26:02", "HLA-A26:03", "HLA-A29:01", "HLA-A29:02", "HLA-A30:01", "HLA-A30:02", - "HLA-A30:03", "HLA-A30:04", "HLA-A31:01", "HLA-A32:01", "HLA-A32:07", "HLA-A32:15", "HLA-A33:01", - "HLA-A33:03", "HLA-A66:01", "HLA-A68:01", "HLA-A68:02", "HLA-A68:23", "HLA-A69:01", "HLA-A74:01", - "HLA-A80:01", "HLA-B07:01", "HLA-B07:02", "HLA-B08:01", "HLA-B08:02", "HLA-B08:03", "HLA-B12:01", - "HLA-B13:02", "HLA-B14:01", "HLA-B14:02", "HLA-B15:01", "HLA-B15:02", "HLA-B15:03", "HLA-B15:08", - "HLA-B15:09", "HLA-B15:10", "HLA-B15:13", "HLA-B15:16", "HLA-B15:17", "HLA-B15:42", "HLA-B18:01", - "HLA-B27:01", "HLA-B27:02", "HLA-B27:03", "HLA-B27:04", "HLA-B27:05", "HLA-B27:06", "HLA-B27:09", - "HLA-B27:10", "HLA-B27:20", "HLA-B35:01", "HLA-B35:02", "HLA-B35:03", "HLA-B35:08", "HLA-B37:01", - "HLA-B38:01", "HLA-B39:01", "HLA-B39:06", "HLA-B39:09", "HLA-B39:10", "HLA-B40:01", "HLA-B40:02", - "HLA-B40:13", "HLA-B41:03", "HLA-B41:04", "HLA-B42:01", "HLA-B42:02", "HLA-B44:01", "HLA-B44:02", - "HLA-B44:03", "HLA-B44:05", "HLA-B45:01", "HLA-B45:06", "HLA-B46:01", "HLA-B48:01", "HLA-B51:01", - "HLA-B51:02", "HLA-B52:01", "HLA-B53:01", "HLA-B54:01", "HLA-B55:01", "HLA-B55:02", "HLA-B56:01", - "HLA-B57:01", "HLA-B57:02", "HLA-B57:03", "HLA-B58:01", "HLA-B58:02", "HLA-B60:01", "HLA-B61:01", - "HLA-B62:01", "HLA-B73:01", "HLA-B81:01", "HLA-B83:01"]) + ["HLA-A*01:01", "HLA-A*02:01", "HLA-A*02:02", "HLA-A*02:03", "HLA-A*02:04", "HLA-A*02:05", "HLA-A*02:06", + "HLA-A*02:07", "HLA-A*02:08", "HLA-A*02:09", "HLA-A*02:10", "HLA-A*02:11", "HLA-A*02:12", "HLA-A*02:14", + "HLA-A*02:16", "HLA-A*02:17", "HLA-A*02:19", "HLA-A*02:50", "HLA-A*03:01", "HLA-A*03:02", "HLA-A*03:19", + "HLA-A*11:01", "HLA-A*11:02", "HLA-A*23:01", "HLA-A*24:01", "HLA-A*24:02", "HLA-A*24:03", "HLA-A*25:01", + "HLA-A*26:01", "HLA-A*26:02", "HLA-A*26:03", "HLA-A*29:01", "HLA-A*29:02", "HLA-A*30:01", "HLA-A*30:02", + "HLA-A*30:03", "HLA-A*30:04", "HLA-A*31:01", "HLA-A*32:01", "HLA-A*32:07", "HLA-A*32:15", "HLA-A*33:01", + "HLA-A*33:03", "HLA-A*66:01", "HLA-A*68:01", "HLA-A*68:02", "HLA-A*68:23", "HLA-A*69:01", "HLA-A*74:01", + "HLA-A*80:01", "HLA-B*07:01", "HLA-B*07:02", "HLA-B*08:01", "HLA-B*08:02", "HLA-B*08:03", "HLA-B*12:01", + "HLA-B*13:02", "HLA-B*14:01", "HLA-B*14:02", "HLA-B*15:01", "HLA-B*15:02", "HLA-B*15:03", "HLA-B*15:08", + "HLA-B*15:09", "HLA-B*15:10", "HLA-B*15:13", "HLA-B*15:16", "HLA-B*15:17", "HLA-B*15:42", "HLA-B*18:01", + "HLA-B*27:01", "HLA-B*27:02", "HLA-B*27:03", "HLA-B*27:04", "HLA-B*27:05", "HLA-B*27:06", "HLA-B*27:09", + "HLA-B*27:10", "HLA-B*27:20", "HLA-B*35:01", "HLA-B*35:02", "HLA-B*35:03", "HLA-B*35:08", "HLA-B*37:01", + "HLA-B*38:01", "HLA-B*39:01", "HLA-B*39:06", "HLA-B*39:09", "HLA-B*39:10", "HLA-B*40:01", "HLA-B*40:02", + "HLA-B*40:13", "HLA-B*41:03", "HLA-B*41:04", "HLA-B*42:01", "HLA-B*42:02", "HLA-B*44:01", "HLA-B*44:02", + "HLA-B*44:03", "HLA-B*44:05", "HLA-B*45:01", "HLA-B*45:06", "HLA-B*46:01", "HLA-B*48:01", "HLA-B*51:01", + "HLA-B*51:02", "HLA-B*52:01", "HLA-B*53:01", "HLA-B*54:01", "HLA-B*55:01", "HLA-B*55:02", "HLA-B*56:01", + "HLA-B*57:01", "HLA-B*57:02", "HLA-B*57:03", "HLA-B*58:01", "HLA-B*58:02", "HLA-B*60:01", "HLA-B*61:01", + "HLA-B*62:01", "HLA-B*73:01", "HLA-B*81:01", "HLA-B*83:01"]) __supported_length = frozenset([5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]) __name = "mhcnuggets-class-1" __version = "2.0" @@ -123,31 +123,29 @@ def version(self): # returns the version of the predictor return self.__version - # the interface defines a function converting Fred2's HLA allele presentation - # into an internal presentation used by different methods. - # for this predictor we won't need it but still have to provide it! - # the function consumes a list of alleles and converts them into the internally used presentation + # Converts FRED2s internal allele representation into the format required by mhcnuggets def convert_alleles(self, alleles): - # we just use the identity function - return alleles + return ['HLA-' + allele.name.replace('*', '') for allele in alleles] - # additionally the interface defines a function `predict` - # that consumes a list of peptides or a single peptide and optionally a list of allele objects - # this method implements the complete prediction routine + # Converts the internal mhcnuggets-class-1 representation back into a FRED2 representation + def revert_allele_repr(self, allele): + allele = allele.replace('HLA-', '') + allele = allele[:1] + '*' + allele[1:] + return allele + + # 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 isinstance(peptides, basestring): - peptides = list(peptides) + 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 = filter(lambda a: a.name in self.supportedAlleles, alleles) - - result = {} + alleles = filter(lambda a: a in self.supportedAlleles, alleles) # fetch peptides as strings peptides = [str(peptide) for peptide in peptides] @@ -158,9 +156,12 @@ def predict(self, peptides, alleles=None, binary=False, **kwargs): for peptide in peptides: file.write(peptide + "\n") - # predict bindings + alleles = self.convert_alleles(alleles) + result = {} + # predict binding affinities for a in alleles: - result[a] = {} + allele_repr = self.revert_allele_repr(a) + result[allele_repr] = {} tmp_output_file = tempfile.NamedTemporaryFile().name mhcnuggets_predict(class_='I', peptides_path=tmp_input_file, @@ -173,17 +174,18 @@ def predict(self, peptides, alleles=None, binary=False, **kwargs): # skip header reader.next() + # assign binding affinities for row in reader: content = row[0].split(',') peptide = content[0] binding_affinity = content[1] if binary: if binding_affinity <= 500: - result[a][peptide] = 1.0 + result[allele_repr][peptide] = 1.0 else: - result[a][peptide] = 0.0 + result[allele_repr][peptide] = 0.0 else: - result[a][peptide] = binding_affinity + result[allele_repr][peptide] = binding_affinity # create EpitopePredictionResult object. This is a multi-indexed DataFrame # with Peptide and Method as multi-index and alleles as columns @@ -264,45 +266,50 @@ def version(self): # returns the version of the predictor return self.__version - # the interface defines a function converting Fred2's HLA allele presentation - # into an internal presentation used by different methods. - # for this predictor we won't need it but still have to provide it! - # the function consumes a list of alleles and converts them into the internally used presentation + # Converts FRED2s internal allele representation into the format required by mhcnuggets-class-2 def convert_alleles(self, alleles): - return [allele.replace(':', '').replace('*', '') for allele in alleles] + return ['HLA-' + allele.name.replace('*', '') for allele in alleles] + + # Converts the internal mhcnuggets-class-2 representation back into a FRED2 representation + def revert_allele_repr(self, allele): + allele = allele.replace('HLA-', '') + # since we need to support single and double mhc2 alleles + allele_split = allele.split('-') + if len(allele_split) > 1: + return allele_split[0][:4] + '*' + allele_split[0][4:] + '-' + allele_split[1][:4] + '*' + allele_split[1][4:] + else: + return allele_split[0][:4] + '*' + allele_split[0][4:] - # additionally the interface defines a function `predict` - # that consumes a list of peptides or a single peptide and optionally a list - # of allele objects - # - # this method implements the complete prediction routine + # 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 isinstance(peptides, basestring): - peptides = list(peptides) + 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 = filter(lambda a: a.name in self.supportedAlleles, alleles) - - result = {} + alleles = filter(lambda a: a in self.supportedAlleles, alleles) # fetch peptides as strings peptides = [str(peptide) for peptide in peptides] + alleles = self.convert_alleles(alleles) + # write peptides temporarily, new line separated tmp_input_file = tempfile.NamedTemporaryFile().name with open(tmp_input_file, 'wb') as file: for peptide in peptides: file.write(peptide + "\n") + result = {} # predict bindings for a in alleles: - result[a] = {} + allele_repr = self.revert_allele_repr(a) + result[allele_repr] = {} tmp_output_file = tempfile.NamedTemporaryFile().name mhcnuggets_predict(class_='II', @@ -322,11 +329,11 @@ def predict(self, peptides, alleles=None, binary=False, **kwargs): binding_affinity = content[1] if binary: if binding_affinity <= 500: - result[a][peptide] = 1.0 + result[allele_repr][peptide] = 1.0 else: - result[a][peptide] = 0.0 + result[allele_repr][peptide] = 0.0 else: - result[a][peptide] = binding_affinity + result[allele_repr][peptide] = binding_affinity # create EpitopePredictionResult object. This is a multi-indexed DataFrame # with Peptide and Method as multi-index and alleles as columns @@ -386,27 +393,31 @@ def version(self): # returns the version of the predictor return self.__version - # the interface defines a function converting Fred2's HLA allele presentation - # into an internal presentation used by different methods. - # for this predictor we won't need it but still have to provide it! - # the function consumes a list of alleles and converts them into the internally used presentation + # converts internal FRED2 HLA representations into an internal representation used by MHCFlurry def convert_alleles(self, alleles): - # we just use the identity function - return alleles - - # additionally the interface defines a function `predict` + return ['HLA-' + allele.name.replace(':', '').replace('*', '') for allele in alleles] + + # Converts the internal MHCFlurry representation back into a FRED2 representation + def revert_allele_repr(self, allele): + allele = allele.replace('HLA-', '') + allele = allele[:1] + '*' + allele[1:3] + ':' + allele[3:] + return allele + + # 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 isinstance(peptides, basestring): - peptides = list(peptides) + 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 = filter(lambda a: a.name in self.supportedAlleles, alleles) + alleles = filter(lambda a: a in self.supportedAlleles, alleles) + + alleles = self.convert_alleles(alleles) # test mhcflurry models are available => download if not p = subprocess.Popen(['mhcflurry-downloads', 'path', 'models_class1'], @@ -417,20 +428,21 @@ def predict(self, peptides, alleles=None, binary=False, **kwargs): # load model predictor = Class1AffinityPredictor.load() + # predict and assign binding affinities result = {} - for a in alleles: - result[a] = {} + allele_repr = self.revert_allele_repr(a) + result[allele_repr] = {} for p in peptides: seq = p.__str__() - binding_affinity = predictor.predict(allele=a, peptides=[seq]) + binding_affinity = predictor.predict(allele=a, peptides=[seq])[0] if binary: if binding_affinity <= 500: - result[a][p] = 1.0 + result[allele_repr][p] = 1.0 else: - result[a][p] = 0.0 + result[allele_repr][p] = 0.0 else: - result[a][p] = binding_affinity + result[allele_repr][p] = binding_affinity # create EpitopePredictionResult object. This is a multi-indexed DataFrame # with Peptide and Method as multi-index and alleles as columns diff --git a/setup.py b/setup.py index 417fd860..b005f3b0 100644 --- a/setup.py +++ b/setup.py @@ -43,7 +43,7 @@ name='Fred2', # Version: - version='2.0.4', + version='2.0.5', description='A Framework for Epitope Detection and Vaccine Design', long_description=readme,