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from Bio.PDB import PDBParser, NeighborSearch
from Bio.PDB.Polypeptide import is_aa
def get_close_residues(pdb_file, protein_chain_id, peptide_chain_id, distance_threshold=3.5):
# Initialize the parser
parser = PDBParser(QUIET=True)
structure = parser.get_structure('complex', pdb_file)
# Assume single model; adjust if multiple models exist
model = structure[0]
# Extract protein and peptide chains
protein_chain = model[protein_chain_id]
peptide_chain = model[peptide_chain_id]
# Extract all atoms from peptide
peptide_atoms = [atom for atom in peptide_chain.get_atoms()]
# Create a NeighborSearch object with all atoms in the structure
all_atoms = list(model.get_atoms())
neighbor_search = NeighborSearch(all_atoms)
# Set to store unique residues
close_residues = set()
# Iterate through each atom in the peptide and find nearby atoms in the protein
for atom in peptide_atoms:
nearby_atoms = neighbor_search.search(atom.coord, distance_threshold, level='A') # 'A' for atoms
for nearby_atom in nearby_atoms:
# Check if the nearby atom is in the protein chain
if nearby_atom.get_parent().get_parent().id == protein_chain_id:
residue = nearby_atom.get_parent()
if is_aa(residue, standard=True):
close_residues.add(residue)
# Sort residues by their sequence position
sorted_residues = sorted(close_residues, key=lambda r: r.get_id()[1])
# Prepare a list of tuples (residue index, residue name)
result = [(res.get_id()[1], res.get_resname()) for res in sorted_residues]
return result
if __name__ == "__main__":
pdb_file = "/home/tc415/discrete-diffusion-guidance/pdbs/UCLH5_docked.pdb" # Replace with your PDB file path
protein_chain_id = "A" # Replace with your protein chain ID
peptide_chain_id = "B" # Replace with your peptide chain ID
close_residues = get_close_residues(pdb_file, protein_chain_id, peptide_chain_id, distance_threshold=3.5)
print("Amino acids in the protein within 3.5 Å of the peptide:")
for res_id, res_name in close_residues:
print(f"Residue {res_id}: {res_name}")
print([res_id for res_id, _ in close_residues])
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