def __main__():
parser = make_parser()
args = parser.parse_args()
LENGTH_OF_SMALLEST = int(args.bp)
FNAME = str(args.plyfile)
SPACERS = int(args.spacers)
overhangs = None
overhangfilename = args.overhangfile
if overhangfilename is not None:
overhangs = read_overhang_file(overhangfilename)
#breakpoint()
segs_list = segment_maker.get_segments(FNAME,
LENGTH_OF_SMALLEST,
SPACERS,
nicks=args.nicks,
overhangs=overhangs)
#now we apply the sequence optimizer.
sequence_optimizer.optimize_sequence(segs_list)
model = mrdna.SegmentModel(
segs_list,
local_twist=True,
dimensions=(5000, 5000, 5000),
)
#model.set_sequence(m13seq(),force=False)
#NUPACK add sequence here!
prefix = "DNA"
run_args = dict(
model=model,
output_name=prefix,
job_id="job-" + prefix,
directory=args.directory,
gpu=args.gpu,
minimization_output_period=int(args.output_period),
coarse_local_twist=args.coarse_local_twist,
fix_linking_number=args.fix_linking_number,
coarse_output_period=int(args.output_period),
fine_output_period=int(args.output_period),
minimization_steps=0, # int(args.minimization_steps),
coarse_steps=int(args.coarse_steps),
fine_steps=int(args.fine_steps),
backbone_scale=args.backbone_scale,
oxdna_steps=args.oxdna_steps,
oxdna_output_period=args.oxdna_output_period)
export_sequences(model, args.seqfile)
simulate(**run_args)
def __main__():
parser = make_parser()
args = parser.parse_args()
LENGTH_OF_SMALLEST = int(args.bp)
FNAME = str(args.plyfile)
SPACERS = int(args.spacers)
segs_list = segment_maker.get_segments(FNAME, LENGTH_OF_SMALLEST, SPACERS)
model = mrdna.SegmentModel(
segs_list,
local_twist=True,
dimensions=(5000, 5000, 5000),
)
#apply sequence here
model.set_sequence(m13seq(), force=False)
prefix = "DNA"
run_args = dict(
model=model,
output_name=prefix,
job_id="job-" + prefix,
directory=args.directory,
gpu=args.gpu,
minimization_output_period=int(args.output_period),
coarse_local_twist=args.coarse_local_twist,
fix_linking_number=args.fix_linking_number,
coarse_output_period=int(args.output_period),
fine_output_period=int(args.output_period),
minimization_steps=0, # int(args.minimization_steps),
coarse_steps=int(args.coarse_steps),
fine_steps=int(args.fine_steps),
backbone_scale=args.backbone_scale,
oxdna_steps=args.oxdna_steps,
oxdna_output_period=args.oxdna_output_period)
export_sequences(model, args.seqfile)
simulate(**run_args)
d1_stem.dna.sequence = rev_comp("GGAAGGAGGAGAGGAGAATA"[d1_toehold_length:])
else:
d1_stem.dna.sequence = rev_comp("GGAAGGAGGAGAGGAGAATA")
d1_loop.dna.sequence = "CATCTA"
d5.dna.sequence = "CTTCCTCACAATCAAAATTTACCTAAAA"
d5_overhang.dna.sequence = "T"*d1_ssDNA
if d1_toehold_length != 0:
d5_toehold.dna.sequence = rev_comp(d1_toehold.dna.sequence)
d5_stem.dna.sequence = rev_comp(d1_stem.dna.sequence)
model = mrdna.SegmentModel(
segs_list,
local_twist = True,
dimensions=(5000,5000,5000),
)
#model.set_sequence('T'*100000)
prefix = "DNA"
run_args = dict(
model = model,
output_name = prefix,
job_id = "job-" + prefix,
directory = args.directory,
gpu = args.gpu,
minimization_output_period = int(args.output_period),
coarse_local_twist = args.coarse_local_twist,