Name: Bio Pdb Geometric Analysis
Author: GPTomics

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Bio Pdb Geometric Analysis | Skills Pool

from Bio.PDB import PDBParser, NeighborSearch, Superimposer
from Bio.PDB import calc_angle, calc_dihedral
import numpy as np

from Bio.PDB import PDBParser

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

chain = structure[0]['A']
atom1 = chain[100]['CA']
atom2 = chain[200]['CA']

# Direct subtraction gives distance
distance = atom1 - atom2
print(f'Distance: {distance:.2f} Angstroms')

# Or use numpy
import numpy as np
distance = np.linalg.norm(atom1.coord - atom2.coord)

import numpy as np
from Bio.PDB import PDBParser

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

ca_atoms = [r['CA'] for r in structure.get_residues() if r.has_id('CA') and r.id[0] == ' ']
n = len(ca_atoms)

dist_matrix = np.zeros((n, n))
for i in range(n):
    for j in range(i + 1, n):
        dist = ca_atoms[i] - ca_atoms[j]
        dist_matrix[i, j] = dist
        dist_matrix[j, i] = dist

print(f'Distance matrix shape: {dist_matrix.shape}')

from Bio.PDB import PDBParser, calc_angle

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

residue = structure[0]['A'][100]
n = residue['N']
ca = residue['CA']
c = residue['C']

# calc_angle takes Vector objects (atom.coord returns array, use atom.get_vector())
angle_rad = calc_angle(n.get_vector(), ca.get_vector(), c.get_vector())
angle_deg = np.degrees(angle_rad)
print(f'N-CA-C angle: {angle_deg:.1f} degrees')

from Bio.PDB import PDBParser, calc_dihedral
import numpy as np

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

chain = structure[0]['A']

# Calculate phi angle (C-N-CA-C)
res_prev = chain[99]
res_curr = chain[100]

phi = calc_dihedral(
    res_prev['C'].get_vector(),
    res_curr['N'].get_vector(),
    res_curr['CA'].get_vector(),
    res_curr['C'].get_vector()
)
print(f'Phi: {np.degrees(phi):.1f} degrees')

# Calculate psi angle (N-CA-C-N)
res_next = chain[101]
psi = calc_dihedral(
    res_curr['N'].get_vector(),
    res_curr['CA'].get_vector(),
    res_curr['C'].get_vector(),
    res_next['N'].get_vector()
)
print(f'Psi: {np.degrees(psi):.1f} degrees')

from Bio.PDB import PDBParser, PPBuilder, calc_dihedral
import numpy as np

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

ppb = PPBuilder()
phi_psi = []

for pp in ppb.build_peptides(structure):
    angles = pp.get_phi_psi_list()
    for residue, (phi, psi) in zip(pp, angles):
        if phi is not None and psi is not None:
            phi_psi.append((residue.resname, np.degrees(phi), np.degrees(psi)))

for name, phi, psi in phi_psi[:10]:
    print(f'{name}: phi={phi:.1f}, psi={psi:.1f}')

from Bio.PDB import PDBParser, NeighborSearch

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Build search tree from all atoms
all_atoms = list(structure.get_atoms())
ns = NeighborSearch(all_atoms)

# Find atoms within radius of a point
center = structure[0]['A'][100]['CA'].coord
radius = 5.0  # Angstroms
neighbors = ns.search(center, radius)
print(f'Found {len(neighbors)} atoms within {radius}A')

# Find atoms within radius of another atom
ca_atom = structure[0]['A'][100]['CA']
neighbors = ns.search(ca_atom.coord, 5.0)

from Bio.PDB import PDBParser, NeighborSearch

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

all_atoms = list(structure.get_atoms())
ns = NeighborSearch(all_atoms)

# Find all atom pairs within distance
contact_distance = 4.0
contacts = ns.search_all(contact_distance, level='R')  # R = residue level

print(f'Found {len(contacts)} residue contacts within {contact_distance}A')
for res1, res2 in contacts[:10]:
    print(f'  {res1.resname}{res1.id[1]} - {res2.resname}{res2.id[1]}')

from Bio.PDB import NeighborSearch

# search_all returns pairs at specified level
# Level codes: A=atom, R=residue, C=chain, M=model, S=structure

atom_contacts = ns.search_all(4.0, level='A')      # Atom pairs
residue_contacts = ns.search_all(4.0, level='R')  # Residue pairs
chain_contacts = ns.search_all(10.0, level='C')   # Chain pairs

from Bio.PDB import PDBParser, Superimposer

parser = PDBParser(QUIET=True)
ref_structure = parser.get_structure('ref', 'reference.pdb')
mobile_structure = parser.get_structure('mobile', 'mobile.pdb')

# Get CA atoms from both structures
ref_atoms = [r['CA'] for r in ref_structure.get_residues() if r.has_id('CA') and r.id[0] == ' ']
mobile_atoms = [r['CA'] for r in mobile_structure.get_residues() if r.has_id('CA') and r.id[0] == ' ']

# Ensure same number of atoms
n = min(len(ref_atoms), len(mobile_atoms))
ref_atoms = ref_atoms[:n]
mobile_atoms = mobile_atoms[:n]

# Superimpose
sup = Superimposer()
sup.set_atoms(ref_atoms, mobile_atoms)
print(f'RMSD before: {sup.rms:.2f} Angstroms')

# Apply transformation to all atoms in mobile structure
sup.apply(mobile_structure.get_atoms())

from Bio.PDB import PDBParser, Superimposer
import numpy as np

parser = PDBParser(QUIET=True)
struct1 = parser.get_structure('s1', 'structure1.pdb')
struct2 = parser.get_structure('s2', 'structure2.pdb')

atoms1 = [r['CA'] for r in struct1.get_residues() if r.has_id('CA') and r.id[0] == ' ']
atoms2 = [r['CA'] for r in struct2.get_residues() if r.has_id('CA') and r.id[0] == ' ']

# Using Superimposer (with alignment)
sup = Superimposer()
sup.set_atoms(atoms1, atoms2)
rmsd_aligned = sup.rms
print(f'RMSD (aligned): {rmsd_aligned:.2f} A')

# Raw RMSD (no alignment)
coords1 = np.array([a.coord for a in atoms1])
coords2 = np.array([a.coord for a in atoms2])
rmsd_raw = np.sqrt(np.mean(np.sum((coords1 - coords2) ** 2, axis=1)))
print(f'RMSD (raw): {rmsd_raw:.2f} A')

from Bio.PDB import PDBParser, CEAligner

parser = PDBParser(QUIET=True)
ref = parser.get_structure('ref', 'reference.pdb')
mobile = parser.get_structure('mobile', 'query.pdb')

# CE alignment works for structures with different sequences
aligner = CEAligner()
aligner.set_reference(ref)
aligner.align(mobile)

print(f'RMSD: {aligner.rms:.2f} A')

# CEAligner automatically modifies mobile structure coordinates

from Bio.PDB import PDBParser
import numpy as np

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Unweighted center (geometric center)
coords = np.array([a.coord for a in structure.get_atoms()])
center = coords.mean(axis=0)
print(f'Geometric center: {center}')

# Mass-weighted center (approximate - uses atom count)
# For accurate mass-weighted, use element masses
atoms = list(structure.get_atoms())
masses = {'C': 12.0, 'N': 14.0, 'O': 16.0, 'S': 32.0, 'H': 1.0}
total_mass = 0
weighted_sum = np.zeros(3)
for atom in atoms:
    mass = masses.get(atom.element, 12.0)
    weighted_sum += mass * atom.coord
    total_mass += mass
center_of_mass = weighted_sum / total_mass
print(f'Center of mass: {center_of_mass}')

import numpy as np
from Bio.PDB import PDBParser

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

coords = np.array([a.coord for a in structure.get_atoms()])
center = coords.mean(axis=0)

# Radius of gyration
rg = np.sqrt(np.mean(np.sum((coords - center) ** 2, axis=1)))
print(f'Radius of gyration: {rg:.2f} A')

from Bio.PDB import PDBParser, NeighborSearch

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Simple approach: residues with few neighbors
all_atoms = list(structure.get_atoms())
ns = NeighborSearch(all_atoms)

surface_residues = []
for residue in structure.get_residues():
    if residue.id[0] != ' ':
        continue
    if not residue.has_id('CA'):
        continue

    ca = residue['CA']
    neighbors = ns.search(ca.coord, 10.0, level='R')
    if len(neighbors) < 15:  # Threshold for surface
        surface_residues.append(residue)

print(f'Surface residues: {len(surface_residues)}')

from Bio.PDB import PDBParser
from Bio.PDB.vectors import Vector, rotaxis

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Get vectors from atoms
atom1 = structure[0]['A'][100]['CA']
atom2 = structure[0]['A'][101]['CA']

v1 = atom1.get_vector()
v2 = atom2.get_vector()

# Vector operations
diff = v2 - v1
print(f'Distance vector: {diff}')
print(f'Length: {diff.norm():.2f}')

# Normalize
unit = diff.normalized()

# Cross product
cross = v1 ** v2

# Dot product
dot = v1 * v2

from Bio.PDB import PDBParser, calc_dihedral
import numpy as np

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Chi1 angle for a residue (N-CA-CB-CG)
residue = structure[0]['A'][100]

if residue.has_id('CB') and residue.has_id('CG'):
    chi1 = calc_dihedral(
        residue['N'].get_vector(),
        residue['CA'].get_vector(),
        residue['CB'].get_vector(),
        residue['CG'].get_vector()
    )
    print(f'Chi1: {np.degrees(chi1):.1f} degrees')

from Bio.PDB import PDBParser
from Bio.PDB.SASA import ShrakeRupley

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Calculate SASA using Shrake-Rupley algorithm
sr = ShrakeRupley()
sr.compute(structure, level='S')  # S=structure, M=model, C=chain, R=residue, A=atom

# Access total SASA
print(f'Total SASA: {structure.sasa:.2f} A^2')

# Access per-residue SASA
for residue in structure.get_residues():
    if hasattr(residue, 'sasa'):
        print(f'{residue.resname}{residue.id[1]}: {residue.sasa:.2f} A^2')

from Bio.PDB import PDBParser
from Bio.PDB.SASA import ShrakeRupley

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

# Higher precision (more points = slower but more accurate)
sr = ShrakeRupley(probe_radius=1.4, n_points=960)
sr.compute(structure, level='R')

# Per-atom SASA
for atom in structure.get_atoms():
    print(f'{atom.name}: {atom.sasa:.2f} A^2')

from Bio.PDB import PDBParser
from Bio.PDB.SASA import ShrakeRupley

parser = PDBParser(QUIET=True)
structure = parser.get_structure('protein', 'protein.pdb')

sr = ShrakeRupley()
sr.compute(structure, level='R')

buried = []
exposed = []
for residue in structure.get_residues():
    if residue.id[0] != ' ':
        continue
    if hasattr(residue, 'sasa'):
        if residue.sasa < 10.0:  # Threshold in A^2
            buried.append(residue)
        else:
            exposed.append(residue)

print(f'Buried: {len(buried)}, Exposed: {len(exposed)}')

Bio Pdb Geometric Analysis

Version Compatibility

Geometric Analysis

Required Imports

Bio Pdb Geometric Analysis

Version Compatibility

Geometric Analysis

Required Imports

Distance Between Atoms

Distance Matrix

Angle Between Three Atoms

Dihedral Angles

Ramachandran Angles for All Residues

Finding Neighbor Atoms

Finding Residue Contacts

Contact Levels

Superimposing Structures

Calculating RMSD

CEAligner for Dissimilar Structures

Center of Mass

Radius of Gyration

Finding Surface Residues

Vector Operations

Chi Angles (Sidechain Dihedrals)

Solvent Accessible Surface Area (SASA)

SASA with Custom Parameters

Identifying Buried vs Exposed Residues

Brenda Database

Clinical Decision Support Documents

Healthcare Cdss Patterns

Nanoclaw Repl

Deep Research

Data Analyst

Bio Pdb Geometric Analysis

Version Compatibility

Geometric Analysis

Required Imports

Bio Pdb Geometric Analysis

Version Compatibility

Geometric Analysis

Required Imports

Distance Between Atoms

Distance Matrix

Angle Between Three Atoms

Dihedral Angles

Ramachandran Angles for All Residues

Finding Neighbor Atoms

Finding Residue Contacts

Contact Levels

Superimposing Structures

Calculating RMSD

CEAligner for Dissimilar Structures

Center of Mass

Radius of Gyration

Finding Surface Residues

Vector Operations

Chi Angles (Sidechain Dihedrals)

Solvent Accessible Surface Area (SASA)

SASA with Custom Parameters

Identifying Buried vs Exposed Residues

Related Skills

Brenda Database

Clinical Decision Support Documents

Healthcare Cdss Patterns

Nanoclaw Repl

Deep Research

Data Analyst