Cryptic binding pocket discovery via conformational ensemble analysis.

Most protein structure predictors return one static conformation. But many disease-relevant proteins are called undruggable not because they are biologically intractable, but because no pocket is visible in their ground state. K-Ras was considered undruggable for thirty years until a transient cryptic pocket was found beneath its switch-II region. That pocket now backs sotorasib and adagrasib.

Lacuna finds those pockets. It generates a conformational ensemble from any input structure, detects pockets in every conformer, clusters them across the ensemble to surface sites that appear only transiently, and ranks them with a fitted model.

lacuna discover kras.pdb --conformers 20 --emit-boltz-constraints --emit-vina-boxes

Install

pip install lacuna-pockets

Optional extras, for better conformational sampling or the sequence-assisted ranker:

pip install "lacuna-pockets[openmm]"   # 100ps implicit-solvent MD
pip install "lacuna-pockets[plm]"      # PLM-assisted ranker (PyTorch, ESM-2)
pip install "lacuna-pockets[boltz]"    # Boltz-2 diffusion sampling (experimental, GPU)
pip install "lacuna-pockets[all]"      # everything

Requires Python 3.10+. The default backend needs no GPU, no force field and no model weights.

Quick start

lacuna discover protein.pdb --conformers 20

Writes a ranked pocket_report.json plus, on request, Boltz YAML constraints and AutoDock Vina boxes ready for docking. Full options in docs/USAGE.md.

How it works

1. Ensemble generation. N conformers from elastic-network normal mode analysis (default), OpenMM implicit-solvent MD, or Boltz-2 diffusion sampling.
2. Pocket detection. Grid-based alpha-point analysis per conformer: distance transform, local maxima in the 1.4-5.5 A interaction zone, clustered into candidates.
3. Cross-ensemble clustering. Greedy centroid merging matches corresponding pockets across every conformer, turning transient cavities into persistent sites with their own statistics.
4. Druggability scoring. Gaussian volume reward centred at 300 A³, plus enclosure, hydrophobicity and aromaticity (Halgren 2009), scored per conformer.
5. Ranking. A fitted linear model over 23 geometric and ensemble-derived features orders the sites. Each also carries a continuous crypticity score.

Documentation

Results

On CryptoBench's designated test fold, Lacuna recovers 55.6% of known cryptic sites in its top five with the zero-dependency default, and 66.1% with the optional PLM-assisted ranker. The latter is level with P2Rank's 63.3% (+2.8%, CI -4.4 to +9.4, spanning zero, so parity rather than a win); the default trails it by 7.8 points. Against MDpocket given the same ensemble, which isolates this pipeline from the sampler, the default gains +11.7% (CI +3.9 to +19.4).

Full results, including where Lacuna loses →

The more interesting result is not Lacuna's score. Across five candidate-generation methods evaluated in six configurations, coverage (whether a qualifying candidate is proposed at all) spans 14.5 points, while conversion (whether a method's own coverage reaches the top five) spans 36.7. fpocket has the highest coverage at 73.7% but the lowest top-5 recovery at 43.6%, while P2Rank converts 95.8% of the sites it covers. Two Lacuna rankers operating on the exact same candidate set differ by 10.6 points of top-5 recovery, isolating ranking directly.

Union coverage saturates at 92.2%, rising to 98.6% for annotated sites containing at least eight residues. Candidate competition is also causal: adding synthetic competitors while holding the true site, real candidate set, and ranker fixed reduces top-5 recovery by 16.8 points on the training folds and 17.0 points on the held-out test fold. Detector consensus provides no measurable gain at a budget of five candidates, but gains 11.8 points at a budget of twenty.

Moore CW. Cryptic binding sites are detected but not ranked: coverage, conversion, and the limits of detector consensus. bioRxiv 2026. doi:10.64898/2026.08.11.743381

Citation

If you use Lacuna, please cite the software paper:

@article{moore2026lacuna,
  author  = {Moore, Clayton W.},
  title   = {Lacuna: Cryptic Binding Pocket Discovery
             via Conformational Ensemble Analysis},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.08.14.744956}
}

If you use the benchmark data or the coverage/conversion decomposition, please also cite the accompanying analysis:

@article{moore2026coverage,
  author  = {Moore, Clayton W.},
  title   = {Cryptic binding sites are detected but not ranked:
             coverage, conversion, and the limits of detector consensus},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.08.11.743381}
}

To cite a specific software version, the archived releases carry their own DOIs under the concept DOI 10.5281/zenodo.20533638, which always resolves to the newest.

Acknowledgements

Lacuna is measured against, and builds on, work released openly by others: fpocket (Le Guilloux et al. 2009), P2Rank (Krivák & Hoksza 2018), IF-SitePred (Carbery et al. 2024) and MDpocket (Schmidtke et al. 2011). Evaluation uses the CryptoBench (Vavra et al. 2024) and PocketMiner (Meller et al. 2023) datasets. Method credits: ANM (Atilgan et al. 2001), SiteMap druggability (Halgren 2009), enclosure scoring (Schmidtke & Barril 2010), ESM-2 (Lin et al. 2023).

License

MIT, free to use, study, modify, share, and embed in closed-source or commercial work, with no copyleft obligation.

Versions 0.2.0 through 0.3.1 were released under AGPL-3.0 and remain available under those terms. MIT applies from 1.0.0 onward. Lacuna moved back to a permissive license because its central recommendation is to combine several detectors, and copyleft makes that combination harder for exactly the people the work is aimed at.