def test_get_residues(array):
ids, names = struc.get_residues(array)
assert ids.tolist() == list(range(1, 21))
assert names.tolist() == [
"ASN", "LEU", "TYR", "ILE", "GLN", "TRP", "LEU", "LYS", "ASP", "GLY",
"GLY", "PRO", "SER", "SER", "GLY", "ARG", "PRO", "PRO", "PRO", "SER"
]
assert len(ids) == struc.get_residue_count(array)
def get_reference_from_structure(
structure_path: str,
positions: t.Optional[t.Container[int]] = None) -> str:
aa_mapping = AminoAcidDict().aa_dict
residues = zip(*bst.get_residues(io.load_structure(structure_path)))
if positions is not None:
residues = (r for r in residues if r[0] in positions)
return "".join([aa_mapping[r[1]] for r in residues])
def analyze_chirality(array):
# Filter backbone + CB
array = array[struc.filter_amino_acids(array)]
array = array[(array.atom_name == "CB") | (struc.filter_backbone(array))]
# Iterate over each residue
ids, names = struc.get_residues(array)
enantiomers = np.zeros(len(ids), dtype=int)
for i, id in enumerate(ids):
coord = array.coord[array.res_id == id]
if len(coord) != 4:
# Glyine -> no chirality
enantiomers[i] = 0
else:
enantiomers[i] = get_enantiomer(coord[0], coord[1], coord[2],
coord[3])
return enantiomers
def test_mass():
"""
Test whether the mass of a residue is the same as the sum of the
masses of its contained atoms.
"""
array = load_structure(join(data_dir, "1l2y.mmtf"))[0]
_, res_names = struc.get_residues(array)
water_mass = strucinfo.mass("H") * 2 + strucinfo.mass("O")
# Mass of water must be subtracted
masses = [strucinfo.mass(res_name) - water_mass for res_name in res_names]
# C-terminus normally has additional oxygen atom
masses[-1] += strucinfo.mass("O")
ref_masses = [strucinfo.mass(res) for res in struc.residue_iter(array)]
# Up to three additional/missing hydrogens are allowed
# (protonation state)
mass_diff = np.abs(
np.array(
[mass - ref_mass for mass, ref_mass in zip(masses, ref_masses)]))
assert (mass_diff // strucinfo.mass("H") <= 3).all()
assert np.allclose((mass_diff % strucinfo.mass("H")), 0, atol=5e-3)
THRESHOLD_DISTANCE = 4.0
# Fetch and load structure
mmtf_file = mmtf.MMTFFile()
mmtf_file.read(rcsb.fetch("2or1", "mmtf"))
structure = mmtf.get_structure(mmtf_file, model=1)
# Separate structure into the DNA and the two identical protein chains
dna = structure[np.isin(structure.chain_id, ["A", "B"])
& (structure.hetero == False)]
protein_l = structure[(structure.chain_id == "L")
& (structure.hetero == False)]
protein_r = structure[(structure.chain_id == "R")
& (structure.hetero == False)]
# Quick check if the two protein chains are really identical
assert len(struc.get_residues(protein_l)) == len(struc.get_residues(protein_r))
# Fast identification of contacts via a cell list:
# The cell list is initiliazed with the coordinates of the DNA
# and later provided with the atom coordinates of the two protein chains
cell_list = struc.CellList(dna, cell_size=THRESHOLD_DISTANCE)
# Sets to store the residue IDs of contact residues
# for each protein chain
id_set_l = set()
id_set_r = set()
for protein, res_id_set in zip((protein_l, protein_r), (id_set_l, id_set_r)):
# For each atom in the protein chain,
# find all atoms in the DNA that are in contact with it
contacts = cell_list.get_atoms(protein.coord, radius=THRESHOLD_DISTANCE)
content = ''.join(f.readlines())
query = 'INSERT INTO interfaces_cif (dimer_id, cif_file, insert_time) VALUES(?,?,?)', (
dimer_id, content,
datetime.strftime(datetime.now(),
"%Y-%m-%d %H:%M:%S"))
sql_queries.append(query)
os.remove("%s.cif" % dimer_id)
seq_1 = ", ".join(
map(
str,
list(
struc.get_residues(extInterface[4][
extInterface[4].chain_id == comb[0]])
[1])))
seq_2 = ", ".join(
map(
str,
list(
struc.get_residues(extInterface[4][
extInterface[4].chain_id == comb[1]])
[1])))
query = 'INSERT INTO interfaces_seq (dimer_id, pdb_id, chain_1, chain_2, sequence_1, sequence_2, insert_time) VALUES(?,?,?,?,?,?,?)', (
dimer_id, pdb_id, comb[0], comb[1], seq_1, seq_2,
datetime.strftime(datetime.now(),
"%Y-%m-%d %H:%M:%S"))
sql_queries.append(query)
def rmsf_plot(topology,
xtc_traj,
start_frame=None,
stop_frame=None,
write_dat_files=None):
# Gromacs does not set the element symbol in its PDB files,
# but Biotite guesses the element names from the atom names,
# emitting a warning
template = strucio.load_structure(topology)
# The structure still has water and ions, that are not needed for our
# calculations, we are only interested in the protein itself
# These are removed for the sake of computational speed using a boolean
# mask
protein_mask = struc.filter_amino_acids(template)
template = template[protein_mask]
residue_names = struc.get_residues(template)[1]
xtc_file = XTCFile()
xtc_file.read(xtc_traj,
atom_i=np.where(protein_mask)[0],
start=start_frame,
stop=stop_frame + 1)
trajectory = xtc_file.get_structure(template)
time = xtc_file.get_time() # Get simulation time for plotting purposes
trajectory = struc.remove_pbc(trajectory)
trajectory, transform = struc.superimpose(trajectory[0], trajectory)
rmsd = struc.rmsd(trajectory[0], trajectory)
figure = plt.figure(figsize=(6, 3))
ax = figure.add_subplot(111)
ax.plot(time, rmsd, color=biotite.colors["dimorange"])
ax.set_xlim(time[0], time[-1])
ax.set_ylim(0, 2)
ax.set_xlabel("Time (ps)")
ax.set_ylabel("RMSD (Å)")
figure.tight_layout()
radius = struc.gyration_radius(trajectory)
figure = plt.figure(figsize=(6, 3))
ax = figure.add_subplot(111)
ax.plot(time, radius, color=biotite.colors["dimorange"])
ax.set_xlim(time[0], time[-1])
ax.set_ylim(14.0, 14.5)
ax.set_xlabel("Time (ps)")
ax.set_ylabel("Radius of gyration (Å)")
figure.tight_layout()
# In all models, mask the CA atoms
ca_trajectory = trajectory[:, trajectory.atom_name == "CA"]
rmsf = struc.rmsf(struc.average(ca_trajectory), ca_trajectory)
figure = plt.figure(figsize=(6, 3))
ax = figure.add_subplot(111)
res_count = struc.get_residue_count(trajectory)
ax.plot(np.arange(1, res_count + 1),
rmsf,
color=biotite.colors["dimorange"])
ax.set_xlim(1, res_count)
ax.set_ylim(0, 1.5)
ax.set_xlabel("Residue")
ax.set_ylabel("RMSF (Å)")
figure.tight_layout()
if write_dat_files == True:
# Write RMSD *.dat file
frames = np.array(range(start_frame - 1, stop_frame), dtype=int)
frames[0] = 0
df = pd.DataFrame(data=rmsd, index=frames, columns=["RMSD Values"])
df.index.name = 'Frames'
df.to_csv('rmsd.dat', header=True, index=True, sep='\t', mode='w')
# Write RMSF *.dat file
df1 = pd.DataFrame(data=rmsf,
index=residue_names,
columns=["RMSF Values"])
df1.index.name = 'Residues'
df1.to_csv('rmsf.dat', header=True, index=True, sep='\t', mode='w')
plt.show()
import biotite.database.rcsb as rcsb
import biotite.structure as struc
import biotite.sequence.graphics as graphics
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.patches import Arc
import numpy as np
# Download the PDB file and read the structure
pdb_file_path = rcsb.fetch("4p5j", "pdb", gettempdir())
pdb_file = pdb.PDBFile.read(pdb_file_path)
atom_array = pdb.get_structure(pdb_file)[0]
nucleotides = atom_array[struc.filter_nucleotides(atom_array)]
# Get the residue names and residue ids of the nucleotides
residue_ids, residue_names = struc.get_residues(nucleotides)
# Create a matplotlib pyplot
fig, ax = plt.subplots(figsize=(8.0, 4.5))
# Setup the axis
ax.set_xlim(0.5, len(residue_ids) + 0.5)
ax.set_ylim(0, len(residue_ids) / 2 + 0.5)
ax.set_aspect("equal")
ax.xaxis.set_major_locator(ticker.MultipleLocator(3))
ax.tick_params(axis='both', which='major', labelsize=8)
ax.set_yticks([])
# Remove the frame
plt.box(False)
