用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/tomevault-io/skills-registry --skill solublempnn命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
| Use when this capability is needed.
> Use when this capability is needed.
Review architecture and API design for the vfs-s3 project. Use when the user mentions @architect, asks to review an issue's design, discuss module boundaries, API shape, or architectural decisions for vfs-s3. Also trigger when the user wants to create an ADR (Architecture Decision Record) or evaluate a technical approach for the project. Intended for dispatch from Codex automation or Claude routines; GitHub trigger phrase: @vfs-s3-bot please prepare design doc Use when this capability is needed.
基于 SOC 职业分类
正在显示 SKILL.md
| name | solublempnn |
| description | > Use when this capability is needed. |
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10 |
| CUDA | 11.0+ | 11.7+ |
| GPU VRAM | 8GB | 16GB (T4) |
| RAM | 8GB | 16GB |
First time? See Installation Guide to set up Modal and biomodals.
SolubleMPNN uses the ProteinMPNN Modal wrapper with soluble model:
cd biomodals
modal run modal_proteinmpnn.py \
--pdb-path backbone.pdb \
--num-seq-per-target 16 \
--sampling-temp 0.1 \
--model-name v_48_020
GPU: T4 (16GB) | Timeout: 600s default
git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN
# Use soluble model weights
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
| Parameter | Default | Range | Description |
|---|---|---|---|
--pdb_path | required | path | Input structure |
--num_seq_per_target | 1 | 1-1000 | Sequences per structure |
--sampling_temp | "0.1" | "0.0001-1.0" | Temperature (string!) |
--model_name | v_48_020 | string | Soluble model variant |
| Model | Description | Use Case |
|---|---|---|
| v_48_002 | Standard | General design |
| v_48_020 | Soluble-trained | E. coli expression |
| v_48_030 | High solubility | Difficult targets |
output/
├── seqs/backbone.fa
└── backbone_pdb/backbone_0001.pdb
$ python protein_mpnn_run.py --pdb_path backbone.pdb --model_name v_48_020 --num_seq_per_target 8
Loading soluble model weights (v_48_020)...
Designing sequences for backbone.pdb
Generated 8 sequences in 2.1 seconds
output/seqs/backbone.fa:
>backbone_0001, score=1.31, global_score=1.24, seq_recovery=0.78
MKTAYIAKQRQISFVKSHFSRQLE...
>backbone_0002, score=1.28, global_score=1.21, seq_recovery=0.81
MKTAYIAKQRQISFVKSQFSRQLD...
What good output looks like:
Should I use SolubleMPNN?
│
├─ What expression system?
│ ├─ E. coli → SolubleMPNN ✓
│ ├─ Mammalian → ProteinMPNN (PTMs matter more)
│ └─ Yeast → Either
│
├─ History of expression problems?
│ ├─ Yes, aggregation → SolubleMPNN ✓
│ ├─ Yes, low yield → SolubleMPNN ✓
│ └─ No → ProteinMPNN is fine
│
├─ What's in the binding site?
│ ├─ Small molecule / ligand → Use LigandMPNN
│ └─ Nothing / protein only → SolubleMPNN ✓
│
└─ Need highest solubility?
├─ Yes → Use v_48_030 model
└─ Standard → Use v_48_020 model
| Campaign Size | Time (T4) | Cost (Modal) | Notes |
|---|---|---|---|
| 100 backbones × 8 seq | 15-20 min | ~$2 | Standard |
| 500 backbones × 8 seq | 1-1.5h | ~$8 | Large campaign |
Expected improvement: +15-30% solubility score vs standard ProteinMPNN.
grep -c "^>" output/seqs/*.fa # Should match backbone_count × num_seq_per_target
Still insoluble: Try v_48_030 (higher solubility bias) Low diversity: Increase temperature to 0.2 Poor folding: Use standard ProteinMPNN and optimize later
| Error | Cause | Fix |
|---|---|---|
RuntimeError: CUDA out of memory | Long protein or large batch | Reduce batch_size |
FileNotFoundError: v_48_020 | Missing model weights | Download soluble weights |
Next: Structure prediction for validation → protein-qc for filtering.
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