"""
High-performance molecular interaction analyzer for HBAT.
This module provides the main analyzer using NumPy for vectorized
calculations of molecular interactions in protein structures.
"""
import math
import time
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
import numpy as np
from ..constants import (
HALOGEN_BOND_ACCEPTOR_ELEMENTS,
HALOGEN_ELEMENTS,
HYDROGEN_BOND_ACCEPTOR_ELEMENTS,
HYDROGEN_BOND_DONOR_ELEMENTS,
HYDROGEN_ELEMENTS,
PI_INTERACTION_ATOMS,
PI_INTERACTION_DONOR,
RESIDUES_WITH_AROMATIC_RINGS,
)
from ..constants.parameters import AnalysisParameters
from .interactions import CooperativityChain, HalogenBond, HydrogenBond, PiInteraction
from .np_vector import NPVec3D, batch_angle_between, compute_distance_matrix
from .pdb_parser import PDBParser
from .structure import Atom, Residue
[docs]
class NPMolecularInteractionAnalyzer:
"""High-performance analyzer for molecular interactions.
This analyzer uses vectorized NumPy operations for efficient
analysis of molecular interactions in protein structures.
:param parameters: Analysis parameters to use
:type parameters: Optional[AnalysisParameters]
"""
[docs]
def __init__(self, parameters: Optional[AnalysisParameters] = None):
"""Initialize analyzer with parameters."""
self.parameters = parameters or AnalysisParameters()
# Validate parameters
validation_errors = self.parameters.validate()
if validation_errors:
raise ValueError(f"Invalid parameters: {'; '.join(validation_errors)}")
self.parser = PDBParser()
self.hydrogen_bonds: List[HydrogenBond] = []
self.halogen_bonds: List[HalogenBond] = []
self.pi_interactions: List[PiInteraction] = []
self.cooperativity_chains: List[CooperativityChain] = []
# Aromatic residues for Ο interactions
self._aromatic_residues = set(RESIDUES_WITH_AROMATIC_RINGS)
# Cache for vectorized data
self._atom_coords: Optional[np.ndarray] = None
self._atom_indices: Dict[str, List[int]] = {}
# Cached atom mappings to avoid repeated creation
self._atom_map: Dict[int, Atom] = {}
self._serial_to_idx: Dict[int, int] = {}
# Optimized residue indexing for fast same-residue filtering
self._residue_to_atoms: Dict[Tuple[str, int, str], List[int]] = {}
self._atom_to_residue: Dict[int, Tuple[str, int, str]] = {}
# Timing and PDB fixing information
self._analysis_start_time: Optional[float] = None
self._analysis_end_time: Optional[float] = None
self._pdb_fixing_info: Dict[str, Any] = {}
# Progress callback for GUI updates
self.progress_callback: Optional[Callable[[str], None]] = None
[docs]
def analyze_file(self, pdb_file: str) -> bool:
"""Analyze a PDB file for molecular interactions."""
self._analysis_start_time = time.time()
self._pdb_fixing_info = {}
# Progress update helper
def update_progress(message: str) -> None:
if self.progress_callback:
self.progress_callback(message)
# First, parse the original file to check if fixing is needed
update_progress("π Reading original PDB file...")
if not self.parser.parse_file(pdb_file):
return False
update_progress("π Analyzing structure...")
# Apply PDB fixing if enabled
if self.parameters.fix_pdb_enabled:
update_progress("π§ Starting PDB fixing...")
try:
original_atoms_count = len(self.parser.atoms)
original_bonds_count = len(self.parser.bonds)
original_hydrogens_count = len(
[a for a in self.parser.atoms if a.is_hydrogen()]
)
update_progress("βοΈ Processing structure with PDB fixer...")
# Fix the PDB file and get path to fixed file
fixed_file_path = self._apply_pdb_fixing(pdb_file)
update_progress("π Parsing fixed structure...")
# Parse the fixed structure
if self.parser.parse_file(fixed_file_path):
new_atoms_count = len(self.parser.atoms)
new_bonds_count = len(self.parser.bonds)
new_hydrogens_count = len(
[a for a in self.parser.atoms if a.is_hydrogen()]
)
# Store PDB fixing information including file path
self._pdb_fixing_info = {
"method": self.parameters.fix_pdb_method,
"original_atoms": original_atoms_count,
"fixed_atoms": new_atoms_count,
"original_hydrogens": original_hydrogens_count,
"fixed_hydrogens": new_hydrogens_count,
"added_hydrogens": new_hydrogens_count
- original_hydrogens_count,
"original_bonds": original_bonds_count,
"redetected_bonds": new_bonds_count,
"fixed_file_path": fixed_file_path,
"applied": True,
}
print(f"PDB fixing applied using {self.parameters.fix_pdb_method}")
print(f"Fixed PDB saved to: {fixed_file_path}")
print(f"Structure now has {new_atoms_count} atoms")
print(f"Re-detected {new_bonds_count} bonds")
else:
raise Exception(
f"Failed to parse fixed PDB file: {fixed_file_path}"
)
except Exception as e:
self._pdb_fixing_info = {"applied": False, "error": str(e)}
print(f"Warning: PDB fixing failed: {e}")
print("Continuing with original structure")
# Use the already parsed original structure
else:
# Not using PDB fixing, already parsed above
self._pdb_fixing_info = {"applied": False}
if not self.parser.has_hydrogens():
print("Warning: PDB file appears to lack hydrogen atoms")
print("Consider enabling PDB fixing or adding hydrogens manually")
update_progress("π Preparing analysis data...")
# Prepare vectorized data
self._prepare_vectorized_data()
# Clear previous results
self.hydrogen_bonds = []
self.halogen_bonds = []
self.pi_interactions = []
self.cooperativity_chains = []
# Analyze interactions with progress updates
update_progress("π Finding hydrogen bonds...")
self._find_hydrogen_bonds_vectorized()
update_progress("π Finding halogen bonds...")
self._find_halogen_bonds_vectorized()
update_progress("π Finding Ο interactions...")
self._find_pi_interactions_vectorized()
update_progress("πΈοΈ Analyzing cooperativity...")
# Find cooperativity chains (still uses graph-based approach)
self._find_cooperativity_chains()
update_progress("β
Analysis complete")
self._analysis_end_time = time.time()
return True
def _prepare_vectorized_data(self) -> None:
"""Prepare atom coordinates and indices for vectorized operations."""
# Extract all atom coordinates
self._atom_coords = np.array(
[
[atom.coords.x, atom.coords.y, atom.coords.z]
for atom in self.parser.atoms
]
)
# Build index mappings for different atom types
self._atom_indices = {
"all": list(range(len(self.parser.atoms))),
"hydrogen": [],
"donor": [],
"acceptor": [],
"halogen": [],
"halogen_acceptor": [],
"aromatic": [],
}
for i, atom in enumerate(self.parser.atoms):
if atom.element in HYDROGEN_ELEMENTS:
self._atom_indices["hydrogen"].append(i)
if atom.element in HYDROGEN_BOND_DONOR_ELEMENTS:
self._atom_indices["donor"].append(i)
if atom.element in HYDROGEN_BOND_ACCEPTOR_ELEMENTS:
self._atom_indices["acceptor"].append(i)
if atom.element in HALOGEN_ELEMENTS:
self._atom_indices["halogen"].append(i)
if atom.element in HALOGEN_BOND_ACCEPTOR_ELEMENTS:
self._atom_indices["halogen_acceptor"].append(i)
if (
atom.res_name in self._aromatic_residues
and atom.name in PI_INTERACTION_ATOMS
):
self._atom_indices["aromatic"].append(i)
# Build optimized residue indexing for fast same-residue filtering
self._build_residue_indices()
# Cache atom mappings for efficient lookups
self._build_atom_mappings()
def _build_residue_indices(self) -> None:
"""Build optimized residue indexing for fast same-residue filtering."""
self._residue_to_atoms.clear()
self._atom_to_residue.clear()
for i, atom in enumerate(self.parser.atoms):
residue_key = (atom.chain_id, atom.res_seq, atom.res_name)
# Map residue to atoms
if residue_key not in self._residue_to_atoms:
self._residue_to_atoms[residue_key] = []
self._residue_to_atoms[residue_key].append(i)
# Map atom to residue
self._atom_to_residue[i] = residue_key
def _build_atom_mappings(self) -> None:
"""Build cached atom mappings for efficient lookups."""
self._atom_map.clear()
self._serial_to_idx.clear()
for i, atom in enumerate(self.parser.atoms):
self._atom_map[atom.serial] = atom
self._serial_to_idx[atom.serial] = i
def _are_same_residue(self, atom1_idx: int, atom2_idx: int) -> bool:
"""Fast same-residue check using pre-computed indices."""
return self._atom_to_residue.get(atom1_idx) == self._atom_to_residue.get(
atom2_idx
)
def _find_hydrogen_bonds_vectorized(self) -> None:
"""Find hydrogen bonds using vectorized NumPy operations."""
if not self._atom_indices["acceptor"]:
return
# Get hydrogen bond donors (heavy atom + bonded hydrogen) like original analyzer
donors = self._get_hydrogen_bond_donors()
if not donors:
return
# Get acceptor coordinates
if self._atom_coords is not None:
a_coords = self._atom_coords[self._atom_indices["acceptor"]]
else:
return
# Extract hydrogen coordinates from donor pairs
h_coords = np.array(
[
[hydrogen.coords.x, hydrogen.coords.y, hydrogen.coords.z]
for _, hydrogen, _, _ in donors
]
)
# Compute distance matrix between hydrogens (from donors) and acceptors
distances = compute_distance_matrix(h_coords, a_coords)
# Find pairs within distance cutoff
h_indices, a_indices = np.where(distances <= self.parameters.hb_distance_cutoff)
# Process pairs in chunks for large datasets
total_pairs = len(h_indices)
chunk_size = 1000 # Process 1000 pairs at a time
for chunk_start in range(0, total_pairs, chunk_size):
chunk_end = min(chunk_start + chunk_size, total_pairs)
# Progress update for large datasets
if total_pairs > chunk_size and self.progress_callback:
progress = int((chunk_start / total_pairs) * 100)
self.progress_callback(f"π Finding hydrogen bonds... {progress}%")
# Small delay to allow GUI updates
time.sleep(0.01)
# Process chunk
for i in range(chunk_start, chunk_end):
h_idx, a_idx = h_indices[i], a_indices[i]
donor_atom, h_atom, donor_idx, h_atom_idx = donors[h_idx]
a_atom = self.parser.atoms[self._atom_indices["acceptor"][a_idx]]
# Skip if same atom
if donor_atom.serial == a_atom.serial:
continue
# Skip if same residue (for local mode) - optimized check
if self.parameters.analysis_mode == "local":
acceptor_idx = self._atom_indices["acceptor"][a_idx]
if self._are_same_residue(donor_idx, acceptor_idx):
continue
# Calculate angle using NPVec3D
donor_vec = NPVec3D(
float(donor_atom.coords.x),
float(donor_atom.coords.y),
float(donor_atom.coords.z),
)
h_vec = NPVec3D(
float(h_atom.coords.x),
float(h_atom.coords.y),
float(h_atom.coords.z),
)
a_vec = NPVec3D(
float(a_atom.coords.x),
float(a_atom.coords.y),
float(a_atom.coords.z),
)
angle_rad = batch_angle_between(donor_vec, h_vec, a_vec)
angle_deg = math.degrees(float(angle_rad))
# Check angle cutoff
if angle_deg >= self.parameters.hb_angle_cutoff:
distance = float(distances[h_idx, a_idx])
donor_acceptor_distance = donor_atom.coords.distance_to(
a_atom.coords
)
# Check donor-acceptor distance cutoff (like original analyzer)
if (
donor_acceptor_distance
> self.parameters.hb_donor_acceptor_cutoff
):
continue
bond_type = f"{donor_atom.element}-H...{a_atom.element}"
donor_residue = f"{donor_atom.chain_id}{donor_atom.res_seq}{donor_atom.res_name}"
acceptor_residue = (
f"{a_atom.chain_id}{a_atom.res_seq}{a_atom.res_name}"
)
hbond = HydrogenBond(
_donor=donor_atom,
hydrogen=h_atom,
_acceptor=a_atom,
distance=distance,
angle=float(angle_rad),
_donor_acceptor_distance=donor_acceptor_distance,
bond_type=bond_type,
_donor_residue=donor_residue,
_acceptor_residue=acceptor_residue,
)
self.hydrogen_bonds.append(hbond)
def _get_hydrogen_bond_donors(self) -> List[Tuple[Atom, Atom, int, int]]:
"""Get potential hydrogen bond donors with optimized indexing.
Returns list of tuples: (donor_atom, hydrogen_atom, donor_idx, hydrogen_idx)
"""
donors = []
# Find hydrogen atoms and their bonded heavy atoms
for h_idx, h_atom in enumerate(self.parser.atoms):
if h_atom.element.upper() not in HYDROGEN_ELEMENTS:
continue
# Get atoms bonded to this hydrogen
bonded_serials = self.parser.get_bonded_atoms(h_atom.serial)
for bonded_serial in bonded_serials:
bonded_atom = self._atom_map.get(bonded_serial)
if bonded_atom is None:
continue
# Check if heavy atom can be donor (N, O, S)
if bonded_atom.element.upper() in HYDROGEN_BOND_DONOR_ELEMENTS:
donor_idx = self._serial_to_idx[bonded_serial]
donors.append((bonded_atom, h_atom, donor_idx, h_idx))
break # Each hydrogen should only bond to one heavy atom
return donors
def _find_halogen_bonds_vectorized(self) -> None:
"""Find halogen bonds using vectorized NumPy operations."""
if (
not self._atom_indices["halogen"]
or not self._atom_indices["halogen_acceptor"]
):
return
# Get coordinates
if self._atom_coords is not None:
x_coords = self._atom_coords[self._atom_indices["halogen"]]
a_coords = self._atom_coords[self._atom_indices["halogen_acceptor"]]
else:
return
# Compute distance matrix
distances = compute_distance_matrix(x_coords, a_coords)
# Find pairs within distance cutoff
x_indices, a_indices = np.where(distances <= self.parameters.xb_distance_cutoff)
# Process pairs in chunks for large datasets
total_pairs = len(x_indices)
chunk_size = 1000 # Process 1000 pairs at a time
for chunk_start in range(0, total_pairs, chunk_size):
chunk_end = min(chunk_start + chunk_size, total_pairs)
# Progress update for large datasets
if total_pairs > chunk_size and self.progress_callback:
progress = int((chunk_start / total_pairs) * 100)
self.progress_callback(f"π Finding halogen bonds... {progress}%")
# Small delay to allow GUI updates
time.sleep(0.01)
# Process chunk
for i in range(chunk_start, chunk_end):
x_idx, a_idx = x_indices[i], a_indices[i]
x_atom = self.parser.atoms[self._atom_indices["halogen"][x_idx]]
a_atom = self.parser.atoms[
self._atom_indices["halogen_acceptor"][a_idx]
]
# Skip if same residue (for local mode) - optimized check
if self.parameters.analysis_mode == "local":
halogen_idx = self._atom_indices["halogen"][x_idx]
acceptor_idx = self._atom_indices["halogen_acceptor"][a_idx]
if self._are_same_residue(halogen_idx, acceptor_idx):
continue
# Find carbon atom bonded to halogen
carbon_atom = self._find_carbon_for_halogen(x_atom)
if not carbon_atom:
continue
# Calculate angle
c_vec = NPVec3D(
float(carbon_atom.coords.x),
float(carbon_atom.coords.y),
float(carbon_atom.coords.z),
)
x_vec = NPVec3D(
float(x_atom.coords.x),
float(x_atom.coords.y),
float(x_atom.coords.z),
)
a_vec = NPVec3D(
float(a_atom.coords.x),
float(a_atom.coords.y),
float(a_atom.coords.z),
)
angle_rad = batch_angle_between(c_vec, x_vec, a_vec)
angle_deg = math.degrees(float(angle_rad))
# Check angle cutoff
if angle_deg >= self.parameters.xb_angle_cutoff:
distance = float(distances[x_idx, a_idx])
bond_type = f"C-{x_atom.element}...{a_atom.element}"
halogen_residue = (
f"{x_atom.chain_id}{x_atom.res_seq}{x_atom.res_name}"
)
acceptor_residue = (
f"{a_atom.chain_id}{a_atom.res_seq}{a_atom.res_name}"
)
xbond = HalogenBond(
halogen=x_atom,
_acceptor=a_atom,
distance=distance,
angle=float(angle_rad),
bond_type=bond_type,
_halogen_residue=halogen_residue,
_acceptor_residue=acceptor_residue,
)
self.halogen_bonds.append(xbond)
def _find_pi_interactions_vectorized(self) -> None:
"""Find Ο interactions using vectorized operations."""
aromatic_centers = self._get_aromatic_centers()
if not aromatic_centers:
return
# Get interaction atoms (H, F, Cl) bonded to carbon like original analyzer
interaction_pairs = self._get_pi_interaction_pairs()
if not interaction_pairs:
return
# Extract center coordinates
center_coords = np.array(
[center["center"].to_array() for center in aromatic_centers]
)
# Check interactions with each carbon-interaction atom pair
for carbon, interaction_atom in interaction_pairs:
# Skip if not a Ο donor element
if carbon.element not in PI_INTERACTION_DONOR:
continue
# Calculate distances to all aromatic centers
h_coord = np.array(
[
interaction_atom.coords.x,
interaction_atom.coords.y,
interaction_atom.coords.z,
]
)
distances = np.linalg.norm(center_coords - h_coord, axis=1)
# Find centers within cutoff
close_centers = np.where(distances <= self.parameters.pi_distance_cutoff)[0]
for center_idx in close_centers:
center_info = aromatic_centers[center_idx]
# Skip same residue (for local mode) - optimized check
if self.parameters.analysis_mode == "local":
carbon_idx = self._serial_to_idx.get(carbon.serial)
if carbon_idx is not None:
# Create residue key for aromatic center
aromatic_residue_key = (
center_info["residue"].chain_id,
center_info["residue"].seq_num,
center_info["residue"].name,
)
carbon_residue_key = self._atom_to_residue.get(carbon_idx)
if carbon_residue_key == aromatic_residue_key:
continue
# Calculate angle
donor_vec = NPVec3D(
float(carbon.coords.x),
float(carbon.coords.y),
float(carbon.coords.z),
)
h_vec = NPVec3D(
float(interaction_atom.coords.x),
float(interaction_atom.coords.y),
float(interaction_atom.coords.z),
)
angle_rad = batch_angle_between(donor_vec, h_vec, center_info["center"])
angle_deg = math.degrees(float(angle_rad))
if angle_deg >= self.parameters.pi_angle_cutoff:
donor_residue = (
f"{carbon.chain_id}{carbon.res_seq}{carbon.res_name}"
)
pi_residue = f"{center_info['residue'].chain_id}{center_info['residue'].seq_num}{center_info['residue'].name}"
# Use NPVec3D directly
pi_center_vec3d = center_info["center"]
pi_int = PiInteraction(
_donor=carbon,
hydrogen=interaction_atom,
pi_center=pi_center_vec3d,
distance=float(distances[center_idx]),
angle=float(angle_rad),
_donor_residue=donor_residue,
_pi_residue=pi_residue,
)
self.pi_interactions.append(pi_int)
def _get_pi_interaction_pairs(self) -> List[Tuple[Atom, Atom]]:
"""Get interaction atoms (H, F, Cl) that are bonded to carbon.
For Ο interactions, we need C-X...Ο geometry, so only atoms
bonded to carbon are potential Ο interaction donors.
Returns list of tuples (carbon, interaction_atom).
"""
interactions = []
# Use cached atom mapping
for atom in self.parser.atoms:
if atom.element.upper() in PI_INTERACTION_ATOMS:
# Check if this atom is bonded to carbon
bonded_serials = self.parser.get_bonded_atoms(atom.serial)
for bonded_serial in bonded_serials:
bonded_atom = self._atom_map.get(bonded_serial)
if bonded_atom is not None and bonded_atom.element.upper() == "C":
interactions.append((bonded_atom, atom))
break # Found at least one carbon, that's sufficient
return interactions
def _find_donor_for_hydrogen(self, hydrogen: Atom) -> Optional[Atom]:
"""Find donor atom for a hydrogen atom."""
for bond in self.parser.bonds:
if bond.involves_atom(hydrogen.serial):
# Find the other atom in the bond
other_serial = bond.get_partner(hydrogen.serial)
if other_serial is not None:
# Find the atom object with this serial
for atom in self.parser.atoms:
if (
atom.serial == other_serial
and atom.element in HYDROGEN_BOND_DONOR_ELEMENTS
):
return atom
return None
def _find_carbon_for_halogen(self, halogen: Atom) -> Optional[Atom]:
"""Find carbon atom bonded to halogen."""
for bond in self.parser.bonds:
if bond.involves_atom(halogen.serial):
# Find the other atom in the bond
other_serial = bond.get_partner(halogen.serial)
if other_serial is not None:
# Find the atom object with this serial
for atom in self.parser.atoms:
if atom.serial == other_serial and atom.element == "C":
return atom
return None
def _find_hydrogen_for_donor(self, donor: Atom) -> Optional[Atom]:
"""Find hydrogen atom bonded to donor."""
for bond in self.parser.bonds:
if bond.involves_atom(donor.serial):
# Find the other atom in the bond
other_serial = bond.get_partner(donor.serial)
if other_serial is not None:
# Find the atom object with this serial
for atom in self.parser.atoms:
if (
atom.serial == other_serial
and atom.element in HYDROGEN_ELEMENTS
):
return atom
return None
def _get_aromatic_centers(self) -> List[Dict[str, Any]]:
"""Get aromatic ring centers using NumPy."""
centers = []
for residue in self.parser.residues.values():
aromatic_center = residue.get_aromatic_center()
if aromatic_center is not None:
centers.append({"residue": residue, "center": aromatic_center})
return centers
def _find_cooperativity_chains(self) -> None:
"""Find cooperativity chains in interactions."""
# Build interaction graph using atom serials as keys (hashable)
interaction_graph: Dict[
int, List[Tuple[int, Union[HydrogenBond, HalogenBond, PiInteraction]]]
] = {}
# Keep mapping from serial to atom for lookups
serial_to_atom = {atom.serial: atom for atom in self.parser.atoms}
# Add hydrogen bonds to graph
for hb in self.hydrogen_bonds:
donor = hb.get_donor()
acceptor = hb.get_acceptor()
# Only add if both are Atom objects (not Vec3D)
if isinstance(donor, Atom) and isinstance(acceptor, Atom):
donor_serial = donor.serial
acceptor_serial = acceptor.serial
if donor_serial not in interaction_graph:
interaction_graph[donor_serial] = []
if acceptor_serial not in interaction_graph:
interaction_graph[acceptor_serial] = []
interaction_graph[donor_serial].append((acceptor_serial, hb))
interaction_graph[acceptor_serial].append((donor_serial, hb))
# Add halogen bonds to graph
for xb in self.halogen_bonds:
donor = xb.get_donor()
acceptor = xb.get_acceptor()
# Only add if both are Atom objects (not Vec3D)
if isinstance(donor, Atom) and isinstance(acceptor, Atom):
donor_serial = donor.serial
acceptor_serial = acceptor.serial
if donor_serial not in interaction_graph:
interaction_graph[donor_serial] = []
if acceptor_serial not in interaction_graph:
interaction_graph[acceptor_serial] = []
interaction_graph[donor_serial].append((acceptor_serial, xb))
interaction_graph[acceptor_serial].append((donor_serial, xb))
# Add pi interactions to graph
for pi in self.pi_interactions:
donor = pi.get_donor()
# Only add if donor is an Atom object (not Vec3D)
if isinstance(donor, Atom):
donor_serial = donor.serial
if donor_serial not in interaction_graph:
interaction_graph[donor_serial] = []
# Note: We can't directly add aromatic center to graph as it's not an Atom
# Instead, we track through the donor only
# Find chains using DFS
visited = set()
for start_serial in interaction_graph:
if start_serial in visited:
continue
# DFS to find connected components
chain_interactions: List[
Union[HydrogenBond, HalogenBond, PiInteraction]
] = []
stack: List[
Tuple[int, Optional[Union[HydrogenBond, HalogenBond, PiInteraction]]]
] = [(start_serial, None)]
chain_serials = set()
while stack:
current_serial, parent_interaction = stack.pop()
if current_serial in chain_serials:
continue
chain_serials.add(current_serial)
visited.add(current_serial)
if parent_interaction:
chain_interactions.append(parent_interaction)
# Add neighbors
for neighbor_serial, interaction in interaction_graph.get(
current_serial, []
):
if neighbor_serial not in chain_serials:
stack.append((neighbor_serial, interaction))
# Create chain if it has at least 2 interactions
if len(chain_interactions) >= 2:
# Calculate chain angles if needed
angles = []
if len(chain_interactions) >= 2:
for i in range(len(chain_interactions) - 1):
angle = self._calculate_chain_angle(
chain_interactions[i], chain_interactions[i + 1]
)
if angle is not None:
angles.append(angle)
chain = CooperativityChain(
interactions=chain_interactions,
chain_length=len(chain_interactions),
chain_type=self._determine_chain_type(chain_interactions),
)
self.cooperativity_chains.append(chain)
def _calculate_chain_angle(self, int1: Any, int2: Any) -> Optional[float]:
"""Calculate angle between two consecutive interactions in a chain."""
# Get key atoms from interactions
atoms1 = self._get_interaction_atoms(int1)
atoms2 = self._get_interaction_atoms(int2)
# Find common atom using serial numbers (hashable)
serials1 = {atom.serial for atom in atoms1}
serials2 = {atom.serial for atom in atoms2}
common_serials = serials1 & serials2
if not common_serials:
return None
common_serial = common_serials.pop()
# Find the actual common atom
common_atom = None
for atom in atoms1:
if atom.serial == common_serial:
common_atom = atom
break
if common_atom is None:
return None
# Get the other atoms
other1 = None
for atom in atoms1:
if atom.serial != common_serial:
other1 = atom
break
other2 = None
for atom in atoms2:
if atom.serial != common_serial:
other2 = atom
break
if other1 is None or other2 is None:
return None
# Calculate angle
vec1 = NPVec3D(
float(other1.coords.x), float(other1.coords.y), float(other1.coords.z)
)
vec_common = NPVec3D(
float(common_atom.coords.x),
float(common_atom.coords.y),
float(common_atom.coords.z),
)
vec2 = NPVec3D(
float(other2.coords.x), float(other2.coords.y), float(other2.coords.z)
)
angle_rad = batch_angle_between(vec1, vec_common, vec2)
return math.degrees(angle_rad)
def _get_interaction_atoms(self, interaction: Any) -> List[Atom]:
"""Get key atoms from an interaction."""
if isinstance(interaction, HydrogenBond):
donor = interaction.get_donor()
acceptor = interaction.get_acceptor()
atoms = []
if isinstance(donor, Atom):
atoms.append(donor)
if isinstance(acceptor, Atom):
atoms.append(acceptor)
return atoms
elif isinstance(interaction, HalogenBond):
donor = interaction.get_donor()
acceptor = interaction.get_acceptor()
atoms = []
if isinstance(donor, Atom):
atoms.append(donor)
if isinstance(acceptor, Atom):
atoms.append(acceptor)
return atoms
elif isinstance(interaction, PiInteraction):
donor = interaction.get_donor()
return [donor] if isinstance(donor, Atom) else []
return []
def _determine_chain_type(self, interactions: List[Any]) -> str:
"""Determine the type of cooperativity chain."""
types = set()
for interaction in interactions:
if isinstance(interaction, HydrogenBond):
types.add("H")
elif isinstance(interaction, HalogenBond):
types.add("X")
elif isinstance(interaction, PiInteraction):
types.add("Ο")
if len(types) == 1:
return f"{''.join(types)}-bond chain"
else:
return "Mixed chain"
def _apply_pdb_fixing(self, pdb_file_path: str) -> str:
"""Apply PDB fixing by processing the original file and saving to a new file.
:param pdb_file_path: Path to the original PDB file
:type pdb_file_path: str
:returns: Path to the fixed PDB file
:rtype: str
"""
import os
from .pdb_fixer import PDBFixer
fixer = PDBFixer()
# Generate output filename (e.g., 6rsa.pdb -> 6rsa_fixed.pdb)
base_dir = os.path.dirname(pdb_file_path)
base_name = os.path.basename(pdb_file_path)
name, ext = os.path.splitext(base_name)
fixed_file_path = os.path.join(base_dir, f"{name}_fixed{ext}")
# Use the new file-to-file fixing method
success = fixer.fix_pdb_file_to_file(
input_pdb_path=pdb_file_path,
output_pdb_path=fixed_file_path,
method=self.parameters.fix_pdb_method,
add_hydrogens=self.parameters.fix_pdb_add_hydrogens,
add_heavy_atoms=self.parameters.fix_pdb_add_heavy_atoms,
convert_nonstandard=self.parameters.fix_pdb_replace_nonstandard,
remove_heterogens=self.parameters.fix_pdb_remove_heterogens,
keep_water=self.parameters.fix_pdb_keep_water,
)
if not success:
raise Exception(f"Failed to fix PDB file: {pdb_file_path}")
return fixed_file_path
[docs]
def get_summary(self) -> Dict[str, Any]:
"""Get comprehensive analysis summary with statistics, PDB fixing info, and timing.
Returns a dictionary containing interaction counts, averages, bond type distributions,
PDB fixing information (if applied), and analysis timing.
:returns: Dictionary containing comprehensive analysis summary
:rtype: Dict[str, Any]
"""
summary: Dict[str, Any] = {
"hydrogen_bonds": {
"count": len(self.hydrogen_bonds),
"average_distance": (
np.mean([hb.distance for hb in self.hydrogen_bonds])
if self.hydrogen_bonds
else 0
),
"average_angle": (
np.mean([math.degrees(hb.angle) for hb in self.hydrogen_bonds])
if self.hydrogen_bonds
else 0
),
},
"halogen_bonds": {
"count": len(self.halogen_bonds),
"average_distance": (
np.mean([xb.distance for xb in self.halogen_bonds])
if self.halogen_bonds
else 0
),
"average_angle": (
np.mean([math.degrees(xb.angle) for xb in self.halogen_bonds])
if self.halogen_bonds
else 0
),
},
"pi_interactions": {
"count": len(self.pi_interactions),
"average_distance": (
np.mean([pi.distance for pi in self.pi_interactions])
if self.pi_interactions
else 0
),
"average_angle": (
np.mean([math.degrees(pi.angle) for pi in self.pi_interactions])
if self.pi_interactions
else 0
),
},
"cooperativity_chains": {
"count": len(self.cooperativity_chains),
"types": [chain.chain_type for chain in self.cooperativity_chains],
},
"total_interactions": len(self.hydrogen_bonds)
+ len(self.halogen_bonds)
+ len(self.pi_interactions),
}
# Add detailed statistics from original get_statistics method
# Round averages for better presentation
if self.hydrogen_bonds:
hb_summary = summary["hydrogen_bonds"]
hb_summary["average_distance"] = round(hb_summary["average_distance"], 2)
hb_summary["average_angle"] = round(hb_summary["average_angle"], 1)
# Bond type distribution
hb_types: Dict[str, int] = {}
for hb in self.hydrogen_bonds:
hb_types[hb.bond_type] = hb_types.get(hb.bond_type, 0) + 1
hb_summary["bond_types"] = hb_types
if self.halogen_bonds:
xb_summary = summary["halogen_bonds"]
xb_summary["average_distance"] = round(xb_summary["average_distance"], 2)
xb_summary["average_angle"] = round(xb_summary["average_angle"], 1)
# Bond type distribution
xb_types: Dict[str, int] = {}
for xb in self.halogen_bonds:
xb_types[xb.bond_type] = xb_types.get(xb.bond_type, 0) + 1
xb_summary["bond_types"] = xb_types
if self.pi_interactions:
pi_summary = summary["pi_interactions"]
pi_summary["average_distance"] = round(pi_summary["average_distance"], 2)
pi_summary["average_angle"] = round(pi_summary["average_angle"], 1)
# Chain length distribution
if self.cooperativity_chains:
chain_lengths: Dict[int, int] = {}
for chain in self.cooperativity_chains:
length = chain.chain_length
chain_lengths[length] = chain_lengths.get(length, 0) + 1
coop_summary = summary["cooperativity_chains"]
coop_summary["chain_lengths"] = chain_lengths
# Add bond detection method breakdown
bond_detection_stats = self.parser.get_bond_detection_statistics()
total_bonds = sum(bond_detection_stats.values())
summary["bond_detection"] = {
"total_bonds": total_bonds,
"methods": bond_detection_stats,
"breakdown": {},
}
# Calculate percentages for each method
if total_bonds > 0:
for method, count in bond_detection_stats.items():
percentage = (count / total_bonds) * 100
breakdown = summary["bond_detection"]["breakdown"]
breakdown[method] = {
"count": count,
"percentage": round(percentage, 1),
}
# Add PDB fixing information if available
if self._pdb_fixing_info:
summary["pdb_fixing"] = self._pdb_fixing_info.copy()
# Add timing information
if (
self._analysis_start_time is not None
and self._analysis_end_time is not None
):
analysis_time = self._analysis_end_time - self._analysis_start_time
summary["timing"] = {
"analysis_duration_seconds": round(analysis_time, 3),
"start_time": self._analysis_start_time,
"end_time": self._analysis_end_time,
}
return summary
