OSCR

Quantifying electrostatic control of docking and binding energetics in functional Cx36 gap junctions.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Results › ESI number at the E2–E2 interface governs GJ formation ↔ cx36_mut_corr.ipynb, lines 1–57 · score 0.82 · K238Q, K238A, K238E, E241A, E230K, E239K
  2. [2] § Results › ESI number at the E2–E2 interface governs GJ formation ↔ cx36_mut_corr.ipynb, lines 1–57 · score 0.82 · K238Q, K238A, K238E, E241A, E230K, E239K
  3. [3] § Results › E241K-containing variants could not form functional GJs ↔ cx36_mut_corr.ipynb, lines 1–57 · score 0.70 · K238E, E241A, E230K, E239K, E241K, hetero
  4. [4] § Results › E241K-containing variants could not form functional GJs ↔ cx36_mut_corr.ipynb, lines 1–57 · score 0.70 · K238E, E241A, E230K, E239K, E241K, hetero
  5. [5] § Methods › Free energy calculation on designed Cx36 variants ↔ cx36_het_dock.ipynb, lines 39–69 · score 0.61 · van der Waals, interaction energies, backbone, bonding, solvation, docking
  6. [6] § Results › The essential role of K238 in forming functional GJs ↔ cx36_het_dock.ipynb, lines 39–69 · score 0.52 · van der Waals, interaction energy, bonds, docked, electrostatic, Cx36

Paper

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The authors' code

Jupyter notebook · 869 lines · 29 KB · no license · 2 matches

  1. # %%
  2. cx36_mut = {
  3. 'hetero': {
  4. 'mut_type':
  5. [
  6. 'K238A', # L -3-2=-5
  7. 'E239K;E230K,E239K', # N 0+2=2
  8. 'K238E,E239K', # O 0+0=0
  9. 'E230K;E230K,E239K', # Q 0+2=2
  10. 'E241A;E230K,E239K', # P -1+2=1
  11. 'K238E,E230K', # R -2-2=-4
  12. 'E230K,E239K', # D 2-2 = 0
  13. 'E230K', # B 0-2 =-2
  14. 'E239K', # C -2
  15. 'E230K,E239K;K238E,E230K', # E 2-2 = 0
  16. 'E230K;K238E', # F 0-4 = -4
  17. 'E239K;K238E', # G 0-4 = -4
  18. 'K238Q', # M -3 - 2 = -5
  19. 'E239K;E230K,E239K', # N 0+2=2
  20. 'K238E;K238E,E230K', # V -4 -2 = -6
  21. 'K238E;K238E,E239K', # W -4 -2 = -6
  22. 'K238E;E230K,E239K,E241K', # a -4 + 4 = 0
  23. 'K238E;E241K', #b -4 + 0 = -4
  24. ],
  25. 'label': ['L', 'N', 'O', 'Q', 'P', 'R', 'D' ,'B', 'C', 'E', 'F', 'G', 'M', 'N', 'V', 'W', 'a', 'b'],
  26. 'ESIs': [3, 3, 3, 3, 3, 2, 4, 5, 5, 4, 4, 4, 3, 3, 1, 1, 2, 2],
  27. 'net_charge' :[-5, 2, 0, 2, 1, -4, 0, -2, -2, 0, -4, -4, -5, 2, -6, -6, 0, -4],
  28. 'Functional': [True, True, True, False, False, False, True, True, True, True, True, True, True, True, False, False, False, False]
  29. },
  31. 'homo': {
  32. 'mut_type':
  33. [
  34. 'E241A;E241A', # J -1*2 = -2
  35. 'E239K;E239K', # I 0*2 = 0
  36. 'E230K;E230K', # H 0*2 = 0
  37. 'K238E,E230K,E239K;K238E,E230K,E239K', # K -2*2 = -4
  38. 'E230K,E239K;E230K,E239K', # S 2*2=4
  39. 'K238E,E230K;K238E,E230K', # T -2*2 = -4
  40. 'K238E,E239K;K238E,E239K', # U -2*2 = -4
  41. 'K238Q;K238Q', # Z -3*2 = -6
  42. 'K238A;K238A', # X -3*2 = -6
  43. 'K238E;K238E', # Y -4*2 = -8
  44. 'E241K;E241K', # c 0*2 = 0
  45. 'E241K,E239K,E230K;E241K,E239K,E230K', # d 2*4 = 8
  46. 'E241K,E239K,E230K,K238E;E241K,E239K,E230K,K238E', # e 2*2 = 4
  47. ],
  48. 'label': ['J', 'I', 'H', 'K','S', 'T', 'U', 'Z', 'X', 'Y', 'c', 'd', 'e'],
  49. 'ESIs': [4, 4, 4, 4, 2, 2, 2, 0, 0, 0, 4, 0, 6],
  50. 'net_charge' : [-2, 0, 0, -4, 4, -4, -4, -6, -6, -8, 0, 8, 4],
  51. 'Functional': [True, True, True, True, False, False, False, False, False, False, False, False, False]
  52. }
  53. }
  54. stru = ['8xgd','8iyg']
  55. for key in cx36_mut:
  56. for ter in cx36_mut[key]:
  57. print(key, ter, len(cx36_mut[key][ter]))
  59. # %%
  60. import pandas as pd
  61. import matplotlib.pyplot as plt
  62. import seaborn as sns
  63. import numpy as np
  64. from scipy.stats import linregress
  65. def pval_to_stars(p):
  66. if p < 0.001:
  67. return '***'
  68. elif p < 0.01:
  69. return '**'
  70. elif p < 0.05:
  71. return '*'
  72. else:
  73. return 'ns'
  75. # %% [markdown]
  76. # # Dataset Combine for difference to WT
  78. # %%
  79. #pdb = '8xgd' # '8xgd','8iyg'
  80. pdb = '8xgd'
  81. file = 'Dif' #Average, Dif, Raw
  83. # %%
  84. all_diff_df = []
  85. use_e241k = True
  86. for mut_sym in ['hetero', 'homo']:
  87. fdx_df = pd.read_csv(f'./{pdb}_{mut_sym}/{file}_{pdb}.fxout', sep='\t', skiprows=8)
  88. e241k_mut_df = pd.read_csv(f'./{pdb}_{mut_sym}/{file}_{pdb}_e241k.fxout', sep='\t', skiprows=8)
  89. #drop_cols
  90. drop_cols = ['Van der Waals clashes', 'cis_bond', 'torsional clash', 'backbone clash', 'disulfide',
  91. 'electrostatic kon', 'helix dipole', 'Sidechain Hbond'] #'Backbone Hbond', ,'Solvation Hydrophobic', 'energy Ionisation'
  92. fdx_df = fdx_df.drop(drop_cols, axis=1)
  93. e241k_mut_df = e241k_mut_df.drop(drop_cols, axis=1)
  95. drop_cols = fdx_df.columns[~(fdx_df != 0).all()]
  96. fdx_df = fdx_df.drop(drop_cols, axis=1)
  97. e241k_mut_df = e241k_mut_df.drop(drop_cols, axis=1)
  98. if use_e241k:
  99. fdx_df = pd.concat([fdx_df, e241k_mut_df], ignore_index=True)
  101. fdx_df['label'] = [lb for lb in cx36_mut[mut_sym]['label'] for i in range(10)]
  102. fdx_df['ESI'] = [esi for esi in cx36_mut[mut_sym]['ESIs'] for i in range(10)]
  103. fdx_df['net_charge'] = [nc for nc in cx36_mut[mut_sym]['net_charge'] for i in range(10)]
  104. fdx_df['mut'] = [mt for mt in cx36_mut[mut_sym]['mut_type'] for i in range(10)]
  105. fdx_df['mut_type'] = [mut_sym for i in range(len(fdx_df))]
  106. fdx_df['functional'] = [fn for fn in cx36_mut[mut_sym]['Functional'] for i in range(10)]
  108. drop_cols = ['Pdb']
  109. fdx_df = fdx_df.drop(drop_cols, axis=1)
  110. all_diff_df.append(fdx_df)
  111. all_diff_df = pd.concat(all_diff_df, ignore_index=True)
  113. # %%
  114. all_diff_df
  116. # %% [markdown]
  117. # ## Direct statitics
  119. # %%
  120. from sklearn.metrics import silhouette_score
  121. import numpy as np
  122. from scipy.stats import mannwhitneyu
  124. # %% [markdown]
  125. # ### All pairs
  127. # %% [markdown]
  128. # Check sample size
  130. # %%
  131. len(all_diff_df[all_diff_df['functional'] == True]), len(all_diff_df[all_diff_df['functional'] == False])
  133. # %% [markdown]
  134. # plot figure
  136. # %%
  137. # Prepare data
  138. all_diff_df['functional_label'] = all_diff_df['functional'].map({True: 'Functional', False: 'Non-functional'})
  139. feature_cols = [col for col in all_diff_df.columns if all_diff_df[col].dtype != 'O' and col not in\
  140. ['functional', 'ESI','functional_label', 'mut','mut_type', 'label', 'net_charge']]
  141. labels = all_diff_df['functional_label']
  143. font_label = 18
  145. # Create PairGrid
  146. g = sns.PairGrid(
  147. all_diff_df,
  148. vars=feature_cols,
  149. hue='functional_label',
  150. palette={'Functional': 'green', 'Non-functional': 'red'},
  151. )
  153. # Map scatter plots
  154. g.map_lower(sns.scatterplot, edgecolor='k', s=60, alpha=0.8)
  155. g.map_diag(sns.kdeplot, fill=True, alpha=0.5) # KDE on diagonal, like pairplot
  156. # Compute and annotate silhouette scores
  157. for i, x_var in enumerate(feature_cols):
  158. for j, y_var in enumerate(feature_cols):
  159. if j > i: # Upper triangle (blank by default)
  160. g.axes[i, j].set_axis_off()
  161. if j < i:
  162. ax = g.axes[i, j]
  163. X = all_diff_df[[y_var, x_var]].values # Note: y_var first for rows, x_var for columns
  165. score = silhouette_score(X, labels)
  166. # Linear regression on all data
  167. slope, intercept, r_val, p_val, _ = linregress(X[:,0], X[:,1])
  168. # Plot the regression line
  169. x_fit = pd.Series(sorted(X[:,0]))
  170. y_fit = slope * x_fit + intercept
  171. ax.plot(x_fit, y_fit, color='grey', linestyle='-', linewidth=1)
  172. ax.set_title(f"S. score: {score:.2f}; R: {r_val:.2f} ({pval_to_stars(p_val)})", fontsize=font_label-3)
  174. if j == i:
  175. ax = g.axes[i, j]
  176. # Compute silhouette score for the diagonal (self-comparison)
  177. X = all_diff_df[[x_var]].values
  178. score = silhouette_score(X, labels)
  179. # Compute p-value (Mann-Whitney U test, non-parametric) for Non-functional vs Functional
  180. #elec_func = all_diff_df[all_diff_df['functional']==True][x_var]
  181. #elec_nonfunc = all_diff_df[all_diff_df['functional']==False][x_var]
  182. #stat, pval = mannwhitneyu(elec_func, elec_nonfunc, alternative='two-sided')
  183. ax.set_title(f"S. score: {score:.2f}", fontsize=font_label-3)
  187. g.add_legend(fontsize=30)
  188. g._legend.set_title("") # Access the legend directly from the PairGrid
  189. #plt.suptitle("Pairwise Scatter Plots with Silhouette Scores", y=1.02, fontsize=30)
  191. for i, feature in enumerate(feature_cols):
  192. if feature == 'total energy':
  193. # Set y-label for the first feature
  194. g.axes[i, 0].set_ylabel('∆∆G', fontsize=font_label-4)
  195. # Set x-label for the first feature
  196. g.axes[-1, i].set_xlabel('∆∆G', fontsize=font_label-2)
  197. elif feature == 'Electrostatics':
  198. # Set y-labels for the first column (leftmost)
  199. g.axes[i, 0].set_ylabel('∆∆Ψ ', fontsize=font_label-4)
  200. # Set x-labels for the bottom row
  201. g.axes[-1, i].set_xlabel('∆∆Ψ', fontsize=font_label-2)
  202. else:
  203. # Set y-labels for the first column (leftmost)
  204. g.axes[i, 0].set_ylabel(f'∆{feature}', fontsize=font_label-4)
  205. # Set x-labels for the bottom row
  206. g.axes[-1, i].set_xlabel(f'∆{feature}', fontsize=font_label-2)
  209. plt.tight_layout()
  210. plt.savefig(f"{pdb}_Pairwise_Plots.svg", format="svg")
  212. # %% [markdown]
  213. # Output Excel for the figure
  215. # %%
  216. if pdb == '8xgd':
  217. all_diff_df[feature_cols+['functional_label']].to_csv(f"Fig_S4A.csv", index=False)
  218. else:
  219. all_diff_df[feature_cols+['functional_label']].to_csv(f"Fig_S5C.csv", index=False)
  221. # %% [markdown]
  222. # ### Single figure of a quantity vs ∆∆G
  224. # %%
  225. all_diff_df_reduced = all_diff_df[~all_diff_df['mut'].isin(['E241A;E241A', 'E241K,E239K,E230K;E241K,E239K,E230K'])]
  227. # %% [markdown]
  228. # Plot data
  230. # %%
  231. from scipy.stats import linregress
  233. # Prepare data
  234. X = all_diff_df_reduced[all_diff_df_reduced['mut_type']=='homo'][['Electrostatics', 'total energy']].values
  235. x = X[:, 0] # Electrostatics
  236. y = X[:, 1] # Total Energy
  238. # Scatter plot
  239. plt.figure(figsize=(6, 5))
  240. plt.scatter(x, y, color='darkcyan', edgecolor='k', s=60, alpha=0.8)
  242. # Linear fit
  243. slope, intercept, r_val, p_val, _ = linregress(x, y)
  244. x_fit = np.linspace(x.min(), x.max(), 100)
  245. y_fit = slope * x_fit + intercept
  246. plt.plot(x_fit, y_fit, color='Grey', linestyle='-', linewidth=2, label='Linear fit')
  248. # Annotation
  249. stats_text = f"Rp = {r_val:.2f} ({pval_to_stars(p_val)})"
  252. # Labels and formatting
  253. plt.xlabel('∆∆ϕ, kcal/mol', fontsize=14)
  254. plt.ylabel('∆∆G, kcal/mol', fontsize=14)
  255. plt.ylim(-21, 41) #(-15, 32)
  256. plt.xlim(-9, 18) #(-20, 22)
  257. ax = plt.gca()
  258. #ax.tick_params(labelbottom=False, labelleft=False)
  261. plt.tight_layout()
  262. print(len(X))
  263. #plt.savefig(f"{pdb}_∆E_vs_∆∆G.svg", format="svg")
  265. # %%
  266. r_val, p_val
  268. # %% [markdown]
  269. # output csv file for the plot
  271. # %%
  272. if pdb == '8xgd':
  273. (all_diff_df_reduced[all_diff_df_reduced['mut_type']=='homo'][['Electrostatics', 'total energy']]).to_csv(f"Fig_1B.csv", index=False)
  275. # %% [markdown]
  276. # ### Multiple quantities vs ∆∆G
  278. # %% [markdown]
  279. # Plot
  281. # %%
  282. from scipy.stats import linregress
  283. # Scatter plot
  284. fig, axes = plt.subplots(figsize=(16, 10), ncols=3, nrows=2)
  285. axes = axes.flatten()
  286. ener_terms = ['Backbone Hbond', 'Van der Waals', 'Solvation Polar', 'Solvation Hydrophobic', 'entropy sidechain', 'entropy mainchain']
  287. ener_term_short = ['BB Hbond', 'VdW', 'Solv Polar', 'Solv Hydrophobic', 'Ent Sidechain', 'Ent Mainchain']
  289. # Prepare elestrostaics data
  290. X_e = all_diff_df_reduced[all_diff_df_reduced['mut_type']=='homo'][['Electrostatics', 'total energy']].values
  291. x_e = X_e[:, 0] # Electrostatics
  292. y_e = X_e[:, 1] # Total Energy
  294. # Scatter plot
  295. #plt.figure(figsize=(6, 5))
  296. #plt.scatter(x, y, color='darkcyan', edgecolor='k', s=60, alpha=0.8)
  298. # Linear fit
  299. slope_e, intercept_e, r_val_e, p_val_e, _ = linregress(x_e, y_e)
  300. x_e_fit = np.linspace(x_e.min(), x_e.max(), 100)
  301. y_e_fit = slope_e * x_e_fit + intercept_e
  303. for i, ax in enumerate(axes):
  304. # Prepare data
  305. X = all_diff_df_reduced[all_diff_df_reduced['mut_type']=='homo'][[ener_terms[i], 'total energy']].values
  306. x = X[:, 0]
  307. y = X[:, 1]
  308. ax.scatter(x, y, color='darkcyan', edgecolor='k', s=60, alpha=0.8)
  309. ax.scatter(x_e, y_e, color='Grey', edgecolor='k', s=20, alpha=0.3)
  311. # Linear fit
  312. slope, intercept, r_val, p_val, _ = linregress(x, y)
  313. x_fit = np.linspace(x.min(), x.max(), 100)
  314. y_fit = slope * x_fit + intercept
  315. ax.plot(x_fit, y_fit, color='k', linestyle='-', linewidth=2, label='Linear fit')
  316. ax.plot(x_e_fit, y_e_fit, color='Grey', linestyle='-', linewidth=2, label='Linear fit (Electrostatics)')
  318. ax.set_xlabel('∆'+ener_term_short[i]+' (kcal/mol)', fontsize=14)
  319. ax.set_ylim(-15, 32)
  320. ax.set_xlim(-20, 22)
  321. if i == 0 or i == 3:
  322. ax.set_ylabel('∆∆G (kcal/mol)', fontsize=14)
  324. # Annotation
  325. stats_text = f"Rp={r_val:.2f}"
  327. ax.set_title('∆∆G vs ∆'+ener_terms[i]+' '+stats_text, fontsize=16)
  329. plt.tight_layout()
  330. plt.savefig(f"{pdb}_∆E_vs_other_SI.svg", format="svg")
  332. # %% [markdown]
  333. # Ouput figure csv file
  335. # %%
  336. all_diff_df_reduced[all_diff_df_reduced['mut_type']=='homo']\
  337. [['total energy','Backbone Hbond', 'Van der Waals', 'Solvation Polar', 'Solvation Hydrophobic', 'entropy sidechain', 'entropy mainchain']].to_csv(f"Fig_S1.csv", index=False)
  339. # %% [markdown]
  340. # ### ∆phi distribution
  342. # %% [markdown]
  343. # Plot
  345. # %%
  346. import seaborn as sns
  347. from scipy.stats import mannwhitneyu
  349. # Prepare data
  350. df = all_diff_df.copy()
  351. df['functional_label'] = df['functional'].map({True: 'Functional', False: 'Non-functional'})
  353. # Extract values
  354. elec_func = df[df['functional_label'] == 'Functional']['Electrostatics']
  355. elec_nonfunc = df[df['functional_label'] == 'Non-functional']['Electrostatics']
  357. # Compute p-value (Mann-Whitney U test, non-parametric)
  358. stat, pval = mannwhitneyu(elec_func, elec_nonfunc, alternative='two-sided')
  360. # Plot
  361. plt.figure(figsize=(7, 5))
  362. sns.histplot(elec_func, color='g', label='Functional', kde=True, stat='density',
  363. bins=12, alpha=0.35, edgecolor=None, kde_kws={'bw_adjust': 1})
  364. sns.histplot(elec_nonfunc, color='r', label='Non-functional', kde=True, stat='density',
  365. bins=12, alpha=0.35, edgecolor=None, kde_kws={'bw_adjust': 1})
  367. #plt.xlabel('∆Electrostatics', fontsize=14)
  368. #plt.ylabel('Frequency', fontsize=14)
  369. plt.yticks([0.0, 0.05, 0.1,0.15,0.2])
  370. ax = plt.gca()
  371. ax.tick_params(labelbottom=False, labelleft=False)
  372. ax.set_xlabel('')
  373. ax.set_ylabel('')
  375. plt.tight_layout()
  376. print(len(df[df['functional']==True]), len(df[df['functional']==False]))
  377. #plt.savefig(f"{pdb}_∆Electrostatics_dist.svg", format="svg")
  379. # %% [markdown]
  380. # Output plot data csv file
  382. # %%
  383. if pdb == '8xgd':
  384. df[['functional_label','Electrostatics']].to_csv(f"Fig_4C.csv", index=False)
  386. # %% [markdown]
  387. # ## ESI vs some energy
  389. # %%
  390. grouped_df = all_diff_df.groupby(['label', 'mut']).agg({
  391. 'net_charge': 'first',
  392. 'ESI': 'first',
  393. 'mut_type': 'first',
  394. 'functional': 'first',
  395. 'total energy': ['mean', 'std'],
  396. 'Electrostatics': ['mean', 'std'],
  397. 'Solvation Polar': ['mean', 'std'],
  398. })
  400. # Flatten the column MultiIndex
  401. grouped_df.columns = ['_'.join(col).strip() if col[1] != 'first' else col[0] for col in grouped_df.columns] #[col[0] if isinstance(col, tuple) else col for col in grouped_df.columns] or ['_'.join(col).strip() if isinstance(col, tuple) else col for col in grouped_df.columns]
  402. grouped_df = grouped_df.reset_index()
  403. grouped_df
  405. # %%
  406. grouped_df_homo = grouped_df[grouped_df['mut_type']=='homo']
  407. grouped_df_homo
  409. # %% [markdown]
  410. # ### Homo samples ESI vs ∆phi
  412. # %%
  413. # drop mut == 'E241A;E241A' and E241K,E239K,E230K;E241K,E239K,E230K
  414. grouped_df_homo_reduced = grouped_df_homo[~grouped_df_homo['mut'].isin(['E241A;E241A', 'E241K,E239K,E230K;E241K,E239K,E230K'])]
  416. # %% [markdown]
  417. # plot
  419. # %%
  420. # ====== USER CONFIGURATION ======
  421. x_col = 'ESI' # x-axis
  422. y_base = 'Electrostatics' # or 'Electrostatics' 'total energy', 'Solvation Polar'
  423. group_col = 'functional' # now using functional: True / False
  425. font_size = 16
  426. font_style = 'Arial'
  428. x_label = 'ESI'
  429. y_label = '∆∆G' if y_base == 'total energy' else '∆Electrostatics'
  430. error_capsize = 10
  431. # =================================
  433. # Derived columns
  434. y_col = f'{y_base}_mean'
  435. y_err = f'{y_base}_std'
  437. # Color mapping by functional
  438. colors = {True: '#00A859', False: 'red'}
  440. # Plot setup
  441. plt.figure(figsize=(8, 6))
  443. # Scatter with error bars colored by functional
  444. for func_value in [True, False]:
  445. sub_df = grouped_df_homo_reduced[grouped_df_homo_reduced[group_col] == func_value]
  446. plt.errorbar(
  447. sub_df[x_col], sub_df[y_col], yerr=sub_df[y_err],
  448. fmt='o', color=colors[func_value], ecolor=colors[func_value],
  449. capsize=error_capsize, capthick=2, markersize=10,
  450. label='Functional' if func_value else 'Non-functional'
  451. )
  453. # Linear regression on all data
  454. x_all = grouped_df_homo_reduced[x_col]
  455. y_all = grouped_df_homo_reduced[y_col]
  456. slope, intercept, r_val, p_val, _ = linregress(x_all, y_all)
  458. # Plot the regression line (optional)
  459. x_fit = pd.Series(sorted(x_all))
  460. y_fit = slope * x_fit + intercept
  461. label_fit = f"Linear fitting"
  462. #plt.plot(x_fit, y_fit, color='darkgrey', linestyle='-', linewidth=1.5, label=label_fit)
  464. ax = plt.gca()
  465. ax.tick_params(labelbottom=False, labelleft=False)
  466. plt.gca().invert_xaxis()
  467. plt.ylim(-7, 18) # 8igy
  468. #plt.ylim(-7, 16) #8xgd
  470. plt.tight_layout()
  471. #plt.savefig(f"{pdb}_{y_label}_vs_ESI.svg", format="svg")
  473. # %% [markdown]
  474. # Output plot data csv file
  476. # %%
  477. if pdb == '8xgd':
  478. grouped_df_homo_reduced[['label','mut','ESI', 'Electrostatics_mean', 'Electrostatics_std', 'total energy_mean','total energy_std','functional']].to_csv(f"Fig_2E.csv", index=False)
  480. # %% [markdown]
  481. # ### Homo samples ESI vs ∆phi Demo version
  483. # %%
  484. # ====== USER CONFIGURATION ======
  485. x_col = 'ESI' # x-axis
  486. y_base = 'Electrostatics' # or 'Electrostatics' 'total energy', 'Solvation Polar'
  487. group_col = 'functional' # now using functional: True / False
  489. font_size = 16
  490. font_style = 'Arial'
  491. plot_title = f'{y_base} vs ESI'
  492. x_label = 'ESI'
  493. y_label = '∆∆G' if y_base == 'total energy' else '∆Electrostatics' + ' (kcal/mol)'
  494. error_capsize = 3
  495. # =================================
  497. # Derived columns
  498. y_col = f'{y_base}_mean'
  499. y_err = f'{y_base}_std'
  501. # Colors by functional
  502. colors = {True: '#00A859', False: 'red'}
  504. # Marker settings for 'functional' True/False
  505. marker_style = {
  506. True: {'marker': 'o', 'facecolors': colors[True], 'edgecolors': 'black', 'label': 'Functional'},
  507. False: {'marker': 'o', 'facecolors': colors[False], 'edgecolors': 'black', 'label': 'Non-functional'}
  508. }
  510. # Plot setup
  511. plt.figure(figsize=(8, 6))
  513. # Scatter plot with error bars
  514. for functional_value in [True, False]:
  515. sub_df = grouped_df_homo_reduced[grouped_df_homo_reduced[group_col] == functional_value]
  516. x = sub_df[x_col]
  517. y = sub_df[y_col]
  518. y_std = sub_df[y_err]
  519. style = marker_style[functional_value]
  521. plt.errorbar(x, y, yerr=y_std, fmt=style['marker'],
  522. markerfacecolor=style['facecolors'], markeredgecolor=style['edgecolors'],
  523. ecolor=style['facecolors'],
  524. markersize=6, capsize=error_capsize, linestyle='none',
  525. label=style['label'])
  526. # Add label text near each point
  527. for i, row in sub_df.iterrows():
  528. plt.text(row[x_col] + 0.2, row[y_col]+1, str(row['mut'].split(';')[0]),
  529. fontsize=font_size-2, color=style['edgecolors'],
  530. ha='left', va='center')
  532. # Linear regression on all data
  533. x_all = grouped_df_homo_reduced[x_col]
  534. y_all = grouped_df_homo_reduced[y_col]
  535. slope, intercept, r_val, p_val, _ = linregress(x_all, y_all)
  537. # Plot the regression line
  538. x_fit = pd.Series(sorted(x_all))
  539. y_fit = slope * x_fit + intercept
  540. label_fit = f"Linear fitting"
  541. plt.plot(x_fit, y_fit, color='black', linestyle='-', linewidth=1.5, label=label_fit)
  543. # Add horizontal line at y=0
  544. plt.axhline(0, color='grey', linestyle='--', linewidth=1.5, label='y=0')
  546. # Add stats textbox
  547. stats_text = f"$R^2$ = {r_val:.3f}\np = {p_val:.1e} ({pval_to_stars(p_val)})"
  548. plt.text(0.7, 0.15, stats_text,
  549. transform=plt.gca().transAxes,
  550. fontsize=font_size,
  551. verticalalignment='top',
  552. bbox=dict(boxstyle="round,pad=0.3", facecolor='lightgrey', alpha=0.5))
  555. # Axis formatting
  556. plt.xlabel(x_label, fontsize=font_size, fontname=font_style)
  557. plt.ylabel(y_label, fontsize=font_size, fontname=font_style)
  558. plt.xticks(fontsize=font_size-1, fontname=font_style)
  559. plt.yticks(fontsize=font_size-1, fontname=font_style)
  560. plt.legend(fontsize=font_size-1)
  561. plt.gca().invert_xaxis()
  563. plt.tight_layout()
  565. # %% [markdown]
  566. # ### ALl samples ESI vs ∆phi excluding E241K-related mutants Demo version
  568. # %%
  569. # drop mut that include 'E241K' samples
  570. grouped_df_reduced = grouped_df[~grouped_df['mut'].str.contains('E241K')]
  572. # %% [markdown]
  573. # Plot
  575. # %%
  576. # ====== USER CONFIGURATION ======
  577. x_col = 'ESI' # x-axis
  578. y_base = 'Electrostatics' # or 'Electrostatics' 'total energy', 'Solvation Polar'
  579. group_col = 'functional' # now using functional: True / False
  581. font_size = 16
  582. font_style = 'Arial'
  583. plot_title = f'{y_base} vs ESI'
  584. x_label = 'ESI'
  585. y_label = '∆∆G' if y_base == 'total energy' else '∆Electrostatics' + ' (kcal/mol)'
  586. error_capsize = 10
  587. # =================================
  589. # Derived columns
  590. y_col = f'{y_base}_mean'
  591. y_err = f'{y_base}_std'
  593. # Colors by functional
  594. colors = {True: '#00A859', False: 'red'}
  596. # Marker settings for 'functional' True/False
  597. marker_style = {
  598. True: {'marker': 'o', 'facecolors': colors[True], 'edgecolors': 'black', 'label': 'Functional'},
  599. False: {'marker': 'o', 'facecolors': colors[False], 'edgecolors': 'black', 'label': 'Non-functional'}
  600. }
  602. # Plot setup
  603. plt.figure(figsize=(8, 6))
  605. # Scatter with error bars colored by functional
  606. for func_value in [True, False]:
  607. sub_df = grouped_df_reduced[grouped_df_reduced[group_col] == func_value]
  608. plt.errorbar(
  609. sub_df[x_col], sub_df[y_col], yerr=sub_df[y_err],
  610. fmt='o', color=colors[func_value], ecolor=colors[func_value],
  611. capsize=error_capsize, capthick=2, markersize=10,
  612. label='Functional' if func_value else 'Non-functional'
  613. )
  614. # Add label text near each point
  615. for i, row in sub_df.iterrows():
  616. plt.text(row[x_col] + 0.2, row[y_col]+1, str(row['label']),
  617. fontsize=font_size-2, color=marker_style[func_value]['edgecolors'],
  618. ha='left', va='center')
  620. # Linear regression on all data
  621. x_all = grouped_df_reduced[x_col]
  622. y_all = grouped_df_reduced[y_col]
  623. slope, intercept, r_val, p_val, _ = linregress(x_all, y_all)
  625. # Plot the regression line
  626. x_fit = pd.Series(sorted(x_all))
  627. y_fit = slope * x_fit + intercept
  628. label_fit = f"Linear fitting"
  629. plt.plot(x_fit, y_fit, color='darkgrey', linestyle='-', linewidth=2, label=label_fit)
  631. # Add stats textbox
  632. stats_text = f"$R$ = {r_val:.3f}\np = {p_val:.1e} ({pval_to_stars(p_val)})"
  633. plt.text(0.7, 0.15, stats_text,
  634. transform=plt.gca().transAxes,
  635. fontsize=font_size,
  636. verticalalignment='top',
  637. bbox=dict(boxstyle="round,pad=0.3", facecolor='lightgrey', alpha=0.5))
  640. # Axis formatting
  641. plt.xlabel(x_label, fontsize=font_size, fontname=font_style)
  642. plt.ylabel(y_label, fontsize=font_size, fontname=font_style)
  643. plt.xticks(fontsize=font_size-1, fontname=font_style)
  644. plt.yticks(fontsize=font_size-1, fontname=font_style)
  645. plt.legend(fontsize=font_size-1)
  646. plt.gca().invert_xaxis()
  648. plt.tight_layout()
  650. # %% [markdown]
  651. # Output plot data csv file
  653. # %%
  654. if pdb == '8xgd':
  655. grouped_df_reduced\
  656. [['label','mut','ESI', 'Electrostatics_mean', 'Electrostatics_std', 'total energy_mean','total energy_std','functional']].to_csv(f"Fig_4D_and_Fig_S4B.csv", index=False)
  657. else:
  658. grouped_df_reduced\
  659. [['label','mut','ESI', 'Electrostatics_mean', 'Electrostatics_std', 'total energy_mean','total energy_std','functional']].to_csv(f"Fig_S5A_and_Fig_S5B.csv", index=False)
  661. # %% [markdown]
  662. # ### ALl samples ESI vs ∆phi excluding E241K-related mutants
  664. # %%
  665. # ====== USER CONFIGURATION ======
  666. x_col = 'ESI' # x-axis
  667. y_base = 'Electrostatics' # or 'Electrostatics' 'total energy', 'Solvation Polar'
  668. group_col = 'functional' # now using functional: True / False
  670. font_size = 16
  671. font_style = 'Arial'
  672. plot_title = f'{y_base} vs ESI'
  673. x_label = 'ESI'
  674. y_label = '∆∆G' if y_base == 'total energy' else '∆Electrostatics'
  675. error_capsize = 10
  676. # =================================
  678. # Derived columns
  679. y_col = f'{y_base}_mean'
  680. y_err = f'{y_base}_std'
  682. # Colors by functional
  683. colors = {True: '#00A859', False: 'red'}
  685. # Marker settings for 'functional' True/False
  686. marker_style = {
  687. True: {'marker': 'o', 'facecolors': colors[True], 'edgecolors': 'black', 'label': 'Functional'},
  688. False: {'marker': 'o', 'facecolors': colors[False], 'edgecolors': 'black', 'label': 'Non-functional'}
  689. }
  691. # Plot setup
  692. plt.figure(figsize=(8, 6))
  694. # Scatter with error bars colored by functional
  695. for func_value in [True, False]:
  696. sub_df = grouped_df_reduced[grouped_df_reduced[group_col] == func_value]
  697. plt.errorbar(
  698. sub_df[x_col], sub_df[y_col], yerr=sub_df[y_err],
  699. fmt='o', color=colors[func_value], ecolor=colors[func_value],
  700. capsize=error_capsize, capthick=2, markersize=10,
  701. label='Functional' if func_value else 'Non-functional'
  702. )
  704. # Linear regression on all data
  705. x_all = grouped_df_reduced[x_col]
  706. y_all = grouped_df_reduced[y_col]
  707. slope, intercept, r_val, p_val, _ = linregress(x_all, y_all)
  709. # Plot the regression line
  710. x_fit = pd.Series(sorted(x_all))
  711. y_fit = slope * x_fit + intercept
  712. label_fit = f"Linear fitting"
  713. plt.plot(x_fit, y_fit, color='darkgrey', linestyle='-', linewidth=2, label=label_fit)
  715. # Add stats textbox
  716. stats_text = f"$R^2$ = {r_val:.3f}\np = {p_val:.1e} ({pval_to_stars(p_val)})"
  718. # Axis formatting
  719. ax = plt.gca()
  720. ax.tick_params(labelbottom=False, labelleft=False)
  721. plt.gca().invert_xaxis()
  722. #plt.ylim(-7, 16) #8xgd
  723. plt.ylim(-7, 18) #8iyg
  725. plt.tight_layout()
  726. plt.savefig(f"{pdb}_{y_label}_vs_ESI_homo_hetero.svg", format="svg")
  728. # %% [markdown]
  729. # ### All samples ESI vs ∆∆G Demo version
  731. # %%
  732. # ====== USER CONFIGURATION ======
  733. x_col = 'ESI' # x-axis
  734. y_base = 'total energy'
  735. group_col = 'functional' # now using functional: True / False
  737. font_size = 16
  738. font_style = 'Arial'
  739. plot_title = f'{y_base} vs ESI'
  740. x_label = 'ESI'
  741. y_label = '∆∆G' if y_base == 'total energy' else '∆Electrostatics' + ' (kcal/mol)'
  742. error_capsize = 10
  743. # =================================
  745. # Derived columns
  746. y_col = f'{y_base}_mean'
  747. y_err = f'{y_base}_std'
  749. # Colors by functional
  750. colors = {True: '#00A859', False: 'red'}
  752. # Marker settings for 'functional' True/False
  753. marker_style = {
  754. True: {'marker': 'o', 'facecolors': colors[True], 'edgecolors': 'black', 'label': 'Functional'},
  755. False: {'marker': 'o', 'facecolors': colors[False], 'edgecolors': 'black', 'label': 'Non-functional'}
  756. }
  758. # Plot setup
  759. plt.figure(figsize=(8, 6))
  761. # Scatter with error bars colored by functional
  762. for func_value in [True, False]:
  763. sub_df = grouped_df_reduced[grouped_df_reduced[group_col] == func_value]
  764. plt.errorbar(
  765. sub_df[x_col], sub_df[y_col], yerr=sub_df[y_err],
  766. fmt='o', color=colors[func_value], ecolor=colors[func_value],
  767. capsize=error_capsize, capthick=2, markersize=10,
  768. label='Functional' if func_value else 'Non-functional'
  769. )
  770. # Add label text near each point
  771. for i, row in sub_df.iterrows():
  772. plt.text(row[x_col] + 0.2, row[y_col]+1, str(row['label']),
  773. fontsize=font_size-2, color=marker_style[func_value]['edgecolors'],
  774. ha='left', va='center')
  776. # Linear regression on all data
  777. x_all = grouped_df_reduced[x_col]
  778. y_all = grouped_df_reduced[y_col]
  779. slope, intercept, r_val, p_val, _ = linregress(x_all, y_all)
  781. # Plot the regression line
  782. x_fit = pd.Series(sorted(x_all))
  783. y_fit = slope * x_fit + intercept
  784. label_fit = f"Linear fitting"
  785. plt.plot(x_fit, y_fit, color='darkgrey', linestyle='-', linewidth=2, label=label_fit)
  787. # Add stats textbox
  788. stats_text = f"$R$ = {r_val:.3f}\np = {p_val:.1e} ({pval_to_stars(p_val)})"
  789. plt.text(0.7, 0.15, stats_text,
  790. transform=plt.gca().transAxes,
  791. fontsize=font_size,
  792. verticalalignment='top',
  793. bbox=dict(boxstyle="round,pad=0.3", facecolor='lightgrey', alpha=0.5))
  796. # Axis formatting
  797. plt.xlabel(x_label, fontsize=font_size, fontname=font_style)
  798. plt.ylabel(y_label, fontsize=font_size, fontname=font_style)
  799. plt.xticks(fontsize=font_size-1, fontname=font_style)
  800. plt.yticks(fontsize=font_size-1, fontname=font_style)
  801. plt.legend(fontsize=font_size-1)
  802. plt.gca().invert_xaxis()
  803. plt.ylim(-21, 41)
  804. plt.tight_layout()
  806. # %% [markdown]
  807. # ### All samples ESI vs ∆∆G
  809. # %%
  810. # ====== USER CONFIGURATION ======
  811. x_col = 'ESI' # x-axis
  812. y_base = 'total energy'
  813. group_col = 'functional' # now using functional: True / False
  815. font_style = 'Arial'
  816. plot_title = f'{y_base} vs ESI'
  817. x_label = 'ESI'
  818. y_label = '∆∆G' if y_base == 'total energy' else '∆Electrostatics'
  819. error_capsize = 10
  820. # =================================
  822. # Derived columns
  823. y_col = f'{y_base}_mean'
  824. y_err = f'{y_base}_std'
  826. # Colors by functional
  827. colors = {True: '#00A859', False: 'red'}
  829. # Marker settings for 'functional' True/False
  830. marker_style = {
  831. True: {'marker': 'o', 'facecolors': colors[True], 'edgecolors': 'black', 'label': 'Functional'},
  832. False: {'marker': 'o', 'facecolors': colors[False], 'edgecolors': 'black', 'label': 'Non-functional'}
  833. }
  835. # Plot setup
  836. plt.figure(figsize=(8, 6))
  838. # Scatter with error bars colored by functional
  839. for func_value in [True, False]:
  840. sub_df = grouped_df_reduced[grouped_df_reduced[group_col] == func_value]
  841. plt.errorbar(
  842. sub_df[x_col], sub_df[y_col], yerr=sub_df[y_err],
  843. fmt='o', color=colors[func_value], ecolor=colors[func_value],
  844. capsize=error_capsize, capthick=2, markersize=10,
  845. label='Functional' if func_value else 'Non-functional'
  846. )
  848. # Linear regression on all data
  849. x_all = grouped_df_reduced[x_col]
  850. y_all = grouped_df_reduced[y_col]
  851. slope, intercept, r_val, p_val, _ = linregress(x_all, y_all)
  853. # Plot the regression line
  854. x_fit = pd.Series(sorted(x_all))
  855. y_fit = slope * x_fit + intercept
  856. label_fit = f"Linear fitting"
  857. plt.plot(x_fit, y_fit, color='darkgrey', linestyle='-', linewidth=2, label=label_fit)
  859. # Add stats textbox
  860. stats_text = f"$R^2$ = {r_val:.3f}\np = {p_val:.1e} ({pval_to_stars(p_val)})"
  862. # Axis formatting
  863. ax = plt.gca()
  864. ax.tick_params(labelbottom=False, labelleft=False)
  865. plt.gca().invert_xaxis()
  866. plt.ylim(-21, 41)
  868. plt.tight_layout()
  869. plt.savefig(f"{pdb}_{y_label}_vs_ESI.svg", format="svg")

cx36_mut_corr.ipynb at commit faf83f4, no license · at the source

Overview

Authors: Robert S Wong1, Zhiyuan Song2, Yu T Zheng1, Honghong Chen1, Haiqing Zhao2, Donglin Bai1
ORCID iDs: Zhiyuan Song, Haiqing Zhao, Donglin Bai
  1. Department of Physiology and Pharmacology, University of Western Ontario, London, ON Canada
  2. Department of Biochemistry and Molecular Biology; Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, Galveston, TX USA
Institutions: Western University (Canada); The University of Texas Medical Branch at Galveston (United States)
Journal: Communications biology, volume 9, issue 1, article 815
Dates: received 21 November 2025; accepted 2 June 2026; published online 11 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10464-w · PMID 42277350 · PMCID PMC13269782 · OpenAlex W7164370178
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials
Keywords: Ion channel signalling, Neurophysiology
MeSH: Gap Junction delta-2 Protein*, Gap Junctions*, Static Electricity*, HEK293 Cells, Humans, Molecular Docking Simulation, Protein Binding (* major topic)
Topic: Connexins and lens biology (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (288241)
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

Connexin36 (Cx36) is broadly expressed in neurons and serves as the principal protein that forms interneuronal gap junctions (GJs), also known as electrical synapses. Recent high-resolution structures of human Cx36 GJ have revealed crucial electrostatic interactions (ESIs) of charged residues between two docked Cx36 hemichannels at the second extracellular (E2) loops. Despite their structural importance, the mechanistic roles of these ESIs remain poorly understood. To investigate their significance, we systematically designed and tested a series of missense variants targeting key E2 interface residues, aiming to disrupt or modulate the electrostatic landscape at the docking interface. Based on the ESI pairs defined from the crystal structure, our combined computational calculations and dual patch-clamp experiments in engineered HEK293 cell pairs suggest that at least three ESI residual pairs per E2–E2 interface are required to support functional GJ formation. Furthermore, we found that these unique ESIs of Cx36 could play a role in its docking specificity to itself, as they rarely form heterotypic GJs with other brain connexins. Overall, these findings provide essential molecular and functional insights into the mechanisms governing Cx36 GJ formation and partner specificity, paving the way for future therapeutic approaches targeting connexin dysfunction in human diseases.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

Zenodo 20303481

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), pandas (2 files), NumPy (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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3 files
At the source: zenodo.org/records/20303481

zhaolabutmb/Cx36_Channel_Electrostatics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: faf83f4505591cf0c499e3533d7ae0991a06646e, 29 May 2026
Languages: Jupyter (2)
Size: 72 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt), 2 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (2 files), pandas (2 files), NumPy (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/zhaolabutmb/Cx36_Channel_Electrostatics

Code availability

All codes and scripts used for computational calculations and plotting are available at GitHub (https://github.com/zhaolabutmb/Cx36_Channel_Electrostatics) and from Zenodo (https://zenodo.org/records/20303481).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Numerical source data underlying patch clamp, computational data, and percentage of morphological GJ plaque figures are provided in Supplementary Data 1. Raw patch clamp recordings are available upon reasonable request to the corresponding author. Computational data including all the calculated datasets can be accessed through Zenodo at https://zenodo.org/records/20303481.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 7 MeSH terms, 1 funder, 58 references.

Cite

This paper

Wong, R. S., Song, Z., Zheng, Y. T., Chen, H., Zhao, H., & Bai, D. (2026). Quantifying electrostatic control of docking and binding energetics in functional Cx36 gap junctions. Communications biology, 9(1), 815. https://doi.org/10.1038/s42003-026-10464-w

BibTeX

@article{wong2026quantifying,
author = {Wong, Robert S and Song, Zhiyuan and Zheng, Yu T and Chen, Honghong and Zhao, Haiqing and Bai, Donglin},
title = {{Quantifying electrostatic control of docking and binding energetics in functional Cx36 gap junctions}},
journal = {Communications biology},
year = {2026},
month = jun,
volume = {9},
number = {1},
pages = {815},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10464-w},
url = {https://doi.org/10.1038/s42003-026-10464-w},
pmid = {42277350},
pmcid = {PMC13269782}
}

RIS

TY - JOUR
AU - Wong, Robert S
AU - Song, Zhiyuan
AU - Zheng, Yu T
AU - Chen, Honghong
AU - Zhao, Haiqing
AU - Bai, Donglin
TI - Quantifying electrostatic control of docking and binding energetics in functional Cx36 gap junctions
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/06/11
VL - 9
IS - 1
SP - 815
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10464-w
UR - https://doi.org/10.1038/s42003-026-10464-w
LA - en
ER -

CSL-JSON

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"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "815",
"DOI": "10.1038/s42003-026-10464-w",
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"PMCID": "PMC13269782",
"ISSN": "2399-3642",
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"URL": "https://doi.org/10.1038/s42003-026-10464-w",
"language": "en",
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"date-parts": [
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6,
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}

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