pdb_utils¶
Functions to manipulate PDB files.
- dms_variants.pdb_utils.reassign_b_factor(input_pdbfile, output_pdbfile, df, metric_col, *, site_col='site', chain_col='chain', missing_metric=0, model_index=0)[source]¶
Reassign B factors in PDB file to some other metric.
B-factor re-assignment is useful because PDB images can be colored by B factor using programs such as
pymolusing commands like:show surface, RBD; spectrum b, white red, RBD, minimum=0, maximum=1
- Parameters:
input_pdbfile (str) – Path to input PDB file.
output_pdbfile (str) – Name of created output PDB file with re-assigned B factors.
df (pandas.DataFrame) – Data frame with metric used to re-assign B factor.
metric_col (str) – Name of column in df that has the numerical metric that the B factor is re-assigned to.
site_col (str) – Name of column in df with site numbers, which should map numbers used in PDB.
chain_col (str) – Name of column in df with chain labels.
missing_metric (float or dict) – How do we handl sites that are missing in df? If a float, reassign B factors for all missing sites to this value. If a dict, should be keyed by chain and assign all missing sites in each chain to indicated value.
model_index (int) – Which model in the PDB to use. If a X-ray structure, there is probably just one model so you can use default of 0.
- Return type:
None
Example
Create data frame df that assigns metric to two sites in chain E:
>>> df = pd.DataFrame({'chain': ['E', 'E'], ... 'site': [333, 334], ... 'metric': [0.5, 1.2]})
Create dict missing_metric that assigns -1 to sites with missing metrics in chain E, and 0 to sites in other chains:
>>> missing_metric = collections.defaultdict(lambda: 0) >>> missing_metric['E'] = -1
Download PDB, do the re-assignment of B factors, read the lines from the resulting re-assigned PDB:
>>> pdb_url = 'https://files.rcsb.org/download/6M0J.pdb' >>> r = requests.get(pdb_url) >>> with tempfile.TemporaryDirectory() as tmpdir: ... original_pdbfile = os.path.join(tmpdir, 'original.pdb') ... with open(original_pdbfile, 'wb') as f: ... _ = f.write(r.content) ... reassigned_pdbfile = os.path.join(tmpdir, 'reassigned.pdb') ... reassign_b_factor(input_pdbfile=original_pdbfile, ... output_pdbfile=reassigned_pdbfile, ... df=df, ... metric_col='metric', ... missing_metric=missing_metric) ... pdb_text = open(reassigned_pdbfile).readlines()
Now spot check some key lines in the output PDB. Chain A has all sites with B factors (last entry) re-assigned to 0:
>>> print(pdb_text[0].strip()) ATOM 1 N SER A 19 -31.455 49.474 2.505 1.00 0.00 N
Chain E has sites 333 and 334 with B-factors assigned to values in df, and other sites (such as 335) assigned to -1:
>>> print('\n'.join(line.strip() for line in pdb_text[5010: 5025])) ATOM 5010 O THR E 333 -34.954 13.568 46.370 1.00 0.50 O ATOM 5011 CB THR E 333 -33.695 14.409 48.627 1.00 0.50 C ATOM 5012 OG1 THR E 333 -34.797 14.149 49.507 1.00 0.50 O ATOM 5013 CG2 THR E 333 -32.495 14.879 49.438 1.00 0.50 C ATOM 5014 N ASN E 334 -35.532 15.604 45.605 1.00 1.20 N ATOM 5015 CA ASN E 334 -36.287 15.087 44.474 1.00 1.20 C ATOM 5016 C ASN E 334 -35.475 15.204 43.182 1.00 1.20 C ATOM 5017 O ASN E 334 -34.533 15.994 43.076 1.00 1.20 O ATOM 5018 CB ASN E 334 -37.622 15.823 44.337 1.00 1.20 C ATOM 5019 CG ASN E 334 -38.660 15.006 43.586 1.00 1.20 C ATOM 5020 OD1 ASN E 334 -38.568 13.776 43.514 1.00 1.20 O ATOM 5021 ND2 ASN E 334 -39.649 15.686 43.016 1.00 1.20 N ATOM 5022 N LEU E 335 -35.849 14.391 42.194 1.00 -1.00 N ATOM 5023 CA LEU E 335 -35.084 14.305 40.955 1.00 -1.00 C ATOM 5024 C LEU E 335 -35.466 15.426 39.992 1.00 -1.00 C
