def test_codon_optimize_with_custom_table():
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(1200, seed=123),
constraints=[EnforceTranslation()],
objectives=[CodonOptimize(
codon_usage_table=biotools.CODON_USAGE_TABLES['b_subtilis'])]
)
assert (problem.objective_scores_sum() < -10)
problem.optimize()
assert (problem.objective_scores_sum() == 0)
def test_codon_optimize_with_custom_table():
table = get_codons_table("b_subtilis")
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(1200, seed=123),
constraints=[EnforceTranslation()],
objectives=[CodonOptimize(codon_usage_table=table)],
logger=None,
)
assert problem.objective_scores_sum() < -10
problem.optimize()
assert problem.objective_scores_sum() == 0
def test_codon_optimize_bestcodon():
numpy.random.seed(123)
protein = random_protein_sequence(3000, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[CodonOptimize(species='e_coli')]
)
assert problem.objective_scores_sum() < 0
problem.optimize()
assert problem.objective_scores_sum() == 0
def test_codon_optimize_harmonized():
numpy.random.seed(123)
protein = random_protein_sequence(500, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[CodonOptimize(species='e_coli', mode='harmonized')]
)
assert (-700 < problem.objective_scores_sum() < -600)
problem.optimize()
assert (-350 < problem.objective_scores_sum())
def test_codon_optimize_harmonized_short_sequence():
protein = "DDDKKKKKK"
sequence = reverse_translate(protein)
harmonization = CodonOptimize(species='b_subtilis', mode='harmonized')
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[harmonization]
)
assert problem.objective_scores_sum() < -7
problem.optimize()
assert -1 < problem.objective_scores_sum()
def test_AvoidRareCodons_as_objective():
numpy.random.seed(123)
sequence = "ATG" "TTT" "ATA" "CCA" "CTT" "TAG"
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[AvoidRareCodons(0.11, "e_coli")],
)
assert abs(problem.objective_scores_sum() + 0.09) < 0.001
problem.optimize()
assert problem.objective_scores_sum() == 0
def test_AvoidRareCodons_as_objective_reversed():
numpy.random.seed(123)
sequence = "ATG" "TTT" "ATA" "CCA" "CTT" "TAG"
rev_sequence = reverse_complement(sequence)
location = (0, len(sequence), -1)
problem = DnaOptimizationProblem(
sequence=rev_sequence,
constraints=[EnforceTranslation(location=location)],
objectives=[AvoidRareCodons(0.11, "e_coli", location=location)],
)
assert abs(problem.objective_scores_sum() + 0.09) < 0.001
problem.optimize()
assert problem.objective_scores_sum() == 0
def test_codon_optimize_harmonize_rca_short_sequence():
protein = random_protein_sequence(500, seed=123)
sequence = reverse_translate(protein)
harmonization = CodonOptimize(species="h_sapiens",
original_species="e_coli",
method="harmonize_rca")
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[harmonization],
logger=None,
)
assert problem.objective_scores_sum() < -123
problem.optimize()
assert -74 < problem.objective_scores_sum()
def test_codon_optimize_match_usage():
numpy.random.seed(123)
protein = random_protein_sequence(500, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[
CodonOptimize(species="e_coli", method="match_codon_usage")
],
logger=None,
)
assert -600 < problem.objective_scores_sum() < -550
problem.optimize()
assert -350 < problem.objective_scores_sum()
def test_EnforceSequence_as_objective():
# Two enzymes, BsmBI(CGTCTC) is GC-rich, EcoRI(GAATTC) is GC-poor, which
# enzyme will be chosen and inserted in the sequence depends on the other
# constraint on GC content
numpy.random.seed(1234)
n_nucleotides = 15
start = 50
location = (start, start + n_nucleotides)
problem = DnaOptimizationProblem(
sequence=25 * "ATGC",
constraints=[AvoidPattern("ATGC")],
objectives=[EnforceSequence("W" * n_nucleotides, location=location)])
assert problem.objective_scores_sum() < 0
problem.resolve_constraints()
problem.optimize()
assert problem.objective_scores_sum() == 0
def test_codon_optimize_match_usage_short_sequence():
numpy.random.seed(123)
protein = "DDDKKKKKK"
sequence = reverse_translate(protein)
harmonization = CodonOptimize(species="b_subtilis",
method="match_codon_usage")
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[harmonization],
logger=None,
)
assert problem.objective_scores_sum() < -5.5
problem.optimize()
assert -0.6 < problem.objective_scores_sum()
print(problem.objective_scores_sum())
assert problem.sequence == "GATGATGACAAGAAAAAGAAAAAAAAA"
def test_codon_optimize_match_usage_gfp_sequence():
sequence = ("ATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTG"
"GTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGCGCGGC"
"GAGGGCGAGGGCGATGCCACCAACGGCAAGCTGACCCTGAAGTTCATC")
spec = CodonOptimize(species="s_cerevisiae", method="match_codon_usage")
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[spec],
logger=None,
)
assert problem.objective_scores_sum() < -61
problem.optimize()
assert problem.objective_scores_sum() > -16
# Just for coverage, we run the compare_frequency function in text mode
spec = problem.objectives[0]
codons = spec.get_codons(problem)
print(spec.compare_frequencies(codons, text_mode=True))
def test_optimization_1():
company_ingen = CommercialDnaOffer(
name="Company InGen",
pricing=PerBasepairPricing(0.08),
sequence_constraints=[NoPatternConstraint(enzyme="AarI")],
)
company_delux = CommercialDnaOffer(
name="Company Delux",
pricing=PerBasepairPricing(0.66),
sequence_constraints=[],
)
assembly_station = DnaAssemblyStation(
name="Gibson Assembly Station",
assembly_method=GibsonAssemblyMethod(
overhang_selector=FixedSizeSegmentSelector(20),
min_segment_length=200,
max_segment_length=1200,
),
supplier=[company_ingen, company_delux],
coarse_grain=20,
# a_star_factor="auto",
)
sequence_path = os.path.join("tests", "data",
"test_optimization_sequence_1.fa")
sequence = load_record(sequence_path)
objective = OptimizeManufacturability(assembly_station)
problem = DnaOptimizationProblem(sequence=sequence, objectives=[objective])
quote = objective.get_quote(problem)
score = problem.objective_scores_sum()
assert -367 < score < -366
problem.randomization_threshold = 0
problem.max_random_iters = 5
problem.optimize()
score = problem.objective_scores_sum()
assert -244 < score < -243
