proteinmpnn

name	proteinmpnn
description	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.
license	Apache-2.0
category	biomodels
metadata	{"display-name":"ProteinMPNN","third_party":[{"kind":"weights","name":"ProteinMPNN","license":"MIT","terms_url":"https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE"}]}

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 ). Either way the repo is cloned in-job — there is no PyPI dist and the checkpoints are bundled in the repo.