adaptyvbio/pdb
> (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.
npx skills add https://github.com/adaptyvbio/protein-design-skills --skill pdb
Note: This skill uses the RCSB PDB web API directly. No Modal deployment needed - all operations run locally via HTTP requests.
# 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"
from Bio.PDB import PDBList
pdbl = PDBList()
pdbl.retrieve_pdb_file("1ABC", pdir="structures/", file_format="pdb")
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)
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"))
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())
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()
query = {
"query": {
"type": "terminal",
"service": "sequence",
"parameters": {
"value": "MKTAYIAKQRQISFVK...",
"evalue_cutoff": 1e-10,
"identity_cutoff": 0.9
}
}
}
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
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
curl -o target.pdb "https://files.rcsb.org/download/XXXX.pdb"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.
Take adaptyvbio/pdb from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.