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Novel Myosin Motor Modulators — GA-II Designed Ligand–Receptor Complexes
Why this target matters. Direct myosin modulation is the first therapeutic strategy to target cardiac contractility at the motor protein itself, a mechanism validated clinically by mavacamten's approval in hypertrophic cardiomyopathy.
22 small molecules generated de novo by the Technetium TC-43.ai engine (GA-II), conditioned on the allosteric modulator site of the myosin motor domain.
Each molecule was constructed against this pocket rather than selected from a compound library — docking (AutoDock Vina) came afterwards, to place and score the generated molecules in the site. Every design is supplied as a single-file protein–ligand complex in PDB format (21 unique ligand structures).
The myosin motor domain is the force-generating ATPase of muscle; allosteric small-molecule modulators of cardiac/skeletal myosin are an established cardiomyopathy modality (e.g., mavacamten).
Receptor note: coordinates correspond to the myosin motor domain (chain A res 33–782). TODO: add the source RCSB PDB accession for the receptor template used to generate these complexes.
Dataset summary
| Complex files | 22 (*_cmpx.pdb) |
| Unique ligand SMILES | 21 |
| Receptor | myosin motor domain (chain A res 33–782) |
| Generator | Technetium GA-II pocket-conditioned generative platform |
| Generation date | 2025-09-13 – 2025-09-28 |
| Pose scoring | AutoDock Vina |
These are de novo, scaffold-constrained generative designs produced by the Technetium GA-II pocket-conditioned generative platform. Each design is docked into the target pocket and scored with AutoDock Vina; a REMARK CORE record preserves the scaffold/attachment context.
Each complex file is self-contained — receptor structure, the ligand's 3D docked pose, and a 2D↔3D atom map all travel inside the single PDB.
Property profile
Physicochemical ranges are computed with RDKit over the 21 unique ligand structures; docking energy is from the generation/docking pipeline.
| Property | Range | Median |
|---|---|---|
| Docking energy (AutoDock Vina) | ≤ -10.9 kcal/mol (down to -14.4) | — |
| Molecular weight | 333.4 – 498.6 Da | 420.5 |
| cLogP | 1.8 – 3.9 | 3.5 |
| TPSA | 68.8 – 99.5 Ų | 87.9 |
| Fsp3 (fraction sp³ C) | 0.2 – 0.5 | 0.4 |
| H-bond donors | 1 – 2 | 1 |
| H-bond acceptors | 4 – 8 | 6 |
| Rotatable bonds | 3 – 7 | 5 |
File format
Each *_cmpx.pdb bundles the receptor and one docked ligand pose:
| Record | Content |
|---|---|
REMARK VINA RESULT <energy> … |
AutoDock Vina docking score (kcal/mol) |
REMARK CORE <smiles> |
the scaffold / attachment context of the design |
REMARK SMILES <smiles> |
the docked ligand (2D structure) |
REMARK SMILES IDX <pos> <serial> … |
map of each SMILES heavy-atom position ↔ its ligand atom serial (the 2D↔3D key) |
ATOM … <chain> |
receptor heavy atoms |
ATOM … UNL (after MODEL 1) |
ligand 3D pose (residue name UNL) |
A combined SMILES index (Novel Myosin Motor Inhibitors.smi, SMILES<TAB>compound-id) is included for quick cheminformatics use.
Usage
import glob
def read_complex(path):
smiles, idx = None, {}
with open(path) as fh:
for line in fh:
if line.startswith("REMARK SMILES IDX"):
toks = line.split()[3:] # flat list of (smiles_pos, atom_serial)
for i in range(0, len(toks), 2):
idx[int(toks[i])] = int(toks[i + 1])
elif line.startswith("REMARK SMILES"):
smiles = line.split(None, 2)[2].strip()
return smiles, idx # idx[smiles_atom_position] -> ligand atom serial
for f in glob.glob("*_cmpx.pdb"):
smi, idx = read_complex(f)
# ligand atoms are the `ATOM ... UNL` records following `MODEL 1`
Provenance & intended use
- These are computationally generated designs and docked poses — not experimentally validated binders. No claim of activity or selectivity is made.
- Intended for machine-learning, cheminformatics, generative-model benchmarking, and docking-pose research on a well-defined target.
Citation
Generated by Technetium Therapeutics. Poses scored with AutoDock Vina.
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