# A NP
# B WT
# C D325I
# D D325A
WT_Ca_85K_interface_clean = BioTek.clean_data_interface_mutant_D325A(
["Kinetic read"], ["B1", "B2", "B3"], "WT")
D325I_Ca_85K_interface_clean = BioTek.clean_data_interface_mutant_D325A(
["Kinetic read"], ["C1", "C2", "C3"], "D325I")
# There was a bug here in the submitted version of the paper where well D2 was listed twice (instead of D2 and D3). In that version of the code, the statistics were computed on D1 + D2 + D2. The bug made data quality look worse than it actually is (i.e. the error bars are much smaller now that all the replicates are included).
D325A_Ca_85K_interface_clean = BioTek.clean_data_interface_mutant_D325A(
["Kinetic read"], ["D1", "D2", "D3"], "D325A")
WT_Ca_85K_interface = BioTek.normalize_for_plot(WT_Ca_85K_interface_clean)
D325I_Ca_85K_interface = BioTek.normalize_for_plot(
D325I_Ca_85K_interface_clean)
D325A_Ca_85K_interface = BioTek.normalize_for_plot(
D325A_Ca_85K_interface_clean)
# # D325I Turbidity After 1 mM Calcium (in 85 mM KCl)
# fig_D325I_Ca_85K_interface = BioTek.plot_vs_WT(WT_Ca_85K_interface, D325I_Ca_85K_interface, "D325I", xLabel, yLabel, 40, 0.13, "CASQ2 D325I Turbidity", "", "lower right", 1.75, golden)
# fig_D325I_Ca_85K_interface.savefig("./output/kinetics_interface_mutation_D325I_85mM_K.pgf")
# fig_D325I_Ca_85K_interface.savefig("./output/kinetics_interface_mutation_D325I_85mM_K.pdf")
# # D325A Turbidity After 1 mM Calcium (in 85 mM KCl)
# fig_D325A_Ca_85K_interface = BioTek.plot_vs_WT(WT_Ca_85K_interface, D325A_Ca_85K_interface, "D325A", xLabel, yLabel, 40, 0.13, "CASQ2 D325A Turbidity", "", "lower right", 1.75, golden)
# fig_D325A_Ca_85K_interface.savefig("./output/kinetics_interface_mutation_D325A_85mM_K.pgf")
# fig_D325A_Ca_85K_interface.savefig("./output/kinetics_interface_mutation_D325A_85mM_K.pdf")
["Kinetic read"], ["B1", "B2", "B3"], "WT with Mg, 0 mins pre-incubation")
WT_Ca_0Mg_clean = BioTek.clean_data_K180R_varying_Mg(["Kinetic read"],
["C1", "C2", "C3"],
"WT, no Mg")
K180R_Mg_90_clean = BioTek.clean_data_K180R_varying_Mg(
["Kinetic read"], ["D1", "D2", "D3"],
"K180R with Mg, 90 mins pre-incubation")
K180R_Mg_0_clean = BioTek.clean_data_K180R_varying_Mg(
["Kinetic read"], ["E1", "E2", "E3"],
"K180R with Mg, 0 mins pre-incubation")
K180R_No_Mg_clean = BioTek.clean_data_K180R_varying_Mg(["Kinetic read"],
["F1", "F2", "F3"],
"K180R without Mg")
WT_Ca_Mg_90mins = BioTek.normalize_for_plot(WT_Ca_Mg_90mins_clean)
WT_Ca_Mg_0mins = BioTek.normalize_for_plot(WT_Ca_Mg_0mins_clean)
WT_Ca_0Mg = BioTek.normalize_for_plot(WT_Ca_0Mg_clean)
K180R_Mg_90 = BioTek.normalize_for_plot(K180R_Mg_90_clean)
K180R_Mg_0 = BioTek.normalize_for_plot(K180R_Mg_0_clean)
K180R_No_Mg = BioTek.normalize_for_plot(K180R_No_Mg_clean)
# Legend postion options
# right
# center left
# upper right
# lower right
# best
# center
# lower left
WT_Ca_85K_interface_clean = BioTek.clean_data_interface_mutants(
["Kinetic read"], ["B7", "B8", "B9"], "WT")
# D325I_Ca_85K_interface = BioTek.clean_data_interface_mutants(["Kinetic read"], ["C7", "C8", "C9"], "D325I")
D50A_Ca_85K_interface_clean = BioTek.clean_data_interface_mutants(
["Kinetic read"], ["D7", "D8", "D9"], "D50A")
D144A_E174A_Ca_85K_interface_clean = BioTek.clean_data_interface_mutants(
["Kinetic read"], ["E7", "E8", "E9"], "D144A E174A")
E184A_E187A_Ca_85K_interface_clean = BioTek.clean_data_interface_mutants(
["Kinetic read"], ["F7", "F8", "F9"], "E184A E187A")
D348A_D350A_Ca_85K_interface_clean = BioTek.clean_data_interface_mutants(
["Kinetic read"], ["G7", "G8", "G9"], "D348A D350A")
D351A_E357A_Ca_85K_interface_clean = BioTek.clean_data_interface_mutants(
["Kinetic read"], ["H7", "H8", "H9"], "D351A E357A")
WT_Ca_85K_interface = BioTek.normalize_for_plot(WT_Ca_85K_interface_clean)
D50A_Ca_85K_interface = BioTek.normalize_for_plot(D50A_Ca_85K_interface_clean)
D144A_E174A_Ca_85K_interface = BioTek.normalize_for_plot(
D144A_E174A_Ca_85K_interface_clean)
E184A_E187A_Ca_85K_interface = BioTek.normalize_for_plot(
E184A_E187A_Ca_85K_interface_clean)
D348A_D350A_Ca_85K_interface = BioTek.normalize_for_plot(
D348A_D350A_Ca_85K_interface_clean)
D351A_E357A_Ca_85K_interface = BioTek.normalize_for_plot(
D351A_E357A_Ca_85K_interface_clean)
#df_WT, df_mutant, mutant_label, xlabel, ylabel, xlim_max, ylim_max, fig_title, legend_title, legend_position, fig_height, fig_width_multiplier
# D325I Turbidity After 1 mM Calcium (in 85 mM KCl)
# fig_D325I_Ca_85K_interface = BioTek.plot_vs_WT(WT_Ca_85K_interface, D325I_Ca_85K_interface, "D325I", xLabel, yLabel, 40, 0.13, "CASQ2 D325I Turbidity", "", "lower right", 1.75, golden)
# fig_D325I_Ca_85K_interface.savefig("./output/kinetics_interface_mutation_D325I_85mM_K.pgf")
##################### CPVT mutants
# This was done in a batch with the mutants. Not using those data in this paper.
# 85 mM KCl
# A7-9 NP, A10-12 D325E
# B7-9 WT, B10-12 R251H
# C7-9 Y55C, C10-12 S173I
# D7-9 F189L, D10-12 K180R
# E7-9 P308L, E10-12 R33Q
WT_Ca_85K_clean = BioTek.clean_data_CPVT_varying_K(["Kinetic read"], ["B7", "B8", "B9"], "WT")
S173I_Ca_85K_clean = BioTek.clean_data_CPVT_varying_K(["Kinetic read"], ["C10", "C11", "C12"], "S173I")
K180R_Ca_85K_clean = BioTek.clean_data_CPVT_varying_K(["Kinetic read"], ["D10", "D11", "D12"], "K180R")
WT_Ca_85K = BioTek.normalize_for_plot(WT_Ca_85K_clean)
S173I_Ca_85K = BioTek.normalize_for_plot(S173I_Ca_85K_clean)
K180R_Ca_85K = BioTek.normalize_for_plot(K180R_Ca_85K_clean)
# S173I Turbidity After 1 mM Calcium (in 85 mM KCl)
fig_S173I_85K = BioTek.plot_vs_WT(WT_Ca_85K, S173I_Ca_85K, "S173I", xLabel, yLabel, 40, 0.13, "CASQ2 S173I Turbidity", "", "upper left",1.75, golden)
fig_S173I_85K.savefig("./output/kinetics_CPVT_mutation_S173I_85mM_K.pgf")
fig_S173I_85K.savefig("./output/kinetics_CPVT_mutation_S173I_85mM_K.pdf")
# K180R Turbidity After 1 mM Calcium (in 85 mM KCl)
fig_K180R_85K = BioTek.plot_vs_WT(WT_Ca_85K, K180R_Ca_85K, "K180R", xLabel, yLabel, 40, 0.13, "CASQ2 K180R Turbidity", "", "upper left",1.75, golden)
fig_K180R_85K.savefig("./output/kinetics_CPVT_mutation_K180R_85mM_K.pgf")
fig_K180R_85K.savefig("./output/kinetics_CPVT_mutation_K180R_85mM_K.pdf")
##########################
matplotlib.rcParams.update({"legend.handlelength": 0.5})
matplotlib.rcParams.update({"legend.frameon": False})
golden = 1.61803398875
less_golden = 1.4
##################### This was done in a batch with some of Jason's mutants. Ignore those.
# A7-9 NP, A10-12 D325E
# B7-9 WT, B10-12 R251H
# C7-9 Y55C, C10-12 S173I
# D7-9 F189L, D10-12 K180R
# E7-9 P308L, E10-12 R33Q
WT_EDTA_clean = BioTek.clean_data_WT_EDTA(["Kinetic read"], ["B1", "B2", "B3"], "WT")
WT_EDTA = BioTek.normalize_for_plot(WT_EDTA_clean)
fig_WT_EDTA = BioTek.plot_WT_EDTA(WT_EDTA, "WT", xLabel, yLabel, 750, 0.025, "WT Turbidity After 1 mM Calcium,\n then 1 mM EDTA (in 0 mM KCl)", "0 mM KCl", "upper left",1.75, golden)
fig_WT_EDTA.savefig("./output/kinetics_WT_EDTA.pgf")
fig_WT_EDTA.savefig("./output/kinetics_WT_EDTA.pdf")
########### Source data for journal.
writer = pd.ExcelWriter("./output/source_data_Ext_Data_Fig_1a.xlsx",engine='xlsxwriter')
sheet_name_ext_1a = "Ext_Data_Fig_1a"
WT_EDTA_clean.to_excel(writer, sheet_name=sheet_name_ext_1a, columns = ["time_seconds","replicate_1","replicate_2","replicate_3"], index = False, startrow = 1)
workbook = writer.book