Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Query AlphaFold protein structure predictions. Use when user asks about protein structure, 3D structure, protein folding, or structure prediction. Triggers on "alphafold", "protein structure", "3D structure", "folding", "pLDDT", "structure prediction".
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Query the AlphaFold EBI API for predicted protein structures.
import requests
import json
BASE_URL = "https://alphafold.ebi.ac.uk/api"
# 1. Get prediction info
def get_alphafold_prediction(uniprot_id):
url = f"{BASE_URL}/prediction/{uniprot_id}"
r = requests.get(url)
r.raise_for_status()
return r.json()
# 2. Download structure file
def download_structure(uniprot_id, output_dir="/workspace/group", fmt="pdb", version="v4"):
filename = f"AF-{uniprot_id}-F1-model_{version}.{fmt}"
url = f"https://alphafold.ebi.ac.uk/files/{filename}"
r = requests.get(url)
r.raise_for_status()
filepath = f"{output_dir}/{filename}"
with open(filepath, 'wb') as f:
f.write(r.content)
return filepath
# 3. Get per-residue confidence (pLDDT)
def get_plddt(uniprot_id):
url = f"{BASE_URL}/prediction/{uniprot_id}"
r = requests.get(url)
data = r.json()
if isinstance(data, list) and data:
cif_url = data[0].get("cifUrl", "")
plddt_url = data[0].get("paeImageUrl", "")
return {"cifUrl": cif_url, "paeImageUrl": plddt_url, "data": data[0]}
return data
# Example
data = get_alphafold_prediction("P04637") # TP53
if isinstance(data, list) and data:
entry = data[0]
print(f"UniProt: {entry.get('uniprotAccession')}")
print(f"Gene: {entry.get('gene', 'N/A')}")
print(f"Organism: {entry.get('organismScientificName', 'N/A')}")
print(f"Model confidence: {entry.get('globalMetricValue', 'N/A')}")
print(f"PDB URL: {entry.get('pdbUrl', 'N/A')}")
print(f"CIF URL: {entry.get('cifUrl', 'N/A')}")
| Endpoint | URL | Use |
|---|---|---|
| Prediction | /api/prediction/{uniprot_id} | Get model info & download URLs |
| Summary | /api/uniprot/summary/{uniprot_id}.json | Brief summary |
| Annotations | /api/annotations/{uniprot_id} | Per-residue annotations |
AF-{UNIPROT_ID}-F1-model_v4.pdbAF-{UNIPROT_ID}-F1-model_v4.cif아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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