Pdb Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/pdb-database/downloadUnzip 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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.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
Fetch the relevant schema to discover searchable attribute names. For structure attributes:
uv run scripts/fetch_schema.py --api search_structure --output schema_structure.txtFor chemical attributes:uv run scripts/fetch_schema.py --api search_chemical --output schema_chemical.txtGrep 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.
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.jsonPass the--count_onlyflag 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_citationattributes overcitationattributes.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.jsonCommon 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.jsonSee 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.jsonImportant: use
full_textsearch as a last resort when there's no more precise attribute search available. Consider using thestruct.titleorrcsb_pubmed_abstract_textattributes 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.
Fetch the schema for the relevant object type. E.g.
uv run scripts/fetch_schema.py --api data_entry --output schema_entry.txtGrep the schema for relevant fields (one keyword at a time, many lines).
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.jsonFiles
- ATTRIBUTION.mdAdded by AutoGineer906 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md8.7 KiB
- references/citation.bib878 B
- scripts/download_coordinate_files.pyScript3.2 KiB
- scripts/fetch_pdb_metadata.pyScript1.8 KiB
- scripts/fetch_schema.pyScript7.5 KiB
- scripts/search_pdb.pyScript5.5 KiB
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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
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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)
- Synced
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