diff --git a/Fred2/EpitopePrediction/ANN.py b/Fred2/EpitopePrediction/ANN.py
new file mode 100644
index 00000000..f6936408
--- /dev/null
+++ b/Fred2/EpitopePrediction/ANN.py
@@ -0,0 +1,442 @@
+# coding=utf-8
+# This code is part of the Fred2 distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+"""
+.. module:: EpitopePrediction.ANN
+   :synopsis: This module contains all classes for ANN-based epitope prediction.
+.. moduleauthor:: heumos
+
+"""
+import subprocess
+import csv
+import tempfile
+from abc import abstractmethod
+
+import pandas
+
+from mhcflurry import Class1AffinityPredictor
+from mhcnuggets.src.predict import predict as mhcnuggets_predict
+from Fred2.Core import EpitopePredictionResult
+from Fred2.Core.Base import AEpitopePrediction
+
+import inspect
+
+
+class BadSignatureException(Exception):
+    pass
+
+
+class SignatureCheckerMeta(type):
+    def __new__(cls, name, baseClasses, d):
+        # For each method in d, check to see if any base class already
+        # defined a method with that name. If so, make sure the
+        # signatures are the same.
+        for methodName in d:
+            f = d[methodName]
+            for baseClass in baseClasses:
+                try:
+                    fBase = getattr(baseClass, methodName).__func__
+                    if not inspect.getargspec(f) == inspect.getargspec(fBase):
+                        raise BadSignatureException(str(methodName))
+                except AttributeError:
+                    # This method was not defined in this base class,
+                    # So just go to the next base class.
+                    continue
+
+        return type(name, baseClasses, d)
+
+
+class AANNEpitopePrediction(AEpitopePrediction):
+    """
+        Abstract base class for ANN predictions.
+        Implements predict functionality
+    """
+
+    @abstractmethod
+    def predict(self, peptides, alleles=None, binary=False, **kwargs):
+        """
+        All ANN based predictors have to implement their custom predict method.
+        Furthermore, all of them have to use the metaclass SignatureCheckerMeta to check for any contract violations.
+        They have to adhere to the following contract
+
+        :param peptides: A single :class:`~Fred2.Core.Peptide.Peptide` or a list of :class:`~Fred2.Core.Peptide.Peptide`
+        :type peptides: list(:class:`~Fred2.Core.Peptide.Peptide`) or :class:`~Fred2.Core.Peptide.Peptide`
+        :param kwargs: optional parameter (not used yet)
+        :return: Returns a :class:`pandas.DataFrame` object with the prediction results
+        :rtype: :class:`pandas.DataFrame`
+        """
+
+
+try:
+    class MHCNuggetsPredictor_1(AANNEpitopePrediction):
+        """
+        Implements MHCNuggets Class I
+
+        .. note::
+            Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins
+            Rohit Bhattacharya, Ashok Sivakumar, Collin Tokheim, Violeta Beleva Guthrie, Valsamo Anagnostou,
+            Victor E. Velculescu, Rachel Karchin (2017) bioRxiv
+        """
+        __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"])
+        __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"
+
+        # 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
+
+        # 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
+        def convert_alleles(self, alleles):
+            # we just use the identity function
+            return 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
+        def predict(self, peptides, alleles=None, binary=False, **kwargs):
+
+            # test whether one peptide or a list
+            if isinstance(peptides, basestring):
+                peptides = list(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 = {}
+
+            # fetch peptides as strings
+            peptides = [str(peptide) for peptide in peptides]
+
+            # 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")
+
+            # predict bindings
+            for a in alleles:
+                result[a] = {}
+                tmp_output_file = tempfile.NamedTemporaryFile().name
+                mhcnuggets_predict(class_='I',
+                                   peptides_path=tmp_input_file,
+                                   mhc=a,
+                                   output=tmp_output_file + a)
+
+                # read predicted binding affinities back
+                with open(tmp_output_file + a, 'rb') as csvfile:
+                    reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
+                    # skip header
+                    reader.next()
+
+                    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
+                            else:
+                                result[a][peptide] = 0.0
+                        else:
+                            result[a][peptide] = binding_affinity
+
+            # create EpitopePredictionResult object. This is a multi-indexed DataFrame
+            # with Peptide and Method as multi-index and alleles as columns
+            df_result = EpitopePredictionResult.from_dict(result)
+            df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
+                                                            names=['Seq', 'Method'])
+            return df_result
+except BadSignatureException:
+    print "Class MHCNuggetsPredictor_1 cannot be constructed, because of a bad method signature (predict)"
+
+try:
+    class MHCNuggetsPredictor_2(AANNEpitopePrediction):
+        """
+        Implements MHCNuggets Class II
+
+        .. note::
+            Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins
+            Rohit Bhattacharya, Ashok Sivakumar, Collin Tokheim, Violeta Beleva Guthrie, Valsamo Anagnostou,
+            Victor E. Velculescu, Rachel Karchin (2017) bioRxiv
+        """
+        __metaclass__ = SignatureCheckerMeta
+        __alleles = frozenset(["HLA-DPA1*01:03-DPB1*02:01", "HLA-DPA1*01:03-DPB1*03:01", "HLA-DPA1*01:03-DPB1*04:01",
+                               "HLA-DPA1*01:03-DPB1*04:02", "HLA-DPA1*02:01-DPB1*01:01", "HLA-DPA1*02:01-DPB1*05:01",
+                               "HLA-DPA1*02:02-DPB1*05:01", "HLA-DPA1*03:01-DPB1*04:02", "HLA-DPB1*01:01",
+                               "HLA-DPB1*02:01", "HLA-DPB1*03:01", "HLA-DPB1*04:01", "HLA-DPB1*04:02", "HLA-DPB1*05:01",
+                               "HLA-DPB1*09:01", "HLA-DPB1*11:01", "HLA-DPB1*14:01", "HLA-DPB1*20:01", "HLA-DQA1*01:01",
+                               "HLA-DQA1*01:01-DQB1*05:01", "HLA-DQA1*01:01-DQB1*05:03", "HLA-DQA1*01:02",
+                               "HLA-DQA1*01:02-DQB1*05:01", "HLA-DQA1*01:02-DQB1*05:02", "HLA-DQA1*01:02-DQB1*06:02",
+                               "HLA-DQA1*01:02-DQB1*06:04", "HLA-DQA1*01:03-DQB1*03:02", "HLA-DQA1*01:03-DQB1*06:01",
+                               "HLA-DQA1*01:03-DQB1*06:03", "HLA-DQA1*01:04-DQB1*05:03", "HLA-DQA1*02:01-DQB1*02:01",
+                               "HLA-DQA1*02:01-DQB1*02:02", "HLA-DQA1*02:01-DQB1*03:01", "HLA-DQA1*02:01-DQB1*03:03",
+                               "HLA-DQA1*02:01-DQB1*04:02", "HLA-DQA1*03:01", "HLA-DQA1*03:01-DQB1*02:01",
+                               "HLA-DQA1*03:01-DQB1*03:01", "HLA-DQA1*03:01-DQB1*03:02", "HLA-DQA1*03:01-DQB1*04:01",
+                               "HLA-DQA1*03:02-DQB1*03:01", "HLA-DQA1*03:02-DQB1*03:03", "HLA-DQA1*03:02-DQB1*04:01",
+                               "HLA-DQA1*03:03-DQB1*04:02", "HLA-DQA1*04:01-DQB1*04:02", "HLA-DQA1*05:01",
+                               "HLA-DQA1*05:01-DQB1*02:01", "HLA-DQA1*05:01-DQB1*03:01", "HLA-DQA1*05:01-DQB1*03:02",
+                               "HLA-DQA1*05:01-DQB1*03:03", "HLA-DQA1*05:01-DQB1*04:02", "HLA-DQA1*05:05-DQB1*03:01",
+                               "HLA-DQA1*06:01-DQB1*04:02", "HLA-DQB1*02:01", "HLA-DQB1*02:02", "HLA-DQB1*03:01",
+                               "HLA-DQB1*03:02", "HLA-DQB1*03:19", "HLA-DQB1*04:02", "HLA-DQB1*05:01", "HLA-DQB1*05:02",
+                               "HLA-DQB1*05:03", "HLA-DQB1*06:02", "HLA-DQB1*06:03", "HLA-DQB1*06:04",
+                               "HLA-DRA0*10:1-DRB1*01:01", "HLA-DRA0*10:1-DRB1*03:01", "HLA-DRA0*10:1-DRB1*04:01",
+                               "HLA-DRA0*10:1-DRB1*04:04", "HLA-DRA0*10:1-DRB1*07:01", "HLA-DRA0*10:1-DRB1*08:01",
+                               "HLA-DRA0*10:1-DRB1*09:01", "HLA-DRA0*10:1-DRB1*11:01", "HLA-DRA0*10:1-DRB1*13:01",
+                               "HLA-DRA0*10:1-DRB1*14:54", "HLA-DRA0*10:1-DRB1*15:01", "HLA-DRA0*10:1-DRB3*01:01",
+                               "HLA-DRA0*10:1-DRB3*02:02", "HLA-DRA0*10:1-DRB3*03:01", "HLA-DRA0*10:1-DRB4*01:03",
+                               "HLA-DRA0*10:1-DRB5*01:01", "HLA-DRB1*01:01", "HLA-DRB1*01:02", "HLA-DRB1*01:03",
+                               "HLA-DRB1*03:01", "HLA-DRB1*03:02", "HLA-DRB1*03:03", "HLA-DRB1*03:04", "HLA-DRB1*03:05",
+                               "HLA-DRB1*04:01", "HLA-DRB1*04:02", "HLA-DRB1*04:03", "HLA-DRB1*04:04", "HLA-DRB1*04:05",
+                               "HLA-DRB1*04:06", "HLA-DRB1*04:07", "HLA-DRB1*04:11", "HLA-DRB1*07:01", "HLA-DRB1*08:01",
+                               "HLA-DRB1*08:02", "HLA-DRB1*08:03", "HLA-DRB1*08:04", "HLA-DRB1*09:01", "HLA-DRB1*10:01",
+                               "HLA-DRB1*11:01", "HLA-DRB1*11:02", "HLA-DRB1*11:03", "HLA-DRB1*11:04", "HLA-DRB1*12:01",
+                               "HLA-DRB1*12:02", "HLA-DRB1*13:01", "HLA-DRB1*13:02", "HLA-DRB1*13:03", "HLA-DRB1*13:04",
+                               "HLA-DRB1*13:05", "HLA-DRB1*14:01", "HLA-DRB1*14:02", "HLA-DRB1*15:01", "HLA-DRB1*15:02",
+                               "HLA-DRB1*15:03", "HLA-DRB1*16:01", "HLA-DRB1*16:02", "HLA-DRB3*01:01", "HLA-DRB3*02:02",
+                               "HLA-DRB3*03:01", "HLA-DRB4*01:01", "HLA-DRB4*01:03", "HLA-DRB5*01:01", "HLA-DRB5*01:02"])
+        __supported_length = frozenset([5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
+        __name = "mhcnuggets-class-2"
+        __version = "2.0"
+
+        # 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
+
+        # 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
+        def convert_alleles(self, alleles):
+            return [allele.replace(':', '').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
+        def predict(self, peptides, alleles=None, binary=False, **kwargs):
+
+            # test whether one peptide or a list
+            if isinstance(peptides, basestring):
+                peptides = list(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 = {}
+
+            # fetch peptides as strings
+            peptides = [str(peptide) for peptide in peptides]
+
+            # 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")
+
+            # predict bindings
+            for a in alleles:
+                result[a] = {}
+                tmp_output_file = tempfile.NamedTemporaryFile().name
+
+                mhcnuggets_predict(class_='II',
+                                   peptides_path=tmp_input_file,
+                                   mhc=a,
+                                   output=tmp_output_file + a)
+
+                # read predicted binding affinities back
+                with open(tmp_output_file + a, 'rb') as csvfile:
+                    reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
+                    # skip header
+                    reader.next()
+
+                    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
+                            else:
+                                result[a][peptide] = 0.0
+                        else:
+                            result[a][peptide] = binding_affinity
+
+            # create EpitopePredictionResult object. This is a multi-indexed DataFrame
+            # with Peptide and Method as multi-index and alleles as columns
+            df_result = EpitopePredictionResult.from_dict(result)
+            df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
+                                                            names=['Seq', 'Method'])
+            return df_result
+except BadSignatureException:
+    print "Class MHCNuggetsPredictor_2 cannot be constructed, because of a bad method signature (predict)"
+
+try:
+    class MHCFlurryPredictor(AANNEpitopePrediction):
+        """
+        Implements MHCFlurry
+
+        .. 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.
+        """
+        __metaclass__ = SignatureCheckerMeta
+        __alleles = frozenset(
+            ["HLA-A*01:01", "HLA-A*02:01", "HLA-A*02:02", "HLA-A*02:03", "HLA-A*02:05", "HLA-A*02:06", "HLA-A*02:07",
+             "HLA-A*02:11", "HLA-A*02:12", "HLA-A*02:16", "HLA-A*02:17", "HLA-A*02:19", "HLA-A*02:50", "HLA-A*03:01",
+             "HLA-A*11:01", "HLA-A*23:01", "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*29:02", "HLA-A*30:01", 'HLA-A*30:02', "HLA-A*31:01", "HLA-A*32:01", "HLA-A*33:01",
+             "HLA-A*66:01", "HLA-A*68:01", "HLA-A*68:02", "HLA-A*68:23", "HLA-A*69:01", "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*14:02", "HLA-B*15:01", "HLA-B*15:02",
+             "HLA-B*15:03", "HLA-B*15:09", "HLA-B*15:17", "HLA-B*18:01", "HLA-B*27:02", "HLA-B*27:03", "HLA-B*27:04",
+             "HLA-B*27:05", "HLA-B*27:06", "HLA-B*35:01", "HLA-B*35:03", "HLA-B*37:01", "HLA-B*38:01", "HLA-B*39:01",
+             "HLA-B*39:06", "HLA-B*40:01", "HLA-B*40:02", "HLA-B*42:01", "HLA-B*44:02", "HLA-B*44:03", "HLA-B*45:01",
+             "HLA-B*46:01", "HLA-B*48:01", "HLA-B*51:01", "HLA-B*53:01", "HLA-B*54:01", "HLA-B*57:01", "HLA-B*58:01",
+             "HLA-B*83:01", "HLA-C*03:03", "HLA-C*04:01", "HLA-C*05:01", "HLA-C*06:02", "HLA-C*07:02", "HLA-C*08:02",
+             "HLA-C*12:03", "HLA-C*14:02", "HLA-C*15:02"])
+        __supported_length = frozenset([8, 9, 10, 11, 12, 13, 14, 15])
+        __name = "mhcflurry"
+        __version = "1.2.2"
+
+        # 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
+
+        # 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
+        def convert_alleles(self, alleles):
+            # we just use the identity function
+            return alleles
+
+        # additionally the interface defines a function `predict`
+        def predict(self, peptides, alleles=None, binary=False, **kwargs):
+
+            # test whether one peptide or a list
+            if isinstance(peptides, basestring):
+                peptides = list(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)
+
+            # test mhcflurry models are available => download if not
+            p = subprocess.Popen(['mhcflurry-downloads', 'path', 'models_class1'],
+                                 stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            if p is not 0:
+                subprocess.call(['mhcflurry-downloads', 'fetch', 'models_class1'])
+
+            # load model
+            predictor = Class1AffinityPredictor.load()
+
+            result = {}
+
+            for a in alleles:
+                result[a] = {}
+                for p in peptides:
+                    seq = p.__str__()
+                    binding_affinity = predictor.predict(allele=a, peptides=[seq])
+                    if binary:
+                        if binding_affinity <= 500:
+                            result[a][p] = 1.0
+                        else:
+                            result[a][p] = 0.0
+                    else:
+                        result[a][p] = binding_affinity
+
+            # create EpitopePredictionResult object. This is a multi-indexed DataFrame
+            # with Peptide and Method as multi-index and alleles as columns
+            df_result = EpitopePredictionResult.from_dict(result)
+            df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
+                                                            names=['Seq', 'Method'])
+            return df_result
+except BadSignatureException:
+    print "Class MHCFlurryPredictor cannot be constructed, because of a bad method signature (predict)"
diff --git a/Fred2/EpitopePrediction/PSSM.py b/Fred2/EpitopePrediction/PSSM.py
index 483ef643..6fbdb7a3 100644
--- a/Fred2/EpitopePrediction/PSSM.py
+++ b/Fred2/EpitopePrediction/PSSM.py
@@ -1,3 +1,4 @@
+# coding=utf-8
 # This code is part of the Fred2 distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -7,17 +8,22 @@
 .. moduleauthor:: schubert
 
 """
-
 import itertools
 import warnings
-import pandas
 import math
+import subprocess
+import csv
+import tempfile
+import pandas
 
 from Fred2.Core.Allele import Allele
 from Fred2.Core.Peptide import Peptide
 from Fred2.Core.Result import EpitopePredictionResult
 from Fred2.Core.Base import AEpitopePrediction
 
+from mhcflurry import Class1AffinityPredictor
+from mhcnuggets.src.predict import predict
+
 
 class APSSMEpitopePrediction(AEpitopePrediction):
     """
@@ -38,13 +44,15 @@ def predict(self, peptides, alleles=None, **kwargs):
         :return: Returns a :class:`~Fred2.Core.Result.EpitopePredictionResult` object with the prediction results
         :rtype: :class:`~Fred2.Core.Result.EpitopePredictionResult`
         """
+
         def __load_allele_model(allele, length):
-            allele_model = "%s_%i"%(allele, length)
-            return getattr(__import__("Fred2.Data.pssms."+self.name+".mat."+allele_model, fromlist=[allele_model]),
-                           allele_model)
+            allele_model = "%s_%i" % (allele, length)
+            return getattr(
+                __import__("Fred2.Data.pssms." + self.name + ".mat." + allele_model, fromlist=[allele_model]),
+                allele_model)
 
         if isinstance(peptides, Peptide):
-            pep_seqs = {str(peptides):peptides}
+            pep_seqs = {str(peptides): peptides}
         else:
             pep_seqs = {}
             for p in peptides:
@@ -54,13 +62,13 @@ 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 itertools.izip(self.convert_alleles(al), al)}
+            alleles_string = {conv_a: a for conv_a, a in itertools.izip(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 itertools.izip(self.convert_alleles(alleles), alleles)}
+            alleles_string = {conv_a: a for conv_a, a in itertools.izip(self.convert_alleles(alleles), alleles)}
 
         result = {}
         pep_groups = pep_seqs.keys()
@@ -68,28 +76,28 @@ def __load_allele_model(allele, length):
         for length, peps in itertools.groupby(pep_groups, key=len):
 
             peps = list(peps)
-            #dynamicaly import prediction PSSMS for alleles and predict
+            # 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]] = {}
                 ##here is the prediction and result object missing##
                 for p in peps:
-                    score = sum(pssm[i].get(p[i], 0.0) for i in xrange(length))+pssm.get(-1, {}).get("con", 0)
+                    score = sum(pssm[i].get(p[i], 0.0) for i in xrange(length)) + pssm.get(-1, {}).get("con", 0)
                     result[alleles_string[a]][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.")
+            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)
         df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
@@ -106,10 +114,12 @@ class Syfpeithi(APSSMEpitopePrediction):
         Rammensee, H. G., Bachmann, J., Emmerich, N. P. N., Bachor, O. A., & Stevanovic, S. (1999).
         SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50(3-4), 213-219.
     """
-    __alleles = frozenset(['HLA-B*15:10', 'HLA-B*41:01', 'HLA-B*37:01', 'HLA-B*27:05', 'HLA-B*38:01', 'HLA-A*02:01', 'HLA-B*47:01', 'HLA-A*26:01',
-                           'HLA-B*37:01', 'HLA-DRB1*11:01', 'HLA-B*50:01', 'HLA-B*07:02', 'HLA-A*68:01', 'HLA-A*24:02', 'HLA-DRB1*15:01', 'HLA-B*15:01',
-                           'HLA-B*45:01', 'HLA-A*11:01', 'HLA-A*03:01', 'HLA-B*40:01', 'HLA-DRB1*03:01', 'HLA-B*39:01', 'HLA-DRB1*01:01', 'HLA-B*51:01',
-                           'HLA-B*39:02', 'HLA-B*08:01', 'HLA-B*18:01', 'HLA-B*44:02', 'HLA-B*49:01', 'HLA-DRB1*07:01', 'HLA-B*14:02', 'HLA-A*01:01'])
+    __alleles = frozenset(
+        ['HLA-B*15:10', 'HLA-B*41:01', 'HLA-B*37:01', 'HLA-B*27:05', 'HLA-B*38:01', 'HLA-A*02:01', 'HLA-B*47:01',
+         'HLA-A*26:01', 'HLA-B*37:01', 'HLA-DRB1*11:01', 'HLA-B*50:01', 'HLA-B*07:02', 'HLA-A*68:01', 'HLA-A*24:02',
+         'HLA-DRB1*15:01', 'HLA-B*15:01', 'HLA-B*45:01', 'HLA-A*11:01', 'HLA-A*03:01', 'HLA-B*40:01', 'HLA-DRB1*03:01',
+         'HLA-B*39:01', 'HLA-DRB1*01:01', 'HLA-B*51:01', 'HLA-B*39:02', 'HLA-B*08:01', 'HLA-B*18:01', 'HLA-B*44:02',
+         'HLA-B*49:01', 'HLA-DRB1*07:01', 'HLA-B*14:02', 'HLA-A*01:01'])
     __supported_length = frozenset([8, 9, 10, 11])
     __name = "syfpeithi"
     __version = "1.0"
@@ -148,7 +158,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
 
 class BIMAS(APSSMEpitopePrediction):
@@ -162,12 +172,12 @@ class BIMAS(APSSMEpitopePrediction):
     """
 
     __alleles = frozenset(['HLA-B*04:01', 'HLA-A*31:01', 'HLA-B*58:01', 'HLA-C*06:02', 'HLA-A*03:01', 'HLA-B*35:01',
-                 'HLA-B*35:01', 'HLA-B*15:01', 'HLA-A*02:05', 'HLA-B*27:05', 'HLA-B*27:05', 'HLA-A*33:02',
-                 'HLA-B*39:01', 'HLA-B*38:01', 'HLA-B*40:', 'HLA-A*24:02', 'HLA-B*51:01', 'HLA-B*07:02', 'HLA-B*08:01',
-                 'HLA-B*51:02', 'HLA-B*40:06', 'HLA-B*40:06', 'HLA-B*51:02', 'HLA-B*37:01', 'HLA-A*11:01',
-                 'HLA-B*08:01', 'HLA-B*44:03', 'HLA-A*68:01', 'HLA-B*51:03', 'HLA-B*52:01', 'HLA-A*02:01',
-                 'HLA-A*01:01', 'HLA-C*07:02', 'HLA-C*03:01', 'HLA-B*40:01', 'HLA-B*51:01', 'HLA-B*39:02',
-                 'HLA-B*52:01', 'HLA-C*04:01', 'HLA-B*27:02', 'HLA-B*39:01'])
+                           'HLA-B*35:01', 'HLA-B*15:01', 'HLA-A*02:05', 'HLA-B*27:05', 'HLA-B*27:05', 'HLA-A*33:02',
+                           'HLA-B*39:01', 'HLA-B*38:01', 'HLA-B*40:', 'HLA-A*24:02', 'HLA-B*51:01', 'HLA-B*07:02',
+                           'HLA-B*08:01', 'HLA-B*51:02', 'HLA-B*40:06', 'HLA-B*40:06', 'HLA-B*51:02', 'HLA-B*37:01',
+                           'HLA-A*11:01', 'HLA-B*08:01', 'HLA-B*44:03', 'HLA-A*68:01', 'HLA-B*51:03', 'HLA-B*52:01',
+                           'HLA-A*02:01', 'HLA-A*01:01', 'HLA-C*07:02', 'HLA-C*03:01', 'HLA-B*40:01', 'HLA-B*51:01',
+                           'HLA-B*39:02', 'HLA-B*52:01', 'HLA-C*04:01', 'HLA-B*27:02', 'HLA-B*39:01'])
     __supported_length = frozenset([8, 9])
     __name = "bimas"
     __version = "1.0"
@@ -206,7 +216,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
     def predict(self, peptides, alleles=None, **kwargs):
         """
@@ -234,10 +244,11 @@ class Epidemix(APSSMEpitopePrediction):
 
         Feldhahn, M., et al. FRED-a framework for T-cell epitope detection. Bioinformatics 2009;25(20):2758-2759.
     """
-    __alleles = frozenset(['HLA-B*27', 'HLA-A*11:01', 'HLA-B*27:05', 'HLA-B*07', 'HLA-B*27', 'HLA-A*01', 'HLA-B*44', 'HLA-A*03',
-                 'HLA-A*25', 'HLA-B*37:01', 'HLA-A*02:01', 'HLA-A*02:01', 'HLA-B*18:01', 'HLA-B*18:01', 'HLA-A*03',
-                 'HLA-A*24', 'HLA-A*25', 'HLA-A*02:01', 'HLA-A*11:01', 'HLA-A*24:02', 'HLA-B*08', 'HLA-B*08',
-                 'HLA-B*51:01', 'HLA-B*51:01'])
+    __alleles = frozenset(
+        ['HLA-B*27', 'HLA-A*11:01', 'HLA-B*27:05', 'HLA-B*07', 'HLA-B*27', 'HLA-A*01', 'HLA-B*44', 'HLA-A*03',
+         'HLA-A*25', 'HLA-B*37:01', 'HLA-A*02:01', 'HLA-A*02:01', 'HLA-B*18:01', 'HLA-B*18:01', 'HLA-A*03',
+         'HLA-A*24', 'HLA-A*25', 'HLA-A*02:01', 'HLA-A*11:01', 'HLA-A*24:02', 'HLA-B*08', 'HLA-B*08',
+         'HLA-B*51:01', 'HLA-B*51:01'])
     __supported_length = frozenset([9, 10, 8, 11])
     __name = "epidemix"
     __version = "1.0"
@@ -276,7 +287,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
 
 class Hammer(APSSMEpitopePrediction):
@@ -288,15 +299,16 @@ class Hammer(APSSMEpitopePrediction):
         Sturniolo, T., et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays
         and virtual HLA class II matrices. Nature biotechnology 1999;17(6):555-561.
     """
-    __alleles = frozenset(['HLA-DRB1*07:03', 'HLA-DRB1*07:01', 'HLA-DRB1*11:28', 'HLA-DRB1*11:21', 'HLA-DRB1*11:20', 'HLA-DRB1*04:26',
-                           'HLA-DRB1*04:23','HLA-DRB1*04:21', 'HLA-DRB5*01:05:', 'HLA-DRB1*08:17', 'HLA-DRB1*13:05', 'HLA-DRB1*13:04',
-                           'HLA-DRB1*13:07', 'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*08:04', 'HLA-DRB1*08:06', 'HLA-DRB1*08:01',
-                           'HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*08:02', 'HLA-DRB1*13:11', 'HLA-DRB1*03:11', 'HLA-DRB1*11:07',
-                           'HLA-DRB1*11:06', 'HLA-DRB1*11:04', 'HLA-DRB1*11:02', 'HLA-DRB1*11:01', 'HLA-DRB1*04:08', 'HLA-DRB1*04:01',
-                           'HLA-DRB1*04:02', 'HLA-DRB1*04:05', 'HLA-DRB1*04:04', 'HLA-DRB1*13:23', 'HLA-DRB1*13:22', 'HLA-DRB1*13:21',
-                           'HLA-DRB1*13:27', 'HLA-DRB1*08:13', 'HLA-DRB1*13:28', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07', 'HLA-DRB1*03:05',
-                           'HLA-DRB1*11:14', 'HLA-DRB1*03:01', 'HLA-DRB1*15:02', 'HLA-DRB1*15:01', 'HLA-DRB1*15:06', 'HLA-DRB1*03:08',
-                           'HLA-DRB1*03:09', 'HLA-DRB1*04:10', 'HLA-DRB5*01:01'])
+    __alleles = frozenset(
+        ['HLA-DRB1*07:03', 'HLA-DRB1*07:01', 'HLA-DRB1*11:28', 'HLA-DRB1*11:21', 'HLA-DRB1*11:20', 'HLA-DRB1*04:26',
+         'HLA-DRB1*04:23', 'HLA-DRB1*04:21', 'HLA-DRB5*01:05:', 'HLA-DRB1*08:17', 'HLA-DRB1*13:05', 'HLA-DRB1*13:04',
+         'HLA-DRB1*13:07', 'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*08:04', 'HLA-DRB1*08:06', 'HLA-DRB1*08:01',
+         'HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*08:02', 'HLA-DRB1*13:11', 'HLA-DRB1*03:11', 'HLA-DRB1*11:07',
+         'HLA-DRB1*11:06', 'HLA-DRB1*11:04', 'HLA-DRB1*11:02', 'HLA-DRB1*11:01', 'HLA-DRB1*04:08', 'HLA-DRB1*04:01',
+         'HLA-DRB1*04:02', 'HLA-DRB1*04:05', 'HLA-DRB1*04:04', 'HLA-DRB1*13:23', 'HLA-DRB1*13:22', 'HLA-DRB1*13:21',
+         'HLA-DRB1*13:27', 'HLA-DRB1*08:13', 'HLA-DRB1*13:28', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07', 'HLA-DRB1*03:05',
+         'HLA-DRB1*11:14', 'HLA-DRB1*03:01', 'HLA-DRB1*15:02', 'HLA-DRB1*15:01', 'HLA-DRB1*15:06', 'HLA-DRB1*03:08',
+         'HLA-DRB1*03:09', 'HLA-DRB1*04:10', 'HLA-DRB5*01:01'])
     __supported_length = frozenset([9])
     __name = "hammer"
     __version = "1.0"
@@ -335,7 +347,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
 
 class SMM(APSSMEpitopePrediction):
@@ -348,17 +360,19 @@ class SMM(APSSMEpitopePrediction):
         biological processes with the stabilized matrix method. BMC Bioinformatics 6:132.
     """
 
-    __alleles = frozenset(['HLA-B*27:20', 'HLA-B*83:01', 'HLA-A*32:15', 'HLA-B*15:17', 'HLA-B*40:13', 'HLA-A*24:02', 'HLA-A*24:03', 'HLA-B*53:01',
-                           'HLA-B*15:01','HLA-B*27:05', 'HLA-B*42:01', 'HLA-B*39:01', 'HLA-B*38:01', 'HLA-A*23:01', 'HLA-A*25:01', 'HLA-C*04:01',
-                           'HLA-A*29:02', 'HLA-A*02:06', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*26:01',
-                           'HLA-C*03:03', 'E*01:01', 'E*01:03', 'HLA-B*58:01', 'HLA-A*31:01', 'HLA-C*06:02', 'HLA-B*07:02', 'HLA-A*66:01',
-                           'HLA-B*57:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-C*14:02', 'HLA-B*35:01', 'HLA-B*15:09', 'HLA-B*35:03', 'HLA-A*80:01',
-                           'HLA-B*15:03', 'HLA-B*15:02', 'HLA-A*32:01', 'HLA-A*02:50', 'HLA-A*32:07', 'HLA-B*58:02', 'HLA-A*69:01', 'HLA-A*68:23',
-                           'HLA-A*11:01', 'HLA-A*03:01', 'HLA-B*73:01', 'HLA-B*40:01', 'HLA-B*44:03', 'HLA-B*46:01', 'HLA-B*40:02', 'HLA-C*12:03',
-                           'HLA-B*44:02', 'HLA-A*30:01', 'HLA-A*02:19', 'HLA-A*30:02', 'HLA-A*02:17', 'HLA-A*02:16', 'HLA-B*51:01', 'HLA-B*45:01',
-                           'HLA-A*02:12', 'HLA-A*02:11', 'HLA-B*54:01', 'HLA-B*08:01', 'HLA-B*18:01', 'HLA-B*08:03', 'HLA-B*08:02', 'HLA-C*05:01',
-                           'HLA-C*15:02', 'HLA-A*33:01', 'HLA-B*14:02', 'HLA-C*07:01', 'HLA-B*48:01', 'HLA-B*15:42', 'HLA-C*07:02', 'HLA-A*01:01',
-                           'HLA-C*08:02'])
+    __alleles = frozenset(
+        ['HLA-B*27:20', 'HLA-B*83:01', 'HLA-A*32:15', 'HLA-B*15:17', 'HLA-B*40:13', 'HLA-A*24:02', 'HLA-A*24:03',
+         'HLA-B*53:01', 'HLA-B*15:01', 'HLA-B*27:05', 'HLA-B*42:01', 'HLA-B*39:01', 'HLA-B*38:01', 'HLA-A*23:01',
+         'HLA-A*25:01', 'HLA-C*04:01', 'HLA-A*29:02', 'HLA-A*02:06', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03',
+         'HLA-A*26:02', 'HLA-A*26:03', 'HLA-A*26:01', 'HLA-C*03:03', 'E*01:01', 'E*01:03', 'HLA-B*58:01', 'HLA-A*31:01',
+         'HLA-C*06:02', 'HLA-B*07:02', 'HLA-A*66:01', 'HLA-B*57:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-C*14:02',
+         'HLA-B*35:01', 'HLA-B*15:09', 'HLA-B*35:03', 'HLA-A*80:01', 'HLA-B*15:03', 'HLA-B*15:02', 'HLA-A*32:01',
+         'HLA-A*02:50', 'HLA-A*32:07', 'HLA-B*58:02', 'HLA-A*69:01', 'HLA-A*68:23', 'HLA-A*11:01', 'HLA-A*03:01',
+         'HLA-B*73:01', 'HLA-B*40:01', 'HLA-B*44:03', 'HLA-B*46:01', 'HLA-B*40:02', 'HLA-C*12:03', 'HLA-B*44:02',
+         'HLA-A*30:01', 'HLA-A*02:19', 'HLA-A*30:02', 'HLA-A*02:17', 'HLA-A*02:16', 'HLA-B*51:01', 'HLA-B*45:01',
+         'HLA-A*02:12', 'HLA-A*02:11', 'HLA-B*54:01', 'HLA-B*08:01', 'HLA-B*18:01', 'HLA-B*08:03', 'HLA-B*08:02',
+         'HLA-C*05:01', 'HLA-C*15:02', 'HLA-A*33:01', 'HLA-B*14:02', 'HLA-C*07:01', 'HLA-B*48:01', 'HLA-B*15:42',
+         'HLA-C*07:02', 'HLA-A*01:01', 'HLA-C*08:02'])
     __supported_length = frozenset([8, 9, 10, 11])
     __name = "smm"
     __version = "1.0"
@@ -397,7 +411,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s_%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s_%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
     def predict(self, peptides, alleles=None, **kwargs):
         """
@@ -427,16 +441,18 @@ class SMMPMBEC(APSSMEpitopePrediction):
     """
 
     __alleles = frozenset(
-        ['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:06', 'HLA-A*02:11', 'HLA-A*02:12', 'HLA-A*02:16', 'HLA-A*02:17',
-         'HLA-A*02:19', 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', '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*29:02', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01', 'HLA-A*32:07', 'HLA-A*32:15',
-         'HLA-A*33:01', 'HLA-A*66:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:23', 'HLA-A*69:01', 'HLA-A*80:01', 'HLA-B*07:02', 'HLA-B*08:01',
-         'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*14:02', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03', 'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*15:42',
-         'HLA-B*18:01', 'HLA-B*27:03', 'HLA-B*27:05', 'HLA-B*27:20', 'HLA-B*35:01', 'HLA-B*35:03', 'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01',
-         'HLA-B*40:02', 'HLA-B*40:13', 'HLA-B*42:01', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*45:06', 'HLA-B*46:01', 'HLA-B*48:01',
-         'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01', 'HLA-B*58:02', 'HLA-B*73:01', 'HLA-B*83:01', 'HLA-C*03:03',
-         'HLA-C*04:01', 'HLA-C*05:01', 'HLA-C*06:02', 'HLA-C*07:01', 'HLA-C*07:02', 'HLA-C*08:02', 'HLA-C*12:03', 'HLA-C*14:02', 'HLA-C*15:02',
-         'E*01:01', 'E*01:03'])
+        ['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:06', 'HLA-A*02:11', 'HLA-A*02:12',
+         'HLA-A*02:16', 'HLA-A*02:17', 'HLA-A*02:19', 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01',
+         '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*29:02',
+         'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01', 'HLA-A*32:07', 'HLA-A*32:15', 'HLA-A*33:01',
+         'HLA-A*66:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*68:23', 'HLA-A*69:01', 'HLA-A*80:01', 'HLA-B*07:02',
+         'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*14:02', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03',
+         'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*15:42', 'HLA-B*18:01', 'HLA-B*27:03', 'HLA-B*27:05', 'HLA-B*27:20',
+         'HLA-B*35:01', 'HLA-B*35:03', 'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*40:13',
+         'HLA-B*42:01', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*45:06', 'HLA-B*46:01', 'HLA-B*48:01',
+         'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01', 'HLA-B*58:02', 'HLA-B*73:01',
+         'HLA-B*83:01', 'HLA-C*03:03', 'HLA-C*04:01', 'HLA-C*05:01', 'HLA-C*06:02', 'HLA-C*07:01', 'HLA-C*07:02',
+         'HLA-C*08:02', 'HLA-C*12:03', 'HLA-C*14:02', 'HLA-C*15:02', 'E*01:01', 'E*01:03'])
     __supported_length = frozenset([8, 9, 10, 11])
     __name = "smmpmbec"
     __version = "1.0"
@@ -475,7 +491,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s_%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s_%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
     def predict(self, peptides, alleles=None, **kwargs):
         """
@@ -506,13 +522,15 @@ class ARB(APSSMEpitopePrediction):
     """
 
     __alleles = frozenset(
-        ['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:06', 'HLA-A*02:11', 'HLA-A*02:12', 'HLA-A*02:16', 'HLA-A*02:19',
-         'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', '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*29:02', 'HLA-A*30:01', 'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01', 'HLA-A*33:01', 'HLA-A*68:01', 'HLA-A*68:02',
-         'HLA-A*69:01', 'HLA-A*80:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*15:01', 'HLA-B*15:02', 'HLA-B*15:03',
-         'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*18:01', 'HLA-B*27:03', 'HLA-B*27:05', 'HLA-B*35:01', 'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01',
-         'HLA-B*40:02', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01', 'HLA-B*46:01', 'HLA-B*48:01', 'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01',
-         'HLA-B*57:01', 'HLA-B*58:01', 'HLA-B*73:01'])
+        ['HLA-A*01:01', 'HLA-A*02:01', 'HLA-A*02:02', 'HLA-A*02:03', 'HLA-A*02:06', 'HLA-A*02:11', 'HLA-A*02:12',
+         'HLA-A*02:16', 'HLA-A*02:19', 'HLA-A*02:50', 'HLA-A*03:01', 'HLA-A*11:01', 'HLA-A*23:01', '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*29:02', 'HLA-A*30:01',
+         'HLA-A*30:02', 'HLA-A*31:01', 'HLA-A*32:01', 'HLA-A*33:01', 'HLA-A*68:01', 'HLA-A*68:02', 'HLA-A*69:01',
+         'HLA-A*80:01', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*08:02', 'HLA-B*08:03', 'HLA-B*15:01', 'HLA-B*15:02',
+         'HLA-B*15:03', 'HLA-B*15:09', 'HLA-B*15:17', 'HLA-B*18:01', 'HLA-B*27:03', 'HLA-B*27:05', 'HLA-B*35:01',
+         'HLA-B*38:01', 'HLA-B*39:01', 'HLA-B*40:01', 'HLA-B*40:02', 'HLA-B*44:02', 'HLA-B*44:03', 'HLA-B*45:01',
+         'HLA-B*46:01', 'HLA-B*48:01', 'HLA-B*51:01', 'HLA-B*53:01', 'HLA-B*54:01', 'HLA-B*57:01', 'HLA-B*58:01',
+         'HLA-B*73:01'])
     __supported_length = frozenset([8, 9, 10, 11])
     __name = "arb"
     __version = "1.0"
@@ -551,7 +569,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
     def predict(self, peptides, alleles=None, **kwargs):
         """
@@ -566,13 +584,15 @@ def predict(self, peptides, alleles=None, **kwargs):
         :return: Returns a :class:`~Fred2.Core.Result.EpitopePredictionResult` object with the prediction results
         :rtype: :class:`~Fred2.Core.Result.EpitopePredictionResult`
         """
+
         def __load_allele_model(allele, length):
-            allele_model = "%s_%i"%(allele, length)
-            return getattr(__import__("Fred2.Data.pssms."+self.name+".mat."+allele_model, fromlist=[allele_model]),
-                           allele_model)
+            allele_model = "%s_%i" % (allele, length)
+            return getattr(
+                __import__("Fred2.Data.pssms." + self.name + ".mat." + allele_model, fromlist=[allele_model]),
+                allele_model)
 
         if isinstance(peptides, Peptide):
-            pep_seqs = {str(peptides):peptides}
+            pep_seqs = {str(peptides): peptides}
         else:
             pep_seqs = {}
             for p in peptides:
@@ -582,33 +602,33 @@ 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 itertools.izip(self.convert_alleles(al), al)}
+            alleles_string = {conv_a: a for conv_a, a in itertools.izip(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 itertools.izip(self.convert_alleles(alleles), alleles)}
+            alleles_string = {conv_a: a for conv_a, a in itertools.izip(self.convert_alleles(alleles), alleles)}
 
         result = {}
         for length, peps in itertools.groupby(pep_seqs.iterkeys(), key=lambda x: len(x)):
             peps = list(peps)
-            #dynamicaly import prediction PSSMS for alleles and predict
+            # 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
 
                 result[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 xrange(length))+pssm.get(-1, {}).get("con", 0)
+                    score = sum(pssm[i].get(p[i], 0.0) for i in xrange(length)) + pssm.get(-1, {}).get("con", 0)
                     score /= -length
                     score -= pssm[-1]["intercept"]
                     score /= pssm[-1]["slope"]
@@ -618,11 +638,11 @@ def __load_allele_model(allele, length):
                     elif score > 1e6:
                         score = 1e6
                     result[alleles_string[a]][pep_seqs[p]] = score
-                    #print a, score, result
+                    # print a, score, result
 
         if not result:
-            raise ValueError("No predictions could be made with " +self.name+" for given input. Check your"
-                             "epitope length and HLA allele combination.")
+            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)
         df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
@@ -641,8 +661,10 @@ class ComblibSidney2008(APSSMEpitopePrediction):
         Immunome Res 4:2.
     """
 
-    __alleles = frozenset(['HLA-B*35:01', 'HLA-B*51:01', 'HLA-B*54:01', 'HLA-B*58:02', 'HLA-A*02:01', 'HLA-A*68:02', 'HLA-B*27:05', 'HLA-B*08:01', 'HLA-B*07:02',
-                 'HLA-A*32:01', 'HLA-B*53:01', 'HLA-A*30:01', 'HLA-B*15:03', 'HLA-B*15:01', 'HLA-B*58:01'])
+    __alleles = frozenset(
+        ['HLA-B*35:01', 'HLA-B*51:01', 'HLA-B*54:01', 'HLA-B*58:02', 'HLA-A*02:01', 'HLA-A*68:02', 'HLA-B*27:05',
+         'HLA-B*08:01', 'HLA-B*07:02', 'HLA-A*32:01', 'HLA-B*53:01', 'HLA-A*30:01', 'HLA-B*15:03', 'HLA-B*15:01',
+         'HLA-B*58:01'])
     __supported_length = frozenset([9])
     __name = "comblibsidney"
     __version = "1.0"
@@ -681,7 +703,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
     def predict(self, peptides, alleles=None, **kwargs):
         """
@@ -698,7 +720,8 @@ def predict(self, peptides, alleles=None, **kwargs):
         """
         return EpitopePredictionResult(
             super(ComblibSidney2008, self).predict(peptides,
-                                                      alleles=alleles, **kwargs).applymap(lambda x: math.pow(10, x)))
+                                                   alleles=alleles,
+                                                   **kwargs).applymap(lambda x: math.pow(10, x)))
 
 
 class TEPITOPEpan(APSSMEpitopePrediction):
@@ -713,111 +736,129 @@ class TEPITOPEpan(APSSMEpitopePrediction):
     """
 
     __alleles = frozenset(
-        ['HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*01:03', 'HLA-DRB1*01:04', 'HLA-DRB1*01:05', 'HLA-DRB1*01:06', 'HLA-DRB1*01:07',
-         'HLA-DRB1*01:08', 'HLA-DRB1*01:09', 'HLA-DRB1*01:10', 'HLA-DRB1*01:11', 'HLA-DRB1*01:12', 'HLA-DRB1*01:13', 'HLA-DRB1*01:14',
-         'HLA-DRB1*01:15', 'HLA-DRB1*01:16', 'HLA-DRB1*01:17', 'HLA-DRB1*01:18', 'HLA-DRB1*01:19', 'HLA-DRB1*01:20', 'HLA-DRB1*01:21',
-         'HLA-DRB1*01:22', 'HLA-DRB1*01:23', 'HLA-DRB1*01:24', 'HLA-DRB1*01:25', 'HLA-DRB1*01:26', 'HLA-DRB1*01:27', 'HLA-DRB1*01:28',
-         'HLA-DRB1*01:29', 'HLA-DRB1*01:30', 'HLA-DRB1*01:31', 'HLA-DRB1*01:32', 'HLA-DRB1*01:34', 'HLA-DRB1*01:35', 'HLA-DRB1*01:36',
-         'HLA-DRB1*03:01', 'HLA-DRB1*03:02', 'HLA-DRB1*03:03', 'HLA-DRB1*03:04', 'HLA-DRB1*03:05', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07',
-         'HLA-DRB1*03:08', 'HLA-DRB1*03:09', 'HLA-DRB1*03:10', 'HLA-DRB1*03:11', 'HLA-DRB1*03:12', 'HLA-DRB1*03:13', 'HLA-DRB1*03:14',
-         'HLA-DRB1*03:15', 'HLA-DRB1*03:16', 'HLA-DRB1*03:17', 'HLA-DRB1*03:18', 'HLA-DRB1*03:19', 'HLA-DRB1*03:20', 'HLA-DRB1*03:21',
-         'HLA-DRB1*03:22', 'HLA-DRB1*03:23', 'HLA-DRB1*03:24', 'HLA-DRB1*03:25', 'HLA-DRB1*03:26', 'HLA-DRB1*03:27', 'HLA-DRB1*03:28',
-         'HLA-DRB1*03:29', 'HLA-DRB1*03:30', 'HLA-DRB1*03:31', 'HLA-DRB1*03:32', 'HLA-DRB1*03:33', 'HLA-DRB1*03:34', 'HLA-DRB1*03:35',
-         'HLA-DRB1*03:36', 'HLA-DRB1*03:37', 'HLA-DRB1*03:38', 'HLA-DRB1*03:39', 'HLA-DRB1*03:40', 'HLA-DRB1*03:41', 'HLA-DRB1*03:42',
-         'HLA-DRB1*03:43', 'HLA-DRB1*03:44', 'HLA-DRB1*03:45', 'HLA-DRB1*03:46', 'HLA-DRB1*03:47', 'HLA-DRB1*03:48', 'HLA-DRB1*03:49',
-         'HLA-DRB1*03:50', 'HLA-DRB1*03:51', 'HLA-DRB1*03:52', 'HLA-DRB1*03:53', 'HLA-DRB1*03:54', 'HLA-DRB1*03:55', 'HLA-DRB1*03:56',
-         'HLA-DRB1*03:57', 'HLA-DRB1*03:58', 'HLA-DRB1*03:59', 'HLA-DRB1*03:60', 'HLA-DRB1*03:61', 'HLA-DRB1*03:62', 'HLA-DRB1*03:63',
-         'HLA-DRB1*03:64', 'HLA-DRB1*04:01', 'HLA-DRB1*04:02', 'HLA-DRB1*04:03', 'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*04:06',
-         'HLA-DRB1*04:07', 'HLA-DRB1*04:08', 'HLA-DRB1*04:09', 'HLA-DRB1*04:10', 'HLA-DRB1*04:11', 'HLA-DRB1*04:12', 'HLA-DRB1*04:13',
-         'HLA-DRB1*04:14', 'HLA-DRB1*04:15', 'HLA-DRB1*04:16', 'HLA-DRB1*04:17', 'HLA-DRB1*04:18', 'HLA-DRB1*04:19', 'HLA-DRB1*04:20',
-         'HLA-DRB1*04:21', 'HLA-DRB1*04:22', 'HLA-DRB1*04:23', 'HLA-DRB1*04:24', 'HLA-DRB1*04:25', 'HLA-DRB1*04:26', 'HLA-DRB1*04:27',
-         'HLA-DRB1*04:28', 'HLA-DRB1*04:29', 'HLA-DRB1*04:30', 'HLA-DRB1*04:31', 'HLA-DRB1*04:32', 'HLA-DRB1*04:33', 'HLA-DRB1*04:34',
-         'HLA-DRB1*04:35', 'HLA-DRB1*04:36', 'HLA-DRB1*04:37', 'HLA-DRB1*04:38', 'HLA-DRB1*04:39', 'HLA-DRB1*04:40', 'HLA-DRB1*04:41',
-         'HLA-DRB1*04:42', 'HLA-DRB1*04:43', 'HLA-DRB1*04:44', 'HLA-DRB1*04:45', 'HLA-DRB1*04:46', 'HLA-DRB1*04:47', 'HLA-DRB1*04:48',
-         'HLA-DRB1*04:49', 'HLA-DRB1*04:50', 'HLA-DRB1*04:51', 'HLA-DRB1*04:52', 'HLA-DRB1*04:53', 'HLA-DRB1*04:54', 'HLA-DRB1*04:55',
-         'HLA-DRB1*04:56', 'HLA-DRB1*04:57', 'HLA-DRB1*04:58', 'HLA-DRB1*04:59', 'HLA-DRB1*04:60', 'HLA-DRB1*04:61', 'HLA-DRB1*04:62',
-         'HLA-DRB1*04:63', 'HLA-DRB1*04:64', 'HLA-DRB1*04:65', 'HLA-DRB1*04:66', 'HLA-DRB1*04:67', 'HLA-DRB1*04:68', 'HLA-DRB1*04:69',
-         'HLA-DRB1*04:70', 'HLA-DRB1*04:71', 'HLA-DRB1*04:72', 'HLA-DRB1*04:73', 'HLA-DRB1*04:74', 'HLA-DRB1*04:75', 'HLA-DRB1*04:76',
-         'HLA-DRB1*04:77', 'HLA-DRB1*04:78', 'HLA-DRB1*04:79', 'HLA-DRB1*04:80', 'HLA-DRB1*04:82', 'HLA-DRB1*04:83', 'HLA-DRB1*04:84',
-         'HLA-DRB1*04:85', 'HLA-DRB1*04:86', 'HLA-DRB1*04:87', 'HLA-DRB1*04:88', 'HLA-DRB1*04:89', 'HLA-DRB1*04:90', 'HLA-DRB1*04:91',
-         'HLA-DRB1*04:92', 'HLA-DRB1*04:93', 'HLA-DRB1*04:95', 'HLA-DRB1*04:96', 'HLA-DRB1*04:97', 'HLA-DRB1*04:98', 'HLA-DRB1*07:01',
-         'HLA-DRB1*07:03', 'HLA-DRB1*07:04', 'HLA-DRB1*07:05', 'HLA-DRB1*07:06', 'HLA-DRB1*07:07', 'HLA-DRB1*07:08', 'HLA-DRB1*07:09',
-         'HLA-DRB1*07:11', 'HLA-DRB1*07:12', 'HLA-DRB1*07:13', 'HLA-DRB1*07:14', 'HLA-DRB1*07:15', 'HLA-DRB1*07:16', 'HLA-DRB1*07:17',
-         'HLA-DRB1*07:18', 'HLA-DRB1*07:19', 'HLA-DRB1*07:20', 'HLA-DRB1*07:21', 'HLA-DRB1*08:01', 'HLA-DRB1*08:02', 'HLA-DRB1*08:03',
-         'HLA-DRB1*08:04', 'HLA-DRB1*08:05', 'HLA-DRB1*08:06', 'HLA-DRB1*08:07', 'HLA-DRB1*08:08', 'HLA-DRB1*08:09', 'HLA-DRB1*08:10',
-         'HLA-DRB1*08:11', 'HLA-DRB1*08:12', 'HLA-DRB1*08:13', 'HLA-DRB1*08:14', 'HLA-DRB1*08:15', 'HLA-DRB1*08:16', 'HLA-DRB1*08:17',
-         'HLA-DRB1*08:18', 'HLA-DRB1*08:19', 'HLA-DRB1*08:20', 'HLA-DRB1*08:21', 'HLA-DRB1*08:22', 'HLA-DRB1*08:23', 'HLA-DRB1*08:24',
-         'HLA-DRB1*08:25', 'HLA-DRB1*08:26', 'HLA-DRB1*08:27', 'HLA-DRB1*08:28', 'HLA-DRB1*08:29', 'HLA-DRB1*08:30', 'HLA-DRB1*08:31',
-         'HLA-DRB1*08:32', 'HLA-DRB1*08:33', 'HLA-DRB1*08:34', 'HLA-DRB1*08:35', 'HLA-DRB1*08:36', 'HLA-DRB1*08:37', 'HLA-DRB1*08:38',
-         'HLA-DRB1*08:39', 'HLA-DRB1*08:40', 'HLA-DRB1*08:41', 'HLA-DRB1*08:42', 'HLA-DRB1*08:43', 'HLA-DRB1*08:44', 'HLA-DRB1*08:45',
-         'HLA-DRB1*09:01', 'HLA-DRB1*09:02', 'HLA-DRB1*09:03', 'HLA-DRB1*09:04', 'HLA-DRB1*09:05', 'HLA-DRB1*09:06', 'HLA-DRB1*09:07',
-         'HLA-DRB1*09:08', 'HLA-DRB1*09:09', 'HLA-DRB1*09:10', 'HLA-DRB1*09:11', 'HLA-DRB1*09:12', 'HLA-DRB1*10:01', 'HLA-DRB1*10:02',
-         'HLA-DRB1*10:03', 'HLA-DRB1*11:01', 'HLA-DRB1*11:02', 'HLA-DRB1*11:03', 'HLA-DRB1*11:04', 'HLA-DRB1*11:05', 'HLA-DRB1*11:06',
-         'HLA-DRB1*11:07', 'HLA-DRB1*11:08', 'HLA-DRB1*11:09', 'HLA-DRB1*11:10', 'HLA-DRB1*11:11', 'HLA-DRB1*11:12', 'HLA-DRB1*11:13',
-         'HLA-DRB1*11:14', 'HLA-DRB1*11:15', 'HLA-DRB1*11:16', 'HLA-DRB1*11:17', 'HLA-DRB1*11:18', 'HLA-DRB1*11:19', 'HLA-DRB1*11:20',
-         'HLA-DRB1*11:21', 'HLA-DRB1*11:22', 'HLA-DRB1*11:23', 'HLA-DRB1*11:24', 'HLA-DRB1*11:25', 'HLA-DRB1*11:26', 'HLA-DRB1*11:27',
-         'HLA-DRB1*11:28', 'HLA-DRB1*11:29', 'HLA-DRB1*11:30', 'HLA-DRB1*11:31', 'HLA-DRB1*11:32', 'HLA-DRB1*11:33', 'HLA-DRB1*11:34',
-         'HLA-DRB1*11:35', 'HLA-DRB1*11:36', 'HLA-DRB1*11:37', 'HLA-DRB1*11:38', 'HLA-DRB1*11:39', 'HLA-DRB1*11:40', 'HLA-DRB1*11:41',
-         'HLA-DRB1*11:42', 'HLA-DRB1*11:43', 'HLA-DRB1*11:44', 'HLA-DRB1*11:45', 'HLA-DRB1*11:46', 'HLA-DRB1*11:47', 'HLA-DRB1*11:48',
-         'HLA-DRB1*11:49', 'HLA-DRB1*11:50', 'HLA-DRB1*11:51', 'HLA-DRB1*11:52', 'HLA-DRB1*11:53', 'HLA-DRB1*11:54', 'HLA-DRB1*11:55',
-         'HLA-DRB1*11:56', 'HLA-DRB1*11:57', 'HLA-DRB1*11:58', 'HLA-DRB1*11:59', 'HLA-DRB1*11:60', 'HLA-DRB1*11:61', 'HLA-DRB1*11:62',
-         'HLA-DRB1*11:63', 'HLA-DRB1*11:64', 'HLA-DRB1*11:65', 'HLA-DRB1*11:66', 'HLA-DRB1*11:67', 'HLA-DRB1*11:68', 'HLA-DRB1*11:69',
-         'HLA-DRB1*11:70', 'HLA-DRB1*11:72', 'HLA-DRB1*11:73', 'HLA-DRB1*11:74', 'HLA-DRB1*11:75', 'HLA-DRB1*11:76', 'HLA-DRB1*11:77',
-         'HLA-DRB1*11:78', 'HLA-DRB1*11:79', 'HLA-DRB1*11:80', 'HLA-DRB1*11:81', 'HLA-DRB1*11:82', 'HLA-DRB1*11:83', 'HLA-DRB1*11:84',
-         'HLA-DRB1*11:85', 'HLA-DRB1*11:86', 'HLA-DRB1*11:87', 'HLA-DRB1*11:88', 'HLA-DRB1*11:89', 'HLA-DRB1*11:90', 'HLA-DRB1*11:91',
-         'HLA-DRB1*11:92', 'HLA-DRB1*11:93', 'HLA-DRB1*11:94', 'HLA-DRB1*11:95', 'HLA-DRB1*11:96', 'HLA-DRB1*11:97', 'HLA-DRB1*11:98',
-         'HLA-DRB1*11:99', 'HLA-DRB1*12:01', 'HLA-DRB1*12:02', 'HLA-DRB1*12:03', 'HLA-DRB1*12:04', 'HLA-DRB1*12:05', 'HLA-DRB1*12:06',
-         'HLA-DRB1*12:07', 'HLA-DRB1*12:08', 'HLA-DRB1*12:09', 'HLA-DRB1*12:10', 'HLA-DRB1*12:11', 'HLA-DRB1*12:12', 'HLA-DRB1*12:13',
-         'HLA-DRB1*12:14', 'HLA-DRB1*12:15', 'HLA-DRB1*12:16', 'HLA-DRB1*12:17', 'HLA-DRB1*12:18', 'HLA-DRB1*12:19', 'HLA-DRB1*12:20',
-         'HLA-DRB1*12:21', 'HLA-DRB1*12:22', 'HLA-DRB1*12:23', 'HLA-DRB1*12:25', 'HLA-DRB1*12:26', 'HLA-DRB1*12:27', 'HLA-DRB1*13:01',
-         'HLA-DRB1*13:02', 'HLA-DRB1*13:03', 'HLA-DRB1*13:04', 'HLA-DRB1*13:05', 'HLA-DRB1*13:06', 'HLA-DRB1*13:07', 'HLA-DRB1*13:08',
-         'HLA-DRB1*13:09', 'HLA-DRB1*13:10', 'HLA-DRB1*13:11', 'HLA-DRB1*13:12', 'HLA-DRB1*13:13', 'HLA-DRB1*13:14', 'HLA-DRB1*13:15',
-         'HLA-DRB1*13:16', 'HLA-DRB1*13:17', 'HLA-DRB1*13:18', 'HLA-DRB1*13:19', 'HLA-DRB1*13:20', 'HLA-DRB1*13:21', 'HLA-DRB1*13:22',
-         'HLA-DRB1*13:23', 'HLA-DRB1*13:24', 'HLA-DRB1*13:25', 'HLA-DRB1*13:26', 'HLA-DRB1*13:27', 'HLA-DRB1*13:28', 'HLA-DRB1*13:29',
-         'HLA-DRB1*13:30', 'HLA-DRB1*13:31', 'HLA-DRB1*13:32', 'HLA-DRB1*13:33', 'HLA-DRB1*13:34', 'HLA-DRB1*13:35', 'HLA-DRB1*13:36',
-         'HLA-DRB1*13:37', 'HLA-DRB1*13:38', 'HLA-DRB1*13:39', 'HLA-DRB1*13:40', 'HLA-DRB1*13:41', 'HLA-DRB1*13:42', 'HLA-DRB1*13:43',
-         'HLA-DRB1*13:44', 'HLA-DRB1*13:45', 'HLA-DRB1*13:46', 'HLA-DRB1*13:47', 'HLA-DRB1*13:48', 'HLA-DRB1*13:49', 'HLA-DRB1*13:50',
-         'HLA-DRB1*13:51', 'HLA-DRB1*13:52', 'HLA-DRB1*13:53', 'HLA-DRB1*13:54', 'HLA-DRB1*13:55', 'HLA-DRB1*13:56', 'HLA-DRB1*13:57',
-         'HLA-DRB1*13:58', 'HLA-DRB1*13:59', 'HLA-DRB1*13:60', 'HLA-DRB1*13:61', 'HLA-DRB1*13:62', 'HLA-DRB1*13:63', 'HLA-DRB1*13:64',
-         'HLA-DRB1*13:65', 'HLA-DRB1*13:66', 'HLA-DRB1*13:67', 'HLA-DRB1*13:68', 'HLA-DRB1*13:69', 'HLA-DRB1*13:70', 'HLA-DRB1*13:71',
-         'HLA-DRB1*13:72', 'HLA-DRB1*13:73', 'HLA-DRB1*13:74', 'HLA-DRB1*13:75', 'HLA-DRB1*13:76', 'HLA-DRB1*13:77', 'HLA-DRB1*13:78',
-         'HLA-DRB1*13:79', 'HLA-DRB1*13:80', 'HLA-DRB1*13:81', 'HLA-DRB1*13:82', 'HLA-DRB1*13:83', 'HLA-DRB1*13:84', 'HLA-DRB1*13:85',
-         'HLA-DRB1*13:86', 'HLA-DRB1*13:87', 'HLA-DRB1*13:88', 'HLA-DRB1*13:89', 'HLA-DRB1*13:90', 'HLA-DRB1*13:91', 'HLA-DRB1*13:92',
-         'HLA-DRB1*13:93', 'HLA-DRB1*13:94', 'HLA-DRB1*13:95', 'HLA-DRB1*13:96', 'HLA-DRB1*13:97', 'HLA-DRB1*13:98', 'HLA-DRB1*13:99',
-         'HLA-DRB1*14:01', 'HLA-DRB1*14:02', 'HLA-DRB1*14:03', 'HLA-DRB1*14:04', 'HLA-DRB1*14:05', 'HLA-DRB1*14:06', 'HLA-DRB1*14:07',
-         'HLA-DRB1*14:08', 'HLA-DRB1*14:09', 'HLA-DRB1*14:10', 'HLA-DRB1*14:11', 'HLA-DRB1*14:12', 'HLA-DRB1*14:13', 'HLA-DRB1*14:14',
-         'HLA-DRB1*14:15', 'HLA-DRB1*14:16', 'HLA-DRB1*14:17', 'HLA-DRB1*14:18', 'HLA-DRB1*14:19', 'HLA-DRB1*14:20', 'HLA-DRB1*14:21',
-         'HLA-DRB1*14:22', 'HLA-DRB1*14:23', 'HLA-DRB1*14:24', 'HLA-DRB1*14:25', 'HLA-DRB1*14:26', 'HLA-DRB1*14:27', 'HLA-DRB1*14:28',
-         'HLA-DRB1*14:29', 'HLA-DRB1*14:30', 'HLA-DRB1*14:31', 'HLA-DRB1*14:32', 'HLA-DRB1*14:33', 'HLA-DRB1*14:34', 'HLA-DRB1*14:35',
-         'HLA-DRB1*14:36', 'HLA-DRB1*14:37', 'HLA-DRB1*14:38', 'HLA-DRB1*14:39', 'HLA-DRB1*14:40', 'HLA-DRB1*14:41', 'HLA-DRB1*14:42',
-         'HLA-DRB1*14:43', 'HLA-DRB1*14:44', 'HLA-DRB1*14:45', 'HLA-DRB1*14:46', 'HLA-DRB1*14:47', 'HLA-DRB1*14:48', 'HLA-DRB1*14:49',
-         'HLA-DRB1*14:50', 'HLA-DRB1*14:51', 'HLA-DRB1*14:52', 'HLA-DRB1*14:53', 'HLA-DRB1*14:54', 'HLA-DRB1*14:55', 'HLA-DRB1*14:56',
-         'HLA-DRB1*14:57', 'HLA-DRB1*14:58', 'HLA-DRB1*14:59', 'HLA-DRB1*14:60', 'HLA-DRB1*14:61', 'HLA-DRB1*14:62', 'HLA-DRB1*14:63',
-         'HLA-DRB1*14:64', 'HLA-DRB1*14:65', 'HLA-DRB1*14:67', 'HLA-DRB1*14:68', 'HLA-DRB1*14:69', 'HLA-DRB1*14:70', 'HLA-DRB1*14:71',
-         'HLA-DRB1*14:72', 'HLA-DRB1*14:73', 'HLA-DRB1*14:74', 'HLA-DRB1*14:75', 'HLA-DRB1*14:76', 'HLA-DRB1*14:77', 'HLA-DRB1*14:78',
-         'HLA-DRB1*14:79', 'HLA-DRB1*14:80', 'HLA-DRB1*14:81', 'HLA-DRB1*14:82', 'HLA-DRB1*14:83', 'HLA-DRB1*14:84', 'HLA-DRB1*14:85',
-         'HLA-DRB1*14:86', 'HLA-DRB1*14:87', 'HLA-DRB1*14:88', 'HLA-DRB1*14:89', 'HLA-DRB1*14:90', 'HLA-DRB1*14:91', 'HLA-DRB1*14:93',
-         'HLA-DRB1*14:94', 'HLA-DRB1*14:95', 'HLA-DRB1*14:96', 'HLA-DRB1*14:97', 'HLA-DRB1*14:98', 'HLA-DRB1*14:99', 'HLA-DRB1*15:01',
-         'HLA-DRB1*15:02', 'HLA-DRB1*15:03', 'HLA-DRB1*15:04', 'HLA-DRB1*15:05', 'HLA-DRB1*15:06', 'HLA-DRB1*15:07', 'HLA-DRB1*15:08',
-         'HLA-DRB1*15:09', 'HLA-DRB1*15:10', 'HLA-DRB1*15:11', 'HLA-DRB1*15:12', 'HLA-DRB1*15:13', 'HLA-DRB1*15:14', 'HLA-DRB1*15:15',
-         'HLA-DRB1*15:16', 'HLA-DRB1*15:18', 'HLA-DRB1*15:19', 'HLA-DRB1*15:20', 'HLA-DRB1*15:21', 'HLA-DRB1*15:22', 'HLA-DRB1*15:23',
-         'HLA-DRB1*15:24', 'HLA-DRB1*15:25', 'HLA-DRB1*15:26', 'HLA-DRB1*15:27', 'HLA-DRB1*15:28', 'HLA-DRB1*15:29', 'HLA-DRB1*15:30',
-         'HLA-DRB1*15:31', 'HLA-DRB1*15:32', 'HLA-DRB1*15:33', 'HLA-DRB1*15:34', 'HLA-DRB1*15:35', 'HLA-DRB1*15:36', 'HLA-DRB1*15:37',
-         'HLA-DRB1*15:38', 'HLA-DRB1*15:39', 'HLA-DRB1*15:40', 'HLA-DRB1*15:41', 'HLA-DRB1*15:42', 'HLA-DRB1*15:43', 'HLA-DRB1*15:44',
-         'HLA-DRB1*15:45', 'HLA-DRB1*15:46', 'HLA-DRB1*15:47', 'HLA-DRB1*15:48', 'HLA-DRB1*15:49', 'HLA-DRB1*15:51', 'HLA-DRB1*15:52',
-         'HLA-DRB1*15:53', 'HLA-DRB1*15:54', 'HLA-DRB1*15:55', 'HLA-DRB1*15:56', 'HLA-DRB1*15:57', 'HLA-DRB1*16:01', 'HLA-DRB1*16:02',
-         'HLA-DRB1*16:03', 'HLA-DRB1*16:04', 'HLA-DRB1*16:05', 'HLA-DRB1*16:07', 'HLA-DRB1*16:08', 'HLA-DRB1*16:09', 'HLA-DRB1*16:10',
-         'HLA-DRB1*16:11', 'HLA-DRB1*16:12', 'HLA-DRB1*16:14', 'HLA-DRB1*16:15', 'HLA-DRB1*16:16', 'HLA-DRB1*16:17', 'HLA-DRB1*16:18',
-         'HLA-DRB3*01:01', 'HLA-DRB3*01:02', 'HLA-DRB3*01:03', 'HLA-DRB3*01:04', 'HLA-DRB3*01:05', 'HLA-DRB3*01:06', 'HLA-DRB3*01:07',
-         'HLA-DRB3*01:08', 'HLA-DRB3*01:09', 'HLA-DRB3*01:10', 'HLA-DRB3*01:11', 'HLA-DRB3*01:12', 'HLA-DRB3*01:13', 'HLA-DRB3*01:14',
-         'HLA-DRB3*01:15', 'HLA-DRB3*02:01', 'HLA-DRB3*02:02', 'HLA-DRB3*02:03', 'HLA-DRB3*02:04', 'HLA-DRB3*02:05', 'HLA-DRB3*02:06',
-         'HLA-DRB3*02:07', 'HLA-DRB3*02:08', 'HLA-DRB3*02:09', 'HLA-DRB3*02:10', 'HLA-DRB3*02:11', 'HLA-DRB3*02:12', 'HLA-DRB3*02:13',
-         'HLA-DRB3*02:14', 'HLA-DRB3*02:15', 'HLA-DRB3*02:16', 'HLA-DRB3*02:17', 'HLA-DRB3*02:18', 'HLA-DRB3*02:19', 'HLA-DRB3*02:20',
-         'HLA-DRB3*02:21', 'HLA-DRB3*02:22', 'HLA-DRB3*02:23', 'HLA-DRB3*02:24', 'HLA-DRB3*02:25', 'HLA-DRB3*02:26', 'HLA-DRB3*02:27',
-         'HLA-DRB3*02:28', 'HLA-DRB3*03:01', 'HLA-DRB3*03:02', 'HLA-DRB3*03:03', 'HLA-DRB4*01:01', 'HLA-DRB4*01:03', 'HLA-DRB4*01:04',
-         'HLA-DRB4*01:05', 'HLA-DRB4*01:06', 'HLA-DRB4*01:07', 'HLA-DRB4*01:08', 'HLA-DRB5*01:01', 'HLA-DRB5*01:02', 'HLA-DRB5*01:04',
-         'HLA-DRB5*01:05', 'HLA-DRB5*01:06', 'HLA-DRB5*01:07', 'HLA-DRB5*01:08', 'HLA-DRB5*01:09', 'HLA-DRB5*01:11', 'HLA-DRB5*01:12',
-         'HLA-DRB5*01:13', 'HLA-DRB5*01:14', 'HLA-DRB5*02:02', 'HLA-DRB5*02:03', 'HLA-DRB5*02:04', 'HLA-DRB5*02:05'])
+        ['HLA-DRB1*01:01', 'HLA-DRB1*01:02', 'HLA-DRB1*01:03', 'HLA-DRB1*01:04', 'HLA-DRB1*01:05', 'HLA-DRB1*01:06',
+         'HLA-DRB1*01:07', 'HLA-DRB1*01:08', 'HLA-DRB1*01:09', 'HLA-DRB1*01:10', 'HLA-DRB1*01:11', 'HLA-DRB1*01:12',
+         'HLA-DRB1*01:13', 'HLA-DRB1*01:14', 'HLA-DRB1*01:15', 'HLA-DRB1*01:16', 'HLA-DRB1*01:17', 'HLA-DRB1*01:18',
+         'HLA-DRB1*01:19', 'HLA-DRB1*01:20', 'HLA-DRB1*01:21', 'HLA-DRB1*01:22', 'HLA-DRB1*01:23', 'HLA-DRB1*01:24',
+         'HLA-DRB1*01:25', 'HLA-DRB1*01:26', 'HLA-DRB1*01:27', 'HLA-DRB1*01:28', 'HLA-DRB1*01:29', 'HLA-DRB1*01:30',
+         'HLA-DRB1*01:31', 'HLA-DRB1*01:32', 'HLA-DRB1*01:34', 'HLA-DRB1*01:35', 'HLA-DRB1*01:36', 'HLA-DRB1*03:01',
+         'HLA-DRB1*03:02', 'HLA-DRB1*03:03', 'HLA-DRB1*03:04', 'HLA-DRB1*03:05', 'HLA-DRB1*03:06', 'HLA-DRB1*03:07',
+         'HLA-DRB1*03:08', 'HLA-DRB1*03:09', 'HLA-DRB1*03:10', 'HLA-DRB1*03:11', 'HLA-DRB1*03:12', 'HLA-DRB1*03:13',
+         'HLA-DRB1*03:14', 'HLA-DRB1*03:15', 'HLA-DRB1*03:16', 'HLA-DRB1*03:17', 'HLA-DRB1*03:18', 'HLA-DRB1*03:19',
+         'HLA-DRB1*03:20', 'HLA-DRB1*03:21', 'HLA-DRB1*03:22', 'HLA-DRB1*03:23', 'HLA-DRB1*03:24', 'HLA-DRB1*03:25',
+         'HLA-DRB1*03:26', 'HLA-DRB1*03:27', 'HLA-DRB1*03:28', 'HLA-DRB1*03:29', 'HLA-DRB1*03:30', 'HLA-DRB1*03:31',
+         'HLA-DRB1*03:32', 'HLA-DRB1*03:33', 'HLA-DRB1*03:34', 'HLA-DRB1*03:35', 'HLA-DRB1*03:36', 'HLA-DRB1*03:37',
+         'HLA-DRB1*03:38', 'HLA-DRB1*03:39', 'HLA-DRB1*03:40', 'HLA-DRB1*03:41', 'HLA-DRB1*03:42', 'HLA-DRB1*03:43',
+         'HLA-DRB1*03:44', 'HLA-DRB1*03:45', 'HLA-DRB1*03:46', 'HLA-DRB1*03:47', 'HLA-DRB1*03:48', 'HLA-DRB1*03:49',
+         'HLA-DRB1*03:50', 'HLA-DRB1*03:51', 'HLA-DRB1*03:52', 'HLA-DRB1*03:53', 'HLA-DRB1*03:54', 'HLA-DRB1*03:55',
+         'HLA-DRB1*03:56', 'HLA-DRB1*03:57', 'HLA-DRB1*03:58', 'HLA-DRB1*03:59', 'HLA-DRB1*03:60', 'HLA-DRB1*03:61',
+         'HLA-DRB1*03:62', 'HLA-DRB1*03:63', 'HLA-DRB1*03:64', 'HLA-DRB1*04:01', 'HLA-DRB1*04:02', 'HLA-DRB1*04:03',
+         'HLA-DRB1*04:04', 'HLA-DRB1*04:05', 'HLA-DRB1*04:06', 'HLA-DRB1*04:07', 'HLA-DRB1*04:08', 'HLA-DRB1*04:09',
+         'HLA-DRB1*04:10', 'HLA-DRB1*04:11', 'HLA-DRB1*04:12', 'HLA-DRB1*04:13', 'HLA-DRB1*04:14', 'HLA-DRB1*04:15',
+         'HLA-DRB1*04:16', 'HLA-DRB1*04:17', 'HLA-DRB1*04:18', 'HLA-DRB1*04:19', 'HLA-DRB1*04:20', 'HLA-DRB1*04:21',
+         'HLA-DRB1*04:22', 'HLA-DRB1*04:23', 'HLA-DRB1*04:24', 'HLA-DRB1*04:25', 'HLA-DRB1*04:26', 'HLA-DRB1*04:27',
+         'HLA-DRB1*04:28', 'HLA-DRB1*04:29', 'HLA-DRB1*04:30', 'HLA-DRB1*04:31', 'HLA-DRB1*04:32', 'HLA-DRB1*04:33',
+         'HLA-DRB1*04:34', 'HLA-DRB1*04:35', 'HLA-DRB1*04:36', 'HLA-DRB1*04:37', 'HLA-DRB1*04:38', 'HLA-DRB1*04:39',
+         'HLA-DRB1*04:40', 'HLA-DRB1*04:41', 'HLA-DRB1*04:42', 'HLA-DRB1*04:43', 'HLA-DRB1*04:44', 'HLA-DRB1*04:45',
+         'HLA-DRB1*04:46', 'HLA-DRB1*04:47', 'HLA-DRB1*04:48', 'HLA-DRB1*04:49', 'HLA-DRB1*04:50', 'HLA-DRB1*04:51',
+         'HLA-DRB1*04:52', 'HLA-DRB1*04:53', 'HLA-DRB1*04:54', 'HLA-DRB1*04:55', 'HLA-DRB1*04:56', 'HLA-DRB1*04:57',
+         'HLA-DRB1*04:58', 'HLA-DRB1*04:59', 'HLA-DRB1*04:60', 'HLA-DRB1*04:61', 'HLA-DRB1*04:62', 'HLA-DRB1*04:63',
+         'HLA-DRB1*04:64', 'HLA-DRB1*04:65', 'HLA-DRB1*04:66', 'HLA-DRB1*04:67', 'HLA-DRB1*04:68', 'HLA-DRB1*04:69',
+         'HLA-DRB1*04:70', 'HLA-DRB1*04:71', 'HLA-DRB1*04:72', 'HLA-DRB1*04:73', 'HLA-DRB1*04:74', 'HLA-DRB1*04:75',
+         'HLA-DRB1*04:76', 'HLA-DRB1*04:77', 'HLA-DRB1*04:78', 'HLA-DRB1*04:79', 'HLA-DRB1*04:80', 'HLA-DRB1*04:82',
+         'HLA-DRB1*04:83', 'HLA-DRB1*04:84', 'HLA-DRB1*04:85', 'HLA-DRB1*04:86', 'HLA-DRB1*04:87', 'HLA-DRB1*04:88',
+         'HLA-DRB1*04:89', 'HLA-DRB1*04:90', 'HLA-DRB1*04:91', 'HLA-DRB1*04:92', 'HLA-DRB1*04:93', 'HLA-DRB1*04:95',
+         'HLA-DRB1*04:96', 'HLA-DRB1*04:97', 'HLA-DRB1*04:98', 'HLA-DRB1*07:01', 'HLA-DRB1*07:03', 'HLA-DRB1*07:04',
+         'HLA-DRB1*07:05', 'HLA-DRB1*07:06', 'HLA-DRB1*07:07', 'HLA-DRB1*07:08', 'HLA-DRB1*07:09', 'HLA-DRB1*07:11',
+         'HLA-DRB1*07:12', 'HLA-DRB1*07:13', 'HLA-DRB1*07:14', 'HLA-DRB1*07:15', 'HLA-DRB1*07:16', 'HLA-DRB1*07:17',
+         'HLA-DRB1*07:18', 'HLA-DRB1*07:19', 'HLA-DRB1*07:20', 'HLA-DRB1*07:21', 'HLA-DRB1*08:01', 'HLA-DRB1*08:02',
+         'HLA-DRB1*08:03', 'HLA-DRB1*08:04', 'HLA-DRB1*08:05', 'HLA-DRB1*08:06', 'HLA-DRB1*08:07', 'HLA-DRB1*08:08',
+         'HLA-DRB1*08:09', 'HLA-DRB1*08:10', 'HLA-DRB1*08:11', 'HLA-DRB1*08:12', 'HLA-DRB1*08:13', 'HLA-DRB1*08:14',
+         'HLA-DRB1*08:15', 'HLA-DRB1*08:16', 'HLA-DRB1*08:17', 'HLA-DRB1*08:18', 'HLA-DRB1*08:19', 'HLA-DRB1*08:20',
+         'HLA-DRB1*08:21', 'HLA-DRB1*08:22', 'HLA-DRB1*08:23', 'HLA-DRB1*08:24', 'HLA-DRB1*08:25', 'HLA-DRB1*08:26',
+         'HLA-DRB1*08:27', 'HLA-DRB1*08:28', 'HLA-DRB1*08:29', 'HLA-DRB1*08:30', 'HLA-DRB1*08:31', 'HLA-DRB1*08:32',
+         'HLA-DRB1*08:33', 'HLA-DRB1*08:34', 'HLA-DRB1*08:35', 'HLA-DRB1*08:36', 'HLA-DRB1*08:37', 'HLA-DRB1*08:38',
+         'HLA-DRB1*08:39', 'HLA-DRB1*08:40', 'HLA-DRB1*08:41', 'HLA-DRB1*08:42', 'HLA-DRB1*08:43', 'HLA-DRB1*08:44',
+         'HLA-DRB1*08:45', 'HLA-DRB1*09:01', 'HLA-DRB1*09:02', 'HLA-DRB1*09:03', 'HLA-DRB1*09:04', 'HLA-DRB1*09:05',
+         'HLA-DRB1*09:06', 'HLA-DRB1*09:07', 'HLA-DRB1*09:08', 'HLA-DRB1*09:09', 'HLA-DRB1*09:10', 'HLA-DRB1*09:11',
+         'HLA-DRB1*09:12', 'HLA-DRB1*10:01', 'HLA-DRB1*10:02', 'HLA-DRB1*10:03', 'HLA-DRB1*11:01', 'HLA-DRB1*11:02',
+         'HLA-DRB1*11:03', 'HLA-DRB1*11:04', 'HLA-DRB1*11:05', 'HLA-DRB1*11:06', 'HLA-DRB1*11:07', 'HLA-DRB1*11:08',
+         'HLA-DRB1*11:09', 'HLA-DRB1*11:10', 'HLA-DRB1*11:11', 'HLA-DRB1*11:12', 'HLA-DRB1*11:13', 'HLA-DRB1*11:14',
+         'HLA-DRB1*11:15', 'HLA-DRB1*11:16', 'HLA-DRB1*11:17', 'HLA-DRB1*11:18', 'HLA-DRB1*11:19', 'HLA-DRB1*11:20',
+         'HLA-DRB1*11:21', 'HLA-DRB1*11:22', 'HLA-DRB1*11:23', 'HLA-DRB1*11:24', 'HLA-DRB1*11:25', 'HLA-DRB1*11:26',
+         'HLA-DRB1*11:27', 'HLA-DRB1*11:28', 'HLA-DRB1*11:29', 'HLA-DRB1*11:30', 'HLA-DRB1*11:31', 'HLA-DRB1*11:32',
+         'HLA-DRB1*11:33', 'HLA-DRB1*11:34', 'HLA-DRB1*11:35', 'HLA-DRB1*11:36', 'HLA-DRB1*11:37', 'HLA-DRB1*11:38',
+         'HLA-DRB1*11:39', 'HLA-DRB1*11:40', 'HLA-DRB1*11:41', 'HLA-DRB1*11:42', 'HLA-DRB1*11:43', 'HLA-DRB1*11:44',
+         'HLA-DRB1*11:45', 'HLA-DRB1*11:46', 'HLA-DRB1*11:47', 'HLA-DRB1*11:48', 'HLA-DRB1*11:49', 'HLA-DRB1*11:50',
+         'HLA-DRB1*11:51', 'HLA-DRB1*11:52', 'HLA-DRB1*11:53', 'HLA-DRB1*11:54', 'HLA-DRB1*11:55', 'HLA-DRB1*11:56',
+         'HLA-DRB1*11:57', 'HLA-DRB1*11:58', 'HLA-DRB1*11:59', 'HLA-DRB1*11:60', 'HLA-DRB1*11:61', 'HLA-DRB1*11:62',
+         'HLA-DRB1*11:63', 'HLA-DRB1*11:64', 'HLA-DRB1*11:65', 'HLA-DRB1*11:66', 'HLA-DRB1*11:67', 'HLA-DRB1*11:68',
+         'HLA-DRB1*11:69', 'HLA-DRB1*11:70', 'HLA-DRB1*11:72', 'HLA-DRB1*11:73', 'HLA-DRB1*11:74', 'HLA-DRB1*11:75',
+         'HLA-DRB1*11:76', 'HLA-DRB1*11:77', 'HLA-DRB1*11:78', 'HLA-DRB1*11:79', 'HLA-DRB1*11:80', 'HLA-DRB1*11:81',
+         'HLA-DRB1*11:82', 'HLA-DRB1*11:83', 'HLA-DRB1*11:84', 'HLA-DRB1*11:85', 'HLA-DRB1*11:86', 'HLA-DRB1*11:87',
+         'HLA-DRB1*11:88', 'HLA-DRB1*11:89', 'HLA-DRB1*11:90', 'HLA-DRB1*11:91', 'HLA-DRB1*11:92', 'HLA-DRB1*11:93',
+         'HLA-DRB1*11:94', 'HLA-DRB1*11:95', 'HLA-DRB1*11:96', 'HLA-DRB1*11:97', 'HLA-DRB1*11:98', 'HLA-DRB1*11:99',
+         'HLA-DRB1*12:01', 'HLA-DRB1*12:02', 'HLA-DRB1*12:03', 'HLA-DRB1*12:04', 'HLA-DRB1*12:05', 'HLA-DRB1*12:06',
+         'HLA-DRB1*12:07', 'HLA-DRB1*12:08', 'HLA-DRB1*12:09', 'HLA-DRB1*12:10', 'HLA-DRB1*12:11', 'HLA-DRB1*12:12',
+         'HLA-DRB1*12:13', 'HLA-DRB1*12:14', 'HLA-DRB1*12:15', 'HLA-DRB1*12:16', 'HLA-DRB1*12:17', 'HLA-DRB1*12:18',
+         'HLA-DRB1*12:19', 'HLA-DRB1*12:20', 'HLA-DRB1*12:21', 'HLA-DRB1*12:22', 'HLA-DRB1*12:23', 'HLA-DRB1*12:25',
+         'HLA-DRB1*12:26', 'HLA-DRB1*12:27', 'HLA-DRB1*13:01', 'HLA-DRB1*13:02', 'HLA-DRB1*13:03', 'HLA-DRB1*13:04',
+         'HLA-DRB1*13:05', 'HLA-DRB1*13:06', 'HLA-DRB1*13:07', 'HLA-DRB1*13:08', 'HLA-DRB1*13:09', 'HLA-DRB1*13:10',
+         'HLA-DRB1*13:11', 'HLA-DRB1*13:12', 'HLA-DRB1*13:13', 'HLA-DRB1*13:14', 'HLA-DRB1*13:15', 'HLA-DRB1*13:16',
+         'HLA-DRB1*13:17', 'HLA-DRB1*13:18', 'HLA-DRB1*13:19', 'HLA-DRB1*13:20', 'HLA-DRB1*13:21', 'HLA-DRB1*13:22',
+         'HLA-DRB1*13:23', 'HLA-DRB1*13:24', 'HLA-DRB1*13:25', 'HLA-DRB1*13:26', 'HLA-DRB1*13:27', 'HLA-DRB1*13:28',
+         'HLA-DRB1*13:29', 'HLA-DRB1*13:30', 'HLA-DRB1*13:31', 'HLA-DRB1*13:32', 'HLA-DRB1*13:33', 'HLA-DRB1*13:34',
+         'HLA-DRB1*13:35', 'HLA-DRB1*13:36', 'HLA-DRB1*13:37', 'HLA-DRB1*13:38', 'HLA-DRB1*13:39', 'HLA-DRB1*13:40',
+         'HLA-DRB1*13:41', 'HLA-DRB1*13:42', 'HLA-DRB1*13:43', 'HLA-DRB1*13:44', 'HLA-DRB1*13:45', 'HLA-DRB1*13:46',
+         'HLA-DRB1*13:47', 'HLA-DRB1*13:48', 'HLA-DRB1*13:49', 'HLA-DRB1*13:50', 'HLA-DRB1*13:51', 'HLA-DRB1*13:52',
+         'HLA-DRB1*13:53', 'HLA-DRB1*13:54', 'HLA-DRB1*13:55', 'HLA-DRB1*13:56', 'HLA-DRB1*13:57', 'HLA-DRB1*13:58',
+         'HLA-DRB1*13:59', 'HLA-DRB1*13:60', 'HLA-DRB1*13:61', 'HLA-DRB1*13:62', 'HLA-DRB1*13:63', 'HLA-DRB1*13:64',
+         'HLA-DRB1*13:65', 'HLA-DRB1*13:66', 'HLA-DRB1*13:67', 'HLA-DRB1*13:68', 'HLA-DRB1*13:69', 'HLA-DRB1*13:70',
+         'HLA-DRB1*13:71', 'HLA-DRB1*13:72', 'HLA-DRB1*13:73', 'HLA-DRB1*13:74', 'HLA-DRB1*13:75', 'HLA-DRB1*13:76',
+         'HLA-DRB1*13:77', 'HLA-DRB1*13:78', 'HLA-DRB1*13:79', 'HLA-DRB1*13:80', 'HLA-DRB1*13:81', 'HLA-DRB1*13:82',
+         'HLA-DRB1*13:83', 'HLA-DRB1*13:84', 'HLA-DRB1*13:85', 'HLA-DRB1*13:86', 'HLA-DRB1*13:87', 'HLA-DRB1*13:88',
+         'HLA-DRB1*13:89', 'HLA-DRB1*13:90', 'HLA-DRB1*13:91', 'HLA-DRB1*13:92', 'HLA-DRB1*13:93', 'HLA-DRB1*13:94',
+         'HLA-DRB1*13:95', 'HLA-DRB1*13:96', 'HLA-DRB1*13:97', 'HLA-DRB1*13:98', 'HLA-DRB1*13:99', 'HLA-DRB1*14:01',
+         'HLA-DRB1*14:02', 'HLA-DRB1*14:03', 'HLA-DRB1*14:04', 'HLA-DRB1*14:05', 'HLA-DRB1*14:06', 'HLA-DRB1*14:07',
+         'HLA-DRB1*14:08', 'HLA-DRB1*14:09', 'HLA-DRB1*14:10', 'HLA-DRB1*14:11', 'HLA-DRB1*14:12', 'HLA-DRB1*14:13',
+         'HLA-DRB1*14:14', 'HLA-DRB1*14:15', 'HLA-DRB1*14:16', 'HLA-DRB1*14:17', 'HLA-DRB1*14:18', 'HLA-DRB1*14:19',
+         'HLA-DRB1*14:20', 'HLA-DRB1*14:21', 'HLA-DRB1*14:22', 'HLA-DRB1*14:23', 'HLA-DRB1*14:24', 'HLA-DRB1*14:25',
+         'HLA-DRB1*14:26', 'HLA-DRB1*14:27', 'HLA-DRB1*14:28', 'HLA-DRB1*14:29', 'HLA-DRB1*14:30', 'HLA-DRB1*14:31',
+         'HLA-DRB1*14:32', 'HLA-DRB1*14:33', 'HLA-DRB1*14:34', 'HLA-DRB1*14:35', 'HLA-DRB1*14:36', 'HLA-DRB1*14:37',
+         'HLA-DRB1*14:38', 'HLA-DRB1*14:39', 'HLA-DRB1*14:40', 'HLA-DRB1*14:41', 'HLA-DRB1*14:42', 'HLA-DRB1*14:43',
+         'HLA-DRB1*14:44', 'HLA-DRB1*14:45', 'HLA-DRB1*14:46', 'HLA-DRB1*14:47', 'HLA-DRB1*14:48', 'HLA-DRB1*14:49',
+         'HLA-DRB1*14:50', 'HLA-DRB1*14:51', 'HLA-DRB1*14:52', 'HLA-DRB1*14:53', 'HLA-DRB1*14:54', 'HLA-DRB1*14:55',
+         'HLA-DRB1*14:56', 'HLA-DRB1*14:57', 'HLA-DRB1*14:58', 'HLA-DRB1*14:59', 'HLA-DRB1*14:60', 'HLA-DRB1*14:61',
+         'HLA-DRB1*14:62', 'HLA-DRB1*14:63', 'HLA-DRB1*14:64', 'HLA-DRB1*14:65', 'HLA-DRB1*14:67', 'HLA-DRB1*14:68',
+         'HLA-DRB1*14:69', 'HLA-DRB1*14:70', 'HLA-DRB1*14:71', 'HLA-DRB1*14:72', 'HLA-DRB1*14:73', 'HLA-DRB1*14:74',
+         'HLA-DRB1*14:75', 'HLA-DRB1*14:76', 'HLA-DRB1*14:77', 'HLA-DRB1*14:78', 'HLA-DRB1*14:79', 'HLA-DRB1*14:80',
+         'HLA-DRB1*14:81', 'HLA-DRB1*14:82', 'HLA-DRB1*14:83', 'HLA-DRB1*14:84', 'HLA-DRB1*14:85', 'HLA-DRB1*14:86',
+         'HLA-DRB1*14:87', 'HLA-DRB1*14:88', 'HLA-DRB1*14:89', 'HLA-DRB1*14:90', 'HLA-DRB1*14:91', 'HLA-DRB1*14:93',
+         'HLA-DRB1*14:94', 'HLA-DRB1*14:95', 'HLA-DRB1*14:96', 'HLA-DRB1*14:97', 'HLA-DRB1*14:98', 'HLA-DRB1*14:99',
+         'HLA-DRB1*15:01', 'HLA-DRB1*15:02', 'HLA-DRB1*15:03', 'HLA-DRB1*15:04', 'HLA-DRB1*15:05', 'HLA-DRB1*15:06',
+         'HLA-DRB1*15:07', 'HLA-DRB1*15:08', 'HLA-DRB1*15:09', 'HLA-DRB1*15:10', 'HLA-DRB1*15:11', 'HLA-DRB1*15:12',
+         'HLA-DRB1*15:13', 'HLA-DRB1*15:14', 'HLA-DRB1*15:15', 'HLA-DRB1*15:16', 'HLA-DRB1*15:18', 'HLA-DRB1*15:19',
+         'HLA-DRB1*15:20', 'HLA-DRB1*15:21', 'HLA-DRB1*15:22', 'HLA-DRB1*15:23', 'HLA-DRB1*15:24', 'HLA-DRB1*15:25',
+         'HLA-DRB1*15:26', 'HLA-DRB1*15:27', 'HLA-DRB1*15:28', 'HLA-DRB1*15:29', 'HLA-DRB1*15:30', 'HLA-DRB1*15:31',
+         'HLA-DRB1*15:32', 'HLA-DRB1*15:33', 'HLA-DRB1*15:34', 'HLA-DRB1*15:35', 'HLA-DRB1*15:36', 'HLA-DRB1*15:37',
+         'HLA-DRB1*15:38', 'HLA-DRB1*15:39', 'HLA-DRB1*15:40', 'HLA-DRB1*15:41', 'HLA-DRB1*15:42', 'HLA-DRB1*15:43',
+         'HLA-DRB1*15:44', 'HLA-DRB1*15:45', 'HLA-DRB1*15:46', 'HLA-DRB1*15:47', 'HLA-DRB1*15:48', 'HLA-DRB1*15:49',
+         'HLA-DRB1*15:51', 'HLA-DRB1*15:52', 'HLA-DRB1*15:53', 'HLA-DRB1*15:54', 'HLA-DRB1*15:55', 'HLA-DRB1*15:56',
+         'HLA-DRB1*15:57', 'HLA-DRB1*16:01', 'HLA-DRB1*16:02', 'HLA-DRB1*16:03', 'HLA-DRB1*16:04', 'HLA-DRB1*16:05',
+         'HLA-DRB1*16:07', 'HLA-DRB1*16:08', 'HLA-DRB1*16:09', 'HLA-DRB1*16:10', 'HLA-DRB1*16:11', 'HLA-DRB1*16:12',
+         'HLA-DRB1*16:14', 'HLA-DRB1*16:15', 'HLA-DRB1*16:16', 'HLA-DRB1*16:17', 'HLA-DRB1*16:18', 'HLA-DRB3*01:01',
+         'HLA-DRB3*01:02', 'HLA-DRB3*01:03', 'HLA-DRB3*01:04', 'HLA-DRB3*01:05', 'HLA-DRB3*01:06', 'HLA-DRB3*01:07',
+         'HLA-DRB3*01:08', 'HLA-DRB3*01:09', 'HLA-DRB3*01:10', 'HLA-DRB3*01:11', 'HLA-DRB3*01:12', 'HLA-DRB3*01:13',
+         'HLA-DRB3*01:14', 'HLA-DRB3*01:15', 'HLA-DRB3*02:01', 'HLA-DRB3*02:02', 'HLA-DRB3*02:03', 'HLA-DRB3*02:04',
+         'HLA-DRB3*02:05', 'HLA-DRB3*02:06', 'HLA-DRB3*02:07', 'HLA-DRB3*02:08', 'HLA-DRB3*02:09', 'HLA-DRB3*02:10',
+         'HLA-DRB3*02:11', 'HLA-DRB3*02:12', 'HLA-DRB3*02:13', 'HLA-DRB3*02:14', 'HLA-DRB3*02:15', 'HLA-DRB3*02:16',
+         'HLA-DRB3*02:17', 'HLA-DRB3*02:18', 'HLA-DRB3*02:19', 'HLA-DRB3*02:20', 'HLA-DRB3*02:21', 'HLA-DRB3*02:22',
+         'HLA-DRB3*02:23', 'HLA-DRB3*02:24', 'HLA-DRB3*02:25', 'HLA-DRB3*02:26', 'HLA-DRB3*02:27', 'HLA-DRB3*02:28',
+         'HLA-DRB3*03:01', 'HLA-DRB3*03:02', 'HLA-DRB3*03:03', 'HLA-DRB4*01:01', 'HLA-DRB4*01:03', 'HLA-DRB4*01:04',
+         'HLA-DRB4*01:05', 'HLA-DRB4*01:06', 'HLA-DRB4*01:07', 'HLA-DRB4*01:08', 'HLA-DRB5*01:01', 'HLA-DRB5*01:02',
+         'HLA-DRB5*01:04', 'HLA-DRB5*01:05', 'HLA-DRB5*01:06', 'HLA-DRB5*01:07', 'HLA-DRB5*01:08', 'HLA-DRB5*01:09',
+         'HLA-DRB5*01:11', 'HLA-DRB5*01:12', 'HLA-DRB5*01:13', 'HLA-DRB5*01:14', 'HLA-DRB5*02:02', 'HLA-DRB5*02:03',
+         'HLA-DRB5*02:04', 'HLA-DRB5*02:05'])
     __supported_length = frozenset([9])
     __name = "tepitopepan"
     __version = "1.0"
@@ -856,7 +897,7 @@ def convert_alleles(self, alleles):
         :return: Returns a string representation of the input :class:`~Fred2.Core.Allele.Allele`
         :rtype: list(str)
         """
-        return ["%s_%s%s"%(a.locus, a.supertype, a.subtype) for a in alleles]
+        return ["%s_%s%s" % (a.locus, a.supertype, a.subtype) for a in alleles]
 
 
 class CalisImm(APSSMEpitopePrediction):
@@ -871,11 +912,14 @@ class CalisImm(APSSMEpitopePrediction):
     """
 
     __alleles = frozenset(
-        ['HLA-B*40:02', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*44:03', 'HLA-B*44:02', 'HLA-A*68:01', 'HLA-A*24:02', 'HLA-B*40:01', 'HLA-A*31:01', 'HLA-B*39:01', 'HLA-A*01:01', 'HLA-B*58:01',
-         'HLA-B*57:01', 'HLA-A*30:02', 'HLA-A*30:01', 'HLA-B*35:01', 'HLA-B*51:01', 'HLA-A*32:01', 'HLA-B*53:01', 'HLA-A*26:01', 'HLA-A*03:01', 'HLA-B*15:02', 'HLA-B*15:01', 'HLA-B*45:01',
-         'HLA-B*54:01', 'HLA-B*18:01', 'HLA-A*68:02', 'HLA-A*69:01', 'HLA-A*02:11', 'HLA-A*11:01', 'HLA-A*23:01', 'HLA-A*33:01', 'HLA-B*46:01', 'HLA-A*02:06', 'HLA-A*02:01', 'HLA-A*02:02',
+        ['HLA-B*40:02', 'HLA-B*07:02', 'HLA-B*08:01', 'HLA-B*44:03', 'HLA-B*44:02', 'HLA-A*68:01', 'HLA-A*24:02',
+         'HLA-B*40:01', 'HLA-A*31:01', 'HLA-B*39:01', 'HLA-A*01:01', 'HLA-B*58:01',
+         'HLA-B*57:01', 'HLA-A*30:02', 'HLA-A*30:01', 'HLA-B*35:01', 'HLA-B*51:01', 'HLA-A*32:01', 'HLA-B*53:01',
+         'HLA-A*26:01', 'HLA-A*03:01', 'HLA-B*15:02', 'HLA-B*15:01', 'HLA-B*45:01',
+         'HLA-B*54:01', 'HLA-B*18:01', 'HLA-A*68:02', 'HLA-A*69:01', 'HLA-A*02:11', 'HLA-A*11:01', 'HLA-A*23:01',
+         'HLA-A*33:01', 'HLA-B*46:01', 'HLA-A*02:06', 'HLA-A*02:01', 'HLA-A*02:02',
          'HLA-A*02:03', 'HLA-A*29:02', 'HLA-B*27:05'])
-    __supported_length = frozenset([9,10,11])
+    __supported_length = frozenset([9, 10, 11])
     __name = "calisimm"
     __version = "1.0"
 
@@ -919,13 +963,15 @@ def predict(self, peptides, alleles=None, **kwargs):
         :return: Returns a :class:`pandas.DataFrame` object with the prediction results
         :rtype: :class:`pandas.DataFrame`
         """
+
         def __load_allele_model(allele, length):
-            allele_model = "%s"%allele
-            return getattr(__import__("Fred2.Data.pssms."+self.name+".mat."+allele_model, fromlist=[allele_model]),
-                           allele_model)
+            allele_model = "%s" % allele
+            return getattr(
+                __import__("Fred2.Data.pssms." + self.name + ".mat." + allele_model, fromlist=[allele_model]),
+                allele_model)
 
         if isinstance(peptides, Peptide):
-            pep_seqs = {str(peptides):peptides}
+            pep_seqs = {str(peptides): peptides}
         else:
             pep_seqs = {}
             for p in peptides:
@@ -933,16 +979,15 @@ def __load_allele_model(allele, length):
                     raise ValueError("Input is not of type Protein or Peptide")
                 pep_seqs[str(p)] = p
 
-
         if alleles is None:
             al = [Allele(a) for a in self.supportedAlleles]
-            alleles_string = {conv_a:a for conv_a, a in itertools.izip(self.convert_alleles(al), al)}
+            alleles_string = {conv_a: a for conv_a, a in itertools.izip(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 itertools.izip(self.convert_alleles(alleles), alleles)}
+            alleles_string = {conv_a: a for conv_a, a in itertools.izip(self.convert_alleles(alleles), alleles)}
 
         result = {}
         pep_groups = pep_seqs.keys()
@@ -950,7 +995,7 @@ 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)
@@ -959,23 +1004,23 @@ def __load_allele_model(allele, length):
                 if alleles_string[a] not in result:
                     result[allele] = {}
 
-                #load matrix
+                # load matrix
                 try:
                     pssm = __load_allele_model(a, length)
                 except ImportError:
                     pssm = []
 
-                importance = self.__importance  if length <= 9 else \
-                             self.__importance[:5] + ((length - 9) * [0.30]) + self.__importance[5:]
+                importance = self.__importance if length <= 9 else \
+                    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]
+                    score = sum(self.__log_enrichment.get(p[i], 0.0) * importance[i]
                                 for i in xrange(length) if i not in pssm)
                     result[allele][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.")
+            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)
         df_result.index = pandas.MultiIndex.from_tuples([tuple((i, self.name)) for i in df_result.index],
@@ -983,4 +1028,4 @@ def __load_allele_model(allele, length):
         return df_result
 
     def convert_alleles(self, alleles):
-        return map(lambda x: x.name.replace("*","").replace(":",""), alleles)
+        return map(lambda x: x.name.replace("*", "").replace(":", ""), alleles)
diff --git a/Fred2/EpitopePrediction/__init__.py b/Fred2/EpitopePrediction/__init__.py
index 7af6131d..7bdbdb61 100644
--- a/Fred2/EpitopePrediction/__init__.py
+++ b/Fred2/EpitopePrediction/__init__.py
@@ -13,6 +13,7 @@
 from Fred2.EpitopePrediction.External import *
 from Fred2.EpitopePrediction.PSSM import *
 from Fred2.EpitopePrediction.SVM import *
+from Fred2.EpitopePrediction.ANN import *
 try:
     from fred_plugin import *
 except ImportError:
diff --git a/setup.py b/setup.py
index 16752141..417fd860 100644
--- a/setup.py
+++ b/setup.py
@@ -43,7 +43,7 @@
     name='Fred2',
 
     # Version:
-    version='2.0.3',
+    version='2.0.4',
 
     description='A Framework for Epitope Detection and Vaccine Design',
     long_description=readme,
@@ -120,6 +120,6 @@
     #ext_modules=[d2s_module],
 
     # Run-time dependencies. (will be installed by pip when FRED2 is installed)
-    install_requires=['setuptools>=18.2', 'pandas', 'pyomo>=4.0','svmlight', 'PyMySQL', 'biopython', 'pyVCF'],
+    install_requires=['setuptools>=18.2', 'pandas', 'pyomo>=4.0','svmlight', 'PyMySQL', 'biopython', 'pyVCF', 'mhcflurry', 'mhcnuggets'],
 
 )