Sample protein sequences to generate backbone conformation ensembles with YAML-configurable denoiser pipelines.
npx skills add https://github.com/LiorZ/protein-design-skills --skill bioemu
Please help me install this Agent Skill. Skill: bioemu Source: https://github.com/LiorZ/protein-design-skills/tree/main/skills/bioemu Command: npx skills add https://github.com/LiorZ/protein-design-skills --skill bioemu
BioEmu enables sampling an ensemble of backbone conformations for a protein monomer from its sequence, delivering an ensemble instead of a single predicted structure, at a fraction of MD cost.
Run a quick test by sampling a small ensemble from a sequence using a modest number of samples and inspect the topology.pdb and samples.xtc outputs.
High-intent search queries and answers about installing and using this skill.
Protein ensemble sampling produces an array of backbone conformations rather than a single static structure by sampling a monomer's sequence. This diffusion-based approach captures conformational variability for downstream analysis.
Estimating folding free-energy landscapes requires generating a protein backbone ensemble at a fraction of MD cost. This diffusion-based conformational sampling provides the structural variance needed to calculate ΔG_fold.
Yes, you can identify cryptic pockets by sampling an ensemble of backbone conformations for a monomeric protein. Exploring this structural variability reveals hidden binding sites that static predictions often miss.
Yes, Linux and Python are required to run this diffusion-based conformational sampling pipeline. Optional GPU acceleration is supported to improve processing speed during the generation of protein ensembles.
You can steer protein backbone sampling toward a collective variable using configurable SMC or FKC methods within the denoiser pipeline. This improves realism or targets specific conformational states during ensemble generation.
The sampling process outputs samples.xtc and topology.pdb files, alongside batch caches. These outputs support trajectory visualization and downstream conformational analysis with resumable and reproducible semantics.
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