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Pdb Database

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

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.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The pdb-database skill provides a structured interface for interacting with the RCSB Protein Data Bank (PDB). It enables users to perform complex searches for biomolecular structures—including proteins, nucleic acids, and ligands—based on sequence, structure, chemical properties, or full-text queries. Additionally, it facilitates the retrieval of specific experimental metadata and the bulk download of coordinate files, ensuring efficient data access for structural biology research.

What it does

  • Performs attribute-based searches for biomolecular structures using JSON queries.
  • Executes similarity searches based on protein sequences, structural motifs, or chemical descriptors.
  • Downloads atomic coordinate files in various formats for specific PDB entries.
  • Retrieves specific experimental metadata using GraphQL queries.
  • Provides tools to discover searchable schema attributes for precise data filtering.
  • Enforces rate limits and best practices for API interaction.

When to use it

  • When you need to find 3D structures of biomolecules based on specific biological or chemical criteria.
  • When you need to perform sequence or structural similarity searches against the PDB.
  • When you need to download coordinate files (e.g., mmcif) for structural analysis.
  • When you need to extract specific metadata about PDB entries without downloading full coordinate files.

Advantages

  • Provides a standardized, script-based workflow for reliable data retrieval.
  • Supports advanced search capabilities including sequence motifs and chemical descriptors.
  • Optimizes data usage by offering both full-file downloads and lightweight metadata queries.
  • Ensures consistent interaction with PDB APIs through provided helper scripts.

Usage examples

“Find all non-human protein structures published in Nature.”
The agent uses a JSON search query to filter entries by taxonomy and journal, returning a list of matching PDB IDs.
“Download the coordinate files for PDB entries 4HHB and 6BEA.”
The agent executes the coordinate download script to save the requested structures to the local workspace.
“Get the experimental methods and titles for entries 1STP and 2JEF.”
The agent runs a GraphQL metadata query and returns the requested information in a structured JSON format.
“Search for structures containing a specific chemical component like a calcium ion.”
The agent performs a text-based chemical search and provides the list of matching PDB entries.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google-deepmind/pdb-database/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip pdb-database.zip -d .agents/skills

Documentation by Google DeepMind

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

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/pdb_database · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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