File size: 3,139 Bytes
638ea38 | 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 | import json
import urllib.request
import numpy as np
import pandas as pd
from Bio.PDB import MMCIFParser, PDBParser, PPBuilder
from common import RES, EXT, abc
from structure_utils import correspondence, tm_score, alphafold_model
PDB = EXT / "pdb"
PDB.mkdir(parents=True, exist_ok=True)
AF = EXT / "alphafold"
A = abc().set_index("gene")
def search(acc):
q = {"query": {"type": "terminal", "service": "text",
"parameters": {"attribute": "rcsb_polymer_entity_container_identifiers.reference_sequence_identifiers.database_accession",
"operator": "exact_match", "value": acc}},
"return_type": "polymer_entity", "request_options": {"return_all_hits": True}}
req = urllib.request.Request("https://search.rcsb.org/rcsbsearch/v2/query", data=json.dumps(q).encode(),
headers={"Content-Type": "application/json"})
body = urllib.request.urlopen(req, timeout=60).read()
return [h["identifier"] for h in json.loads(body)["result_set"]] if body else []
def ca(structure, chain=None):
model = structure[0]
chains = [model[chain]] if chain else list(model)
res = [r for c in chains for r in c if "CA" in r and r.id[0] == " "]
seq = "".join(str(pp.get_sequence()) for c in chains for pp in PPBuilder().build_peptides(c))
return seq, np.array([r["CA"].coord for r in res], dtype=float)
rows = []
for gene, acc in A.uniprot.items():
for ent in search(acc):
entry, eid = ent.split("_")
meta = json.load(urllib.request.urlopen(f"https://data.rcsb.org/rest/v1/core/entry/{entry}", timeout=60))
pe = json.load(urllib.request.urlopen(f"https://data.rcsb.org/rest/v1/core/polymer_entity/{entry}/{eid}", timeout=60))
chain = pe["rcsb_polymer_entity_container_identifiers"]["auth_asym_ids"][0]
f = PDB / f"{entry}.cif"
if not f.exists():
urllib.request.urlretrieve(f"https://files.rcsb.org/download/{entry}.cif", f)
s_exp, x_exp = ca(MMCIFParser(QUIET=True).get_structure(entry, f), chain)
s_af, x_af = ca(PDBParser(QUIET=True).get_structure(gene, alphafold_model(acc, AF)))
i_exp, i_af = correspondence(s_exp, s_af)
tm, rmsd = tm_score(x_exp[i_exp], x_af[i_af], len(s_exp))
rows.append({"gene": gene, "pdb": entry, "chain": chain, "method": meta["exptl"][0]["method"],
"resolution": (meta["rcsb_entry_info"].get("resolution_combined") or [np.nan])[0],
"year": meta["rcsb_accession_info"]["initial_release_date"][:4],
"doi": (meta.get("citation") or [{}])[0].get("pdbx_database_id_DOI"),
"title": meta["struct"]["title"], "modelled_residues": len(s_exp),
"uniprot_length": len(s_af), "coverage": len(s_exp) / len(s_af),
"tm_score_af_vs_experimental": tm, "rmsd_core": rmsd})
out = pd.DataFrame(rows)
out.to_csv(RES / "experimental_structures.csv", index=False)
pd.set_option("display.width", 220)
pd.set_option("display.max_colwidth", 70)
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