pdb-database

name	pdb-database
description	Access the RCSB Protein Data Bank (PDB) to search, download, and programmatically retrieve 3D macromolecular structures and metadata; use when you need structure discovery (text/sequence/3D similarity) or automated structural data ingestion for structural biology and drug discovery workflows.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

Use this skill when you need to:

• Find protein/nucleic acid 3D structures by keywords, organism, experimental method, or resolution.
• Identify related structures via sequence similarity (e.g., homolog search for modeling).
• Identify related structures via 3D structure similarity (e.g., fold-level comparisons).
• Download coordinates (PDB/mmCIF) for downstream analysis, visualization, docking, or modeling.
• Run batch retrieval of metadata/coordinates to feed pipelines in drug discovery, protein engineering, or structural bioinformatics.

Key Features

• Text and attribute-based search over RCSB PDB entries.
• Sequence similarity search with configurable thresholds (e-value, identity).
• Structure similarity search using an existing entry as a query.
• Programmatic metadata retrieval via the RCSB Data API (schema-based or GraphQL).
• Direct coordinate downloads in PDB and mmCIF formats.
• Batch processing patterns for multiple PDB IDs.

Dependencies

• rcsb-api (latest recommended; provides rcsbapi.search and rcsbapi.data)
• requests>=2.0 (HTTP downloads)
• biopython>=1.80 (optional; parsing/analyzing PDB coordinates)

Install (example):

uv pip install rcsb-api requests biopython

Example Usage

The following script is end-to-end runnable: it searches for a target, fetches metadata, downloads coordinates, and parses the structure.

#!/usr/bin/env python3
import pathlib
import requests

from rcsbapi.search import TextQuery, AttributeQuery
from rcsbapi.search.attrs import rcsb_entry_info
from rcsbapi.data import fetch, Schema

from Bio.PDB import PDBParser


def download_text(url: str, out_path: pathlib.Path) -> None:
    r = requests.get(url, timeout=60)
    r.raise_for_status()
    out_path.write_text(r.text, encoding="utf-8")


def main():
    out_dir = pathlib.Path("pdb_out")
    out_dir.mkdir(exist_ok=True)

    # 1) Search: hemoglobin entries with resolution < 2.0 Å
    q_text = TextQuery("hemoglobin")
    q_res = AttributeQuery(
        attribute=rcsb_entry_info.resolution_combined,
        operator="less",
        value=2.0,
    )
    query = q_text & q_res

    pdb_ids = list(query())[:5]
    if not pdb_ids:
        raise SystemExit("No results found.")
    pdb_id = pdb_ids[0]
    print(f"Selected PDB ID: {pdb_id}")

    # 2) Fetch entry metadata
    entry = fetch(pdb_id, schema=Schema.ENTRY)
    title = entry.get("struct", {}).get("title")
    method = (entry.get("exptl") or [{}])[0].get("method")
    resolution = (entry.get()  {}).get()
    deposit_date = (entry.get()  {}).get()

    ()
    ()
    ()
    ()
    ()

    
    pdb_path = out_dir / 
    cif_path = out_dir / 

    download_text(, pdb_path)
    download_text(, cif_path)
    ()

    
    parser = PDBParser(QUIET=)
    structure = parser.get_structure(pdb_id, (pdb_path))

    atom_count = (  _  structure.get_atoms())
    chain_ids = ({chain.  chain  structure.get_chains()})
    ()
    ()
    ()


 __name__ == :
    main()

Implementation Details

Search Modes and Query Composition

• Text search uses free-text matching over entry annotations (titles, keywords, descriptions).
• Attribute search filters by structured fields (e.g., organism, method, resolution).
• Sequence similarity search typically supports:
  • evalue_cutoff: lower is more stringent (fewer, more confident hits).
  • identity_cutoff: fraction identity threshold (e.g., 0.9 for near-identical).
• Structure similarity search uses an existing structure (e.g., an entry_id) as the geometric reference.
• Queries can be combined with boolean logic:
  • query1 & query2 (AND)
  • query1 | query2 (OR)
  • ~query (NOT), where supported by the client

Data Retrieval (Schema vs GraphQL)

• Schema-based fetch (e.g., Schema.ENTRY, Schema.POLYMER_ENTITY) is convenient for common objects and stable access patterns.
• GraphQL fetch is best when you need a custom selection of fields in one request (reduce round-trips and payload).

Example GraphQL pattern:

from rcsbapi.data import fetch

query = """
{
  entry(entry_id: "4HHB") {
    struct { title }
    exptl { method }
    rcsb_entry_info { resolution_combined deposited_atom_count }
  }
}
"""
data = fetch(query_type="graphql", query=query)

Coordinate Downloads and Formats

• PDB: legacy text format; widely supported but less expressive for large/complex structures.
• mmCIF (PDBx): modern standard; preferred for completeness and large structures.

Direct download endpoints:

• https://files.rcsb.org/download/{PDB_ID}.pdb
• https://files.rcsb.org/download/{PDB_ID}.cif

Batch Processing Pattern

For batch metadata retrieval, iterate over IDs and call fetch(pdb_id, schema=Schema.ENTRY); handle exceptions per-ID to keep pipelines robust. For large batches, consider rate limiting and caching to avoid repeated downloads.

Reference Documentation

If present in this repository, consult:

• references/api_reference.md for advanced endpoint usage, query patterns, schema notes, rate limits, and troubleshooting.