Pdb DatabaseSAFE

Kosmos: An AI Scientist for Autonomous Discovery - An implementation and adaptation to be driven by Claude Code or API - Based on the Kosmos AI Paper - https://arxiv.org/abs/2511.02824

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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] = {
            "t

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.