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

Running it

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"

Two flags trip almost everyone the first time. --sampling_temp is parsed as a space-separated string so one run can sweep several temperatures; a single value needs no quoting, but a multi-value sweep must be quoted ("0.1 0.2 0.3"), and commas never split — "0.1,0.2" fails the float cast. --pdb_path_chains is also space-separated inside one quoted argument ("A B"); a comma is kept as part of the chain ID.

Designs land in out/seqs/<pdb_stem>.fa. The first record is the input sequence; each design header carries score= (mean negative log-likelihood — lower is more confident), global_score=, and seq_recovery=. ProteinMPNN writes sequences only — it does not thread them back onto the backbone; if you need designed-sequence PDBs, the ligandmpnn runner writes them to backbones/ automatically and accepts --model_type protein_mpnn for the same weights.

A flat chain map in --fixed_positions_jsonl silently redesigns every residue

--fixed_positions_jsonl expects one JSON object per line keyed by the PDB stem first, then chain, then a list of 1-indexed residue numbers: {"backbone": {"A": [10, 11, 12], "B": []}}. Passing the inner {"A": [...]} directly — the obvious guess — is silently treated as "no PDB matched," and every position is redesigned. The bundled helper_scripts/make_fixed_positions_dict.py writes the correct shape from a chain and range string and is worth the extra call; the same outer-stem rule applies to --chain_id_jsonl and --tied_positions_jsonl.

Checkpoints — which one to pick

--model_name	training noise	use
v_48_002	0.02 Å	highest recovery; close-to-native redesigns
v_48_020 (default)	0.20 Å	de novo backbones — tolerates RFdiffusion imperfection
v_48_030	0.30 Å	very rough backbones; lowest recovery
--use_soluble_model	—	swaps to the soluble-trained set; see solublempnn

Errors worth recognizing

You see	It means / do this
KeyError: 'A'	Chain letter not in the PDB — grep '^ATOM' file.pdb | cut -c22 | sort -u to see what is.
JSONDecodeError on a *_jsonl flag	The flag wants a file path, not inline JSON; write the file first.
All positions redesigned despite --fixed_positions_jsonl	Outer PDB-stem key missing — see the gotcha above.
ModuleNotFoundError for relative imports	Script run from the wrong cwd — cd into the cloned repo first; the imports are repo-relative.

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Next: fold the designs in complex with the target via boltz, chai1, or esmfold2 and filter on ipTM.