bio-alignment-structural
Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.
npx skills add BioTender-max/awesome-bio-agent-skills --skill structural-alignment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: Foldseek 8+, TM-align 20220412+, US-align 20231222+, Foldmason 1+, BioPython 1.83+, pymol-open-source 3.0+ Before using code patterns, verify installed versions match. If versions differ: - CLI: `foldseek --version`, `TMalign`, `USalign`, `foldmason --version` - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Structural Alignment **"Align two protein structures"** → Compute backbone-aware superposition and a fold-similarity score (TM-score, RMSD, or LDDT). - CLI pairwise: `TMalign A.pdb B.pdb`, `USalign A.pdb B.pdb` - CLI search at scale: `foldseek easy-search query/ AFDB result.m8 tmp/` - CLI structural MSA: `foldmason easy-msa structures/*.pdb out tmp/` - Python pairwise: `Bio.PDB.Superimposer`, or `subprocess` wrapping `TMalign` / `USalign` (see `examples/tm_align_pairwise.py`) - GUI / scripted molecular-graphics superposition: ChimeraX `matchmaker`, PyMOL `super`/`cealign` **"Find structural homologs of an AlphaFold model"** → Search
- Version Compatibility
- When to Use Structural Alignment
- Twilight-Zone Threshold Exceptions
- Pairwise Structural Alignment Tool Selection
- Foldseek vs DALI: Modern Comparison
- TM-score Threshold Caveats
- TM-align Pairwise Run
- Foldseek-Multimer for Database-Scale Complex Search
- Bio.PDB.Superimposer
- Structural Search at Scale: Foldseek
- pLDDT-Filtering AlphaFold Structures Before Foldseek
- Structural Multiple Sequence Alignment
- Foldmason easy-msa
- Hybrid Sequence-Structure Approaches
TMalign chainA.pdb chainB.pdb -o superposed.sup TMalign chainA.pdb chainB.pdb -outfmt 2 USalign chainA.pdb chainB.pdb -mol prot -outfmt 2 USalign complex_A.pdb complex_B.pdb -mm 1 -ter 0 foldseek easy-multimersearch query_complex.pdb afdb_multimer result tmp/ foldseek easy-multimersearch query_complex.pdb afdb_multimer result tmp/ --multimer-tm-threshold 0.5 foldseek easy-multimercluster *.pdb cluster_result tmp/ --multimer-tm-threshold 0.65 Search query structures against AFDB (default: --alignment-type 2) foldseek easy-search query.pdb afdb_database result.m8 tmp/ Refine top hits with full TM-align rotation (slower but global TM-score)
What does the bio-alignment-structural skill do?
Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill structural-alignment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
