Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
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