## Connections
### Web (via AlphaFold DB)
1. Go to https://alphafold.ebi.ac.uk/entry/Q7RTU9 (STRC)
2. Click "AlphaMissense" tab
3. View heatmap of all possible substitutions
4. Download full CSV
### Download Pre-computed Data
# Full dataset (4.3GB)
gsutil cp gs://dm_alphamissense/AlphaMissense_hg38.tsv.gz .
# Or per-gene from AlphaFold DB API
curl "https://alphafold.ebi.ac.uk/api/prediction/Q7RTU9" | python3 -c "
import sys,json
d = json.load(sys.stdin)[0]
print(d.get('amAnnotationUrl', 'N/A'))
"
### Python (from downloaded TSV)
import pandas as pd
am = pd.read_csv("AlphaMissense_hg38.tsv.gz", sep='\t', comment='#')
strc = am[am['uniprot_id'] == 'Q7RTU9']
e1659a = strc[(strc['protein_variant'] == 'E1659A')]
print(e1659a) # Score: 0.9016
VERIFIED — STRC E1659A score: 0.9016 (likely pathogenic, ≥0.840 threshold per Pejaver 2022). Used as PP3_Moderate evidence in ACMG classification.