def load_user_options(args, location):
assert(isinstance(location, Location))
#set enforce translation to the whole thing
constraints = []
objectives = []
if args.harmonized:
opt_mode = 'harmonized'
else:
opt_mode = 'best_codon'
objectives += [
CodonOptimize(species=args.species, location=location, mode=opt_mode)
]
constraints += [
EnforceTranslation(location=location)
]
if args.avoid_homopolymers:
constraints += [
AvoidPattern(HomopolymerPattern("A",args.avoid_homopolymers),location=location),
AvoidPattern(HomopolymerPattern("T",args.avoid_homopolymers),location=location),
AvoidPattern(HomopolymerPattern("G",args.avoid_homopolymers),location=location),
AvoidPattern(HomopolymerPattern("C",args.avoid_homopolymers),location=location)]
if args.avoid_hairpins:
constraints += [AvoidHairpins(location=location)]
if args.avoid_patterns:
constraints += [AvoidPattern(pattern,location=location) for pattern in args.avoid_patterns]
#NOTE! Printing this to a template is broken
if args.avoid_restriction_sites:
constraints += [AvoidPattern(EnzymeSitePattern(enzy),location=location) for enzy in args.avoid_restriction_sites]
if args.constrain_global_GC_content:
constraints += [EnforceGCContent(mini=args.global_GC_content_min, maxi=args.global_GC_content_max, location=location)]
if args.constrain_local_GC_content:
constraints += [EnforceGCContent(mini=args.local_GC_content_min, maxi=args.global_GC_content_max, window=args.local_GC_content_window, location=location)]
if args.constrain_terminal_GC_content:
constraints += [EnforceTerminalGCContent(mini=args.terminal_GC_content_min, maxi=args.terminal_GC_content_max, window_size=8, location=location)]
if args.constrain_CAI:
constraints += [ConstrainCAI(species=args.species, minimum=args.constrain_CAI_minimum, location=location)]
if args.optimize_dicodon_frequency:
objectives += [MaximizeDicodonAdaptiveIndex()]
if args.kmers:
objectives += [MinimizeKmerScore(k=args.kmers, boost=args.avoid_kmers_boost, location=location)]
if args.avoid_secondary_structure:
objectives += [MinimizeSecondaryStructure(max_energy=args.avoid_secondary_structure_max_e, location=location, boost=args.avoid_secondary_structure_boost)]
if args.avoid_initiator_secondary_structure:
objectives += [MinimizeSecondaryStructure(max_energy=args.avoid_initiator_secondary_structure_max_e, location=location, optimize_initiator=True, boost=args.avoid_initiator_secondary_structure_boost)]
return objectives, constraints
def optimize(self, codon_table):
self.optimize_frequent(codon_table)
# return
opt_codons = self.__vaccine_codons_gen.copy()
self.__vaccine_codons_gen.clear()
vac_strand = self.get_strand(opt_codons)
#vir_strand = self.get_strand(self.__virus_codons)
codon_table = pct.get_codons_table(codon_table)
problem = DnaOptimizationProblem(
sequence=vac_strand,
constraints=[
EnforceTranslation(genetic_table='Standard',
start_codon='ATG'),
EnforceGCContent(mini=0.54, maxi=0.9, window=120)
],
objectives=[
CodonOptimize(method="use_best_codon",
codon_usage_table=codon_table)
]
)
problem.resolve_constraints()
problem.optimize()
self.__vaccine_codons_gen = []
count = 1
vcodon = ""
for x in problem.sequence:
if count % 3 == 0:
vcodon += x
self.__vaccine_codons_gen.append(vcodon)
vcodon = ""
else:
vcodon += x
count += 1
return
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_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_as_hard_constraint():
numpy.random.seed(123)
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(2000, seed=123),
constraints=[
EnforceTranslation(location=Location(1000, 1300)),
CodonOptimize(location=Location(1000, 1300), species='e_coli')
]
)
assert not problem.all_constraints_pass()
problem.resolve_constraints()
assert problem.all_constraints_pass()
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_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_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_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_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"
random_protein_sequence,
reverse_translate,
CodonOptimize,
EnforceTranslation,
AvoidPattern,
EnforceGCContent,
)
protein = random_protein_sequence(1000, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[
EnforceTranslation(),
AvoidPattern("BsmBI_site"),
EnforceGCContent(mini=0.4, maxi=0.6, window=60),
],
objectives=[CodonOptimize(species="s_cerevisiae")],
)
print("\nBefore optimization:\n")
print(problem.constraints_text_summary())
print(problem.objectives_text_summary())
problem.resolve_constraints(final_check=True)
problem.optimize()
print("\nAfter optimization:\n")
print(problem.constraints_text_summary())
print(problem.objectives_text_summary())
"""Example of use of the AvoidPAttern specification"""
from dnachisel import (DnaOptimizationProblem, random_protein_sequence,
reverse_translate, CodonOptimize, EnforceTranslation,
AvoidPattern, EnforceGCContent)
protein = random_protein_sequence(1000, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[
EnforceTranslation(),
AvoidPattern("BsmBI_site"),
EnforceGCContent(mini=0.4, maxi=0.6, window=60)
],
objectives=[CodonOptimize(species='s_cerevisiae')])
print("\nBefore optimization:\n")
print(problem.constraints_text_summary())
print(problem.objectives_text_summary())
problem.resolve_constraints(final_check=True)
problem.optimize()
print("\nAfter optimization:\n")
print(problem.constraints_text_summary())
print(problem.objectives_text_summary())
"""Example of use of the AvoidPAttern specification"""
from dnachisel import (DnaOptimizationProblem, random_dna_sequence,
CodonOptimize, Location, EnforceTranslation)
problem = DnaOptimizationProblem(
sequence=random_dna_sequence(2000, seed=123),
constraints=[
EnforceTranslation(location=Location(1000, 1300)),
CodonOptimize(location=Location(1000, 1300), species='e_coli')
])
print("\nBefore resolution:\n")
print(problem.constraints_text_summary())
problem.resolve_constraints()
print("\nAfter resolution:\n")
print(problem.constraints_text_summary())
codon_table_11[k][k2] = RSCU_list[k2]
print("\nOptimizing codons for input gene list")
#Read gene fasta sequence and initiate optimizer
problem = DnaOptimizationProblem(
sequence=gene,
constraints=[
EnforceTranslation(),
AvoidPattern("BsmBI_site", "BamHI"),
EnforceTranslation(),
EnforceGCContent(mini=0.35, maxi=0.65, window=50), #TWIST: 25% and 65% GC
],
objectives=[CodonOptimize(codon_usage_table=codon_table_11)],
)
if taxid and not input_path:
print("\nOptimizing codons for taxonomic ID: " + taxid)
#Read gene fasta sequence and initiate optimizer
if not protein_flag:
problem = DnaOptimizationProblem(
sequence=gene,
constraints=[
#EnforceSequence(sequence = "ATG", location=(0, 2)),
AvoidChanges(location=(0, 2)),
AvoidPattern("BsmBI_site", "BamHI"),
EnforceTranslation(),
EnforceGCContent(mini=0.35, maxi=0.65, window=50), #TWIST: 25% and 65% GC
"""Example of use of the CodonOptimize specification."""
from dnachisel import (DnaOptimizationProblem, random_protein_sequence,
CodonOptimize, reverse_translate, EnforceTranslation)
protein = random_protein_sequence(3000, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(sequence=sequence,
constraints=[EnforceTranslation()],
objectives=[CodonOptimize('e_coli')])
print("\nBefore optimization:\n")
print(problem.objectives_text_summary())
problem.optimize()
print("\nAfter optimization:\n")
print(problem.objectives_text_summary())
CDS_constraints = []
for (start, end, strand) in CDS_list:
if strand == 1:
promoter_region = (start - 30, start - 1)
else:
promoter_region = (end + 1, end + 30)
CDS_constraints += [
AvoidChanges(promoter_region),
EnforceTranslation((start, end, strand)),
]
# DEFINE OBJECTIVES
objectives = [EnforceGCContent(0.51, boost=10000)] + [
CodonOptimize("e_coli", location=(start, end, strand))
for (start, end, strand) in CDS_list
]
# DEFINE AND SOLVE THE PROBLEM
problem = DnaOptimizationProblem(
sequence=record,
constraints=dna_provider_constraints + CDS_constraints,
objectives=objectives,
)
print("\n\n=== Initial Status ===")
print(problem.constraints_text_summary(failed_only=True))
print(problem.objectives_text_summary())
"""Example of use of the AvoidPAttern specification"""
from dnachisel import (DnaOptimizationProblem, random_protein_sequence,
reverse_translate, CodonOptimize, EnforceTranslation,
AvoidPattern, EnforceGCContent)
protein = random_protein_sequence(1000, seed=123)
sequence = reverse_translate(protein)
problem = DnaOptimizationProblem(
sequence=sequence,
constraints=[
EnforceTranslation(),
AvoidPattern(enzyme="BsmBI"),
EnforceGCContent(mini=0.4, maxi=0.6, window=60)
],
objectives=[
CodonOptimize(species='s_cerevisiae')
]
)
print ("\nBefore optimization:\n")
print (problem.constraints_text_summary())
print (problem.objectives_text_summary())
problem.resolve_constraints(final_check=True)
problem.optimize()
print ("\nAfter optimization:\n")
print (problem.constraints_text_summary())
print (problem.objectives_text_summary())
def domesticate(
self,
dna_sequence=None,
protein_sequence=None,
is_cds="default",
codon_optimization=None,
extra_constraints=(),
extra_objectives=(),
final_record_target=None,
edit=False,
barcode="",
barcode_spacer="AA",
report_target=None,
):
"""Domesticate a sequence.
Parameters
----------
dna_sequence
The DNA sequence string to domesticate.
protein_sequence
Amino-acid sequence of the protein, which will be converted into
a DNA sequence string.
is_cds
If True, sequence edits are restricted to synonymous mutations.
codon_optimization
Either None for no codon optimization or the name of an organism
supported by DnaChisel.
extra_constraints
List of extra constraints to apply to the domesticated sequences.
Each constraint is either a DnaChisel constraint or a function
(dna_sequence => DnaChisel constraint).
extra_objectives
List of extra optimization objectives to apply to the domesticated
sequences. Each objective is either a DnaChisel constraint or a
function (dna_sequence => DnaChisel constraint).
final_record_target
Path to the file where to write the final genbank.
edit
Turn to True to allow sequence edits (if it is false and no all
constraints are originally satisfied, a failed domestication result
(i.e. with attribute ``success`` set to False) will be returned.
report_target
Target for the sequence optimization report (a folder path, or a zip
path).
barcode
A sequence of DNA that will be added to the left of the sequence once
the domestication is done.
barcode_spacer
Nucleotides to be added between the barcode and the enzyme (optional,
the idea here is that they will make sure to avoid the creation of
unwanted cutting sites).
Returns
-------
final_record, edits_record, report_data, success, msg
"""
if is_cds == "default":
is_cds = self.cds_by_default
if isinstance(dna_sequence, SeqRecord):
problem = DnaOptimizationProblem.from_record(dna_sequence)
for spec in problem.constraints + problem.objectives:
spec.location += len(self.left_flank)
extra_constraints = list(extra_constraints) + problem.constraints
extra_objectives = list(extra_constraints) + problem.objectives
if protein_sequence is not None:
is_cds = True
dna_sequence = reverse_translate(protein_sequence)
constraints = [
c(dna_sequence) if hasattr(c, "__call__") else c
for c in list(extra_constraints) + self.constraints
]
location = Location(len(self.left_flank),
len(self.left_flank) + len(dna_sequence))
if is_cds:
constraints.append(EnforceTranslation(location=location))
objectives = [
o(dna_sequence) if hasattr(o, "__call__") else o
for o in list(extra_objectives) + self.objectives
]
if codon_optimization:
objectives.append(
CodonOptimize(species=codon_optimization, location=location))
if self.minimize_edits:
objectives.append(AvoidChanges())
extended_sequence = self.left_flank + dna_sequence + self.right_flank
if (not is_cds) and (not edit):
constraints.append(AvoidChanges())
problem = DnaOptimizationProblem(
extended_sequence,
constraints=constraints,
objectives=objectives,
logger=self.logger,
)
all_constraints_pass = problem.all_constraints_pass()
no_objectives = (len(problem.objectives) - self.minimize_edits) == 0
report_data = None
optimization_successful = True
message = ""
# print (all_constraints_pass, no_objectives)
if not (all_constraints_pass and no_objectives):
problem.n_mutations = self.simultaneous_mutations
if report_target is not None:
(success, message, report_data) = problem.optimize_with_report(
target=report_target, project_name=self.name)
optimization_successful = success
else:
report_data = None
try:
problem.resolve_constraints()
problem.optimize()
except Exception as err:
message = str(err)
optimization_successful = False
report_data = None
final_record = problem.to_record(
with_original_features=True,
with_original_spec_features=False,
with_constraints=False,
with_objectives=False,
)
edits_record = problem.to_record(
with_original_features=True,
with_original_spec_features=False,
with_constraints=False,
with_objectives=False,
with_sequence_edits=True,
)
if final_record_target is not None:
SeqIO.write(final_record, final_record_target, "genbank")
return DomesticationResult(
problem.sequence_before,
final_record,
edits_record,
report_data,
optimization_successful,
message,
)
