---
name: pdb-20a152819515
description: >
  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.
license: MIT
category: utilities
tags: [database, structure, fetch]
---

# 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.


---
Source attribution: NeverSight
Original project: https://github.com/NeverSight/skills_feed/blob/c7a8170df1a85e57ee661f0932a7968ebb7bbdb0/data/skills-md/adaptyvbio/protein-design-skills/pdb/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: NeverSight
Original project: https://github.com/NeverSight/skills_feed/blob/c7a8170df1a85e57ee661f0932a7968ebb7bbdb0/data/skills-md/adaptyvbio/protein-design-skills/pdb/SKILL.md
Attribution basis: Source repository record

Fields of work: Education & Research, Data & Analytics
Roles: Researcher, Data Scientist
Specialties: Database
Tools: Rcsb Pdb, Uniprot
Country applicability: Country not specified
Classification basis: Source category, role and specialization labels
Instruction SHA-256: d9b83367123c2c7fab5286dea026910a9cd2a235d6379a80b63d3a88fd225035