Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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).
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" --use_soluble_model
The runner uses repo-relative imports, so the cd line is load-bearing —
invoking the script by absolute path from elsewhere fails with
ModuleNotFoundError. If you want threaded designed-sequence PDBs as well,
the LigandMPNN runner accepts --model_type soluble_mpnn (see ligandmpnn
for that path; it needs ProDy in addition to torch). The flag surface is
otherwise identical to proteinmpnn (or ligandmpnn for the second form),
including the string-typed temperature and the fixed-position JSONL keyed by
PDB stem — see proteinmpnn for the parsing quirks. The repo
ships soluble weights at v_48_010 and v_48_020 only; asking for
--model_name v_48_002 --use_soluble_model errors on a missing checkpoint, so
leave --model_name at its default.
Output is out/seqs/<stem>.fa with score= and seq_recovery= in each
header. Expect recovery against a native structure to drop a few points
relative to vanilla — that is the prior working, not a bug.
Soluble weights shift the distribution; they do not enforce a hydrophobicity
ceiling. If a particular surface patch keeps coming back hydrophobic, that
patch is likely structurally load-bearing and the network is paying the
solubility cost to keep the fold. Layering --omit_AAs "CW" or a per-position
bias on top is fine, but check that the resulting designs still fold (via
boltz or esmfold2) before assuming the constraint was free.
The training set is "structures that were soluble enough to crystallise," which correlates with but is not the same as "expresses solubly in E. coli at 37 °C." For campaigns where expression yield is the bottleneck, rank the soluble-MPNN output by an orthogonal sequence-based predictor before committing wet-lab slots; treat the MPNN bias as widening the funnel, not replacing the filter.
Next: fold the designs with boltz or esmfold2 to confirm the backbone
is still recovered, then carry survivors into the expression screen.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
日本語の概要は準備中です。原文の説明を表示しています。
Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
日本語の概要は準備中です。原文の説明を表示しています。
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.
日本語の概要は準備中です。原文の説明を表示しています。
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare reproducible setup instructions and validate a user-managed named software environment on an Open-Science SSH Compute Host, including direct SSH and Slurm hosts. Use when a remote job needs packages, modules, cache variables, or a repeatable activation that the host does not already provide.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to create or manage a Specialist agent or create, revise, publish, or delete a Skill through the conversational `/Customize` entry. Routes Skill work to the internal skill-creator and handles Specialist work through the JavaScript host.agents SDK.
日本語の概要は準備中です。原文の説明を表示しています。