Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Note: This skill uses the UniProt REST API directly. No Modal deployment needed - all operations run locally via HTTP requests.
# FASTA format
curl "https://rest.uniprot.org/uniprotkb/P00533.fasta"
# JSON format with annotations
curl "https://rest.uniprot.org/uniprotkb/P00533.json"
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
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']}")
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}, ...]
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)
# Use UniProt BLAST
# https://www.uniprot.org/blast
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")
# 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
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
| Endpoint | Description |
|---|---|
/uniprotkb/{id}.fasta | FASTA sequence |
/uniprotkb/{id}.json | Full entry JSON |
/uniprotkb/search | Search entries |
/uniprotkb/stream | Batch download |
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 colabfold for structure.
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.