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Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be respected, or to get threaded designed-sequence PDBs out of any MPNN run.
Passed 21 of 22 test cases with the skill, and 12 of 22 without it.
and 15 more in the full report
10 fixed12 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
While setting up the environment for running graph-based protein sequence design from a newly cloned repository, execution fails with `AttributeError: module 'numpy' has no attribute 'int'`. What is the precise fix for this script in the openfold dependency?
| Checked | Without skill | With skill |
|---|---|---|
| Applies sed to replace np.int with np.int64 in openfold/np/residue_constants.py | Missed | Passed |
The instructions your model reads when the skill loads.
LigandMPNN extends the ProteinMPNN graph with non-protein atoms — small
molecules, nucleic acids, and metals are visible to the network — so it is the
right inverse-folding tool whenever the design surface includes a bound ligand
or cofactor that vanilla proteinmpnn would ignore. The same run.py is also
the most convenient runner for the other MPNN families because, unlike the
original ProteinMPNN script, it threads designs back onto the input structure
and writes PDBs alongside the FASTA. Code and weights are MIT
(github.com/dauparas/LigandMPNN). The model is small enough to run on CPU —
for a handful of designs on one structure that is seconds and usually faster
than dispatching, so the normal path is local with
pip install torch numpy biopython ProDy ml_collections dm-tree; a GPU helps
for batched campaigns.
pip install torch numpy biopython ProDy ml_collections dm-tree
git clone --depth 1 https://github.com/dauparas/LigandMPNN.git ligandmpnn
cd ligandmpnn
sed -i 's/np\.int\b/np.int64/g' openfold/np/residue_constants.py # repo pins numpy 1.23; alias removed in >=1.24
bash get_model_params.sh ./model_params
python run.py \
--model_type ligand_mpnn \
--checkpoint_ligand_mpnn ./model_params/ligandmpnn_v_32_010_25.pt \
--pdb_path complex.pdb \
--out_folder out \
--batch_size 8 --number_of_batches 4 \
--temperature 0.1 \
--fixed_residues "A45 A46 A47 A48"
Residue selections are space-separated {chain}{resnum} tokens inside one
quoted string ("A45 A46 B10"; insertion codes append directly, "B82A").
That is the format for --fixed_residues and --redesigned_residues;
--bias_AA_per_residue and --omit_AA_per_residue instead take a path to a
JSON file whose keys use the same {chain}{resnum} form, and
--chains_to_design is comma-separated ("A,B"). If you want to redesign only the pocket, naming the pocket residues
in --redesigned_residues is usually shorter than fixing everything else.
test cases passed, vs 12 / 22 without
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