solublempnn
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).