add local frame info for molecules

data/pdbbind.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3e7a0f58df3e45dec1c90b30f2bd8ab62c601a1073dafa1d426e0f36413ecf30
-size 141173725
+oid sha256:7fda9d4f86f91a5469a2bcaa3ecedd009e4e27abf412a1de377a65095033481b
+size 179039843

pdbbind.py

@@ -11,6 +11,10 @@ import dask.array as da
 
 from rdkit import Chem
 
+from spyrmsd import molecule
+from spyrmsd import graph
+import networkx as nx
+
 import os
 import re
 import sys
@@ -25,6 +29,55 @@ max_seq = 2046 # = 2048 - 2 (accounting for [CLS] and [SEP])
 max_smiles = 510 # = 512 - 2
 chunk_size = '1G'
 
+def rot_from_two_vecs(e0_unnormalized, e1_unnormalized):
+    """Create rotation matrices from unnormalized vectors for the x and y-axes.
+    This creates a rotation matrix from two vectors using Gram-Schmidt
+    orthogonalization.
+    Args:
+    e0_unnormalized: vectors lying along x-axis of resulting rotation
+    e1_unnormalized: vectors lying in xy-plane of resulting rotation
+    Returns:
+    Rotations resulting from Gram-Schmidt procedure.
+    """
+    # Normalize the unit vector for the x-axis, e0.
+    e0 = e0_unnormalized / np.linalg.norm(e0_unnormalized)
+
+    # make e1 perpendicular to e0.
+    c = np.dot(e1_unnormalized, e0)
+    e1 = e1_unnormalized - c * e0
+    e1 = e1 / np.linalg.norm(e1)
+
+    # Compute e2 as cross product of e0 and e1.
+    e2 = np.cross(e0, e1)
+
+    # local to space frame
+    return np.stack([e0,e1,e2]).T
+
+def get_local_frames(mol):
+    # get the two nearest neighbors of every atom on the molecular graph
+    # ties are broken using canonical ordering
+    g = molecule.Molecule.from_rdkit(mol).to_graph()
+
+    R = []
+    for node in g:
+        length = nx.single_source_shortest_path_length(g, node)
+
+        neighbor_a = [n for n,l in length.items() if l==1][0]
+
+        try:
+            neighbor_b = [n for n,l in length.items() if l==1][1]
+        except:
+            # get next nearest neighbor
+            neighbor_b = [n for n,l in length.items() if l==2][0]
+
+        xyz = np.array(mol.GetConformer().GetAtomPosition(node))
+        xyz_a = np.array(mol.GetConformer().GetAtomPosition(neighbor_a))
+        xyz_b = np.array(mol.GetConformer().GetAtomPosition(neighbor_b))
+
+        R.append(rot_from_two_vecs(xyz_a-xyz, xyz_b-xyz))
+
+    return R
+
 def parse_complex(fn):
     try:
         name = os.path.basename(fn)
@@ -59,14 +112,20 @@ def parse_complex(fn):
 
         k = 0
         token_pos = []
+        token_rot = []
+
+        frames = get_local_frames(mol)
+
         for i,token in enumerate(masked_tokens):
             if token != '':
                 token_pos.append(tuple(mol.GetConformer().GetAtomPosition(atom_order[k])))
+                token_rot.append(frames[atom_order[k]].flatten().tolist())
                 k += 1
             else:
                 token_pos.append((np.nan, np.nan, np.nan))
+                token_rot.append(np.eye(3).flatten().tolist())
 
-        return name, seq, smi, xyz_receptor, token_pos
+        return name, seq, smi, xyz_receptor, token_pos, token_rot
 
     except Exception as e:
         print(e)
@@ -82,14 +141,19 @@ if __name__ == '__main__':
     comm = MPI.COMM_WORLD
     with MPICommExecutor(comm, root=0) as executor:
         if executor is not None:
-            result = executor.map(parse_complex, filenames)
+            result = executor.map(parse_complex, filenames, chunksize=32)
             result = list(result)
             names = [r[0] for r in result if r is not None]
             seqs = [r[1] for r in result if r is not None]
             all_smiles = [r[2] for r in result if r is not None]
             all_xyz_receptor = [r[3] for r in result if r is not None]
             all_xyz_ligand = [r[4] for r in result if r is not None]
+            all_rot_ligand = [r[5] for r in result if r is not None]
 
             import pandas as pd
-            df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles, 'receptor_xyz': all_xyz_receptor, 'ligand_xyz': all_xyz_ligand})
-            df.to_parquet('data/pdbbind.parquet')
+            df = pd.DataFrame({'name': names, 'seq': seqs,
+                               'smiles': all_smiles,
+                               'receptor_xyz': all_xyz_receptor,
+                               'ligand_xyz': all_xyz_ligand,
+                               'ligand_rot': all_rot_ligand})
+            df.to_parquet('data/pdbbind.parquet',index=False)

pdbbind.slurm

@@ -1,9 +1,9 @@
 #!/bin/bash
 #SBATCH -J preprocess_pdbbind
-#SBATCH -p gpu
+#SBATCH -p batch
 #SBATCH -A STF006
 #SBATCH -t 3:00:00
-#SBATCH -N 2
-#SBATCH --ntasks-per-node=16
+#SBATCH -N 4
+#SBATCH --ntasks-per-node=8
 
 srun python pdbbind.py

pdbbind_complexes.py

@@ -57,7 +57,7 @@ _URLs = {name: _URL+_file_names[name] for name in _file_names}
 class PDBBindComplexes(datasets.ArrowBasedBuilder):
     """List of protein sequences, ligand SMILES, and complex coordinates."""
 
-    VERSION = datasets.Version("1.1.0")
+    VERSION = datasets.Version("1.2.0")
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset