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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.
Passed 22 of 22 test cases with the skill, and 17 of 22 without it.
and 4 more in the full report
5 fixed17 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
I generated a de novo binder complex `rf_out.pdb` with chains A and B using RFdiffusion. I want to generate 50 redesigned sequences across both chains together at temperature 0.1, sending output to `./mpnn_output`. Please give me the exact terminal execution script to clone the repository and execute the run.
| Checked | Without skill | With skill |
|---|---|---|
| Changes directory into `proteinmpnn` before running `protein_mpnn_run.py`. | Missed | Passed |
| Uses `--pdb_path_chains "A B"` with space-separated chain IDs inside quotes. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
test cases passed, vs 17 / 22 without
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