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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