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Modified quickmodel to create a csv file of DeltaG and Kd estimates. #118

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Modified quickmodel to create a csv file of DeltaG and Kd estimates. #118

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537c15c
Modified quickmodel to output an extra csv file of DeltaG and Kd esti…
MehtapIsik Jul 14, 2017
aaf1170
Corrected mean and median values in csv output of Quickmodel.
MehtapIsik Jul 14, 2017
bc56beb
Merge branch 'master' into quickmodel-summary
MehtapIsik Aug 8, 2017
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42 changes: 38 additions & 4 deletions scripts/quickmodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,8 +33,11 @@ def quick_model(inputs, nsamples=1000, nthin=20):
Thinning interval ; number of MCMC steps per sample collected
"""

[complex_fluorescence, ligand_fluorescence] = parser.get_data_using_inputs(inputs)

[complex_fluorescence, ligand_fluorescence] = parser.get_data_using_inputs(inputs)
# List to store DeltaG and Kd estimates for generating summary csv table.
deltaG_output_summary_list = []
Kd_output_summary_list = []

for name in complex_fluorescence.keys():

Expand Down Expand Up @@ -65,8 +68,8 @@ def quick_model(inputs, nsamples=1000, nthin=20):
assay_volume=inputs['assay_volume'], DG_prior='uniform')

import datetime
my_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
my_datetime_filename = datetime.datetime.now().strftime("%Y-%m-%d %H%M")
my_datetime = datetime.datetime.now().strftime("%Y-%m-%d_%H:%M")
my_datetime_filename = datetime.datetime.now().strftime("%Y-%m-%d_%H%M")

nburn = 0 # no longer need burn-in since we do automated equilibration detection
niter = nthin*nsamples # former total simulation time
Expand Down Expand Up @@ -232,9 +235,40 @@ def quick_model(inputs, nsamples=1000, nthin=20):
metadata['Lstated'] = metadata['Lstated'].tolist()

import json
with open('%s-%s.json'%(name,my_datetime), 'w') as outfile:
with open('%s-%s.json'%(name,my_datetime_filename), 'w') as outfile:
json.dump(metadata, outfile, sort_keys = True, indent = 4, ensure_ascii=False)

# Append Delta G and K_d estimates to summary list, for writing out a summary table.
protein_name = name.split("-")[0]
ligand_name = name.split("-")[1]
DeltaG_median = interval[1]
DeltaG_lower_CI = interval[0] # 95% Credibility intervals
DeltaG_upper_CI = interval[2]
DeltaG_mean = DeltaG_equil
DeltaG_STD = dDeltaG_equil
Kd_median = Kd_interval[1]
Kd_lower_CI = Kd_interval[0]
Kd_upper_CI = Kd_interval[2]
Kd_mean = Kd
Kd_STD = dKd
deltaG_output_summary_list.append((protein_name, ligand_name, "DeltaG", DeltaG_median, DeltaG_lower_CI,
DeltaG_upper_CI, DeltaG_mean, DeltaG_STD, "k_BT"))
Kd_output_summary_list.append((protein_name, ligand_name, "K_d", Kd_median, Kd_lower_CI, Kd_upper_CI,
Kd_mean, Kd_STD, "M"))

# Save DeltaG and K_d estimates of all analyzed experiments in a summary csv file.
summary_file_name = "%s-delG-and-Kd-summary-%s.csv" % (protein_name, my_datetime_filename)
writer = open(summary_file_name, "w")
# Write column names
writer.write("%s,%s,%s,%s,%s,%s,%s,%s,%s\n"%("Protein", "Ligand", "Parameter", "Median", "Lower 95% CrI",
"Upper 95% CrI", "Mean", "STD", "Unit"))
# Write values
for summary_tuple in deltaG_output_summary_list:
writer.write("%s,%s,%s,%.3g,%.3g,%.3g,%.3g,%.3g,%s\n" %summary_tuple)
for summary_tuple in Kd_output_summary_list:
writer.write("%s,%s,%s,%.3g,%.3g,%.3g,%.3g,%.3g,%s\n" %summary_tuple)


def entry_point():

import sys
Expand Down