pdb-database

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" } } ] }'

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:

See https://search.rcsb.org/#search-services for more details.

Full text search workflow

Searches all text associated with an entry. Example:

Important: use full_text search as a last resort when there's no more precise attribute search available. Consider using the struct.title or rcsb_pubmed_abstract_text attributes instead.

File download workflow

To download full PDB entries, use the download_coordinate_files.py script. Use this when you need access to atomic coordinates, when asked for a pdb / mmcif file, or when non-specifically asked to fetch a PDB code. Example:

Metadata query workflow

This flow is significantly more efficient than downloading full coordinate files when you only need a few pieces of metadata about each entry / entity.

1. Fetch the schema for the relevant object type. E.g. uv run scripts/fetch_schema.py --api data_entry --output schema_entry.txt

2. Grep the schema for relevant fields (one keyword at a time, many lines).

3. Compose and run a GraphQL metadata query: uv run scripts/fetch_pdb_metadata.py --query '<GraphQL>' --output results.json

For step 3: Example queries