--- tags: - proteins - molecules - chemistry - SMILES - complex structures --- ## 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 ```