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Agent skill

Solublempnn

by JimLiu in JimLiu/science-skills

Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al.

Apache-2.0Auto-check passedResearch & Science

Install Solublempnn

skills CLI
$ npx skills add JimLiu/science-skills --skill solublempnn -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JimLiu/science-skills solublempnn --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/solublempnn .claude/skills/solublempnn && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
solublempnn
GitHub stars
227
Used in
4 other repos
Token cost
~1.1k tokens
SKILL.md length
449 words
Files
1
Skills in repo
27
Repo updated
First seen
Licence
Apache-2.0

At a glance

Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al.

  • Tasks that involve Protein structure and design
  • SKILL.md covers Running it, Hydrophobic surface patches… and "Crystallisable" training set…
  • Calls pip, git and python; reaches github.com

What it does

Solublempnn is an agent skill from JimLiu/science-skills. 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.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Protein structure and design. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Protein structure and design

Example prompts

  • “/solublempnn”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit fb309c3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • git
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Solublempnn loads about 1.1k tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 449 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from JimLiu/science-skills at commit fb309c3, republished under its Apache-2.0 licence (© JimLiu). 449 words, ~1,065 tokens.

Download SKILL.mdSave it as .claude/skills/solublempnn/SKILL.md (or your agent's skills folder).
name
solublempnn
description
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.
license
Apache-2.0
category
biomodels
metadata.display-name
SolubleMPNN

SolubleMPNN

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).

Running it

bash
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.

Show full SKILL.md (172 more words)Show less

Hydrophobic surface patches still recur where the fold needs them

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.

"Crystallisable" training set ≠ "soluble in your host" — keep an orthogonal filter

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.

© JimLiu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/solublempnn of JimLiu/science-skills.

Open the folder on GitHubat commit fb309c3

Used in 4 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in JimLiu/science-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Solublempnn next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Alphafoldadaptyvbio/protein-design-skills1634 repos~1.2kAutomated safety check: PassMIT
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Complexa Binder DesignNVIDIA-BioNeMo/bionemo-agent-toolkit478—~3.1kAutomated safety check: NotesApache-2.0

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Categories

Questions about Solublempnn

What does Solublempnn do?

Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. Solublempnn is an agent skill from JimLiu/science-skills. Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al.

When should I use Solublempnn?

Solublempnn fits situations like: tasks that involve Protein structure and design.

How do I install Solublempnn in Claude Code?

Run `npx skills add JimLiu/science-skills --skill solublempnn -a claude-code`. Or copy the skill folder (skills/solublempnn in JimLiu/science-skills) into .claude/skills/solublempnn in your project. Claude Code loads it when a task matches its description.

How do I install Solublempnn in Codex?

Run `npx skills add JimLiu/science-skills --skill solublempnn -a codex`. Or copy the skill folder (skills/solublempnn in JimLiu/science-skills) into .agents/skills/solublempnn in your project. Codex loads it when a task matches its description.

Can I use Solublempnn in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JimLiu/science-skills --skill solublempnn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/solublempnn, .gemini/skills/solublempnn, .github/skills/solublempnn and .opencode/skills/solublempnn in your project.

What does Solublempnn need to run?

Going by SKILL.md and its folder, Solublempnn needs the command-line tools its instructions call (pip, git and python). Our summary lists: Python 3.

Does Solublempnn access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Solublempnn safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Solublempnn use?

Solublempnn is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Solublempnn use?

About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Solublempnn?

Skills that share tags, products or a category with Solublempnn: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Alphafold (adaptyvbio/protein-design-skills, 163 stars), Bindcraft (adaptyvbio/protein-design-skills, 163 stars) and Pymol Visualization (ChatMol/ChatMol, 372 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Solublempnn?

JimLiu (a GitHub user) maintains it in JimLiu/science-skills, which has 227 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on July 1, 2026.

Source: JimLiu/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.