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import multiprocessing as mp
import os
import subprocess
import warnings
from glob import glob
from itertools import combinations
import numpy as np
from biotite.structure.io import load_structure
from biotite.structure.io.pdb import PDBFile
from pxdbench.globals import TMALIGN_PATH
from pxdbench.utils import convert_cifs_to_pdbs, str2bool
warnings.filterwarnings("ignore", module="biotite")
def run_tmalign(pdb1, pdb2, tmalign_path="TMalign"):
"""
Run TM-align on two PDB files and return the output.
Args:
pdb1 (str): Path to the first PDB file
pdb2 (str): Path to the second PDB file
tmalign_path (str): Path to the TM-align executable
Returns:
str: The output from TM-align
"""
cmd = [tmalign_path, pdb1, pdb2]
try:
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
return result.stdout
except subprocess.CalledProcessError as e:
print(f"Error running TMalign: {e}")
return None
def extract_tmscore(tmalign_output):
"""
Extract the TM-score from the TM-align output.
Args:
tmalign_output (str): The output from TM-align
Returns:
float: The TM-score (average of the two normalized scores), or None if not found
"""
if tmalign_output is None:
return None
# TM-align returns two TM-scores (normalized by different lengths)
tm_scores = []
for line in tmalign_output.split("\n"):
if "TM-score=" in line:
try:
tm_score = float(line.split("=")[1].split("(")[0].strip())
tm_scores.append(tm_score)
except (IndexError, ValueError):
continue
if tm_scores:
return sum(tm_scores) / len(tm_scores)
return None
def cluster_worker(args):
pdb1, pdb2, i, j, tmalign_path = args
tmalign_output = run_tmalign(pdb1, pdb2, tmalign_path)
tm_score = extract_tmscore(tmalign_output)
return (i, j, tm_score)
def calculate_pairwise_tmscores(pdb_files, tmalign_path):
"""
Calculate pairwise TM-scores for a list of PDB files, in parallel.
Args:
pdb_files (list): List of paths to PDB files
tmalign_path (str): Path to the TM-align executable
Returns:
numpy.ndarray: Matrix of pairwise TM-scores
"""
n = len(pdb_files)
tm_matrix = np.zeros((n, n))
# Set diagonal to 1.0
np.fill_diagonal(tm_matrix, 1.0)
# Create list of pairs (i, j)
pairs = list(combinations(range(n), 2))
# Prepare arguments for multiprocessing
args_list = [(pdb_files[i], pdb_files[j], i, j, tmalign_path) for i, j in pairs]
num_workers = min(40, mp.cpu_count())
with mp.Pool(processes=num_workers) as pool:
results = pool.map(cluster_worker, args_list)
for i, j, tm_score in results:
if tm_score is not None:
tm_matrix[i, j] = tm_score
tm_matrix[j, i] = tm_score
return tm_matrix
def greedy_clustering(pdb_files, tm_matrix, threshold):
"""
Perform greedy clustering based on TM-scores.
This implementation selects the structure with the most
unassigned neighbors above the threshold as the next cluster center.
Args:
pdb_files (list): List of paths to PDB files
tm_matrix (numpy.ndarray): Matrix of pairwise TM-scores
threshold (float): TM-score threshold for clustering
Returns:
list: List of clusters, where each cluster is a list of PDB file indices
"""
n = len(pdb_files)
assigned = [False] * n
clusters = []
print(f"Performing greedy clustering with TM-score threshold {threshold}...")
while not all(assigned):
# Find unassigned structure with most unassigned neighbors
max_neighbors = -1
center_idx = -1
for i in range(n):
if assigned[i]:
continue
# Count unassigned neighbors (including self)
count = sum(
1 for j in range(n) if not assigned[j] and tm_matrix[i, j] >= threshold
)
if count > max_neighbors:
max_neighbors = count
center_idx = i
if center_idx == -1:
break # No unassigned structures left
# Create new cluster
current_cluster = [center_idx]
assigned[center_idx] = True
# Add all similar unassigned structures to the cluster
for j in range(n):
if not assigned[j] and tm_matrix[center_idx, j] >= threshold:
current_cluster.append(j)
assigned[j] = True
clusters.append(current_cluster)
print(f"Created cluster {len(clusters)} with {len(current_cluster)} structures")
return clusters
def save_tm_matrix(tm_matrix, pdb_files, output_file):
"""
Save the TM-score matrix to a file.
Args:
tm_matrix (numpy.ndarray): Matrix of pairwise TM-scores
pdb_files (list): List of paths to PDB files
output_file (str): Path to the output file
"""
with open(output_file, "w") as f:
# Write header
f.write("# TM-score matrix\n")
f.write("# Format: <pdb_i> <pdb_j> <tm_score>\n\n")
n = len(pdb_files)
for i in range(n):
for j in range(i, n): # Only upper triangle including diagonal
pdb_i = os.path.basename(pdb_files[i])
pdb_j = os.path.basename(pdb_files[j])
tm_score = tm_matrix[i, j]
f.write(f"{pdb_i} {pdb_j} {tm_score:.4f}\n")
def extract_chain_from_pdb(
pdb_dir: str,
pdb_files: list[str],
chain_id: str,
):
new_file_list = []
folder_path = f"{pdb_dir}/tmp"
os.makedirs(folder_path, exist_ok=True)
for input_file_path in pdb_files:
name = input_file_path.split("/")[-1].split(".")[0]
pdb_file_path = f"{pdb_dir}/tmp/{name}.pdb"
# we only consider input pdb file
structure = load_structure(input_file_path)
# make sure chain id is in structure
chain_structure = structure[structure.chain_id == chain_id]
if len(chain_structure) == 0:
raise ValueError(f"chain {chain_id} not in {input_file_path}")
pdb_file = PDBFile()
pdb_file.set_structure(chain_structure)
pdb_file.write(pdb_file_path)
new_file_list.append(pdb_file_path)
print(f"finish extract chain")
return folder_path, new_file_list
def main():
parser = argparse.ArgumentParser(
description="Perform pairwise TM-align and greedy clustering of PDB files"
)
parser.add_argument(
"--input_dir", required=True, help="Directory containing PDB/CIF files"
)
parser.add_argument("--output_dir", type=str, default=None)
parser.add_argument(
"--threshold",
type=float,
default=0.5,
help="TM-score threshold for clustering (default: 0.5)",
)
parser.add_argument(
"--clusters_output",
default="clusters.txt",
help="Output file for clustering results (default: clusters.txt)",
)
parser.add_argument(
"--matrix_output",
default="tm_matrix.txt",
help="Output file for TM-score matrix (default: tm_matrix.txt)",
)
parser.add_argument(
"--tmalign_path",
default=TMALIGN_PATH,
help="Path to the TM-align executable (default: TMalign in PATH)",
)
parser.add_argument(
"--is_mmcif", default=False, type=str2bool, help="input file type, mmcif or pdb"
)
parser.add_argument("--binder_chain", default=None, help="only calculate one chain")
args = parser.parse_args()
# Get all PDB files in the directory
if args.is_mmcif:
pdb_dir, pdb_names, _, _ = convert_cifs_to_pdbs(
args.input_dir,
out_pdb_dir=os.path.join(args.input_dir, "converted_pdbs"),
)
pdb_files = sorted([os.path.join(pdb_dir, fn + ".pdb") for fn in pdb_names])
else:
pdb_dir = args.input_dir
pdb_files = sorted(glob(os.path.join(args.input_dir, "*.pdb")))
if not pdb_files:
print(f"No PDB files found in {args.input_dir}")
return
if args.binder_chain is not None:
if len(args.binder_chain) > 1:
args.binder_chain = args.binder_chain[0]
print(
f"Use the chain ID in the PDB file -- trim it to one char: {args.binder_chain}"
)
pdb_dir, pdb_files = extract_chain_from_pdb(
pdb_dir, pdb_files, args.binder_chain
)
print(f"Found {len(pdb_files)} PDB files")
if args.output_dir is None:
output_dir = os.path.join(args.input_dir, "postprocess")
else:
output_dir = args.output_dir
os.makedirs(output_dir, exist_ok=True)
# Calculate pairwise TM-scores
tm_matrix = calculate_pairwise_tmscores(pdb_files, args.tmalign_path)
# Save the TM-score matrix
save_tm_matrix(tm_matrix, pdb_files, os.path.join(output_dir, args.matrix_output))
print(f"TM-score matrix saved to {os.path.join(output_dir, args.matrix_output)}")
# Perform greedy clustering
clusters = greedy_clustering(pdb_files, tm_matrix, args.threshold)
# Write clustering results
with open(os.path.join(output_dir, args.clusters_output), "w") as f:
f.write(f"# Clustering with TM-score threshold: {args.threshold}\n")
f.write(f"# Number of clusters: {len(clusters)}\n\n")
for i, cluster in enumerate(clusters):
f.write(f"Cluster {i+1} (size: {len(cluster)}):\n")
# Write the representative (center) first
f.write(f" {os.path.basename(pdb_files[cluster[0]])} (center)\n")
# Write the rest of the cluster members
for idx in cluster[1:]:
f.write(f" {os.path.basename(pdb_files[idx])}\n")
f.write("\n")
print(f"Clustering completed. Found {len(clusters)} clusters.")
print(f"Results written to {os.path.join(output_dir, args.clusters_output)}")
if __name__ == "__main__":
main()
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