def __init__(self, cmd='molrep', **kwargs):
self.parameters = [
Switch(['-h', 'help'], ''),
Switch(['-i', 'interactive'], ''),
Option(['-f', 'hklin'], '', equate=False, filename=True),
Option(['-m', 'xyzin'], '', equate=False, filename=True),
Option(['-m2', 'xyzin2'], '', equate=False, filename=True),
Option(['-mx', 'fixed_xyzin'], '', equate=False, filename=True),
Option(['-s', 'seqin'], '', equate=False, filename=True),
Option(['-s2', 'seqin2'], '', equate=False, filename=True),
Option(['-k', 'keyin'], '', equate=False, filename=True),
Option(['-po', 'out_dir'], '', equate=False),
Option(['-ps', 'out_scr'], '', equate=False),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
def __init__(self, cmd='dssp', **kwargs):
self.parameters = [
Switch(['-v', 'verbose'],
'Verbose output'),
Option(['-i', 'input'],
"Input structure",
equate=False,
filename=True,
is_required=True),
Option(['-o', 'output'],
"Output DSSP file",
equate=False,
filename=True,
is_required=True),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
def __init__(self, cmd='TMscore', **kwargs):
self.parameters = [
Switch([
'-c', 'complex'
], 'Run TM-score to compare two complex structures with multiple chains'
),
Option(['-d', 'norm_scale'],
'TM-score normalized with an assigned scale d0',
equate=False),
Option(['-l', 'norm_length'],
'TM-score normalized by a specific length',
equate=False),
Argument(['model'],
"Input model structure",
filename=True,
is_required=True),
Argument(['native'],
"Input native structure",
filename=True,
is_required=True),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
def __init__(self, cmd='TMalign', **kwargs):
self.parameters = [
Argument(['chain1'],
'first PDB structure',
filename=True,
is_required=True),
Argument(['chain2'],
'second PDB structure',
filename=True,
is_required=True),
Option(['-i', 'aln_in'],
'an alignment specified in fasta file',
equate=False,
filename=True),
Option(['-I', 'aln_out'],
'stick the alignment to this file',
equate=False,
filename=True),
Option(
['-o', 'superposition'],
"output the superposition to 'TM.sup', 'TM.sup_all' and 'TM.sup_atm'",
equate=False,
filename=True),
Switch(
['-a', 'normalized'],
'TM-score normalized by the average length of two proteins'),
Option(['-L', 'assigned_length'],
'TM-score normalized by an assigned length (>L_min)',
equate=False),
Option(['-d', 'scale_factor'],
'TM-score scaled by an assigned d0',
equate=False),
Option(['-m', 'rotation_matrix'],
'output TM-align rotation matrix',
equate=False,
filename=True),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
def __init__(self, cmd='maxcluster', **kwargs):
if not self.options_ok(**kwargs):
msg = "Unknown combination: Please use one of the following:" \
" -e [file] PDB experiment" \
" -p [file] PDB prediction" \
" OR" \
" -l [file] File containing a list of PDB model fragments" \
" OR" \
" -e [file] PDB experiment" \
" -l [file] File containing a list of PDB model fragments"
raise RuntimeError(msg)
self.parameters = [
Option(['-e', 'pdb_experiment'],
'PDB experiment',
equate=False,
filename=True),
Option(['-p', 'pdb_prediction'],
'PDB prediction',
equate=False,
filename=True),
Option(['-l', 'pdb_list'],
'File containing a list of PDB model fragments',
equate=False,
filename=True),
# OPTIONS
Option(['-L', 'log_level'],
'Log level (default is 4 for single MaxSub, 1 for lists)',
equate=False),
Option(['-d', 'distance_cutoff'],
'The distance cut-off for search (default auto-calibrate)',
equate=False),
Option(
['-N', 'norm_length'],
'The normalisation length for TM score (default is length of experiment)',
equate=False),
Switch(['-rmsd', 'rmsd'], 'Perform only RMSD fit'),
Option(['-i', 'maxsubdom_iterations'],
'MaxSubDom iterations (default = 1)',
equate=False),
Switch(['-in', 'sequence_independent'],
'Sequence independant mode'),
# CLUSTERING OPTIONS
Option(['-C', 'cluster_method'], "Cluster method:"
" 0 - No clustering"
" 1 - Single linkage"
" 2 - Average linkage"
" 3 - Maximum linkage"
" 4 - Neighbour pairs (min size)"
" 5 - Neighbour pairs (absolute size)"
"(default = 5)",
equate=False),
Option(
['-T', 'init_cluster_threshold'],
'Initial clustering threshold (default RMSD = 4; MaxSub = 0.5)',
equate=False),
Option(
['-Tm', 'max_cluster_threshold'],
'Maximum clustering threshold (default RMSD = 8; MaxSub = 0.8)',
equate=False),
Option(
['-a', 'adj_cluster_threshold'],
'Clustering threshold adjustment (default RMSD = 0.2; MaxSub = 0.05)',
equate=False),
Option(['-is', 'init_cluster_size'],
'Initial cluster size (default = 50)',
equate=False),
Option(['-ms', 'min_cluster_size'],
'Minimum cluster size (default = 5)',
equate=False),
Option(['-s', 'score_threshold'],
'3D-jury score threshold (default = 0.2)',
equate=False),
Option(['-P', 'pair_threshold'],
'3D-jury pair threshold (default = 20)',
equate=False)
]
AbstractCommandline.__init__(self, cmd, **kwargs)
def __init__(self, cmd='theseus', **kwargs):
if 'pdb_files' in kwargs and (isinstance(kwargs['pdb_files'], list) or
isinstance(kwargs['pdb_files'], tuple)):
kwargs['pdb_files'] = " ".join(kwargs['pdb_files'])
self.parameters = [
Option(
['-a', 'atoms'], "atoms to include in superposition"
" 0 = alpha carbons and phosphorous atoms"
" 1 = backbone"
" 2 = all"
" 3 = alpha and beta carbons"
" 4 = all heavy atoms (all but hydrogens)"
" or"
" a colon-delimited string specifying the atom-types PDB-style"
"e.g., -a ' CA : N'"
"selects the alpha carbons and backone nitrogens",
equate=False),
Switch(['-f', 'first_model'],
"only read the first model of a multi-model PDB file"),
Option(['-i', 'niteration'],
"maximum iterations {200}",
equate=False),
Switch(['-l', 'least_square'],
"superimpose with conventional least squares method"),
Option(['-s', 'residues_incl'],
"residues to select (e.g. -s15-45:50-55) {all}",
equate=False),
Option(['-S', 'residues_excl'],
"residues to exclude (e.g. -S15-45:50-55) {none}",
equate=False),
Switch(['-v'
'ml_variance_weighting'],
"use ML variance weighting (no correlations)"),
# Input/output options
Switch(['--amber', 'amber'],
"for reading AMBER8 formatted PDB files"),
Option(
['-A', 'alignment'],
"sequence alignment file to use as a guide (CLUSTAL or A2M format)",
equate=False,
filename=True),
Switch(['-F', 'print_fasta'],
"print FASTA files of the sequences in PDB files and quit"),
Switch([
'-I', 'no_superposition'
], "just calculate statistics for input file (don't superposition)"
),
Option(
['-M', 'sequence_map'],
"file that maps sequences in the alignment file to PDB files",
equate=False,
filename=True),
Option(['-r', 'root_name'],
'root name for output files {theseus}',
equare=False),
# Principal components analysis
Switch(
['-C', 'covariance_matrix'],
"use covariance matrix for PCA (correlation matrix is default)"
),
Option(['-P', 'principal_components'],
"# of principal components to calculate {0}",
equate=False),
# Morphometrics
Switch(['-d', 'scale_factors'],
"calculate scale factors (for morphometrics)"),
Switch(['-q', 'rohlf_files'],
"read and write Rohlf TPS morphometric landmark files"),
ArgumentList(['pdb_files'],
'Input pdb files',
filename=True,
is_required=True),
]
AbstractCommandline.__init__(self, cmd, **kwargs)