Hi Jing,

it depends what exactly  statistics you want to get. Running

phenix.model_statistics model.pdb

will likely get you most you want to see, a sample output looks like:

Deviations from Ideal Values.
  Bond      :  0.008   0.054    992
  Angle     :  1.071   6.317   1346
  Chirality :  0.063   0.191    143
  Planarity :  0.007   0.050    177
  Dihedral  : 27.365 179.733    369
  Min Nonbonded Distance : 2.116

Molprobity Statistics.
  All-atom Clashscore : 2.62
  Ramachandran Plot:
    Outliers :  0.81 %
    Allowed  :  6.50 %
    Favored  : 92.68 %
  Rotamer Outliers :  2.91 %
  Cbeta Deviations :  0.00 %
  Peptide Plane:
    Cis-proline     : 0.00 %
    Cis-general     : 0.00 %
    Twisted Proline : 0.00 %
    Twisted General : 0.00 %

Rama-Z (Ramachandran plot Z-score):
Interpretation: bad |Rama-Z| > 3; suspicious 2 < |Rama-Z| < 3; good |Rama-Z| < 2.
Scores for whole/helix/sheet/loop are scaled independently;
therefore, the values are not related in a simple manner.
  whole: -1.36 (0.65), residues: 123
  helix: -1.04 (0.99), residues: 10
  sheet:  0.56 (1.03), residues: 21
  loop : -1.34 (0.57), residues: 92

In expect way you can put together a Python script and that would loop over models, get an object-container from calling model_statistics and programmatically parse that object to extract information you need.

Pavel

On 5/15/20 11:54, Jing Liu wrote:
Hi,

I had generate a batch of pdb models (40-100) in rosetta and am wondering whether there is a way to evaluate these models in batch with Molprobity. For example, can I use phenix command line to do it and how? Or should I download the Molprobity and do it on my local computer? Really appreciate if anyone can share their experience.

Jing Liu, PhD
Ted Jardetzky lab
Department of Structural Biology
Stanford University 

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