# Solublempnn

Inverse-fold a backbone with SolubleMPNN, ProteinMPNN retrained on a soluble-PDB...

- Repository: https://github.com/aipoch/open-science (5414 stars)
- Category: Design
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aipoch/open-science --skill solublempnn -g -a claude-code -y`
Codex: `npx skills add aipoch/open-science --skill solublempnn -g -a codex -y`
Cursor: `npx skills add aipoch/open-science --skill solublempnn -g -a cursor -y`
Gemini CLI: `npx skills add aipoch/open-science --skill solublempnn -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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_target 16 \
  --sampling_temp "0.1" --use_soluble_model
```

The rest: https://github.com/aipoch/open-science/blob/HEAD/resources/skills/solublempnn/SKILL.md