/pdb_database

name: pdb-database
description: >
  Use when you want to search for or download experimentally-determined 3D
  structures for biomolecules (proteins, nucleic acids, bound ligands).
  Supports searching by sequence similarity, structure similarity, chemical
  and other attributes. Also use to get metadata about biomolecular structure
  experiments.

RCSB Protein Data Bank skill

Prerequisites

1. **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure `uv` is installed and on PATH. 2. **User Notification**: If .licenses/pdb_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.rcsb.org/pages/usage-policy, then (2) create the file recording the notification text and timestamp.

Core Rules

• **Always prefer to use the provided scripts**. Only as a last resort use

`curl`, `urllib`, raw HTTP requests, or any other method to access PDB APIs. The scripts automatically enforce required rate limits.

• **Always redirect output to a file**. Parse output with e.g. `jq`, `grep`,

or a short Python snippet. Do NOT print large API responses to stdout to avoid truncation.

• **Notification**: If this skill is used, ensure this is mentioned in the

output.

• **Explain your queries** On completing a task that used PDB JSON/GraphQL

queries, explain in clear language what your query did so the user can correct any bad assumptions.

Attribute-based search workflow

1. **Fetch the relevant schema** to discover searchable attribute names. For structure attributes: `uv run scripts/fetch_schema.py --api search_structure --output schema_structure.txt` For chemical attributes: `uv run scripts/fetch_schema.py --api search_chemical --output schema_chemical.txt`

2. **Grep the schema** to find relevant attributes. Grep one keyword at a time and examine many lines — there are lots of similar attributes and you must choose the **best match** for the user's intent.

3. **Compose and run a JSON search query** using the discovered attributes: `uv run scripts/search_pdb.py --query '<JSON>' --return_type <RETURN_TYPE> --output results.json` Pass the `--count_only` flag to get just the number of matching entries.

For step 2: some basic PDB concepts (helpful for attribute choice)

• **Entity**: A unique molecule found in a structure.
• **Instance / Chain**: A particular copy of an entity. E.g. if a structure

contains two protein chains with the same sequence, they are the same entity but different instances / chains.

• **Assembly**: A biologically relevant collection of instances / chains. This

may be the same as the deposited structure, a subset, or multiple copies.

• **Label vs Auth**: Polymer instances get letter labels ("A", "B", "AA") and

their monomers are numbered. There are author-assigned ("auth") and PDB-internal ("label") schemes. The label scheme is more consistent and is always used in scripts and APIs. However, users and papers may refer to the author scheme (clarify which scheme is being used if necessary).

• **Chemical component**: A small molecule / monomer, with an ID matching

`[A-Z]{1,3}`

• **Primary citation**: The main publication about a structure. Prefer

`primary_citation` attributes over `citation` attributes.

• **Resolution**: Frequently used measure of structure quality (lower is

better). Usually prefer `rcsb_entry_info.resolution_combined`, which accounts for different experimental methods.

For step 3: Example queries

# Non-human proteins published in Nature, newest first
uv run scripts/search_pdb.py --query '{ "type": "group", "logical_operator": "and", "nodes": [ { "type": "terminal", "service": "text", "parameters": { "operator": "exact_match", "negation": true, "value": "Homo sapiens", "attribute": "rcsb_entity_source_organism.taxonomy_lineage.name" } }, { "type": "terminal", "service": "text", "parameters": { "operator": "exact_match", "value": "Nature", "attribute": "rcsb_primary_citation.rcsb_journal_abbrev" } } ] }' --return_type entry --sort_by rcsb_accession_info.initial_release_date --sort_direction desc --page_start 0 --rows 100 --output results.json

# Structures containing the chemical component CA (Ca2+ ion)
uv run scripts/search_pdb.py --query '{ "type": "terminal", "service": "text_chem", "parameters": { "operator": "exact_match", "value": "CA", "attribute": "rcsb_chem_comp_container_identifiers.comp_id" } }' --return_type entry --output results.json

# Number of entries with disulfide bonds
uv run scripts/search_pdb.py --query '{ "type": "terminal", "service": "text", "parameters": { "operator": "exact_match", "value": "disulfide bridge", "attribute": "rcsb_polymer_struct_conn.connect_type" } }' --return_type entry --count-only --output count.json

Common operators: `exact_match`, `equals`, `exists`, `contains_phrase`, `contains_words`, `in`, `greater`, `less`

Similarity-based search workflow

Similarity searches do not require a schema fetch. Basic examples:

# Sequence similarity
uv run scripts/search_pdb.py --query '{ "query": { "type": "terminal", "service": "sequence", "parameters": { "evalue_cutoff": 1, "identity_cutoff": 0.9, "sequence_type": "protein", "value": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQ" } }, "request_options": { "scoring_strategy": "sequence" } }' --return_type polymer_entity --output results.json

# Structure similarity
uv run scripts/search_pdb.py --query '{ "type": "terminal", "service": "structure", "parameters": { "value": {"entry_id": "6LU7", "asym_id": "A"}, "number_of_candidates": 2000 } }' --return_type polymer_entity --output results.json

# Sequence motif match
uv run scripts/search_pdb.py --query '{ "type": "terminal", "service": "seqmotif", "parameters": { "value": "C-x(2,4)-C-x(3)-[LIVMFYW