Foldseek

Foldseek Structure Search

Prerequisites

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

Databases

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

Next: Download hits with pdb skill → use for scaffold design.

Inputs

• One or more query structures in PDB or mmCIF format.
• A chosen database target such as PDB, AFDB, or a local structural collection.
• Search settings like sensitivity, output mode, and result count.

Outputs

• Ranked structural hits with alignment statistics and hit annotations.
• Evidence about novelty, scaffold reuse, or remote structural homology.
• Candidate templates or analogs to inspect with pdb or follow up in design decisions.

Next Step

Inspect the top structural hits with pdb, then use the results to refine scaffold choice or novelty claims.