class OptiTopeTestCase(unittest.TestCase):
"""
Unittest for OptiTope
"""
def setUp(self):
self.proteins=[]
self.alleles = [Allele("HLA-A*01:01"),Allele("HLA-B*07:02"), Allele("HLA-C*03:01")]
self.peptides = [Peptide(p) for p in """SFSIFLLAL
GHRMAWDMM
VYEADDVIL
CFTPSPVVV
FLLLADARV
GPADGMVSK
YLYDHLAPM
GLRDLAVAV
GPTPLLYRL
TWVLVGGVL
IELGGKPAL
LAGGVLAAV
QYLAGLSTL
NFVSGIQYL
VLSDFKTWL
ARPDYNPPL
KLLPRLPGV
RHTPVNSWL
GLYLFNWAV
ALYDVVSTL
RRCRASGVL
WPLLLLLLA
VTYSLTGLW
YFVIFFVAA""".split()]
self.result= EpitopePredictorFactory("NetMHC").predict(self.peptides, self.alleles)
self.thresh = {"A*01:01":0,"B*07:02":0,"C*03:01":0}
def test_selection_without_constraints(self):
"""
tests if minimal selection withotu additional constraints (except the knapsack capacity) works
#peptides obtainedn by perfroming optimization with same input and parameters by
etk.informatik.uni-tuebingen.de/optitope
:return:
"""
opt = OptiTope(self.result, self.thresh, k=3, solver="cplex", verbosity=0)
r =opt.solve()
self.assertTrue(len(set(str(p) for p in r) - set(["GPTPLLYRL", "QYLAGLSTL", "ALYDVVSTL"])) == 0 )
def test_allele_cov_constraint(self):
"""
tests the allele converage constraints
:return:
"""
#self.alleles.extend([Allele("HLA-A*02:01"),Allele("HLA-B*15:01")])
#self.thresh.update({"A*02:01":0,"B*15:01":0})
self.result= EpitopePredictorFactory("BIMAS").predict(self.peptides, self.alleles)
print self.result[self.alleles[0:2]]
opt = OptiTope(self.result,self.thresh,k=3,solver="cplex",verbosity=1)
opt.activate_allele_coverage_const(0.8)
r = opt.solve()
res_df = self.result.xs(self.result.index.values[0][1], level="Method")
peps = [str(p) for p in r]
probs = {"A*01:01":1, "A*02:01":1, "B*07:02":1,"B*15:01":1,"C*03:01":1.0}
res_df = res_df.loc[peps, :]
res_df = res_df[[a for a in self.alleles]]
res_df = res_df[res_df.apply(lambda x: any(x[a] > self.thresh[a.name] for a in self.alleles), axis=1)]
print res_df.apply(lambda x: sum( x[c]*probs[c.name] for c in res_df.columns),axis=1)
self.assertTrue(len(set(str(p) for p in r) - set(["ALYDVVSTL", "KLLPRLPGV", "GPTPLLYRL"])) == 0 )
def test_epitope_conservation_constraint(self):
import random
self.result = EpitopePredictorFactory("BIMAS").predict(self.peptides, self.alleles)
conservation = {}
print self.result.index.levels[0]
for e in self.result.index.levels[0]:
conservation[str(e)] = random.random()
pt = OptiTope(self.result, self.thresh, k=3, solver="cplex" ,verbosity=1)
pt.activate_epitope_conservation_const(0.5, conservation=conservation)
for e in pt.solve():
print e, conservation[e]
