vas2201
commented
3 days ago I am currently employing a threshold range of 0.5 to 0.8 to filter network connections. Despite this, I am still observing long-range network artifacts in the ks_gcc and gcc analyses. I am trying to replicate the results depicted in Figures 3D1 and 3D2 of the publication. Can you also provide me an example for the following functions of pymol like input files and nodes, that would be great help ?.
cmd.extend('gradient_color', gradient_color) cmd.extend("draw_network_from_df", draw_network_from_df) cmd.extend("draw_shortest_path", draw_shortest_path) cmd.extend("draw_network_from_adjacency", draw_network_from_adjacency)
Any guidance on this matter would be greatly appreciated.
Hello fmaschietto,
I am using the code snippet below to create a visualization in PyMOL, following the instructions provided in the notebook. Based on my understanding, the correlation network should be filtered to include only connections within 0-5 angstroms. However, in PyMOL, I am observing longer connections, as shown in the attached figure. Could you please advise on this if possible?.
open Pymol locally from pymol import cmd, util import seaborn as sns import warnings warnings.filterwarnings('ignore') cmd.delete('all') cmd.bg_color('black')
place yourself in the
visualize_networkfoldercd visualize_network
execute pymol locally calling
pymolfrom inside the directory.provide path to pdb and data_frame data_frame: change accordingly
path = '/Desktop/workflow2025/mdigest-master/input_files/MptpA/Results/mdigest-RESULTS/RESULTS/MptpA/' cmd.load(path+ '1u2p.pdb', '1u2p') cmd.color('grey80', '1u2p') cmd.remove('!(polymer)') cmd.run('Desktop/workflow2025/mdigest-master/input_files/MptpA/Results/mdigest-RESULTS/RESULTS/MptpA/visualize_networks/draw_network_pymol.py') cmd.hide('lines', '')
cmd.set_view((\ 0.264859468, -0.952457845, -0.150496021,\ -0.843045175, -0.304483652, 0.443325162,\ -0.468080193, 0.009453356, -0.883612871,\ 0.000000000, 0.000000000, -133.785675049,\ 8.123708725, 6.384021759, 22.244569778,\ 105.477645874, 162.093704224, -20.000000000))
draw_network_from_df(path + 'network_Ptpa_filter_d_0_5.pkl', which='gcc_Q75L', w1='gcc_Q75L', w2='gcc_WT', color_sign=True, sns_palette=sns.color_palette("tab20"), label='gcc_diff', edge_norm=.5)