uniprot
Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence design, use proteinmpnn.
- License 许可
- MIT
- 最近更新
- 2026-11-12
- Stars
- ★ 6
- Forks
- 0
01 前言 Front Matter
- name
- uniprot
- tags
- [ "database", "sequence", "annotation" ]
- license
- MIT
- category
- utilities
- description
- Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence design, use proteinmpnn.
03 SKILL.md 正文
UniProt Database Access
Note: This skill uses the UniProt REST API directly. No Modal deployment needed - all operations run locally via HTTP requests.
Fetching Sequences
By Accession
# FASTA format
curl "https://rest.uniprot.org/uniprotkb/P00533.fasta"
# JSON format with annotations
curl "https://rest.uniprot.org/uniprotkb/P00533.json"
Using Python
import requests
def get_uniprot_sequence(accession):
"""Fetch sequence from UniProt."""
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta"
response = requests.get(url)
if response.ok:
lines = response.text.strip().split('\n')
header = lines[0]
sequence = ''.join(lines[1:])
return header, sequence
return None, None
Getting Annotations
Full Entry
def get_uniprot_entry(accession):
"""Fetch full UniProt entry as JSON."""
url = f"https://rest.uniprot.org/uniprotkb/{accession}.json"
response = requests.get(url)
return response.json() if response.ok else None
entry = get_uniprot_entry("P00533")
print(f"Protein: {entry['proteinDescription']['recommendedName']['fullName']['value']}")
Domain Boundaries
def get_domains(accession):
"""Extract domain annotations."""
entry = get_uniprot_entry(accession)
domains = []
for feature in entry.get('features', []):
if feature['type'] == 'Domain':
domains.append({
'name': feature.get('description', ''),
'start': feature['location']['start']['value'],
'end': feature['location']['end']['value']
})
return domains
# Example: EGFR domains
domains = get_domains("P00533")
# [{'name': 'Kinase', 'start': 712, 'end': 979}, ...]
Searching UniProt
By Gene Name
def search_uniprot(query, organism=None, limit=10):
"""Search UniProt by query."""
url = "https://rest.uniprot.org/uniprotkb/search"
params = {
"query": query,
"format": "json",
"size": limit
}
if organism:
params["query"] += f" AND organism_id:{organism}"
response = requests.get(url, params=params)
return response.json()['results']
# Search for human EGFR
results = search_uniprot("EGFR", organism=9606)
By Sequence Similarity (BLAST)
# Use UniProt BLAST
# https://www.uniprot.org/blast
Cross-References
Get PDB Structures
def get_pdb_references(accession):
"""Get PDB structures for UniProt entry."""
entry = get_uniprot_entry(accession)
pdbs = []
for xref in entry.get('uniProtKBCrossReferences', []):
if xref['database'] == 'PDB':
pdbs.append({
'pdb_id': xref['id'],
'method': xref.get('properties', [{}])[0].get('value', ''),
'chains': xref.get('properties', [{}])[1].get('value', '')
})
return pdbs
# Example: PDB structures for EGFR
pdbs = get_pdb_references("P00533")
Common Use Cases
Target Selection
# 1. Find protein by name
results = search_uniprot("insulin receptor", organism=9606)
# 2. Get accession
accession = results[0]['primaryAccession'] # e.g., P06213
# 3. Get domains
domains = get_domains(accession)
# 4. Find PDB structure
pdbs = get_pdb_references(accession)
# 5. Download best structure for design
Sequence Alignment Info
def get_sequence_variants(accession):
"""Get natural variants from UniProt."""
entry = get_uniprot_entry(accession)
variants = []
for feature in entry.get('features', []):
if feature['type'] == 'Natural variant':
variants.append({
'position': feature['location']['start']['value'],
'original': feature.get('alternativeSequence', {}).get('originalSequence', ''),
'variant': feature.get('alternativeSequence', {}).get('alternativeSequences', [''])[0],
'description': feature.get('description', '')
})
return variants
API Reference
| Endpoint | Description |
|---|---|
/uniprotkb/{id}.fasta |
FASTA sequence |
/uniprotkb/{id}.json |
Full entry JSON |
/uniprotkb/search |
Search entries |
/uniprotkb/stream |
Batch download |
Troubleshooting
Entry not found: Check accession format (e.g., P00533) Rate limits: Add delay between requests Large downloads: Use stream endpoint with pagination
Next: Use sequence with esm for embeddings or chai / boltz for structure.
04 同仓库更多 Skills
branding
When the user wants to define, audit, or apply brand strategy—purpose, values, positioning, storytelling, voice, narrative (not only visuals). Also use when the user mentions "brand strategy", "brand story", "brand storytelling" or "brand voice". For typography, colors, design tokens, and frontend visuals, use brand-visual-generator.
brand-visual-generator
When the user wants to define, audit, or apply visual identity (typography, colors, spacing, design tokens, frontend aesthetics). Also use when the user mentions "brand style guide", "visual identity", "design system" or "typography". For brand story, positioning, and voice, use branding.
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
frontend-design
Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
inbox-guardian-for-gmail
Review a personal Gmail inbox with local, owner-approved spam rules. Use for audit-first quarantine, Trash, repeated-evidence sender learning, and header review. Do not use for automatic unsubscribe requests or unreviewed mailbox actions.