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Pdb

Community
BioTender-max
pdb

Fetch and analyze protein structures from RCSB PDB. Use this skill when: (1) Need to download a structure by PDB ID, (2) Search for similar structures, (3) Prepare target for binder design, (4) Extract specific chains or domains, (5) Get structure metadata. For sequence lookup, use uniprot. For binder design workflow, use binder-design.

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namepdb
Stars
204
Forks
36
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Pdb AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/adaptyv/pdb
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/adaptyv/pdb .claude/skills/pdb
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pdb in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pdb on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pdb is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PDB Database Access

Note: This skill uses the RCSB PDB web API directly. No Modal deployment needed - all operations run locally via HTTP requests.

Fetching Structures

By PDB ID

bash
# Download PDB file
curl -o 1alu.pdb "https://files.rcsb.org/download/1ALU.pdb"

# Download mmCIF
curl -o 1alu.cif "https://files.rcsb.org/download/1ALU.cif"

Using Python

python
from Bio.PDB import PDBList

pdbl = PDBList()
pdbl.retrieve_pdb_file("1ABC", pdir="structures/", file_format="pdb")

Using RCSB API

python
import requests

def fetch_pdb(pdb_id: str, format: str = "pdb") -> str:
    """Fetch structure from RCSB PDB."""
    url = f"https://files.rcsb.org/download/{pdb_id}.{format}"
    response = requests.get(url)
    response.raise_for_status()
    return response.text

def fetch_fasta(pdb_id: str) -> str:
    """Fetch sequence in FASTA format."""
    url = f"https://www.rcsb.org/fasta/entry/{pdb_id}"
    return requests.get(url).text

# Example usage
pdb_content = fetch_pdb("1ALU")
with open("1ALU.pdb", "w") as f:
    f.write(pdb_content)

Structure Preparation

Selecting Chains

python
from Bio.PDB import PDBParser, PDBIO, Select

class ChainSelect(Select):
    def __init__(self, chain_id):
        self.chain_id = chain_id

    def accept_chain(self, chain):
        return chain.id == self.chain_id

# Extract chain A
parser = PDBParser()
structure = parser.get_structure("protein", "1abc.pdb")
io = PDBIO()
io.set_structure(structure)
io.save("chain_A.pdb", ChainSelect("A"))

Trimming to Binding Region

python
def trim_around_residues(pdb_file, center_residues, buffer=10.0):
    """Trim structure to region around specified residues."""
    parser = PDBParser()
    structure = parser.get_structure("protein", pdb_file)

    # Get center coordinates
    center_coords = []
    for res in structure.get_residues():
        if res.id[1] in center_residues:
            center_coords.extend([a.coord for a in res.get_atoms()])

    center = np.mean(center_coords, axis=0)

    # Keep residues within buffer
    class RegionSelect(Select):
        def accept_residue(self, res):
            for atom in res.get_atoms():
                if np.linalg.norm(atom.coord - center) < buffer:
                    return True
            return False

    io = PDBIO()
    io.set_structure(structure)
    io.save("trimmed.pdb", RegionSelect())

Searching PDB

RCSB Search API

python
import requests

query = {
    "query": {
        "type": "terminal",
        "service": "full_text",
        "parameters": {
            "value": "EGFR kinase domain"
        }
    },
    "return_type": "entry"
}

response = requests.post(
    "https://search.rcsb.org/rcsbsearch/v2/query",
    json=query
)
results = response.json()

By Sequence Similarity

python
query = {
    "query": {
        "type": "terminal",
        "service": "sequence",
        "parameters": {
            "value": "MKTAYIAKQRQISFVK...",
            "evalue_cutoff": 1e-10,
            "identity_cutoff": 0.9
        }
    }
}

Structure Analysis

Get Chain Info

python
def get_structure_info(pdb_file):
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure("protein", pdb_file)

    info = {
        "chains": [],
        "total_residues": 0
    }

    for model in structure:
        for chain in model:
            residues = list(chain.get_residues())
            info["chains"].append({
                "id": chain.id,
                "length": len(residues),
                "first_res": residues[0].id[1],
                "last_res": residues[-1].id[1]
            })
            info["total_residues"] += len(residues)

    return info

Find Interface Residues

python
def find_interface_residues(pdb_file, chain_a, chain_b, distance=4.0):
    """Find residues at interface between two chains."""
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure("complex", pdb_file)

    interface_a = set()
    interface_b = set()

    for res_a in structure[0][chain_a].get_residues():
        for res_b in structure[0][chain_b].get_residues():
            for atom_a in res_a.get_atoms():
                for atom_b in res_b.get_atoms():
                    if atom_a - atom_b < distance:
                        interface_a.add(res_a.id[1])
                        interface_b.add(res_b.id[1])

    return interface_a, interface_b

Common Tasks for Binder Design

Target Preparation Checklist

  1. Download structure: curl -o target.pdb "https://files.rcsb.org/download/XXXX.pdb"
  2. Identify target chain
  3. Remove waters and ligands (if needed)
  4. Trim to binding region + buffer
  5. Identify potential hotspots
  6. Renumber if needed

Troubleshooting

Structure not found: Check PDB ID format (4 characters) Multiple models: Select first model for design Missing residues: Check for gaps in structure


Next: Use structure with boltzgen (recommended) or rfdiffusion for design.

Frequently asked questions

What does the Pdb AI skill do?

Fetch and analyze protein structures from RCSB PDB. Use this skill when: (1) Need to download a structure by PDB ID, (2) Search for similar structures, (3) Prepare target for binder design, (4) Extract specific chains or domains, (5) Get structure metadata. For sequence lookup, use uniprot. For binder design workflow, use binder-design.

Why use Pdb on TypingMind?

Because you install it once and use it with any model. Pdb is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pdb in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/adaptyv/pdb. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pdb?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pdb?

As many as you like. As long as a model supports skills, you can use Pdb with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pdb AI skill free?

Yes. It is published on GitHub by BioTender-max under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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