You are reading the documentation for version 3.2 of ProMod3. You may also want to read the documentation for: 1.3 2.0 2.1 3.0 3.1 3.3

Model Checking¶

This chapter describes additional functionality to check models. Some of this functionality is used within the modelling pipeline.

Detecting Ring Punches¶

promod3.modelling.GetRings(ent)¶

Get rings for a protein structure. A ring is only added if all ring-atoms exist or if it is a proline and three of the atoms exist (center and radii are estimated then).

Parameters:	ent (`EntityHandle` or `EntityView`) – Structure for which to detect rings.
Returns:	`list` of rings to perform ring checks. Each ring is a named tuple with: center (`Vec3`), plane (`Plane`), radius (`float`), residue (`ResidueHandle`).

promod3.modelling.GetRingPunches(rings, ent)¶

Get list of residues with rings that are punched by the given structure.

Parameters:	rings – List of rings as provided by `GetRings()`. ent (`EntityHandle` or `EntityView`) – Structure for which to detect punches.
Returns:	`list` of residues (`ResidueHandle`) which have a punched ring.

promod3.modelling.HasRingPunches(rings, ent)¶

Check if any ring is punched by the given structure. This check is faster than using GetRingPunches().

Parameters:	rings – List of rings as provided by `GetRings()`. ent (`EntityHandle` or `EntityView`) – Structure for which to detect punches.
Returns:	True, iff any ring is punched
Return type:	`bool`

promod3.modelling.FilterCandidates(candidates, model, gap, orig_indices=[])¶

Remove loop candidates if they cause ring punches.

Parameters:

candidates (LoopCandidates) – Loop candidates meant to fill gap within model. Offending candidates are removed from this list.
model (EntityHandle) – Model for which loop is to be filled.
gap (StructuralGap.) – Gap for which loop is to be filled.
orig_indices (list) – Mapping to old indexing of candidates. If given, it must have as many elements as candidates.

promod3.modelling.FilterCandidatesWithSC(candidates, model, gap, orig_indices=[])¶: Remove loop candidates if they (with sidechain) cause ring punches. See FilterCandidates().

Model Checking With MolProbity¶

promod3.modelling.RunMolProbity(target_pdb, molprobity_bin=None)¶

Run MolProbity from Phenix on a given PDB file.

MolProbity score computation: (formula from molprobity source code)

clashscore = result["Clashscore"]
rota_out = result["Rotamer outliers"]
rama_iffy = 100. - result["Ramachandran favored"]
mpscore = (( 0.426 * math.log(1 + clashscore) ) +
         ( 0.33 * math.log(1 + max(0, rota_out - 1)) ) +
         ( 0.25 * math.log(1 + max(0, rama_iffy - 2)) )) + 0.5

Parameters:	target_pdb (`str`) – Path to PDB file on which to do analysis. molprobity_bin (`str`) – Path to `phenix.molprobity` executable. If None, it searches for it in the `PATH` or (if set) in the env. variable `MOLPROBITY_EXECUTABLE`. The function was tested with `Phenix 1.9-1692` and with `MolProbity 4.2` which also includes it.
Returns:	Dictionary with scores produced by MolProbity. Entries: “Ramachandran outliers” (percentage [0,100] as `float`) “Ramachandran favored” (percentage [0,100] as `float`) “Rotamer outliers” (percentage [0,100] as `float`) “C-beta deviations” (`int`) “Clashscore” (`float`) “MolProbity score” (`float`) “RMS(bonds)” (`float`) “RMS(angles)” (`float`)
Return type:	`dict`
Raises:	`FileNotFound` if the “phenix.molprobity” executable is not found.