conkit.plot.modelvalidation module¶
A module to produce a model validation plot
It uses one external program:
map_align for contact map alignment
* This program needs to be installed separately from https://github.com/sokrypton/map_align*
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class
ModelValidationFigure
(model, prediction, sequence, dssp, map_align_exe=None, dist_bins=None, l_factor=0.5, **kwargs)[source]¶ Bases:
conkit.plot.figure.Figure
A Figure object specifc for a model validation. This figure represents the proabbility that each given residue in the model is involved in a model error. This is donw by feeding a trained classfier the differences observed between the predicted distogram and the observed inter-residue contacts and distances at the PDB model.
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dssp
¶ The DSSP output for the PDB model that will be validated
Type: Bio.PDB.DSSP.DSSP
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dist_bins
¶ A list of tuples with the boundaries of the distance bins to use in the calculation [default: CASP2 bins]
Type: list, tuple
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l_factor
¶ The L/N factor used to filter the contacts before finding the False Negatives [default: 0.5]
Type: float
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absent_residues
¶ The residues not observed in the model that will be validated (only if in PDB format)
Type: set
Examples
>>> from Bio.PDB import PDBParser >>> from Bio.PDB.DSSP import DSSP >>> p = PDBParser() >>> structure = p.get_structure('TOXD', 'toxd/toxd.pdb')[0] >>> dssp = DSSP(structure, 'toxd/toxd.pdb', dssp='mkdssp', acc_array='Wilke') >>> import conkit >>> sequence = conkit.io.read('toxd/toxd.fasta', 'fasta').top >>> model = conkit.io.read('toxd/toxd.pdb', 'pdb').top_map >>> prediction = conkit.io.read('toxd/toxd.npz', 'rosettanpz').top_map >>> conkit.plot.ModelValidationFigure(model, prediction, sequence, dssp)
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dist_bins
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model
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prediction
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sequence
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