--- name: pdb 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: adaptyvbio Original project: https://github.com/adaptyvbio/protein-design-skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod 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. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: adaptyvbio Original project: https://github.com/adaptyvbio/protein-design-skills Attribution basis: Pinned per-package origin registry Fields of work: Data & Analytics, Education & Research Roles: Business Intelligence Analyst, Data Scientist, Researcher Specialties: Cross Platform, Database Tools: Rcsb Pdb Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 95671eb6b9def6c0089a3226cf23a371b5615c58cbef1f39e5a366c36190d66f Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.