Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill "bio-pdb-structure-navigation" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/OpenClaw-Medical-Skills && cp -r /tmp/OpenClaw-Medical-Skills/skills/bio-pdb-structure-navigation ~/.claude/skills/bio-pdb-structure-navigationThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
name: bio-pdb-structure-navigation
description: Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.
tool_type: python
primary_tool: Bio.PDB
---
## Version Compatibility
Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Structure Navigation
**"Access residues and atoms in a PDB structure"** → Navigate the Structure-Model-Chain-Residue-Atom hierarchy to iterate over components, extract sequences, and access atomic coordinates.
- Python: `structure[0]['A'][100]['CA'].get_vector()` for direct access
Navigate the Structure-Model-Chain-Residue-Atom (SMCRA) hierarchy to access and iterate over structure components.
## Required Imports
```python
from Bio.PDB import PDBParser, PPBuilder, Selection
from Bio.Data.PDBData import protein_letters_3to1
```
## SMCRA Hierarchy
```
Structure
|
+-- Model (0, 1, ...) # NMR ensembles, crystal asymmetric unit
|
+-- Chain (A, B, ...) # Polypeptide chains, ligands
|
+-- Residue # Amino acids, nucleotides, hetero groups
|
+-- Atom # Individual atoms
```
## Accessing Hierarchy Levels
```python
from Bio.PDB import PDBParser
parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')
# Access by index/ID
model = structure[0] # First model
chain = model['A'] # Chain A
residue = chain[100] # Residue 100 (simple numbering)
residue = chain[(' ', 100, ' ')] # Full residue ID (hetfield, resseq, icode)
atom = residue['CA'] # C-alpha atom
```
## Iterating Over Structure
```python
# Iterate all levels
for model in structure:
for chain in model:
for residue in chain:
for atom in residue:
print(f'{chain.id}:{residue.id[1]}:{atom.name}')
# Shortcut iterators (all levels below current)
for chain in structure.get_chains():
print(f'Chain: {chain.id}')
for residue in structure.get_residues():
print(f'Residue: {residue.resname}')
for atom in structure.get_atoms():
print(f'Atom: {atom.name} at {atom.coord}')
```
## Residue Identification
```python
# Residue ID is a tuple: (hetfield, resseq, icode)
for residue in chain:
hetfield, resseq, icode = residue.id
print(f'Residue {resseq}{icode}: {residue.resname}')
# hetfield values:
# ' ' - standard amino acid
# 'W' - water
# 'H_xxx' - hetero residue (ligand, modified residue)
# Filter standard residues only
standard_residues = [r for r in chain if r.id[0] == ' ']
# Filter water
waters = [r for r in chain if r.id[0] == 'W']
# Filter hetero atoms (ligands)
hetero = [r for r in chain if r.id[0].startswith('H_')]
```
## Atom Properties
```python
for atom in residue:
print(f'Name: {atom.name}')
print(f'Element: {atom.element}')
print(f'Coordinates: {atom.coord}')
print(f'B-factor: {atom.bfactor}')
print(f'Occupancy: {atom.occupancy}')
print(f'Full ID: {atom.full_id}')
print(f'Serial number: {atom.serial_number}')
```
## Getting Full Identifiers
```python
# Full hierarchical ID from any entity
atom = structure[0]['A'][100]['CA']
print(atom.get_full_id())
# ('protein', 0, 'A', (' ', 100, ' '), ('CA', ' '))
# Components: (structure_id, model_id, chain_id, residue_id, atom_id)
```
## Checking for Children
```python
# Check if entity has child
if chain.has_id(100):
residue = chain[100]
# Check if residue has atom
if residue.has_id('CA'):
ca = residue['CA']
# Get list of all children
chains = structure[0].get_list()
residues = chain.get_list()
atoms = residue.get_list()
```
## Getting Parent Entity
```python
# Navigate up hierarchy
atom = structure[0]['A'][100]['CA']
residue = atom.get_parent()
chain = residue.get_parent()
model = chain.get_parent()
structure = model.get_parent()
```
## Extracting Polypeptide Sequences
```python
from Bio.PDB import PDBParser, PPBuilder
parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')
ppb = PPBuilder()
for pp in ppb.build_peptides(structure):
seq = pp.get_sequence()
print(f'Polypeptide: {seq}')
print(f'Length: {len(seq)}')
# Get all sequences as list
sequences = [pp.get_sequence() for pp in ppb.build_peptides(structure)]
```
## Using CaPPBuilder for Broken Chains
```python
from Bio.PDB import CaPPBuilder
# Use when backbone is incomplete
# Connects residues if CA atoms are within 4.3 Angstroms
ppb = CaPPBuilder()
for pp in ppb.build_peptides(structure):
print(f'Fragment: {pp.get_sequence()}')
```
## Converting Residue Names
```python
from Bio.Data.PDBData import protein_letters_3to1
# Three-letter to one-letter conversion
three_letter = 'ALA'
one_letter = protein_letters_3to1.get(three_letter, 'X')
print(f'{three_letter} -> {one_letter}') # ALA -> A
# Build sequence manually
sequence = ''
for residue in chain:
if residue.id[0] == ' ': # Standard residue
code = protein_letters_3to1.get(residue.resname, 'X')
sequence += code
print(f'Sequence: {sequence}')
```
## Using Selection.unfold_entities
```python
from Bio.PDB import Selection
# Extract entities at specific level
# Codes: S=structure, M=model, C=chain, R=residue, A=atom
# Get all residues from structure
residues = Selection.unfold_entities(structure, 'R')
print(f'Total residues: {len(residues)}')
# Get all atoms from a chain
atoms = Selection.unfold_entities(chain, 'A')
print(f'Atoms in chain: {len(atoms)}')
# Get all chains from model
chains = Selection.unfold_entities(model, 'C')
```
## Handling Disordered Atoms
```python
# Check for disorder
if atom.is_disordered():
print(f'Atom {atom.name} has multiple conformations')
print(f'Alt locations: {atom.disordered_get_id_list()}')
# Select specific conformation
atom.disordered_select('A')
print(f'Coord for alt A: {atom.coord}')
# Get all conformations
for altloc in atom.disordered_get_id_list():
atom.disordered_select(altloc)
print(f' {altloc}: {atom.coord}')
# Get unpacked list (all conformations)
all_atoms = atom.disordered_get_list()
```
## Handling Disordered Residues
```python
# Point mutations at same position
if residue.is_disordered():
print(f'Disordered residue at {residue.id}')
names = residue.disordered_get_id_list()
print(f'Alternative residues: {names}')
# Select specific residue type
residue.disordered_select('ALA')
```
## Finding Specific Atoms
```python
# Get backbone atoms
backbone_names = ['N', 'CA', 'C', 'O']
for residue in chain:
backbone = [residue[name] for name in backbone_names if residue.has_id(name)]
# Get all C-alpha atoms
ca_atoms = [r['CA'] for r in structure.get_residues() if r.has_id('CA')]
print(f'Found {len(ca_atoms)} CA atoms')
# Get sidechain atoms
for residue in chain:
sidechain = [a for a in residue if a.name not in ['N', 'CA', 'C', 'O']]
```
## Filtering by Residue Type
```python
# Get only amino acids
amino_acids = [r for r in chain if r.id[0] == ' ']
# Get specific amino acid types
arginines = [r for r in chain if r.resname == 'ARG']
charged = [r for r in chain if r.resname in ['ARG', 'LYS', 'ASP', 'GLU']]
# Get hetero atoms
ligands = [r for r in chain if r.id[0].startswith('H_')]
for lig in ligands:
print(f'Ligand: {lig.resname} at position {lig.id[1]}')
```
## Counting Entities
```python
# Count at each level
n_models = len(list(structure.get_models()))
n_chains = len(list(structure.get_chains()))
n_residues = len(list(structure.get_residues()))
n_atoms = len(list(structure.get_atoms()))
print(f'Models: {n_models}, Chains: {n_chains}')
print(f'Residues: {n_residues}, Atoms: {n_atoms}')
# Count per chain
for chain in structure.get_chains():
n_res = len([r for r in chain if r.id[0] == ' '])
print(f'Chain {chain.id}: {n_res} amino acids')
```
## Working with NMR Ensembles
```python
# NMR structures have multiple models
parser = PDBParser(QUIET=True)
structure = parser.get_structure('nmr', 'nmr_structure.pdb')
n_models = len(list(structure.get_models()))
print(f'NMR ensemble with {n_models} conformers')
# Iterate over models
for model in structure:
# Each model is a separate conformation
ca_coords = [r['CA'].coord for r in model.get_residues() if r.has_id('CA')]
print(f'Model {model.id}: {len(ca_coords)} CA atoms')
```
## Related Skills
- structure-io - Parse and write structure files
- geometric-analysis - Measure distances, angles, RMSD
- structure-modification - Modify coordinates and properties
- sequence-manipulation/seq-objects - Work with extracted sequences
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