pdb-database
Skill2 starsChanged 3 months agoMITView on GitHub
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.
13 other projects use a version of this, 4 of them edited.
Adopted by
Most recently taken up by
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- MarieLynneBlock/arcanum-artifextheir own version, first seen Nov 1, 2026
- davidanthonyn/claude-skillstheir own version, first seen Oct 20, 2026
- Osmosy/vector-geneedited, first seen Oct 13, 2026
- garri333/Skillstheir own version, first seen Oct 11, 2026
- wu-yc/LabClawtheir own version, first seen Oct 8, 2026
Other versions13 projects use a version of this.
Word for word · 0
None found.
Edited · 4
- google-deepmind/science-skills★ 3.4k98% same
- BeyondTheProtocol/polaris★ 4798% same
- uppifyagency/boilerplate-saas-2026★ 1100% same
- Osmosy/vector-gene★ 098% same
Wrote their own version · 9
- wu-yc/LabClaw★ 1.1ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- jaechang-hits/SciAgent-Skills★ 386same name
- qinyan-ai/qinyan-academic-skills★ 80same name
- MarieLynneBlock/arcanum-artifex★ 4same name
- BrightGold70/skills★ 1same name
- garri333/Skills★ 1same name
- yf8578/claude-skills-collection★ 1same name
- davidanthonyn/claude-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.
0
Common
Most of the 13 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 20
uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH. - Very specific: line 22
User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill directory then (1) prominently notify the user to check …
- Very specific: line 29
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 …
- Distinctive: line 35
Notification: If this skill is used, ensure this is mentioned in the output.
“Common” is measured: kept by at least 6 in 10 of the 13 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.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.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.jsonDoes it work?
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