Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/naity/FM4Lifenpx agentmods add skills/naity/fm4life/proteinmpnnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/naity/fm4life/proteinmpnn)<a href="https://agentmods.dev/skills/naity/fm4life/proteinmpnn"><img src="https://agentmods.dev/badge/skills/naity/fm4life/proteinmpnn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/naity/fm4life/proteinmpnn"><img src="https://agentmods.dev/badge/skills/naity/fm4life/proteinmpnn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00092 | $0.01859 |
| Opus 5 | $0.00046 | $0.00929 |
| Sonnet 5 | $0.00018 | $0.00372 |
| Haiku 4.5 | $0.00009 | $0.00186 |
Grade A, and why
proteinmpnn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ProteinMPNN: Inverse Folding for Protein Sequence Design
Overview
ProteinMPNN designs amino acid sequences for a given protein backbone structure. It is the sequence design step in the modern protein design pipeline:
RFdiffusion (backbone) → ProteinMPNN (sequence) → AlphaFold2 (validation)
Given a backbone PDB, ProteinMPNN outputs sequences that are predicted to fold into that structure. It can design multiple chains simultaneously, lock specific residues, enforce symmetry, and score existing sequences.
Core capabilities:
- Sequence design — generate sequences for any protein backbone
- Scoring — evaluate how well a given sequence fits a structure
- Fixed positions — lock active site or interface residues
- Tied positions — enforce symmetric sequences across chains (homooligomers)
- Amino acid bias — favor or disfavor specific amino acids globally or per-residue
- PSSM guidance — incorporate evolutionary information
Installation
# Create conda environment
conda create --name mlfold python=3.9
conda activate mlfold
# Install PyTorch with CUDA
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
# Clone repository
git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN
No pip install — run directly from the repository.
Model Checkpoints
| Checkpoint | Use case |
|---|---|
v_48_020.pt |
Default; best general-purpose model |
v_48_030.pt |
Latest checkpoint |
v_48_010.pt |
Intermediate checkpoint |
v_48_002.pt |
Early checkpoint |
soluble_model_weights/v_48_020.pt |
Soluble proteins only |
ca_model_weights/v_48_020.pt |
CA-only backbone (faster) |
Default: v_48_020.pt. Use --use_soluble_model or --ca_only to switch model families.
Core Workflows
1. Minimal monomer design
python protein_mpnn_run.py \
--pdb_path input.pdb \
--pdb_path_chains "A" \
--num_seq_per_target 8 \
--sampling_temp "0.1" \
--out_folder results/
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 218 lines · 92 tokens per session scan A e35a5ee4cc61
proteinmpnn is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 5mo ago), licensed MIT. It adds 92 tokens to every session and 1,859 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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