Merge pull request #3 from sokrypton/main

v2.3.1

MANIFEST.in

@@ -0,0 +1 @@
+include alphafold/common/stereo_chemical_props.txt

alphafold/common/confidence.py

@@ -112,8 +112,7 @@ def predicted_tm_score(
     logits: np.ndarray,
     breaks: np.ndarray,
     residue_weights: Optional[np.ndarray] = None,
-    asym_id: Optional[np.ndarray] = None,
-    interface: bool = False) -> np.ndarray:
+    asym_id: Optional[np.ndarray] = None) -> np.ndarray:
   """Computes predicted TM alignment or predicted interface TM alignment score.
 
   Args:
@@ -123,8 +122,7 @@ def predicted_tm_score(
     residue_weights: [num_res] the per residue weights to use for the
       expectation.
     asym_id: [num_res] the asymmetric unit ID - the chain ID. Only needed for
-      ipTM calculation, i.e. when interface=True.
-    interface: If True, interface predicted TM score is computed.
+      ipTM calculation.
 
   Returns:
     ptm_score: The predicted TM alignment or the predicted iTM score.
@@ -136,10 +134,10 @@ def predicted_tm_score(
     residue_weights = np.ones(logits.shape[0])
 
   bin_centers = _calculate_bin_centers(breaks)
+  num_res = residue_weights.shape[0]
 
-  num_res = int(np.sum(residue_weights))
   # Clip num_res to avoid negative/undefined d0.
-  clipped_num_res = max(num_res, 19)
+  clipped_num_res = max(np.sum(residue_weights), 19)
 
   # Compute d_0(num_res) as defined by TM-score, eqn. (5) in Yang & Skolnick
   # "Scoring function for automated assessment of protein structure template
@@ -154,15 +152,14 @@ def predicted_tm_score(
   # E_distances tm(distance).
   predicted_tm_term = np.sum(probs * tm_per_bin, axis=-1)
 
-  pair_mask = np.ones(shape=(num_res, num_res), dtype=bool)
-  if interface:
-    pair_mask *= asym_id[:, None] != asym_id[None, :]
+  if asym_id is None:
+    pair_mask = np.ones((num_res,num_res), dtype=bool)
+  else:
+    pair_mask = asym_id[:, None] != asym_id[None, :]
 
   predicted_tm_term *= pair_mask
 
-  pair_residue_weights = pair_mask * (
-      residue_weights[None, :] * residue_weights[:, None])
-  normed_residue_mask = pair_residue_weights / (1e-8 + np.sum(
-      pair_residue_weights, axis=-1, keepdims=True))
+  pair_residue_weights = pair_mask * (residue_weights[None, :] * residue_weights[:, None])
+  normed_residue_mask = pair_residue_weights / (1e-8 + np.sum(pair_residue_weights, axis=-1, keepdims=True))
   per_alignment = np.sum(predicted_tm_term * normed_residue_mask, axis=-1)
-  return np.asarray(per_alignment[(per_alignment * residue_weights).argmax()])
+  return np.asarray(per_alignment[(per_alignment * residue_weights).argmax()])

alphafold/common/residue_constants.py

@@ -24,8 +24,8 @@
 
 # Internal import (35fd).
 
-
-stereo_chemical_props_path = 'alphafold/common/stereo_chemical_props.txt'
+from . import __file__ 
+stereo_chemical_props_path = os.path.join(os.path.dirname(__file__), f'stereo_chemical_props.txt')  
 
 # Distance from one CA to next CA [trans configuration: omega = 180].
 ca_ca = 3.80209737096
@@ -122,7 +122,7 @@
 # 4,5,6,7: 'chi1,2,3,4-group'
 # The atom positions are relative to the axis-end-atom of the corresponding
 # rotation axis. The x-axis is in direction of the rotation axis, and the y-axis
-# is defined such that the dihedral-angle-definiting atom (the last entry in
+# is defined such that the dihedral-angle-defining atom (the last entry in
 # chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate).
 # format: [atomname, group_idx, rel_position]
 rigid_group_atom_positions = {
@@ -771,10 +771,10 @@ def _make_rigid_transformation_4x4(ex, ey, translation):
 # and an array with (restype, atomtype, coord) for the atom positions
 # and compute affine transformation matrices (4,4) from one rigid group to the
 # previous group
-restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=np.int)
+restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int)
 restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
 restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32)
-restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=np.int)
+restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int)
 restype_atom14_mask = np.zeros([21, 14], dtype=np.float32)
 restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32)
 restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32)