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solublempnn

Maintainer FreedomIntelligence · Last updated April 1, 2026

Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.

OpenClawNanoClawAnalysisReproductionsolublempnn🔬 omics & computational biologyprotein structure & designsolubility

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/solublempnn

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • python protein_mpnn_run.py \ --pdb_path backbone.pdb \ --out_folder output/ \ --num_seq_per_target 16 \ --sampling_temp "0.1" \ --model_name "v_48_020" # Soluble model.

Source Doc

Excerpt From SKILL.md

How to run

First time? See Installation Guide to set up Modal and biomodals.

Option 1: Modal (recommended)

SolubleMPNN uses the ProteinMPNN Modal wrapper with soluble model:

GPU: T4 (16GB) | Timeout: 600s default

Option 2: Local installation

git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN

Use cases

  • Use solublempnn in research workflows aligned with this subject area.
  • Follow the upstream documentation for the full working procedure.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

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