### Pdb Database
Works with
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 simi..."
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpdb-databaseExecute the skills CLI command in your project's root directory to begin installation:
Fetches pdb-database from google-deepmind/science-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate pdb-database. Access via /pdb-database in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
2
total installs
2
this week
0
upvotes
Run in your terminal
2
installs
2
this week
—
stars
| 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. |
uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.curl, urllib, raw HTTP requests, or any other method to access PDB APIs.
The scripts automatically enforce required rate limits.jq, grep,
or a short Python snippet. Do NOT print large API responses to stdout to
avoid truncation.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
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.
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.
[A-Z]{1,3}primary_citation attributes over citation attributes.rcsb_entry_info.resolution_combined, which
accounts for different experimental methods.# 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 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.
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_textsearch as a last resort when there's no more precise attribute search available. Consider using thestruct.titleorrcsb_pubmed_abstract_textattributes instead.
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>
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.txt
Grep 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
# 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
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
google-deepmind/science-skills
google-deepmind/science-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
BuilderIO/skills
We added pdb-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
pdb-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
pdb-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: pdb-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
pdb-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
pdb-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: pdb-database is focused, and the summary matches what you get after install.
pdb-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend pdb-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added pdb-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
showing 1-10 of 52