Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Query InterPro for protein domains and families. Use when user asks about protein domains, functional sites, protein families, domain architecture, or motifs. Triggers on "interpro", "protein domain", "domain architecture", "protein family", "functional site", "motif".
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See how to installReview the instructions the agent will receive before you install.
Query the InterPro REST API for protein domains, families, and functional sites.
import requests
import json
BASE_URL = "https://www.ebi.ac.uk/interpro/api"
# 1. Get protein annotation (domains/families for a UniProt ID)
def get_protein_domains(uniprot_id):
url = f"{BASE_URL}/protein/uniprot/{uniprot_id}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 2. Get InterPro entry details
def get_interpro_entry(interpro_id):
url = f"{BASE_URL}/entry/interpro/{interpro_id}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 3. Search InterPro by text
def search_interpro(query, max_results=10):
url = f"{BASE_URL}/entry/interpro"
params = {"search": query, "page_size": max_results}
r = requests.get(url, params=params, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 4. Get domain matches for a protein
def get_domain_matches(uniprot_id):
url = f"{BASE_URL}/protein/uniprot/{uniprot_id}/entry/interpro"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# Example: TP53 domains
domains = get_domain_matches("P04637")
for result in domains.get("results", []):
meta = result.get("metadata", {})
name = meta.get("name", "N/A")
ipr_type = meta.get("type", "N/A")
accession = meta.get("accession", "N/A")
proteins = result.get("proteins", [])
if proteins:
locations = proteins[0].get("entry_protein_locations", [])
for loc in locations:
for frag in loc.get("fragments", []):
start = frag.get("start", "?")
end = frag.get("end", "?")
print(f"{accession} ({ipr_type}): {name} [{start}-{end}]")
domain — Structural/functional domainfamily — Protein familyhomologous_superfamily — Distant homologsrepeat — Repeated motifsite — Active/binding siteNo reviews yet. Tell others what you thought.
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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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.
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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.
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