proteinmpnn
Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while holding interface residues fixed, or to generate a temperature-swept set of sequences for downstream folding.
Profile →npx skills add xuzhougeng/wisp-science --skill proteinmpnn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ProteinMPNN ProteinMPNN is the default inverse-folding step in the binder pipeline: a message-passing network that sees backbone geometry only, so it is the right choice when the design surface is protein–protein and the wrong one as soon as a ligand, nucleic acid, or metal is part of the interface — `ligandmpnn` adds those atoms to the graph with a near-identical CLI, and `solublempnn` swaps in weights trained on soluble structures for an expression-biased prior. Code and weights are MIT (github.com/dauparas/ProteinMPNN). The model is small enough to run on CPU — for a handful of sequences on one backbone that is seconds and usually faster than dispatching a remote job; a GPU helps for batched campaigns (hundreds of backbones or large `--num_seq_per_target`). Either way the repo is cloned in-job — there is no PyPI dist and the checkpoints are bundled in the repo. ## Running it ```bash pip install torch numpy # if not already present git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn cd proteinmpnn python protein_mpnn_run.py \ --pdb_path backbone.pdb --pdb_path_chains "A" \ --out_folder out --num_seq_per_target 16 --sampling_temp "0.1" ``` Two flags trip
- Running it
- A flat chain map in --fixedpositionsjsonl silently redesigns every residue
- Checkpoints — which one to pick
- Wisp execution
- Errors worth recognizing
pip install torch numpy # if not already present git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn cd proteinmpnn python protein_mpnn_run.py \
What does the proteinmpnn skill do?
Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while holding interface residues fixed, or to generate a temperature-swept set of sequences for downstream folding.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill proteinmpnn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.