Spaces:
Build error
Build error
File size: 3,495 Bytes
bfcb8b7 | 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 | """
HDT v3.0: Structural Bioinformatics Layer
Automates protein structure acquisition from RCSB PDB and AlphaFold DB.
"""
import os
import requests
import pandas as pd
from pathlib import Path
BASE_DIR = Path(__file__).parent.parent
STRUCTURE_DIR = BASE_DIR / 'data' / 'structures'
STRUCTURE_DIR.mkdir(parents=True, exist_ok=True)
# Recommended PDB IDs for top targets (manually curated for quality/human)
TARGET_PDB_MAPPING = {
'MTOR': '4IPH',
'IL1B': '1ITB',
'IL6': '1ALU',
'TNF': '1TNF',
'IFNg': '1HIG',
'NLRP3': '7PZW' # Human NLRP3 decamer (Cryo-EM)
}
def download_pdb(pdb_id):
"""Download a PDB file from RCSB."""
print(f"Downloading PDB: {pdb_id}...")
url = f"https://files.rcsb.org/download/{pdb_id}.pdb"
output_path = STRUCTURE_DIR / f"{pdb_id}.pdb"
try:
response = requests.get(url)
response.raise_for_status()
with open(output_path, 'wb') as f:
f.write(response.content)
print(f"Saved to {output_path.name}")
return output_path
except Exception as e:
print(f"Error downloading {pdb_id}: {e}")
return None
def download_alphafold(af_id):
"""Download an AlphaFold structure (PDB format)."""
print(f"Downloading AlphaFold: {af_id}...")
# URL format: https://alphafold.ebi.ac.uk/files/AF-Q96P20-F1-model_v4.pdb
url = f"https://alphafold.ebi.ac.uk/files/{af_id}-model_v4.pdb"
output_path = STRUCTURE_DIR / f"{af_id}.pdb"
try:
response = requests.get(url)
response.raise_for_status()
with open(output_path, 'wb') as f:
f.write(response.content)
print(f"Saved to {output_path.name}")
return output_path
except Exception as e:
print(f"Error downloading {af_id}: {e}")
return None
def main():
print("="*50)
print("PHASE 2: STRUCTURAL BIOINFORMATICS (STRUCTURE ACQUISITION)")
print("="*50)
# Load v3.0 ranked targets to identify top hits
ranked_path = BASE_DIR / 'outputs' / 'tables' / 'targets_ranked_v3.csv'
if not ranked_path.exists():
print("Ranked targets (v3.0) not found. Run run_pipeline_v3.py first.")
return
targets_df = pd.read_csv(ranked_path)
top_targets = targets_df['Symbol'].head(6).tolist()
log_data = []
for gene in top_targets:
struct_id = TARGET_PDB_MAPPING.get(gene)
if not struct_id:
print(f"No predefined structure mapping for {gene}. Skipping...")
continue
if struct_id.startswith('AF-'):
path = download_alphafold(struct_id)
source = 'AlphaFold'
else:
path = download_pdb(struct_id)
source = 'RCSB_PDB'
if path:
log_data.append({
'Gene': gene,
'Structure_ID': struct_id,
'Source': source,
'File_Path': str(path.relative_to(BASE_DIR))
})
# Save acquisition log
log_df = pd.DataFrame(log_data)
log_path = STRUCTURE_DIR / 'structure_log.csv'
log_df.to_csv(log_path, index=False)
print("\n" + "="*50)
print(f"Acquisition complete. {len(log_df)} structures saved to {STRUCTURE_DIR.name}/")
print(f"Log saved to {log_path.name}")
print("="*50)
if __name__ == "__main__":
main()
|