Functions
def	sample_folding
	Methods. More...

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

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

tuple	scorefxn_high = get_fa_scorefxn()

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

tuple	mc = MonteCarlo(test_pose, scorefxn_low, kT)

tuple	trial = TrialMover(folding_mover, mc)

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

tuple	jd = PyJobDistributor(job_output, jobs, scorefxn_high)

list	scores = [0]

int	counter = 0

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

string	default = '../test/data/test_in.pdb'

string	help = 'the PDB file containing the protein to fold'

tuple	pose = Pose()

	fasta_filename = options.fasta_filename

tuple	f = open(fasta_filename, 'r')

tuple	sequence = f.readlines()

	pdb_filename = options.pdb_filename;

	long_frag_filename = options.long_frag_filename

tuple	long_frag_length = int(options.long_frag_length)

	short_frag_filename = options.short_frag_filename

tuple	short_frag_length = int(options.short_frag_length)

tuple	kT = float(options.kT)

tuple	long_inserts = int(options.long_inserts)

tuple	short_inserts = int(options.short_inserts)

tuple	cycles = int(options.cycles)

tuple	jobs = int(options.jobs)

	job_output = options.job_output

Function Documentation

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(), fmt.print(), basic::options::OptionKeys::relax::range.range, sum(), and basic::options::OptionKeys::in::file.xyz.

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 name, pyrosetta.distributed.io.pose_from_sequence, and basic::options::OptionKeys::relax::range.range.

Variable Documentation

int demo.D060_Folding.counter = 0

Referenced by copy_asymm_unit_to_other_subunits(), supercharge.design_supercharge(), main(), utility::graph::Graph.output_connectivity(), utility::graph::Digraph.output_connectivity(), sequence_tolerance_main(), Solver.Solve(), and numeric::UniformRotationSampler.UniformRotationSampler().

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

string demo.D060_Folding.default = '../test/data/test_in.pdb'

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

demo.D060_Folding.fasta_filename = options.fasta_filename

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

for each trajectory, try cycles number of applications

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

add any other moves you desire

string demo.D060_Folding.help = 'the PDB file containing the protein to fold'

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

Referenced by main(), and stepwise_monte_carlo().

demo.D060_Folding.job_output = options.job_output

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

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

demo.D060_Folding.long_frag_filename = options.long_frag_filename

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

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

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

tuple demo.D060_Folding.parser = optparse.OptionParser()

INTERPRETING RESULTS.

COMMANDLINE COMPATIBILITY

demo.D060_Folding.pdb_filename = options.pdb_filename;

tuple demo.D060_Folding.pose = Pose()

tuple demo.D060_Folding.scorefxn_high = get_fa_scorefxn()

Referenced by demo.D080_Loop_modeling.sample_single_loop_modeling().

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

AddPyMOLObserver(test_pose, True)

list demo.D060_Folding.scores = [0]

Referenced by add_rosetta_options_10(), calc_rmsf_and_avrg(), main(), and save_per_residue_scores().

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

demo.D060_Folding.short_frag_filename = options.short_frag_filename

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

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

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

Functions

Variables

Function Documentation

Variable Documentation