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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.
Passed 19 of 20 test cases with the skill, and 11 of 20 without it.
and 6 more in the full report
9 fixed13 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
To generate 3D PDB structures of soluble sequence designs using the LigandMPNN runner, what Python package dependency is required in addition to PyTorch?
| Checked | Without skill | With skill |
|---|---|---|
| identifies ProDy as the required package dependency | Missed | Passed |
The instructions your model reads when the skill loads.
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).
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 runner uses repo-relative imports, so the cd line is load-bearing —
invoking the script by absolute path from elsewhere fails with
ModuleNotFoundError. If you want threaded designed-sequence PDBs as well,
the LigandMPNN runner accepts --model_type soluble_mpnn (see ligandmpnn
for that path; it needs ProDy in addition to torch). The flag surface is
otherwise identical to proteinmpnn (or ligandmpnn for the second form),
including the string-typed temperature and the fixed-position JSONL keyed by
PDB stem — see proteinmpnn for the parsing quirks. The repo
ships soluble weights at v_48_010 and v_48_020 only; asking for
--model_name v_48_002 --use_soluble_model errors on a missing checkpoint, so
leave --model_name at its default.
Output is out/seqs/<stem>.fa with score= and seq_recovery= in each
test cases passed, vs 11 / 20 without
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