Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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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.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
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.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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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.
The English summary is being prepared. Showing the original description.