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AF2_kinase_families_stackedbarplot.py
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AF2_kinase_families_stackedbarplot.py
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### Author: Noah Herrington, Ph.D.
### Email: [email protected]
import os
import warnings
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt, rc, rcParams
def norm(conf_list):
norm_conf_list = []
minimum = min(conf_list)
maximum = max(conf_list)
rang_e = maximum - minimum
for item in conf_list:
item_norm = (item - minimum) / rang_e
norm_conf_list.append(item_norm)
return norm_conf_list
def avg(conf_list):
avg_conf_list = []
sum_all = sum(conf_list)
for item in conf_list:
item_frac = item / sum_all
avg_conf_list.append(item_frac)
return avg_conf_list
warnings.filterwarnings("ignore")
font = {'weight' : 'bold',
'size' : 11}
rc('font', **font)
rcParams['figure.dpi'] = 300
kinase_families = {}
family_names = {}
families_df = pd.read_csv(os.getcwd() + "/kinfam.csv")
for idx,row in families_df.iterrows():
new_family = row['Group']
if new_family not in family_names.keys():
family_names[new_family] = {}
family_names[new_family]["CIDI"] = 0
family_names[new_family]["CIDO"] = 0
family_names[new_family]["CODI"] = 0
family_names[new_family]["CODO"] = 0
family_names[new_family]["DFGinter"] = 0
family_names[new_family]["Unassigned"] = 0
kinase_families[row['HGNC Name']] = row['Group']
kinase_families['GRK2'] = "AGC"
kinase_families['GRK3'] = "AGC"
kinase_families['PDPK2P'] = "AGC"
kinase_families['RSKR'] = "AGC"
kinase_families['SIK1B'] = "CAMK"
kinase_families['CDK11A'] = "CMGC"
kinase_families['CSNK2A3'] = "CMGC"
kinase_families['PAK5'] = "STE"
kinase_families['MAP3K20'] = "STE"
kinase_families['MAP3K21'] = "STE"
classified_df = pd.read_csv(os.getcwd() + "/kinases_classified.csv")
used_uniprots = []
for idx,row in classified_df.iterrows():
filename = row['Input']
dfg = row['Spatial_label']
chelix = row['C-helix_label']
kinase,rank,rank_num,model,model_num,ptm,seed,seed_num,unrelax = filename.split("_")
try:
kinase,domain = kinase.split("-")
except:
pass
else:
pass
family = kinase_families[kinase]
if dfg == 'DFGin' and chelix == 'Chelix-in':
family_names[family]['CIDI'] += 1
elif dfg == 'DFGin' and chelix == 'Chelix-out':
family_names[family]['CODI'] += 1
elif dfg == 'DFGout' and chelix == 'Chelix-in':
family_names[family]['CIDO'] += 1
elif dfg == 'DFGout' and chelix == 'Chelix-out':
family_names[family]['CODO'] += 1
elif dfg == 'DFGinter':
family_names[family]['DFGinter'] += 1
elif dfg == 'Unassigned':
family_names[family]['Unassigned'] += 1
family_names = dict(sorted(family_names.items()))
del family_names['Atypical']
del family_names['RGC']
families = list(family_names.keys())
conformations = {}
conformations["CIDI"] = []
conformations["CIDO"] = []
conformations["CODI"] = []
conformations["CODO"] = []
conformations["DFGinter"] = []
conformations["Unassigned"] = []
print("AF2 Structure Count")
print("-------------------")
list_of_lists = []
for family in family_names.keys():
family_conf_list = []
family_conf_list.append(family_names[family]["CIDI"])
family_conf_list.append(family_names[family]["CIDO"])
family_conf_list.append(family_names[family]["CODI"])
family_conf_list.append(family_names[family]["CODO"])
family_conf_list.append(family_names[family]["DFGinter"])
family_conf_list.append(family_names[family]["Unassigned"])
avg_family_conf_list = avg(family_conf_list)
list_of_lists.append(avg_family_conf_list)
sum_structures = 0
sum_structures += family_names[family]["CIDI"]
sum_structures += family_names[family]["CIDO"]
sum_structures += family_names[family]["CODI"]
sum_structures += family_names[family]["CODO"]
sum_structures += family_names[family]["DFGinter"]
sum_structures += family_names[family]["Unassigned"]
print("{}: {}".format(family, sum_structures))
print("CIDI Fraction: {}".format(family_names[family]["CIDI"] / sum_structures * 100, "%"))
print("CIDO Fraction: {}".format(family_names[family]["CIDO"] / sum_structures * 100, "%"))
print("CODI Fraction: {}".format(family_names[family]["CODI"] / sum_structures * 100, "%"))
print("CODO Fraction: {}".format(family_names[family]["CODO"] / sum_structures * 100, "%"))
print("DFGinter Fraction: {}".format(family_names[family]["DFGinter"] / sum_structures * 100, "%"))
print("Unassigned Fraction: {}".format(family_names[family]["Unassigned"] / sum_structures * 100, "%"))
confs = ["CIDI","CIDO","CODI","CODO","DFGinter","Unassigned"]
for i in range(6):
for j in range(8):
conf = confs[i]
conformations[conf].append(list_of_lists[j][i])
fig,ax = plt.subplots()
bottom = np.zeros(8)
colors = ['#000140','#002CB3','#4263B7','#7280C7','#B5B0EA','#808080']
for conf,conf_count,color in zip(conformations.keys(),conformations.values(), colors):
p = ax.bar(families, conf_count, color=color, label=conf, bottom=bottom)
bottom += conf_count
plt.xlabel("Group", fontsize=13)
plt.ylabel("Fraction of Structures", fontsize=13)
plt.ylim(0,1.1)
plt.show()