Agent skill

Drug Protein Prep

by learningmatter-mit in learningmatter-mit/AtomisticSkills

Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.

MITAuto-check passedResearch & Science

Install Drug Protein Prep

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a claude-code

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

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-prep --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/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drug-protein-prep .claude/skills/drug-protein-prep && 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/

Paste into Claude Code

Install the "drug-protein-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep into .claude/skills/drug-protein-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-protein-prep", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-prep --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/drug-protein-prep .agents/skills/drug-protein-prep && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "drug-protein-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep into .agents/skills/drug-protein-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-protein-prep", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-prep --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/drug-protein-prep .cursor/skills/drug-protein-prep && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "drug-protein-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep into .cursor/skills/drug-protein-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-protein-prep", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/drug-protein-prep

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-prep --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/drug-protein-prep .gemini/skills/drug-protein-prep && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "drug-protein-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep into .gemini/skills/drug-protein-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-protein-prep", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-prep

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/drug-protein-prep .github/skills/drug-protein-prep && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "drug-protein-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep into .github/skills/drug-protein-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-protein-prep", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install learningmatter-mit/AtomisticSkills drug-protein-prep --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/drug-protein-prep .opencode/skills/drug-protein-prep && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "drug-protein-prep" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/drug-protein-prep into .opencode/skills/drug-protein-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-protein-prep", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: drug-protein-prep
GitHub stars: 176
Token cost: ~1.1k tokens
SKILL.md length: 305 words
Files: 6 (incl. scripts)
Skills in repo: 129
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.

Works in 6 steps: Prepare a receptor to PDB (Cleanup +… → Convert to PDBQT (for AutoDock Vina) → Keep cofactors/metal ions → …
Research & Science work in your project
SKILL.md covers Goal, Instructions, Examples and Constraints
Runs Python scripts from its folder

What it does

Drug Protein Prep is an agent skill from learningmatter-mit/AtomisticSkills. Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `examples/1iep_receptor/1IEP_summary.json`, `examples/README.md` and `scripts/prepare_protein.py`).

It sits in Research & Science. It works with Model Context Protocol. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

When your agent uses it

Research & Science work in your project

Example prompts

“/drug-protein-prep”

Requirements

Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Prepare a receptor to PDB (Cleanup + Hydrogens)
Convert to PDBQT (for AutoDock Vina)
Keep cofactors/metal ions
Use a biological assembly (recommended when oligomerization matters)
Prepare from a local structure file
Validate the output (strongly recommended)

What it can do on your machine

Read from SKILL.md and the folder at commit 6257444. 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
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- 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

Drug Protein Prep loads about 1.1k tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 305 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~42
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); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 305 words, ~1,068 tokens.

Download SKILL.mdSave it as .claude/skills/drug-protein-prep/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: drug-protein-prep
description: Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.
metadata.category: drug-discovery
metadata.venv: cpu

protein-prep

<!-- mcp-tools-note -->

[!NOTE] Steps written server.tool are MCP tool calls: drugdisc.convert_to_pdbqt is the convert_to_pdbqt tool of the drugdisc server (mcp__drugdisc__convert_to_pdbqt, or mcp__plugin_atomistic-skills_drugdisc__convert_to_pdbqt when installed as a plugin). Without a connected server, run the same tools from the shell. Tools named in one command share a process, so a model loaded by load_model stays loaded:
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python -m src.mcp_server.cli drugdisc convert_to_pdbqt key=value

Goal

To prepare protein (and optionally nucleic acid) receptor structures for molecular docking (e.g., AutoDock Vina) by:

retrieving coordinates from RCSB PDB (optional),
fixing common structural issues (missing atoms, nonstandard residues),
adding hydrogens at a target pH.

Note: This skill handles structure cleanup and protonation. To convert the result to PDBQT for docking, use the drugdisc.convert_to_pdbqt tool.

Instructions

1. Prepare a receptor to PDB (Cleanup + Hydrogens)

This script manages missing atoms, nonstandard residues, and protonation.

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/prepare_protein.py \
  --pdb_id 1iep \
  --chains A \
  --ph 7.0 \
  --heterogens none \
  --missing_residues ignore \
  --output_dir protein_prep/

2. Convert to PDBQT (for AutoDock Vina)

Use the MCP tool to convert the prepared PDB to PDBQT format.

bash

drugdisc.convert_to_pdbqt(
    input_data="protein_prep/1IEP_prepared.pdb",
    output_path="protein_prep/1IEP.pdbqt",
    input_type="pdb"
)

3. Keep cofactors/metal ions

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/prepare_protein.py \
  --pdb_id 1iep \
  --chains A \
  --heterogens non-water \
  --delete_resname SO4 GOL \
  --output_dir protein_prep_keep_cofactors/

4. Use a biological assembly (recommended when oligomerization matters)

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/prepare_protein.py \
  --pdb_id 1iep \
  --assembly 1 \
  --chains A \
  --output_dir protein_prep_assembly1/

5. Prepare from a local structure file

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/prepare_protein.py \
  --pdb_file receptor.pdb \
  --heterogens none \
  --output_dir protein_prep_local/

6. Validate the output (strongly recommended)

After preparation:

Inspect the JSON summary for missing residues, nonstandard residue replacements, and atoms added.
Visually inspect the binding site and check for:
- correct oligomeric state,
- retained/removed cofactors and metal ions,
- sensible protonation (especially histidines),
- alternate locations resolved appropriately.

If protonation is critical, consider a hydrogen optimization / pKa-aware tool (e.g., Reduce/Reduce2, PROPKA/PDB2PQR/H++), then regenerate PDBQT from the protonated receptor.

Examples

Full Workflow: HIV-1 Protease

Prepare the structure:

bash

${CLAUDE_SKILL_DIR}/../../venv/run cpu+openmm python ${CLAUDE_SKILL_DIR}/scripts/prepare_protein.py \
  --pdb_id 1hsg \
  --chains A B \
  --heterogens none \
  --ph 7.0 \
  --output_dir hiv_prep/

Convert to PDBQT:

bash

drugdisc.convert_to_pdbqt(
    input_data="hiv_prep/1HSG_prepared.pdb",
    output_path="hiv_prep/1HSG.pdbqt",
    input_type="pdb"
)

Constraints

Environment: Requires cpu+openmm.
Core dependencies: pdbfixer, openmm.
Protonation: Default pH-based hydrogen addition is a baseline.
Missing residues: By default, missing residues are ignored to avoid introducing uncertain loop models.
PDBQT: PDBQT conversion is delegated to the drugdisc.convert_to_pdbqt tool (which uses Meeko).

Author: Matthew Cox Contact: GitHub @mcox3406

Files

SKILL.md and 5 other files (scripts) in skills/drug-protein-prep of learningmatter-mit/AtomisticSkills.

SKILL.md
examples/1iep_receptor/1IEP_prepared.pdb
examples/1iep_receptor/1IEP_prepared.pdbqt
examples/1iep_receptor/1IEP_summary.json
examples/README.md
scripts/prepare_protein.py

Open the folder on GitHubat commit 6257444

Compare with similar skills

Drug Protein Prep 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.

Drug Protein Prep compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Drug Protein Prep this skilllearningmatter-mit/AtomisticSkills	176	—	~1.1k	Automated safety check: Pass	MIT	today
Deep Researchjordan-gibbs/hyperresearch	3.8k	—	~1.2k	Automated safety check: Pass	MIT	6 days ago
Annotate Paper54yyyu/zotero-mcp	5.3k	—	~1.5k	Automated safety check: Pass	MIT	today
Paper Searchopenags/paper-search-mcp	2.8k	—	~1.2k	Automated safety check: Notes	MIT	6 days ago
NSFC Literature Review WriterHuiyuLi-2000/Chinese-Grant-Writer-Skills	432	1 repos	~1.4k	Automated safety check: Notes	MIT	3 mo ago
Research LitCurryTang/Amadeus	176	6 repos	~963	Automated safety check: Notes	None	6 mo ago

Similar skills

Deep Research
jordan-gibbs/hyperresearch
Deep research with hyperresearch, for Claude Code and OpenAI Codex.
3.8k GitHub stars~1.2k tokensUpdated 6 days ago
Research & ScienceAuto-check passed
Annotate Paper
54yyyu/zotero-mcp
Read the open paper and write study annotations into its PDF with zotero-cli - a context box on the title, a four-part summary on the abstract, role-coded abstract highlights, one box per figure…
5.3k GitHub stars~1.5k tokensUpdated today
Research & ScienceAuto-check passed
Paper Search
openags/paper-search-mcp
Search, download, and read academic papers from 20+ sources (arXiv, PubMed, Semantic Scholar, CrossRef, etc).
2.8k GitHub stars~1.2k tokensUpdated 6 days ago
Research & ScienceAuto-check: notes
NSFC Literature Review Writer
HuiyuLi-2000/Chinese-Grant-Writer-Skills
Writes the research-status literature review and critique section of an NSFC grant proposal, backed by a bundled multi-source literature search.
432 GitHub starsUsed in 1 repo~1.4k tokens
Research & ScienceAuto-check: notes
Research Lit
CurryTang/Amadeus
Search and analyze research papers, find related work, summarize key ideas.
176 GitHub starsUsed in 6 repos~963 tokens
Research & ScienceAuto-check: notes
Research Review
GRIND-Lab-Core/night_owl_research_agent
Get a deep critical review of research idea from GPT via Codex MCP.
106 GitHub starsUsed in 6 repos~1.1k tokens
Research & ScienceAuto-check: notes

Questions about Drug Protein Prep

What does Drug Protein Prep do?

Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens. Drug Protein Prep is an agent skill from learningmatter-mit/AtomisticSkills. Prepare macromolecular receptor structures (PDB/mmCIF or RCSB PDB ID) for docking or simulation by fixing common structure issues and adding hydrogens.

When should I use Drug Protein Prep?

Drug Protein Prep fits situations like: research & Science work in your project.

How do I install Drug Protein Prep in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a claude-code`. Or copy the skill folder (skills/drug-protein-prep in learningmatter-mit/AtomisticSkills) into .claude/skills/drug-protein-prep in your project. Claude Code loads it when a task matches its description.

How do I install Drug Protein Prep in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a codex`. Or copy the skill folder (skills/drug-protein-prep in learningmatter-mit/AtomisticSkills) into .agents/skills/drug-protein-prep in your project. Codex loads it when a task matches its description.

Can I use Drug Protein Prep 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 learningmatter-mit/AtomisticSkills --skill drug-protein-prep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-protein-prep, .gemini/skills/drug-protein-prep, .github/skills/drug-protein-prep and .opencode/skills/drug-protein-prep in your project.

What does Drug Protein Prep need to run?

Going by SKILL.md and its folder, Drug Protein Prep needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Drug Protein Prep access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Drug Protein Prep 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Drug Protein Prep use?

Drug Protein Prep is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug Protein Prep 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 Drug Protein Prep?

Skills that share tags, products or a category with Drug Protein Prep: Deep Research (jordan-gibbs/hyperresearch, 3.8k stars), Annotate Paper (54yyyu/zotero-mcp, 5.3k stars), Paper Search (openags/paper-search-mcp, 2.8k stars) and NSFC Literature Review Writer (HuiyuLi-2000/Chinese-Grant-Writer-Skills, 432 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Protein Prep?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.

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

Install Drug Protein Prep

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

protein-prep

Goal

Instructions

1. Prepare a receptor to PDB (Cleanup + Hydrogens)

2. Convert to PDBQT (for AutoDock Vina)

3. Keep cofactors/metal ions

4. Use a biological assembly (recommended when oligomerization matters)

5. Prepare from a local structure file

6. Validate the output (strongly recommended)

Examples

Full Workflow: HIV-1 Protease

Constraints

Files

Compare with similar skills

Similar skills

More from learningmatter-mit/AtomisticSkills

Related

Questions about Drug Protein Prep