About alterlab-pdb
A skill for structural biology researchers and computational workflows to search RCSB PDB and retrieve macromolecular coordinates and metadata. It supports text, attribute, sequence, and structural-similarity searches, as well as batch operations.
Key features
- 01Search by text and attributes
- 02Sequence and structure similarity searches
- 03Retrieve entry metadata
- 04Download multiple coordinate formats
- 05Process entries in batches
Use cases
- 01Find experimental structures of drug targets
- 02Compare protein or nucleic acid structures
- 03Download structures for computational analysis
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ alterlab-ieu/ alterlab-academic-skills --skill alterlab-pdbAfter installing, type/alterlab-pdbin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
evals/evals.jsonreferences/api_reference.mdscripts/query_pdb.pyDetails
From the project READMEPDB 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 ~260,000 experimentally determined entries (2026-09) plus computed structure models (CSMs) imported from AlphaFold DB and ModelArchive.
Scripts
scripts/query_pdb.py — RCSB Search + Data + file APIs (stdlib only, JSON to stdout):
python scripts/query_pdb.py search hemoglobin --rows 25 # full-text search (entry IDs)
python scripts/query_pdb.py entry 4HHB # entry metadata
python scripts/query_pdb.py download 4HHB --format cif # download coordinates
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
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| AI-predicted structure for a protein without experimental data | alterlab-alphafold-db |
| Protein sequence, function annotation, or accession mapping | alterlab-uniprot |
| Domain/family classification of a protein | alterlab-interpro |
| Predicting a new complex or co-folding structure | alterlab-boltz |
| Docking a ligand into a structure | alterlab-diffdock |
Core Capabilities
1. Searching for Structures
Find PDB entries using various search criteria:
Text Search: Search by protein name, keywords, or descriptions
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.)
from rcsbapi.search import AttributeQuery
from rcsbapi.search import search_attributes as attrs
# Find human protein structures (idiomatic form — recommended)
query = attrs.rcsb_entity_source_organism.scientific_name == "Homo sapiens"
results = list(query())
# OR explicit AttributeQuery with a dotted-path STRING (not the Attr object):
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
from rcsbapi.search import SeqSimilarityQuery
query = SeqSimilarityQuery(
value="MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQGVDDAFYTLVREIRKHKEKMSKDGKKKKKKSKTKCVIM",
evalue_cutoff=0.1,
identity_cutoff=0.9,
sequence_type="protein"
)
results = list(query())
Structure Similarity: Find structures with similar 3D geometry
from rcsbapi.search import StructSimilarityQuery
# structure_search_type is "entry_id" (default), "file_url", or "file_upload";
# pass assembly_id or chain_id to pick what is compared ("entry"/"chain"/"assembly"
# are not valid search types and return HTTP 400)
query = StructSimilarityQuery(
entry_id="4HHB", # Hemoglobin
assembly_id="1", # or chain_id="A"
)
results = list(query())
Combining Queries: Use logical operators to build complex searches
from rcsbapi.search import search_attributes as attrs
# High-resolution human proteins
query1 = attrs.rcsb_entity_source_organism.scientific_name == "Homo sapiens"
query2 = attrs.rcsb_entry_info.resolution_combined < 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:
from rcsbapi.data import DataQuery
# Get entry-level data
query = DataQuery(
input_type="entries",
input_ids=["4HHB"],
return_data_list=["struct.title", "exptl.method"],
)
data = query.exec() # synchronous; returns a dict
entry = data["data"]["entries"][0]
print(entry["struct"]["title"])
print(entry["exptl"][0]["method"])
Polymer Entity Information:
from rcsbapi.data import DataQuery
# Get protein/nucleic acid information
query = DataQuery(
input_type="polymer_entities",
input_ids=["4HHB_1"],
return_data_list=["entity_poly.pdbx_seq_one_letter_code"],
)
data = query.exec()
entity = data["data"]["polymer_entities"][0]
print(entity["entity_poly"]["pdbx_seq_one_letter_code"])
Building Queries (GraphQL under the hood):
from rcsbapi.data import DataQuery
# DataQuery builds the GraphQL query for you from input_type/input_ids/return_data_list;
# there is no separate fetch(query_type="graphql", ...) entry point.
query = DataQuery(
input_type="entries",
input_ids=["4HHB"],
return_data_list=[
"struct.title",
"exptl.method",
"rcsb_entry_info.resolution_combined",
"rcsb_entry_info.deposited_atom_count",
],
)
# Inspect the auto-generated GraphQL / open it in the editor:
print(query.get_editor_link())
data = query.exec()
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:
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:
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:
from rcsbapi.data import DataQuery
# Get experimental details
query = DataQuery(
input_type="entries",
input_ids=["4HHB"],
return_data_list=[
"rcsb_entry_info.resolution_combined",
"exptl.method",
"rcsb_accession_info.deposit_date",
],
)
data = query.exec()["data"]["entries"][0]
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[0] if resolution else None} Å") # resolution_combined is a list
print(f"Method: {method}")
print(f"Deposited: {deposition_date}")
5. Batch Operations
Process multiple structures efficiently:
from rcsbapi.data import DataQuery
pdb_ids = ["4HHB", "1MBN", "1GZX"] # Hemoglobin, myoglobin, etc.
# A single DataQuery can fetch all entries at once. Use fully qualified paths:
# source organism lives on polymer entities, not on the entry.
query = DataQuery(
input_type="entries",
input_ids=pdb_ids,
return_data_list=[
"rcsb_id",
"struct.title",
"rcsb_entry_info.resolution_combined",
"polymer_entities.rcsb_entity_source_organism.scientific_name",
],
)
results = {}
for data in query.exec()["data"]["entries"]:
pdb_id = data["rcsb_id"]
resolution = (data.get("rcsb_entry_info") or {}).get("resolution_combined") or [None]
entities = data.get("polymer_entities") or [{}]
organisms = (entities[0].get("rcsb_entity_source_organism") or [{}])
results[pdb_id] = {
"title": data["struct"]["title"],
"resolution": resolution[0],
"organism": organisms[0].get("scientific_name"),
}
# 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 (rcsb-api, current major version
1.x; examples here target >=1.7):
uv pip install "rcsb-api>=1.7"
The rcsb-api package provides unified access to both Search and Data APIs through
the rcsbapi.search and rcsbapi.data modules. (The older rcsbsearchapi package
is superseded by rcsb-api and its import rcsbsearchapi path is gone — prefer
rcsb-api for new code.)
The scripts/query_pdb.py helper needs none of this — it hits the public REST APIs
with only the Python standard library.
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