File size: 4,018 Bytes
5ad5b1b 1d7245f 28974f4 d13e5f1 1d7245f 36d87d5 bc9f870 36d87d5 bc9f870 36d87d5 bc9f870 36d87d5 1d7245f 28974f4 1d7245f 2eec835 1d7245f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
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
```
|