pdb-database▌
google-deepmind/science-skills · updated Jun 4, 2026
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### Pdb Database
- ›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..."
| 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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill 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.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.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.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_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.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
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
How to use pdb-database on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add pdb-database
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches pdb-database from GitHub repository google-deepmind/science-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate pdb-database. Access the skill through slash commands (e.g., /pdb-database) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★52 reviews- ★★★★★Diya Diallo· Dec 20, 2024
We added pdb-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Nikhil Diallo· Dec 12, 2024
pdb-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Liam White· Dec 12, 2024
pdb-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Diya Chawla· Dec 4, 2024
Keeps context tight: pdb-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sakshi Patil· Nov 23, 2024
pdb-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Diya Bhatia· Nov 11, 2024
pdb-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nia Torres· Nov 11, 2024
Solid pick for teams standardizing on skills: pdb-database is focused, and the summary matches what you get after install.
- ★★★★★Diya Lopez· Nov 3, 2024
pdb-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aarav Patel· Nov 3, 2024
I recommend pdb-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Nia Flores· Oct 22, 2024
We added pdb-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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