Denoise and rebuild atomic coordinates in protein-ligand complexes to generate stochastic ensembles.
npx skills add https://github.com/LiorZ/protein-design-skills --skill placer
Please help me install this Agent Skill. Skill: placer Source: https://github.com/LiorZ/protein-design-skills/tree/main/skills/placer Command: npx skills add https://github.com/LiorZ/protein-design-skills --skill placer
Denoises and rebuilds atomic coordinates in protein-ligand complexes to generate stochastic ensembles that reveal conformational heterogeneity and support refinement, scoring, and validation of binding poses.
Start PLACER inside the provided container on a ligand-containing complex to generate an ensemble of refined poses.
High-intent search queries and answers about installing and using this skill.
Refine docking poses by denoising and rebuilding atomic coordinates for the ligand and proximal sidechains, generating a stochastic ensemble of 50-200 models with per-model scores to validate binding pose realism.
Conformational ensemble generation produces 50-200 stochastic models from a ligand-containing complex to reveal conformational heterogeneity, allowing you to assess binding site plausibility by reranking poses with per-model scores.
Yes, this refinement process runs inside an Apptainer or Singularity SIF container with bundled weights and scripts, taking ligand-containing PDB or mmCIF inputs and outputting multimodel PDBs.
You can explicitly specify fixed ligands, mutations, and ligand chemistry as inputs before running the refinement, ensuring the stochastic ensemble generation respects your defined structural constraints.
Analyze conformational heterogeneity by generating a stochastic ensemble of refined poses, which denoises atomic coordinates and provides per-model scores to evaluate pose variability across the binding pocket.
This approach requires ligand-containing PDB or mmCIF inputs and execution within an Apptainer or Singularity container, limiting its use to environments supporting containerized workflows.
HolobiomicsLabGenerate docking poses and confidence scores for protein-ligand complexes.
jaechang-hitsPredict protein-ligand binding poses from PDB and SMILES inputs.
OwnLabAIGenerate protein-ligand docking poses with diffusion models and confidence scores.
yf8578Run molecular dynamics simulations with OpenMM and analyze trajectories using MDAnalysis.
