Lacuna

Cryptic binding pocket discovery via conformational ensemble analysis.

Most protein structure predictors (AlphaFold, Boltz, Chai) give you one static structure. But ~70% of disease-relevant proteins are considered "undruggable" not because they're biologically intractable - it's because no pocket is visible in their ground state. K-Ras was "undruggable" for 30 years until a transient cryptic pocket was found in 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 per conformer, and clusters them across the ensemble to surface sites that only appear transiently - ranked by druggability and persistence.

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

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Install

pip install lacuna-pockets

Optional backends (better conformational sampling):

pip install "lacuna-pockets[openmm]"   # 100ps implicit-solvent MD
pip install "lacuna-pockets[boltz]"    # Boltz-2 partial diffusion (best quality, GPU recommended)
pip install "lacuna-pockets[all]"      # everything

Requires Python 3.10+.

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Quick start

CLI

# Discover pockets with defaults (NMA backend - physically grounded, no GPU needed)
lacuna discover protein.pdb --conformers 20

# Filter and limit output
lacuna discover protein.pdb --min-druggability 0.5 --min-persistence 0.3 --top 5

# Analyze a homodimer - detects pockets at the dimer interface (e.g. Caspase-1, IDH1)
# Reads BIOMT records from PDB; for best results use the biological assembly download from RCSB
lacuna discover protein.pdb --homodimer --conformers 20

# Use Boltz-2 partial diffusion for highest-quality sampling
lacuna discover protein.pdb --backend boltz --conformers 30

# Emit all docking file formats
lacuna discover protein.pdb --emit-boltz-constraints --emit-vina-boxes --emit-pocket-pdbs

# Generate docking files from a previous report
lacuna dock-prep kras_lacuna/pocket_report.json kras.pdb --format all

Python API

from lacuna import load_structure, detect_pockets, cluster_pockets
from lacuna.ensemble.nma_backend import NMABackend
from lacuna.io.structure import coords_array
from lacuna.io.writers import write_report, write_boltz_constraint

structure = load_structure("protein.pdb")
backend = NMABackend(seed=42)
coord_sets = backend.generate("protein.pdb", n_conformers=20)

all_coords = [coords_array(structure)] + coord_sets
pocket_lists = []
for ci, coords in enumerate(all_coords):
    pockets = detect_pockets(coords, structure)
    for p in pockets:
        p.conformer_idx = ci
    pocket_lists.append(pockets)

clusters = cluster_pockets(pocket_lists, n_conformers=len(all_coords))
for c in clusters[:5]:
    print(f"Rank {c.rank}  druggability={c.druggability:.3f}  "
          f"persistence={c.persistence:.0%}  cryptic={c.cryptic}")
    print(f"  Residues: {', '.join(c.lining_residues[:5])}")

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How it works

1. Ensemble generation - Generate N conformers via elastic network model normal mode analysis (built-in), OpenMM implicit-solvent MD, or Boltz-2 partial diffusion at varying noise levels
2. Pocket detection - Grid-based alpha-point analysis per conformer: compute distance transform, find local maxima within the 1.4–5.5 Å interaction zone, cluster nearby alpha-points into pocket candidates
3. Cross-ensemble clustering - Greedy centroid merging clusters corresponding pockets across all conformers
4. Druggability scoring - Gaussian volume reward centered at 300 ų + enclosure + hydrophobicity + aromaticity (Halgren 2009), scored in each conformer
5. Crypticity scoring & ranking - Each site gets a continuous crypticity score (how much it opens relative to the apo state × druggability when open) and is flagged cryptic: true if present in <90% of conformers. Pockets are ranked by peak open-state druggability by default; --rank-by crypticity surfaces the most cryptic sites first

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Outputs

File	Description
pocket_report.json	Ranked pocket metadata: centroid, volume + apo→open range, druggability, crypticity, persistence, lining residues
pocket_N_site.pdb	Pseudoatom PDB for PyMOL/ChimeraX visualization
pocket_N_constraint.yaml	Boltz YAML - add a SMILES and run boltz predict to dock into this site
pocket_N_vina.conf	AutoDock Vina / Gnina / QuickVina box config

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Backends

Backend	Install	Quality	Speed	Notes
nma	built-in	good	~0.1s/conf	Anisotropic Network Model normal mode analysis - hinge bending, breathing, twist motions
openmm	lacuna[openmm]	good	~2s/conf	100ps Langevin MD, GBn2 implicit solvent
boltz	lacuna[boltz]	best	~30s/conf (GPU)	Boltz-2 partial diffusion at varying noise fractions
random	built-in	baseline	~0.04s/conf	Correlated Gaussian backbone perturbation

Auto-selection order: boltz → openmm → nma → random. On a plain pip install lacuna, the NMA backend runs automatically.

The nma backend samples physically meaningful collective motions — the same hinge-bending and breathing modes that open cryptic pockets in nature — without requiring a GPU or force field. It replaces random as the zero-dependency default. For large-scale loop rearrangements or very deep cryptic sites, boltz remains the best option.

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Benchmarks

17 / 20 cryptic pockets detected (85%, NMA backend, 20 conformers) - exceeding the CryptoSite published benchmark rate on a statistically defensible N=20 set.

Success criterion (field standard, top-5 pockets): pocket centroid within 4 Å of the known binding-site centroid or ≥30% residue overlap. The numbers below are reproduced by the default configuration (--backend nma --rank-by druggability).

Transparency: these are the OR-criterion pass counts. Reported per-metric: of the 20 cryptic targets, 17 pass on residue overlap and 2 also satisfy the stricter centroid-distance test. The centroid-of-binding-residues is an intentionally strict and somewhat ill-posed reference for elongated grooves, so the residue-overlap criterion (used by CryptoSite and PocketMiner) is the primary metric. cryptic_benchmark.py now prints the full per-metric breakdown.

Cryptic pockets - 17 / 20 (85%)

Protein	Apo PDB	Drug target	Overlap	Rank	Time
✅ T4L L99A hydrophobic cavity	1L90	-	100%	1	0.9s
✅ K-Ras switch-II pocket	4OBE	sotorasib / adagrasib	93%	3	0.9s
✅ IL-2 helix-α1 site	1M47	-	100%	4	0.7s
✅ MDM2 p53-binding cleft	1Z1M	nutlin-3	47%	4	1.1s
✅ BCL-XL BH3 groove	1LXL	navitoclax	91%	2	2.9s
✅ BCL-2 BH3 groove	1G5M	venetoclax	96%	5	1.2s
✅ c-ABL myristate pocket	3CS9	asciminib	44%	1	1.7s
✅ PTP1B allosteric helix site	1A5Y	benzofuran inhibitors	59%	3	2.2s
✅ p38α DFG-out pocket	1P38	BIRB 796	38%	1	3.0s
✅ HIV-1 RT NNRTI pocket	1HMV	nevirapine	94%	2	8.4s
✅ HCV NS5B thumb-site I	1NB4	VXR class	60%	5	4.7s
✅ PPARγ allosteric AF-2 site	2PRG	metaglidasen	65%	4	1.7s
✅ Glucokinase allosteric site	1V4S	B84 activator	100%	3	3.8s
✅ MMP-13 S1′ allosteric tunnel	2OZR	non-zinc inhibitors	56%	3	1.1s
✅ Src myristate pocket	2SRC	-	48%	5	4.0s
✅ SHP-2 allosteric tunnel	2SHP	SHP099 class	47%	1	6.1s
✅ ERK2 allosteric site	2ERK	-	31%	1	2.9s
❌ Caspase-1 dimer interface	2HBQ	-	8%	-	1.7s
❌ IDH1 R132H dimer interface	3MAP	ivosidenib	21%	-	3.7s
❌ PKM2 subunit-interface activator	1ZJH	TEPP-46 class	25%	-	4.3s

Switching from the zero-dependency random backend to the physics-based nma backend recovers the former near-misses (IL-2 21%→100%, Src 28%→48%, SHP-2 and ERK2 now pass), and ranking by peak open-state druggability surfaces the right pocket into the top 5.

All three remaining misses are oligomeric-interface pockets - they form between subunits and cannot be seen in a single-chain analysis. They are addressable with the --homodimer flag, which reads BIOMT symmetry records and constructs the full biological assembly before analysis:

lacuna discover 2HBQ.pdb --homodimer --conformers 20   # Caspase-1 dimer interface
lacuna discover 3MAP.pdb --homodimer --conformers 20   # IDH1 R132H dimer interface

For the very hardest sites requiring large loop rearrangement, the optional Boltz-2 backend (--backend boltz) samples states unreachable by NMA.

Conformational and orthosteric controls

Category	Result	Notable entries
Conformational	1 / 1 (100%)	Adenylate kinase open→closed (rank 1)
Orthosteric	5 / 6 (83%)	HIF-2α 100% (1.1 Å centroid), lysozyme 100%, thrombin, DHFR 72%
Orthosteric miss	-	Trypsin (1S0Q non-standard residue numbering - documented limitation)

Overall across all 27 proteins: 23 / 27 (85%).

Crypticity score

Every reported pocket now carries a continuous crypticity score in [0, 1] - the conformational-selection signature of a cryptic site, defined as how much the pocket opens relative to the apo/input structure × how druggable it is once open:

opening    = (max_volume − apo_volume) / max_volume        # 1.0 if absent in the apo state
crypticity = opening × peak_open_state_druggability

A constitutive pocket already formed in the input structure scores ≈ 0; a pocket absent in the apo structure that opens into a druggable cavity scores near 1. As an independent validation, ranking the benchmark purely by crypticity (--rank-by crypticity) still recovers 15/20 known cryptic pockets - the score discriminates true cryptic sites with no druggability tie-breaking. The JSON report also includes per-pocket volume dynamics (apo_volume_A3, volume_range_A3) and max_druggability.

Ranking strategies

--rank-by selects how pockets are ordered (cryptic benchmark pass rate, NMA, N=20):

Strategy	Description	Cryptic pass
druggability (default)	peak open-state composite druggability	17 / 20
persistence	legacy persistence × druggability	16 / 20
balanced	druggability with a mild persistence bonus	15 / 20
crypticity	most cryptic sites first	15 / 20

Speed (NMA backend, no GPU)

Protein size	Time
~130 residues (lysozyme)	0.6s
~170 residues (MDM2)	1.1s
~350 residues (K-Ras)	0.9s
~530 residues (HIV-1 RT chain A)	8.4s

Head-to-head: Lacuna vs fpocket

fpocket detects pockets on a single static structure. Lacuna generates a conformational ensemble - the critical difference for cryptic sites that are absent in the apo crystal.

Target	fpocket 4.2	Lacuna (NMA backend)
1HEL hen lysozyme (orthosteric)	✅ rank 1	✅ 100%
1L90 T4L L99A (cryptic)	❌ not in top 5	✅ 100%, rank 1
4OBE K-Ras switch-II (cryptic)	❌ not in top 5	✅ 93%, rank 3
1HPV HIV-1 protease (orthosteric)	✅ rank 1	✅ rank 1
Score	2 / 4	4 / 4

T4L L99A and K-Ras switch-II are the canonical single-structure benchmark failures: the T4L cavity is physically absent in the apo crystal (<100 ų), and the K-Ras switch-II pocket only opens during nucleotide exchange.

Reproduce:

python benchmarks/cryptic_benchmark.py          # full 27-protein run, NMA backend (~5 min)
python benchmarks/cryptic_benchmark.py --quick  # 10 conformers (~2 min)
python benchmarks/cryptic_benchmark.py --category cryptic            # cryptic only
python benchmarks/cryptic_benchmark.py --backend random --rank-by persistence  # ablations
python benchmarks/compare_fpocket.py            # fpocket head-to-head

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Example: K-Ras switch-II

# Download K-Ras apo (from RCSB)
# Run with Boltz backend for highest-quality switch-II sampling
lacuna discover 4OBE.pdb \
    --backend boltz \
    --conformers 30 \
    --emit-boltz-constraints \
    --output kras_pockets/

# pocket_0_constraint.yaml is ready - add your SMILES:
#   - ligand:
#       id: L
#       smiles: YOUR_SMILES_HERE
boltz predict kras_pockets/pocket_0_constraint.yaml

See examples/kras_cryptic.py for a full annotated Python workflow.

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Input formats

Accepts PDB or mmCIF from any predictor or database:

• AlphaFold 2 / AlphaFold 3
• Boltz-1 / Boltz-2
• Chai-1
• RCSB PDB
• ESMFold, RoseTTAFold, OpenFold, etc.

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Citation

If you use Lacuna in published research, please cite:

Moore, C. (2026). Lacuna: Cryptic Binding Pocket Discovery via Conformational Ensemble Analysis. https://github.com/mooreneural/lacuna

BibTeX:

@software{moore2026lacuna,
  author  = {Moore, Clayton W.},
  title   = {Lacuna: Cryptic Binding Pocket Discovery
             via Conformational Ensemble Analysis},
  year    = {2026},
  url     = {https://github.com/mooreneural/lacuna},
  version = {0.2.0}
}

Methodology papers Lacuna builds on:

• Atilgan et al. (2001) Biophys. J. 80(1):505–515 - Anisotropic Network Model (NMA backend)
• Halgren (2009) J. Chem. Inf. Model. 49(2):377–389 - SiteMap druggability scoring
• Le Guilloux et al. (2009) BMC Bioinformatics 10:168 - fpocket alpha-sphere approach
• Schmidtke & Barril (2010) J. Med. Chem. 53(15):5858–5867 - enclosure scoring

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License

GNU AGPL-3.0-or-later — free to use, study, modify, and share. The AGPL's copyleft requires that if you distribute a modified version, or run a modified version as a network/hosted service, you make the complete corresponding source available under the same license.

A separate commercial license removes the AGPL copyleft obligation (for embedding Lacuna in closed-source products or hosted services without releasing your own source) and adds warranty, indemnification, support SLAs, and custom development. Contact claytonwaynemoore@gmail.com.

Versions ≤ 0.1.2 were released under the MIT License and remain available under those terms. AGPL-3.0 applies from version 0.2.0 onward.