Model.assess_normalized_dope() — assess a model with the normalized DOPE method

assess_normalized_dope()

Output:

Z-score

This command assesses the quality of the model using the normalized DOPE method. This is a Z-score; positive scores are likely to be poor models, while scores lower than -1 or so are likely to be native-like.

The normalized DOPE score is derived from the statistics of raw DOPE scores^6.4. See Selection.assess_dope() for more information on these raw scores.

When using AutoModel or LoopModel, automatic normalized DOPE assessment of each model can be requested by adding assess.normalized_dope to AutoModel.assess_methods or LoopModel.loop.assess_methods respectively.

Example: examples/assessment/assess_normalized_dope.py

# Example for: Model.assess_normalized_dope()

from modeller import *
from modeller.scripts import complete_pdb

env = Environ()
env.libs.topology.read(file='$(LIB)/top_heav.lib')
env.libs.parameters.read(file='$(LIB)/par.lib')

# Read a model previously generated by Modeller's AutoModel class
mdl = complete_pdb(env, '../atom_files/1fdx.B99990001.pdb')

zscore = mdl.assess_normalized_dope()