Foldseek Structure Search
Prerequisites
| Requirement | Minimum | Recommended |
|---|
| Python | 3.8+ | 3.10 |
| RAM | 8GB | 16GB |
| Disk | 10GB | 50GB (for local databases) |
How to run
Note: Foldseek can run locally or via web server. No GPU required.
Option 1: Web Server (Quick; rate-limited, use sparingly)
# Upload structure to web server
curl -X POST "https://search.foldseek.com/api/ticket" \
-F "q=@query.pdb" \
-F "database[]=afdb50" \
-F "database[]=pdb100"
Option 2: Local installation
# Install Foldseek
conda install -c conda-forge -c bioconda foldseek
# Search PDB
foldseek easy-search query.pdb /path/to/pdb100 results.m8 tmp/
# Search AlphaFold DB
foldseek easy-search query.pdb /path/to/afdb50 results.m8 tmp/
Option 3: Python API
import subprocess
import pandas as pd
def foldseek_search(query_pdb, database, output="results.m8"):
"""Run Foldseek search."""
subprocess.run([
"foldseek", "easy-search",
query_pdb, database, output, "tmp/",
"--format-output", "query,target,pident,alnlen,evalue,bits"
])
return pd.read_csv(output, sep="\t",
names=["query", "target", "pident", "alnlen", "evalue", "bits"])
Key parameters
| Parameter | Default | Description |
|---|
--min-seq-id | 0.0 | Minimum sequence identity |
-e | 0.001 | E-value threshold |
--alignment-type | 2 | 0=3Di, 1=TM, 2=3Di+AA |
--max-seqs | 300 | Max hits to pass through prefilter; reducing this affects sensitivity |
Databases
| Database | Description | Size |
|---|
pdb100 | PDB clustered at 100% | ~200K structures |
afdb50 | AlphaFold DB at 50% | ~67M structures |
swissprot | SwissProt structures | ~500K structures |
cath50 | CATH domains | ~50K domains |
Output format
# results.m8 (tabular)
query target pident alnlen evalue bits
query 1abc_A 85.2 120 1e-45 180.5
query 2def_B 72.1 115 1e-32 145.2
Sample output
Successful run
$ foldseek easy-search query.pdb pdb100 results.m8 tmp/
[INFO] Loading database: pdb100 (194,527 entries)
[INFO] Searching...
[INFO] Found 127 hits
Top 5 hits:
1. 1abc_A - 85.2% identity, E=1e-45
2. 2def_B - 72.1% identity, E=1e-32
3. 3ghi_C - 68.5% identity, E=1e-28
4. 4jkl_A - 55.3% identity, E=1e-18
5. 5mno_B - 42.1% identity, E=1e-10
Decision tree
Should I use Foldseek?
│
├─ What are you searching?
│ ├─ By 3D structure → Foldseek ✓
│ ├─ By sequence → Use BLAST (uniprot skill)
│ └─ Both → Run both, compare results
│
└─ What do you need?
├─ Find structural homologs → Foldseek ✓
├─ Remote homolog detection → Foldseek ✓
├─ Structural clustering → Foldseek ✓
└─ Functional annotation → Cross-reference with UniProt
Common use cases
Find similar designs
# Compare your design to PDB
foldseek easy-search design.pdb pdb100 similar_natural.m8 tmp/
Novelty check
# Ensure design is novel (low similarity to known)
foldseek easy-search design.pdb afdb50 novelty.m8 tmp/
# Novel if: top hit identity < 30%
Scaffold search
# Find scaffolds for motif grafting
foldseek easy-search motif.pdb pdb100 scaffolds.m8 tmp/ \
--min-seq-id 0.0 -e 10
Verify
wc -l results.m8 # Number of hits
Troubleshooting
No hits: Lower e-value threshold, try larger database Too many hits: Increase min-seq-id threshold Slow search: Use smaller database
Error interpretation
| Error | Cause | Fix |
|---|
Database not found | Wrong path | Check database location |
Invalid PDB | Malformed structure | Validate PDB format |
Out of memory | Large database | Use more RAM or web server |
Next: Download hits with pdb skill → use for scaffold design.
