Table 1 Sample code for the evaluation of protein backbone dihedral angles and fragment quality
| Action | Code Sample |
|---|---|
| Load |
import rstoolbox as rs
import matplotlib.pyplot as plt import seaborn as sns |
| Read |
# With Rosetta installed, a single structure is scored. The
# function will return multiple score terms, sequence, # secondary structure and phi/psi angles. ref = rs.io.get_sequence_and_structure(‘1kx8_d2.pdb’) |
|
# Loading Rosetta fragments
seqfrags = rs.io.parse_rosetta_fragments(‘seq.200.9mers’) # With Rosetta, structural similarity of the fragments can be measured seqfrags = seqfrags. add_quality_measure (None, ‘mota_1kx8_d2.pdb’) strfrags = rs.io.parse_rosetta_fragments(‘str.200.9mers’) strfrags = strfrags. add_quality_measure (None, ‘mota_1kx8_d2.pdb’) | |
|
# Loading
ab initio
data
abseq = rs.io.parse_rosetta_file(‘abinitio_seqfrags.minsilent.gz’) abstr = rs.io.parse_rosetta_file(‘abinitio_strfrags.minsilent.gz’) | |
| Plot |
fig = plt.figure(figsize = (170 / 25.4, 170 / 25.4))
grid = (3, 6) |
|
# There are 4 flavours of Ramachandran plots available depending on the
# targeted residues: GENERAL, GLY, PRE-PRO and PRO. ax1 = plt.subplot2grid(grid, (0, 0), colspan = 2) # Ramachandran is plotted for a single decoy (selected as parameter 1). # As a decoy can contain multiple chains, the chain identifier is an # ubiquitous attribute in multiple functions of the library. rs.plot.plot_ramachandran_single (ref.iloc[0], ‘A’, ax1) ax1 = plt.subplot2grid(grid, (0, 2), fig = fig, colspan = 2) rs. plot.plot_ramachandran_single (ref.iloc[0], ‘A’, ax1, ‘PRE-PRO’) ax1 = plt.subplot2grid(grid, (0, 4), colspan = 2) rs.plot.plot_ramachandran_single (ref.iloc[0], ‘A’, ax1, ‘PRO’) | |
|
# Show RMSD match of fragments to the corresponding sequence for a
# selected region ax1 = plt.subplot2grid(grid, (1, 0), colspan = 3) ax2 = plt.subplot2grid(grid, (1, 3), colspan = 3, sharey = ax1) rs.plot.plot_fragments (seqfrags. slice_region (21, 56), strfrags.slice_region(21, 56), ax1, ax2) rs.utils.add_top_title (ax1, ‘sequence-based 9mers’) rs.utils.add_top_title (ax2, ‘structure-based 9mers’) | |
|
# DataFrames can directly work with widely spread plotting functions
ax1 = plt.subplot2grid(grid, (2, 0), colspan = 3) sns.scatterplot(x = “rms”, y = “score”, data = abseq, ax = ax1) ax2 = plt.subplot2grid(grid, (2, 3), colspan = 3, sharey = ax1, sharex = ax1) sns.scatterplot(x = “rms”, y = “score”, data = abstr, ax = ax2) rs.utils.add_top_title (ax1, ‘sequence-based fragments’) rs.utils.add_top_title (ax2, ‘structure-based fragments’) | |
|
plt.tight_layout()
plt.savefig(‘BMC_Fig2.png’, dpi = 300) |
