File size: 6,071 Bytes
8efb4bd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | import os
import subprocess
from typing import Tuple, List, Dict, Optional
import Bio.PDB
import Bio.SeqUtils
import numpy as np
import scipy.spatial
# prints all pairwise dockqs given a reference and a single model
INTERFACE_MIN_ATOM_DIST = 8
RMSD_PATH = "/cs/labs/dina/dina/projects/rmsd/rmsd3.linux"
# TODO: change this to your path
WORK_DIR = "~/tmp/check_dockq"
def get_pdb_model_readonly(pdb_path: str):
pdb_parser = Bio.PDB.PDBParser(QUIET=True)
pdb_struct = pdb_parser.get_structure("original_pdb", pdb_path)
return next(iter(pdb_struct))
def _get_chained_res_id(res) -> Tuple[str, int]:
return res.parent.id, res.get_id()[1]
def _get_clean_res_ids(pdb_path) -> List[Tuple[str, int]]:
return [_get_chained_res_id(res) for res in get_pdb_model_readonly(pdb_path).get_residues()
if "CA" in res and Bio.SeqUtils.seq1(res.get_resname()) != "X"]
def extract_chains(pdb_path: str, output_path: str, chains_to_save: List[str]):
pdb_parser = Bio.PDB.PDBParser(QUIET=True)
pdb_struct = pdb_parser.get_structure("original_pdb", pdb_path)
assert len(list(pdb_struct)) == 1, "can't extract if more than one model"
model = next(iter(pdb_struct))
chains = list(model.get_chains())
for chain_id in chains_to_save:
assert len([c for c in chains if c.id == chain_id]) == 1, f"Missing chain {chain_id} from {pdb_path}"
for chain in chains:
if chain.id not in chains_to_save:
model.detach_child(chain.id)
io = Bio.PDB.PDBIO()
io.set_structure(pdb_struct)
io.save(output_path)
def prepare_chains_pdbs(ref_path: str, sample_path: str, output_folder: str, chain_map: Optional[Dict[str, str]]) \
-> Dict[str, Tuple[str, str]]:
ref_pdb_model = get_pdb_model_readonly(ref_path)
sample_pdb_model = get_pdb_model_readonly(sample_path)
if chain_map is None:
chain_map = {c.id: c.id for c in ref_pdb_model.get_chains() if c.id in sample_pdb_model}
chain_to_files = {}
for chain_name in chain_map.keys():
print("Preparing", chain_name)
output_pdb_path1 = os.path.join(output_folder, f"ref_{chain_name}.pdb")
output_pdb_path2 = os.path.join(output_folder, f"sample_{chain_name}.pdb")
chain_to_files[chain_name] = (output_pdb_path1, output_pdb_path2)
chain_ref = ref_pdb_model[chain_name]
chain_sample = sample_pdb_model[chain_map[chain_name]]
chain_ref_res_ids = {res.get_id()[1] for res in chain_ref.get_residues() if "CA" in res}
chain_sample_res_ids = {res.get_id()[1] for res in chain_sample.get_residues() if "CA" in res}
joined_res_ids = chain_ref_res_ids.intersection(chain_sample_res_ids)
ref_res_to_remove = []
for res in chain_ref.get_residues():
if res.get_id()[1] not in joined_res_ids:
ref_res_to_remove.append(res)
for res in ref_res_to_remove:
res.parent.detach_child(res.id)
sample_res_to_remove = []
for res in chain_sample.get_residues():
if res.get_id()[1] not in joined_res_ids:
sample_res_to_remove.append(res)
for res in sample_res_to_remove:
res.parent.detach_child(res.id)
io = Bio.PDB.PDBIO()
io.set_structure(chain_ref)
io.save(output_pdb_path1)
io = Bio.PDB.PDBIO()
io.set_structure(chain_sample)
io.save(output_pdb_path2)
return chain_to_files
def is_chains_coords_close(chain1_ca: np.ndarray, chain2_ca: np.ndarray):
if len(chain1_ca) == 0 or len(chain2_ca) == 0:
return False
return np.min(scipy.spatial.distance.cdist(chain1_ca, chain2_ca)) < INTERFACE_MIN_ATOM_DIST
def get_interacting_chains(pdb_path) -> List[Tuple[str, str]]:
pdb_parser = Bio.PDB.PDBParser(QUIET=True)
pdb_struct = pdb_parser.get_structure("original_pdb", pdb_path)
model = next(iter(pdb_struct))
chains = list(model.get_chains())
close_pairs = []
for i in range(len(chains)):
chain_i_ca = np.array([res['CA'].coord for res in chains[i].get_residues() if 'CA' in res])
for j in range(i + 1, len(chains)):
chain_j_ca = np.array([res['CA'].coord for res in chains[j].get_residues() if 'CA' in res])
if is_chains_coords_close(chain_i_ca, chain_j_ca):
close_pairs.append((chains[i].get_id(), chains[j].get_id()))
return [(sorted(i)[0], sorted(i)[1]) for i in close_pairs]
def print_all_dockq(ref_path, sample_path, chain_map):
tmpdir = os.path.join(WORK_DIR, "tmp")
os.makedirs(tmpdir, exist_ok=True)
chain_to_files = prepare_chains_pdbs(ref_path, sample_path, tmpdir, chain_map)
interacting_chains = get_interacting_chains(ref_path)
trans_path = os.path.join(tmpdir, "transformation.txt")
open(trans_path, "w").write("1 0 0 0 0 0 0\n")
tmp_output_path = os.path.join(tmpdir, "output.txt")
scores = {}
for c1, c2 in interacting_chains:
print("Running", c1, c2, chain_to_files[c1], chain_to_files[c2])
subprocess.run(f"{RMSD_PATH} {chain_to_files[c1][0]} {chain_to_files[c1][1]} "
f"{chain_to_files[c2][0]} {chain_to_files[c2][1]} "
f"{trans_path} -o {tmp_output_path}", shell=True)
dockqs = [float(line.split(" | ")[2].strip()) for line in open(tmp_output_path, "r").read().split("\n")[1:-3]
if len(line) > 0]
print("DockQ:", dockqs)
scores[(c1, c2)] = dockqs[0]
print("Final scores:", scores)
if __name__ == '__main__':
ref_path = "afm3_dataset/input_complexes/7ZKQ.pdb"
sample_path = "outputClustered_6.pdb"
chain_map = None
# sample_path = "7ZKQ_22_AA_CC_TT_bb_unrelaxed_rank_003_alphafold2_multimer_v3_model_5_seed_000.pdb"
# chain_map = {"2": "A", "A": "B", "C": "C", "T": "D", "b": "E"}
print_all_dockq(ref_path, sample_path, chain_map) |