Python-based
Hierarchical
ENvironment
for
Integrated
Xtallography
Superpose moving model on fixed model. Optionally morph the moving model to be similar to the fixed model and trim it back to the parts that match.
The superpose_models tool can use either sequence-based alignment with least-squares fitting or SSM-based (secondary-structure-matching) superposition.
The LSQ fitting method consists of least-squares superposition of two selected parts from two pdb files. If no selections is provided for fixed and moving models the whole content of both input PDB files is used for superposition. If the number of atoms in fixed and moving models is different and the models contain amino-acid residues then the sequence alignment is performed and the matching residues (CA atoms by default, can be changed by the user) are used for superposition. Note that selected (and/or matching) atoms are the atoms used to find the superposition operators while these operators are applied to the whole moving structure.
SSM matching in phenix.superpose_models consists of identifying secondary structure in the two models, indexing all pairs of secondary structure elements, and finding sets of matching pairs in the two structures. Normally the basic unit of a match is a set of three pairs of secondary structure elements (they can be overlapping) with the same spatial arrangement in the two structures. Such a match is called a triple in superpose_models.
A matching triples yields a unique orientation of the moving model. Matching triples are grouped into sets in which all the triples yield nearly the same orientation. Then the transformations suggested by the largest groups are each tested by using them to superimpose the moving model on the fixed model and noting the number of residues that superimpose.
LSQ fitting. This is the default superposition method.
SSM matching. This is an alternative to LSQ fitting. It can be useful if your models do not have similar sequences
Morphing. The best-fitting superimposed moving models can be morphed by calculation of a real-space distortion field that changes over a typical distance of about 10 A (set by the parameter distortion_field_length). This is a way to superpose one model on another and then make them as similar as possible by applying a smooth distortion to the moving model
Trimming. The superposed (and optionally morphed) moving model can be trimmed to remove segments that are very different from the fixed model.
SSM match to map. You can supply a map and specify ssm_match_to_map=True. The tool will search for helices and strands in the map and generate a target helices_strands model. Then a brute force SSM matching of your moving model to the helices_strands model is carried out and matches are scored with map-model correlation. This option can also be carried out with the dock_in_map tool. Note this option does not support trimming or morphing.
Brute force SSM. You can supply a target model that has helices and strands. Helices and strands in your moving model will be matched to those in the target model to superpose it. You can also supply a map_file and specify score_superpose_brute_force_with_cc if you wish. Note this option does not support trimming or morphing.
Running superpose_models is easy. From the command-line you can type:
phenix.superpose_models fixed_model=fixed_model.pdb \ moving_model=moving_model.pdb morph=true trim=true
This will superimpose moving_model.pdb on fixed_model.pdb, morph it (gradual adjustment of atomic positions with location along the chain and in space), and trim moving_model back to the region where they are similar.
To superpose a model on a map you can say:
phenix.superpose_models map_file=map.ccp4 moving_model=moving_model.pdb
This will find helices and strands in map.ccp4 and superpose moving_model.pdb on those secondary structure elements.