Detalle del Skill
pdb-database
Specialized structural-biology data retrieval for scientific DS.
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SKILL.md
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---
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
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
```bash
# 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.naLeer la fuente completa en GitHub (abre una página externa)