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This skill helps you locate, retrieve, and analyze 3D structures from the PDB database, enabling efficient data access for drug discovery and research.

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---
name: pdb-database
description: "Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery."
---

# PDB Database

## Overview

RCSB PDB is the worldwide repository for 3D structural data of biological macromolecules. Search for structures, retrieve coordinates and metadata, perform sequence and structure similarity searches across 200,000+ experimentally determined structures and computed models.

## When to Use This Skill

This skill should be used when:
- Searching for protein or nucleic acid 3D structures by text, sequence, or structural similarity
- Downloading coordinate files in PDB, mmCIF, or BinaryCIF formats
- Retrieving structural metadata, experimental methods, or quality metrics
- Performing batch operations across multiple structures
- Integrating PDB data into computational workflows for drug discovery, protein engineering, or structural biology research

## Core Capabilities

### 1. Searching for Structures

Find PDB entries using various search criteria:

**Text Search:** Search by protein name, keywords, or descriptions
```python
from rcsbapi.search import TextQuery
query = TextQuery("hemoglobin")
results = list(query())
print(f"Found {len(results)} structures")
```

**Attribute Search:** Query specific properties (organism, resolution, method, etc.)
```python
from rcsbapi.search import AttributeQuery
from rcsbapi.search.attrs import rcsb_entity_source_organism

# Find human protein structures
query = AttributeQuery(
    attribute=rcsb_entity_source_organism.scientific_name,
    operator="exact_match",
    value="Homo sapiens"
)
results = list(query())
```

**Sequence Similarity:** Find structures similar to a given sequence
```python
from rcsbapi.search import SequenceQuery

query = SequenceQuery(
    value="MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM",
    evalue_cutoff=0.1,
    identity_cutoff=0.9
)
results = list(query())
```

**Structure Similarity:** Find structures with similar 3D geometry
```python
from rcsbapi.search import StructSimilarityQuery

query = StructSimilarityQuery(
    structure_search_type="entry",
    entry_id="4HHB"  # Hemoglobin
)
results = list(query())
```

**Combining Queries:** Use logical operators to build complex searches
```python
from rcsbapi.search import TextQuery, AttributeQuery
from rcsbapi.search.attrs import rcsb_entry_info

# High-resolution human proteins
query1 = AttributeQuery(
    attribute=rcsb_entity_source_organism.scientific_name,
    operator="exact_match",
    value="Homo sapiens"
)
query2 = AttributeQuery(
    attribute=rcsb_entry_info.resolution_combined,
    operator="less",
    value=2.0
)
combined_query = query1 & query2  # AND operation
results = list(combined_query())
```

### 2. Retrieving Structure Data

Access detailed information about specific PDB entries:

**Basic Entry Information:**
```python
from rcsbapi.data import Schema, fetch

# Get entry-level data
entry_data = fetch("4HHB", schema=Schema.ENTRY)
print(entry_data["struct"]["title"])
print(entry_data["exptl"][0]["method"])
```

**Polymer Entity Information:**
```python
# Get protein/nucleic acid information
entity_data = fetch("4HHB_1", schema=Schema.POLYMER_ENTITY)
print(entity_data["entity_poly"]["pdbx_seq_one_letter_code"])
```

**Using GraphQL for Flexible Queries:**
```python
from rcsbapi.data import fetch

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

### 3. Downloading Structure Files

Retrieve coordinate files in various formats:

**Download Methods:**
- **PDB format** (legacy text format): `https://files.rcsb.org/download/{PDB_ID}.pdb`
- **mmCIF format** (modern standard): `https://files.rcsb.org/download/{PDB_ID}.cif`
- **BinaryCIF** (compressed binary): Use ModelServer API for efficient access
- **Biological assembly**: `https://files.rcsb.org/download/{PDB_ID}.pdb1` (for assembly 1)

**Example Download:**
```python
import requests

pdb_id = "4HHB"

# Download PDB format
pdb_url = f"https://files.rcsb.org/download/{pdb_id}.pdb"
response = requests.get(pdb_url)
with open(f"{pdb_id}.pdb", "w") as f:
    f.write(response.text)

# Download mmCIF format
cif_url = f"https://files.rcsb.org/download/{pdb_id}.cif"
response = requests.get(cif_url)
with open(f"{pdb_id}.cif", "w") as f:
    f.write(response.text)
```

### 4. Working with Structure Data

Common operations with retrieved structures:

**Parse and Analyze Coordinates:**
Use BioPython or other structural biology libraries to work with downloaded files:
```python
from Bio.PDB import PDBParser

parser = PDBParser()
structure = parser.get_structure("protein", "4HHB.pdb")

# Iterate through atoms
for model in structure:
    for chain in model:
        for residue in chain:
            for atom in residue:
                print(atom.get_coord())
```

**Extract Metadata:**
```python
from rcsbapi.data import fetch, Schema

# Get experimental details
data = fetch("4HHB", schema=Schema.ENTRY)

resolution = data.get("rcsb_entry_info", {}).get("resolution_combined")
method = data.get("exptl", [{}])[0].get("method")
deposition_date = data.get("rcsb_accession_info", {}).get("deposit_date")

print(f"Resolution: {resolution} Å")
print(f"Method: {method}")
print(f"Deposited: {deposition_date}")
```

### 5. Batch Operations

Process multiple structures efficiently:

```python
from rcsbapi.data import fetch, Schema

pdb_ids = ["4HHB", "1MBN", "1GZX"]  # Hemoglobin, myoglobin, etc.

results = {}
for pdb_id in pdb_ids:
    try:
        data = fetch(pdb_id, schema=Schema.ENTRY)
        results[pdb_id] = {
            "title": data["struct"]["title"],
            "resolution": data.get("rcsb_entry_info", {}).get("resolution_combined"),
            "organism": data.get("rcsb_entity_source_organism", [{}])[0].get("scientific_name")
        }
    except Exception as e:
        print(f"Error fetching {pdb_id}: {e}")

# Display results
for pdb_id, info in results.items():
    print(f"\n{pdb_id}: {info['title']}")
    print(f"  Resolution: {info['resolution']} Å")
    print(f"  Organism: {info['organism']}")
```

## Python Package Installation

Install the official RCSB PDB Python API client:

```bash
# Current recommended package
uv pip install rcsb-api

# For legacy code (deprecated, use rcsb-api instead)
uv pip install rcsbsearchapi
```

The `rcsb-api` package provides unified access to both Search and Data APIs through the `rcsbapi.search` and `rcsbapi.data` modules.

## Common Use Cases

### Drug Discovery
- Search for structures of drug targets
- Analyze ligand binding sites
- Compare protein-ligand complexes
- Identify similar binding pockets

### Protein Engineering
- Find homologous structures for modeling
- Analyze sequence-structure relationships
- Compare mutant structures
- Study protein stability and dynamics

### Structural Biology Research
- Download structures for computational analysis
- Build structure-based alignments
- Analyze structural features (secondary structure, domains)
- Compare experimental methods and quality metrics

### Education and Visualization
- Retrieve structures for teaching
- Generate molecular visualizations
- Explore structure-function relationships
- Study evolutionary conservation

## Key Concepts

**PDB ID:** Unique 4-character identifier (e.g., "4HHB") for each structure entry. AlphaFold and ModelArchive entries start with "AF_" or "MA_" prefixes.

**mmCIF/PDBx:** Modern file format that uses key-value structure, replacing legacy PDB format for large structures.

**Biological Assembly:** The functional form of a macromolecule, which may contain multiple copies of chains from the asymmetric unit.

**Resolution:** Measure of detail in crystallographic structures (lower values = higher detail). Typical range: 1.5-3.5 Å for high-quality structures.

**Entity:** A unique molecular component in a structure (protein chain, DNA, ligand, etc.).

## Resources

This skill includes reference documentation in the `references/` directory:

### references/api_reference.md
Comprehensive API documentation covering:
- Detailed API endpoint specifications
- Advanced query patterns and examples
- Data schema reference
- Rate limiting and best practices
- Troubleshooting common issues

Use this reference when you need in-depth information about API capabilities, complex query construction, or detailed data schema information.

## Additional Resources

- **RCSB PDB Website:** https://www.rcsb.org
- **PDB-101 Educational Portal:** https://pdb101.rcsb.org
- **API Documentation:** https://www.rcsb.org/docs/programmatic-access/web-apis-overview
- **Python Package Docs:** https://rcsbapi.readthedocs.io/
- **Data API Documentation:** https://data.rcsb.org/
- **GitHub Repository:** https://github.com/rcsb/py-rcsb-api

Overview

This skill provides programmatic access to the RCSB Protein Data Bank (PDB) to find, inspect, and download 3D structures of proteins and nucleic acids. It supports text, attribute, sequence, and structure similarity searches, retrieval of metadata via GraphQL or schema endpoints, and downloading coordinates in PDB/mmCIF/BinaryCIF formats. Use it to integrate structural data into drug discovery, protein engineering, or computational biology pipelines.

How this skill works

The skill queries RCSB search APIs to locate entries by text, attributes, sequence similarity, or 3D structure matches and returns lists of PDB IDs. It uses the data API and GraphQL endpoints to fetch entry-level and entity-level metadata, experimental details, and quality metrics. Coordinates and assemblies are downloaded directly from RCSB file servers (PDB, mmCIF, BinaryCIF) and can be processed with common libraries like BioPython.

When to use it

  • Search for structures by protein name, organism, ligand, or other annotations
  • Find homologs or structural analogs using sequence or structure similarity
  • Download coordinate files (PDB, mmCIF, BinaryCIF) for modeling or visualization
  • Retrieve experimental metadata, resolution, deposition dates, and quality metrics
  • Batch-process lists of PDB IDs for large-scale analyses or pipelines

Best practices

  • Prefer mmCIF or BinaryCIF for large or complex structures; legacy PDB is useful for quick inspection
  • Use attribute filters (organism, resolution, method) to narrow results before downloading files
  • Cache fetched metadata and coordinate files to reduce API load and avoid rate limits
  • Validate sequence/chain mappings when combining data from multiple entries or assemblies
  • Use GraphQL for custom, minimal payloads to improve performance

Example use cases

  • Identify high-resolution human protein structures for a drug target and download biological assemblies for docking
  • Run a sequence-similarity search to find homologous structures for comparative modeling
  • Batch-extract resolution, method, and organism metadata for hundreds of PDB IDs for quality assessment
  • Fetch mmCIF files and parse coordinates with BioPython to compute per-residue metrics or prepare inputs for MD simulations
  • Retrieve structure alignments or structure-similarity hits to study binding site conservation

FAQ

Which file format should I choose: PDB, mmCIF, or BinaryCIF?

Use mmCIF for the modern, full-featured format; BinaryCIF is best for efficient, compressed access; legacy PDB is acceptable for simple, human-readable inspection.

How do I limit API usage and avoid rate limits?

Batch requests, cache results locally, and request only required fields (GraphQL) or minimal schemas. Spread large jobs over time and respect documented rate limits.