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pdb-database

google-deepmind/science-skills

Search and download experimentally-determined 3D biomolecule structures from the RCSB Protein Data Bank.

What is pdb-database?

Access the RCSB Protein Data Bank to search for and download 3D structures of proteins, nucleic acids, and bound ligands. Use this skill when you need to find structures by sequence similarity, structure similarity, chemical properties, or other attributes, or when you need metadata about biomolecular structure experiments.

  • Search structures by sequence similarity, structure similarity, chemical descriptors, or sequence motifs
  • Search by biomolecule attributes (organism, journal, resolution, experimental method, etc.) using attribute-based queries
  • Download coordinate files in PDB or mmCIF format for structures of interest
  • Retrieve metadata about entries and entities without downloading full coordinate files
  • Perform full-text searches across all associated entry information
  • Access chemical component information and ligand data

How to install pdb-database

npx skills add https://github.com/google-deepmind/science-skills --skill pdb-database
Prerequisites
  • uv must be installed and on PATH (see uv skill)
  • User must review RCSB PDB usage policy at https://www.rcsb.org/pages/usage-policy before first use
  • License notification file will be created in workspace root on first use
Claude Code
Cursor
Windsurf
Cline

How to use pdb-database

  1. 1.For attribute-based searches: fetch the relevant schema using fetch_schema.py to discover searchable attributes
  2. 2.Grep the schema output to identify the best-matching attributes for your search criteria
  3. 3.Compose a JSON search query using the discovered attributes and run search_pdb.py
  4. 4.For similarity searches: construct a query specifying sequence, structure, motif, or chemical similarity parameters
  5. 5.For metadata queries: fetch the schema for your object type, identify relevant fields, and run fetch_pdb_metadata.py
  6. 6.For coordinate files: use download_coordinate_files.py with PDB IDs and desired format (mmcif or pdb)
  7. 7.Redirect all output to files and parse with jq, grep, or Python rather than printing to stdout

Use cases

Good for
  • Find all protein structures from a specific organism published in a particular journal
  • Identify structures similar to a query protein sequence or 3D structure
  • Discover structures containing specific chemical components (e.g., metal ions, cofactors)
  • Retrieve high-resolution structures meeting specific quality criteria
  • Find structures matching a sequence motif or chemical descriptor
Who it's for
  • Structural biologists and computational biologists
  • Drug discovery and molecular modeling researchers
  • Bioinformaticians analyzing protein families and evolution
  • Students and educators in structural biology
  • Researchers performing structure-based analysis or validation

pdb-database FAQ

What's the difference between label and auth numbering schemes?

Polymer instances get letter labels (A, B, AA) with PDB-internal label numbering or author-assigned (auth) numbering. The label scheme is more consistent and is always used in scripts and APIs, but users and papers may refer to the author scheme.

What does resolution mean and why does it matter?

Resolution is a frequently-used measure of structure quality where lower values indicate better quality. Use rcsb_entry_info.resolution_combined, which accounts for different experimental methods.

What's the difference between an entity and an instance/chain?

An entity is a unique molecule found in a structure, while an instance or chain is a particular copy of that entity. For example, if a structure contains two protein chains with the same sequence, they are the same entity but different instances.

When should I use full-text search vs. attribute search?

Use full-text search only as a last resort when there's no more precise attribute search available. Consider using struct.title or rcsb_pubmed_abstract_text attributes instead for more targeted results.

What's the difference between downloading coordinate files and querying metadata?

Downloading full coordinate files gives you atomic coordinates but is slower. Metadata queries are significantly more efficient when you only need a few pieces of information about each entry or entity.

Full instructions (SKILL.md)

Source of truth, from google-deepmind/science-skills.


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)-[LIVMFYWC]-x(8)-H-x(3,5)-H.", "pattern_type": "prosite", "sequence_type": "protein" } }' --return_type polymer_entity --output results.json
# Chemical descriptor match
uv run scripts/search_pdb.py --query '{ "type": "terminal", "service": "chemical", "parameters": { "value": "InChI=1S/C8H9NO2/c1-6(10)9-7-2-4-8(11)5-3-7/h2-5,11H,1H3,(H,9,10)", "type": "descriptor", "descriptor_type": "InChI", "match_type": "graph-strict" } }' --return_type mol_definition --output results.json

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

Full text search workflow

Searches all text associated with an entry. Example:

uv run scripts/search_pdb.py --query '{ "type": "terminal", "service": "full_text", "parameters": { "value": "isopeptide + ( collagen | fibrinogen )" } }' --return_type entry --output results.json

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:

uv run scripts/download_coordinate_files.py --ids "4HHB,6BEA" --format "mmcif" --output_dir <OUTPUT_DIR>

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

# Fetch structure titles and experimental methods
uv run scripts/fetch_pdb_metadata.py --query '{ entries(entry_ids: ["1STP", "2JEF", "1CDG"]) { rcsb_id struct { title } exptl { method } } }' --output results.json
# Fetch polymer entity taxonomy and cluster membership
uv run scripts/fetch_pdb_metadata.py --query '{ polymer_entities(entity_ids:["2CPK_1","3WHM_1","2D5Z_1"]) { rcsb_id rcsb_entity_source_organism { ncbi_taxonomy_id ncbi_scientific_name } rcsb_cluster_membership { cluster_id identity } } }' --output results.json
# Fetch polymer entity external sequence database accessions
uv run scripts/fetch_pdb_metadata.py --query '{ entries(entry_ids:["7NHM", "5L2G"]){ polymer_entities { rcsb_id rcsb_polymer_entity_container_identifiers { reference_sequence_identifiers { database_accession database_name } } } } }' --output results.json