Functions
def	sample_folding (sequence, long_frag_filename, long_frag_length, short_frag_filename, short_frag_length, kT=3.0, long_inserts=1, short_inserts=3, cycles=40, jobs=1, job_output='fold_output')
	Methods. More...

def	guess_disulfides (pose, cutoff=6.0)
	if you create a custom protocol, you may have additional variables to reset, such as kT More...

Variables
	extra_options

	scorefxn_low = create_score_function('score3')
	AddPyMOLObserver(test_pose, True) More...

	scorefxn_high = get_fa_scorefxn()

	folding_mover = protocols.moves.SequenceMover()
	add any other moves you desire More...

	mc = MonteCarlo(test_pose, scorefxn_low, kT)

	trial = TrialMover(folding_mover, mc)

	folding = protocols.moves.RepeatMover(trial, cycles)
	for each trajectory, try cycles number of applications More...

	jd = PyJobDistributor(job_output, jobs, scorefxn_high)

list	scores = [0]*(jobs + 1)

int	counter = 0

	parser = optparse.OptionParser()
	INTERPRETING RESULTS. More...

	dest

	default

	help

	options

	args

	pose = Pose()

	fasta_filename = options.fasta_filename

	f = open(fasta_filename, 'r')

	sequence = f.readlines()

	pdb_filename = options.pdb_filename;

	long_frag_filename = options.long_frag_filename

	long_frag_length = int(options.long_frag_length)

	short_frag_filename = options.short_frag_filename

	short_frag_length = int(options.short_frag_length)

	kT = float(options.kT)

	long_inserts = int(options.long_inserts)

	short_inserts = int(options.short_inserts)

	cycles = int(options.cycles)

	jobs = int(options.jobs)

	job_output = options.job_output

Function Documentation

◆ guess_disulfides()

def demo.D060_Folding.guess_disulfides	(	pose,
		cutoff = `6.0`
	)

if you create a custom protocol, you may have additional variables to reset, such as kT

if you create a custom protocol, this section will most likely change, many protocols exist as single Movers or can be chained together in a sequence (see above) so you need only apply the final Mover b. apply the refinement protocol

c. export the lowest scoring decoy structure for this trajectory -recover the lowest scoring decoy structure

if you want to see the decoy scores, uncomment the line below scorefxn_high( test_pose )

A quick method for probing a protein for cysteine residues close to each
    other (within  <cutoff>  )

References ObjexxFCL.len(), print(), range, sum(), and basic::options::OptionKeys::in::file.xyz.

◆ sample_folding()

def demo.D060_Folding.sample_folding	(	sequence,
		long_frag_filename,
		long_frag_length,
		short_frag_filename,
		short_frag_length,
		kT = `3.0`,
		long_inserts = `1`,
		short_inserts = `3`,
		cycles = `40`,
		jobs = `1`,
		job_output = `'fold_output'`
	)

Methods.

Performs
    exporting structures to a PyMOL instance
    Output structures are named  <job_output>_(job#).pdb

References pyrosetta.distributed.io.pose_from_sequence, and range.

Variable Documentation

◆ args

demo.D060_Folding.args

◆ counter

int demo.D060_Folding.counter = 0

Referenced by align_backbone_by_chunk(), CustomMover.apply(), JDtestmover.apply(), atom_line(), detail.compile_to_parts(), MainWindow.compute_com_vect(), copy_asymm_unit_to_other_subunits(), detail.count_parts(), do_oom(), OutputManager.dump_pose(), ZincSiteEvaluator.evaluate_mutations_native(), ga_main(), get_frag_rmsd(), apps::pilot::AlignChunkMover.get_local_sequence_mapping(), get_start_index(), idealize_tf_pose(), InstantiateModulesBenchmark.instantiate_filters(), InstantiateModulesBenchmark.instantiate_movers(), InstantiateModulesBenchmark.instantiate_packer_palettes(), InstantiateModulesBenchmark.instantiate_res_selectors(), InstantiateModulesBenchmark.instantiate_reslvl_taskops(), InstantiateModulesBenchmark.instantiate_simple_metrics(), InstantiateModulesBenchmark.instantiate_taskops(), jitterloop(), main(), MSA_design(), multivec2sf(), utility::graph::Digraph.output_connectivity(), utility::graph::Graph.output_connectivity(), perturb_bb_and_relax(), phosphate_stats(), pose_from_posedata(), processChildrenIntensification(), read_pdbs(), rotateUntilCys(), FragMonteCarlo.run(), sequence_tolerance_main(), sequence_tolerance_mf_main(), set_pose_conformation(), shift_center_and_PCA(), CenRotDockingMover.slide_into_contact(), Solver.Solve(), swapalignmentvector(), test_membrane_chain(), trim_and_add_jump_data(), numeric::UniformRotationSampler.UniformRotationSampler(), and NativeEnsemble.write_stats_to_db().

◆ cycles

demo.D060_Folding.cycles = int(options.cycles)

◆ default

demo.D060_Folding.default

◆ dest

demo.D060_Folding.dest

◆ extra_options

demo.D060_Folding.extra_options

◆ f

demo.D060_Folding.f = open(fasta_filename, 'r')

◆ fasta_filename

demo.D060_Folding.fasta_filename = options.fasta_filename

◆ folding

demo.D060_Folding.folding = protocols.moves.RepeatMover(trial, cycles)

for each trajectory, try cycles number of applications

◆ folding_mover

demo.D060_Folding.folding_mover = protocols.moves.SequenceMover()

add any other moves you desire

◆ help

demo.D060_Folding.help

◆ jd

demo.D060_Folding.jd = PyJobDistributor(job_output, jobs, scorefxn_high)

Referenced by AssignClusterToolMover.apply(), TestJD2Mover.apply(), ui::task::Node.data_download_finished(), ui::task::Node.data_is_fresh(), ui::task::Updater.listen_to_updates(), main(), ui::task::TaskCancelDialog.run(), ui::task::TaskSubmit.s_on_queues_finished(), stepwise_monte_carlo(), ui::task::TaskSyncer_TaskStrategy.task_data_upload(), and ui::task::TaskSyncer_TaskStrategy.task_queuing().

◆ job_output

demo.D060_Folding.job_output = options.job_output

◆ jobs

demo.D060_Folding.jobs = int(options.jobs)

Referenced by InterfaceDDGBindJobInputter.fill_jobs(), protocols::jd2::RECONMPIJobDistributor.go(), protocols::jd2::RECONMPIJobDistributor.go_hardcoded(), main(), and NativeEnsemble.write_schema_to_db().

◆ kT

demo.D060_Folding.kT = float(options.kT)

◆ long_frag_filename

demo.D060_Folding.long_frag_filename = options.long_frag_filename

◆ long_frag_length

demo.D060_Folding.long_frag_length = int(options.long_frag_length)

◆ long_inserts

demo.D060_Folding.long_inserts = int(options.long_inserts)

◆ mc

demo.D060_Folding.mc = MonteCarlo(test_pose, scorefxn_low, kT)

◆ options

demo.D060_Folding.options

◆ parser

demo.D060_Folding.parser = optparse.OptionParser()

INTERPRETING RESULTS.

COMMANDLINE COMPATIBILITY

◆ pdb_filename

demo.D060_Folding.pdb_filename = options.pdb_filename;

◆ pose

demo.D060_Folding.pose = Pose()

◆ scorefxn_high

demo.D060_Folding.scorefxn_high = get_fa_scorefxn()

Referenced by demo.D080_Loop_modeling.sample_single_loop_modeling().

◆ scorefxn_low

demo.D060_Folding.scorefxn_low = create_score_function('score3')

AddPyMOLObserver(test_pose, True)

◆ scores

list demo.D060_Folding.scores = [0]*(jobs + 1)

◆ sequence

string demo.D060_Folding.sequence = f.readlines()

◆ short_frag_filename

demo.D060_Folding.short_frag_filename = options.short_frag_filename

◆ short_frag_length

demo.D060_Folding.short_frag_length = int(options.short_frag_length)

◆ short_inserts

demo.D060_Folding.short_inserts = int(options.short_inserts)

◆ trial

demo.D060_Folding.trial = TrialMover(folding_mover, mc)

Functions

Variables