foldseek
Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.
Best use case
foldseek is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.
Teams using foldseek should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/foldseek/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How foldseek Compares
| Feature / Agent | foldseek | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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)
```bash
# 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
```bash
# 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
```python
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
```bash
# Compare your design to PDB
foldseek easy-search design.pdb pdb100 similar_natural.m8 tmp/
```
### Novelty check
```bash
# 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
```bash
# Find scaffolds for motif grafting
foldseek easy-search motif.pdb pdb100 scaffolds.m8 tmp/ \
--min-seq-id 0.0 -e 10
```
---
## Verify
```bash
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 |
---
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