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Cannot get the config names for the dataset.
Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/jglaser/pdb_protein_ligand_complexes/pdb_protein_ligand_complexes.py or any data file in the same directory. Couldn't find 'jglaser/pdb_protein_ligand_complexes' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in jglaser/pdb_protein_ligand_complexes. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/jglaser/pdb_protein_ligand_complexes/pdb_protein_ligand_complexes.py or any data file in the same directory. Couldn't find 'jglaser/pdb_protein_ligand_complexes' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in jglaser/pdb_protein_ligand_complexes.
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How to use the data sets
This dataset contains about 36,000 unique pairs of protein sequences and ligand SMILES, and the coordinates of their complexes from the PDB.
SMILES are assumed to be tokenized by the regex from P. Schwaller.
Ligand selection criteria
Only ligands
- that have at least 3 atoms,
- a molecular weight >= 100 Da,
- and which are not among the 280 most common ligands in the PDB (this includes common additives like PEG, ADP, ..)
are considered.
Use the already preprocessed data
Load a test/train split using
import pandas as pd
train = pd.read_pickle('data/pdb_train.p')
test = pd.read_pickle('data/pdb_test.p')
Receptor features contain protein frames and side chain angles in OpenFold/AlphaFold format.
Ligand tokens which do not correspond to atoms have nan
as their coordinates.
Documentation by example:
>>> import pandas as pd
>>> test = pd.read_pickle('data/pdb_test.p')
>>> test.head(5)
pdb_id lig_id ... ligand_xyz_2d ligand_bonds
0 7k38 VTY ... [(-2.031355975502858, -1.6316778784387098, 0.0... [(0, 1), (1, 4), (4, 5), (5, 10), (10, 9), (9,...
1 6prt OWA ... [(4.883261310160714, -0.37850716807626705, 0.0... [(11, 18), (18, 20), (20, 8), (8, 7), (7, 2), ...
2 4lxx FNF ... [(8.529427756002057, 2.2434809270065372, 0.0),... [(51, 49), (49, 48), (48, 46), (46, 53), (53, ...
3 4lxx FON ... [(-10.939694946697701, -1.1876214529096956, 0.... [(13, 1), (1, 0), (0, 3), (3, 4), (4, 7), (7, ...
4 7bp1 CAQ ... [(-1.9485571585149868, -1.499999999999999, 0.0... [(4, 3), (3, 1), (1, 0), (0, 7), (7, 9), (7, 6...
[5 rows x 8 columns]
>>> test.columns
Index(['pdb_id', 'lig_id', 'seq', 'smiles', 'receptor_features', 'ligand_xyz',
'ligand_xyz_2d', 'ligand_bonds'],
dtype='object')
>>> test.iloc[0]['receptor_features']
{'rigidgroups_gt_frames': array([[[[-5.3122622e-01, 2.0922849e-01, -8.2098854e-01,
1.7295000e+01],
[-7.1005428e-01, -6.3858479e-01, 2.9670244e-01,
-9.1399997e-01],
[-4.6219218e-01, 7.4056256e-01, 4.8779655e-01,
3.3284000e+01],
[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
1.0000000e+00]],
...
[[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
-3.5030000e+00],
[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
2.6764999e+01],
[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
1.5136000e+01],
[ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,
1.0000000e+00]]]], dtype=float32), 'torsion_angles_sin_cos': array([[[-1.90855725e-09, 3.58859784e-02],
[ 1.55730803e-01, 9.87799530e-01],
[ 6.05505241e-01, -7.95841312e-01],
...,
[-2.92459433e-01, -9.56277928e-01],
[ 9.96634814e-01, -8.19697779e-02],
[ 0.00000000e+00, 0.00000000e+00]],
...
[[ 2.96455977e-04, -9.99999953e-01],
[-8.15660990e-01, 5.78530158e-01],
[-3.17915569e-01, 9.48119024e-01],
...,
[ 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00]]])}
>>> test.iloc[0]['receptor_features'].keys()
dict_keys(['rigidgroups_gt_frames', 'torsion_angles_sin_cos'])
>>> test.iloc[0]['ligand_xyz']
[(22.289, 11.985, 9.225), (21.426, 11.623, 7.959), (nan, nan, nan), (nan, nan, nan), (21.797, 11.427, 6.574), (20.556, 11.56, 5.792), (nan, nan, nan), (20.507, 11.113, 4.552), (nan, nan, nan), (19.581, 10.97, 6.639), (20.107, 10.946, 7.954), (nan, nan, nan), (nan, nan, nan), (19.645, 10.364, 8.804)]
Manual update from PDB
# download the PDB archive into folder pdb/
sh rsync.sh 24 # number of parallel download processes
# extract sequences and coordinates in parallel
sbatch pdb.slurm
# or
mpirun -n 42 parse_complexes.py # desired number of tasks
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