solublempnn
Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.
Profile →npx skills add xuzhougeng/wisp-science --skill solublempnn --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.
# SolubleMPNN SolubleMPNN is not a separate package — it is the ProteinMPNN architecture retrained on a soluble-PDB subset, which shifts the output distribution away from the surface hydrophobics that the full-PDB model happily places (because many of them are buried at crystallographic or membrane interfaces in the training set). Reach for it when the goal is soluble yield in a heterologous host; stick with `proteinmpnn` when native-like recovery matters more, since the soluble prior trades a few points of recovery for the surface bias. Code and weights are MIT (github.com/dauparas/ProteinMPNN, `soluble_model_weights`; also exposed via github.com/dauparas/LigandMPNN). 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 GPU helps for batched campaigns. Either way the repo is cloned in-job (no PyPI dist; checkpoints bundled). ## 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_targe
- Running it
- Wisp execution
- Hydrophobic surface patches still recur where the fold needs them
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 solublempnn skill do?
Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill solublempnn --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.