Here we will analyze two competition assay conducted as a rough beginning to understand how to best design competition assays to the fluorescent kinase inhibitors (bosutinib, bosutinib isomer, erlotinib, and gefitinib) used in other assays in this repository.
The first (1st) part of this will be looking at data collected trying to compete off bosutinib from Src kinase with imatinib (conducted on March 11, 2015). The second (2nd) part of this will be looking at data collected trying to compete off gefitinib from Src kinase with imatinib (conducted on October 30, 2015). The third (3rd) part will be some simple modeling to see if these experiments follow our expectations and how we can better design the experiments to get better results from the competition assay. Then in a fourth (4th) section we'll work a little on a PYMC model to get affinities from the competition assay.
In [1]:
#import needed libraries
import re
import os
from lxml import etree
import pandas as pd
import pymc
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
The first attempt at a Bosutinib-Imatinib competition assay was on March 11, 2015. The full description of the assay can be found here. In short (and very similar to as described in the lab-protocols repository) 100 uL of 0.5 $\mu$M Src with a titration of Bosutinib up to 20 $\mu$M in every other row was prepared. In one of two plates 10 $\mu$M Imatinib was added to the whole plate to try to compete off the Bosutinib.
In [2]:
def get_wells_from_section(path):
reads = path.xpath("*/Well")
wellIDs = [read.attrib['Pos'] for read in reads]
data = [(float(s.text), r.attrib['Pos'])
for r in reads
for s in r]
datalist = {
well : value
for (value, well) in data
}
welllist = [
[
datalist[chr(64 + row) + str(col)]
if chr(64 + row) + str(col) in datalist else None
for row in range(1,9)
]
for col in range(1,13)
]
return welllist
In [3]:
file_BOS= "data/2015-03-11 18-35-16_plate_1.xml"
file_name = os.path.splitext(file_BOS)[0]
In [4]:
root = etree.parse(file_BOS)
In [5]:
Sections = root.xpath("/*/Section")
much = len(Sections)
print "****The xml file " + file_BOS + " has %s data sections:****" % much
for sect in Sections:
print sect.attrib['Name']
In [6]:
#Just going to work with topread for now
TopRead = root.xpath("/*/Section")[0]
welllist = get_wells_from_section(TopRead)
Bos_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])
In [7]:
sns.set_palette("Paired", 10)
sns.set_context("notebook", rc={"lines.linewidth": 2.5})
Bos_dataframe.plot(figsize=(6, 4), title=file_name)
plt.xlim(-0.5,11.5)
Out[7]:
In [8]:
Bos_dataframe
Out[8]:
In [9]:
file_BOS_IMA= "data/Ima_WIP_SMH_SrcBos_Extend_013015_mdfx_20150311_18.xml"
file_name = os.path.splitext(file_BOS_IMA)[0]
root = etree.parse(file_BOS_IMA)
Sections = root.xpath("/*/Section")
much = len(Sections)
print "****The xml file " + file_BOS_IMA + " has %s data sections:****" % much
for sect in Sections:
print sect.attrib['Name']
In [10]:
#Just going to work with topread for now
TopRead = root.xpath("/*/Section")[0]
welllist = get_wells_from_section(TopRead)
BosIma_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])
sns.set_palette("Paired", 10)
sns.set_context("notebook", rc={"lines.linewidth": 2.5})
BosIma_dataframe.plot(figsize=(6, 4), title=file_name)
plt.xlim(-0.5,11.5)
Out[10]:
In [11]:
plt.plot(BosIma_dataframe[:].values, 'r');
plt.plot(Bos_dataframe[:].values, 'k');
plt.text(8,450,'Bosutinib',fontsize=15)
plt.text(8,420,'Imatinib + Bosutinib',fontsize=15,color='red')
Out[11]:
The first attempt at a Gefitinib-Imatinib competition assay was on October 30, 2015. The full description of the assay can be found here. In short (and very similar to as described in the lab-protocols repository) 100 uL of 0.5 $\mu$M Src with a titration of Gefitinib up to 20 $\mu$M in every other row was prepared. In one of two plates 10 $\mu$M Imatinib was added to the whole plate to try to compete off the Gefitinib. Note the documentation here could be better, which could be why this data doesn't look particularly great.
In [12]:
file_GEF = "data/Gef_2015-10-30 17-55-48_plate_1.xml"
file_name = os.path.splitext(file_GEF)[0]
In [13]:
root = etree.parse(file_GEF)
In [14]:
Sections = root.xpath("/*/Section")
much = len(Sections)
print "****The xml file " + file_GEF + " has %s data sections:****" % much
for sect in Sections:
print sect.attrib['Name']
In [15]:
#Just going to work with topread for now
TopRead = root.xpath("/*/Section")[0]
welllist = get_wells_from_section(TopRead)
Gef_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])
Gef_dataframe.plot(figsize=(6, 4), title=file_name)
plt.xlim(-0.5,11.5)
Out[15]:
In [16]:
file_GEF_IMA= "data/GefIma_2015-10-30 17-51-13_plate_1.xml"
file_name = os.path.splitext(file_GEF_IMA)[0]
root = etree.parse(file_GEF_IMA)
Sections = root.xpath("/*/Section")
much = len(Sections)
print "****The xml file " + file_GEF_IMA + " has %s data sections:****" % much
for sect in Sections:
print sect.attrib['Name']
In [17]:
#Just going to work with topread for now
TopRead = root.xpath("/*/Section")[0]
welllist = get_wells_from_section(TopRead)
GefIma_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])
sns.set_palette("Paired", 10)
sns.set_context("notebook", rc={"lines.linewidth": 2.5})
GefIma_dataframe.plot(figsize=(6, 4), title=file_name)
plt.xlim(-0.5,11.5)
Out[17]:
In [18]:
plt.plot(GefIma_dataframe[:].values, 'r');
plt.plot(Gef_dataframe[:].values, 'k');
plt.text(8,230,'Gefitinib',fontsize=15)
plt.text(8,210,'Imatinib + Gefitinib',fontsize=15,color='red')
Out[18]:
So now let's look at what our expected data might look like. Here we are looking at inhibitor affinities for Src. Some initial placeholder data from here again: http://www.guidetopharmacology.org/GRAC/LigandScreenDisplayForward?ligandId=5710&screenId=2
In [19]:
Kd_Bos = 1.0e-9 # M
Kd_Gef = 3800e-9 # M
Kd_Ima = 3000e-9 # M
From our assay setup we know the Src concentration is 0.5 $\mu$M.
In [20]:
Ptot = 0.5e-6 # M
Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M
First let's just plot our two component binding for Bosutinib and Gefitinib.
In [21]:
# Now we can use this to define a function that gives us PL from Kd, Ptot, and Ltot.
def two_component_binding(Kd, Ptot, Ltot):
"""
Parameters
----------
Kd : float
Dissociation constant
Ptot : float
Total protein concentration
Ltot : float
Total ligand concentration
Returns
-------
P : float
Free protein concentration
L : float
Free ligand concentration
PL : float
Complex concentration
"""
PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot)) # complex concentration (uM)
P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)
L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)
return [P, L, PL]
In [22]:
[Lb, Pb, PLb] = two_component_binding(Kd_Bos, Ptot, Ltot)
[Lg, Pg, PLg] = two_component_binding(Kd_Gef, Ptot, Ltot)
In [23]:
# y will be complex concentration
# x will be total ligand concentration
Bos, = plt.semilogx(Ltot,PLb,'green', label='Bosutinib')
Gef, = plt.semilogx(Ltot,PLg,'violet', label = 'Gefitinib')
plt.xlabel('$[L]_{tot}$')
plt.ylabel('$[PL]$')
plt.ylim(0,6e-7)
plt.legend(loc=3);
Now let's see how we would expect Imatinib to effect this.
From our assay setup we know the Imatinib concentration is
In [24]:
Lima = 10e-6 # M
In [25]:
#Competitive binding function
def three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A):
"""
Parameters
----------
Ptot : float
Total protein concentration
Ltot : float
Total tracer(fluorescent) ligand concentration
Kd_L : float
Dissociation constant
Atot : float
Total competitive ligand concentration
Kd_A : float
Dissociation constant
Returns
-------
P : float
Free protein concentration
L : float
Free ligand concentration
A : float
Free ligand concentration
PL : float
Complex concentration
Kd_L_app : float
Apparent dissociation constant of L in the presence of A
Usage
-----
[P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)
"""
Kd_L_app = Kd_L*(1+Atot/Kd_A)
PL = 0.5 * ((Ptot + Ltot + Kd_L_app) - np.sqrt((Ptot + Ltot + Kd_L_app)**2 - 4*Ptot*Ltot)) # complex concentration (uM)
P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)
L = Ltot - PL; # free tracer ligand concentration in sample cell after n injections (uM)
A = Atot - PL; # free competitive ligand concentration in sample cell after n injections (uM)
return [P, L, A, PL, Kd_L_app]
In [26]:
[Pbi, Lbi, Abi, PLbi, Kd_bima] = three_component_competitive_binding(Ptot, Ltot, Kd_Bos, Lima, Kd_Ima)
[Pgi, Lgi, Agi, PLgi, Kd_gima] = three_component_competitive_binding(Ptot, Ltot, Kd_Gef, Lima, Kd_Ima)
In [27]:
# y will be complex concentration
# x will be total ligand concentration
plt.title('Src competition assay')
Bos, = plt.semilogx(Ltot,PLb,'green', label='Bosutinib')
Bos_Ima, = plt.semilogx(Ltot,PLbi,'cyan', label='Bosutinib + Ima')
Gef, = plt.semilogx(Ltot,PLg,'violet', label = 'Gefitinib')
Gef_Ima, = plt.semilogx(Ltot,PLgi,'pink', label = 'Gefitinib + Ima')
plt.xlabel('$[L]_{tot}$')
plt.ylabel('$[PL]$')
plt.ylim(0,6e-7)
plt.legend(loc=3);
HMMM.
So I was right to think that Gefitinib should work despite the fact that Bosutinib didn't, but...
In [28]:
#Using expected Kd's from same website as above
Kd_Bos_Abl = 0.1e-9 # M
Kd_Gef_Abl = 480e-9 # M
Kd_Ima_Abl = 21.0e-9 # M
In [29]:
[Lb_Abl, Pb_Abl, PLb_Abl] = two_component_binding(Kd_Bos_Abl, Ptot, Ltot)
[Lg_Abl, Pg_Abl, PLg_Abl] = two_component_binding(Kd_Gef_Abl, Ptot, Ltot)
In [30]:
[Pbi_Abl, Lbi_Abl, Abi_Abl, PLbi_Abl, Kd_bima_Abl] = three_component_competitive_binding(Ptot, Ltot, Kd_Bos_Abl, Lima, Kd_Ima_Abl)
[Pgi_Abl, Lgi_Abl, Agi_Abl, PLgi_Abl, Kd_gima_Abl] = three_component_competitive_binding(Ptot, Ltot, Kd_Gef_Abl, Lima, Kd_Ima_Abl)
In [31]:
# y will be complex concentration
# x will be total ligand concentration
Bos, = plt.semilogx(Ltot,PLb_Abl,'green', label='Bosutinib')
Bos_Ima, = plt.semilogx(Ltot,PLbi_Abl,'cyan', label='Bosutinib + Ima')
Gef, = plt.semilogx(Ltot,PLg_Abl,'violet', label = 'Gefitinib')
Gef_Ima, = plt.semilogx(Ltot,PLgi_Abl,'pink', label = 'Gefitinib + Ima')
plt.title('Abl competition assay')
plt.xlabel('$[L]_{tot}$')
plt.ylabel('$[PL]$')
plt.ylim(0,6e-7)
plt.legend(loc=3);
Looks promising!
In [32]:
def get_wells_from_section(path):
reads = path.xpath("*/Well")
wellIDs = [read.attrib['Pos'] for read in reads]
data = [(s.text, r.attrib['Pos'])
for r in reads
for s in r]
datalist = {
well : value
for (value, well) in data
}
welllist = [
[
datalist[chr(64 + row) + str(col)]
if chr(64 + row) + str(col) in datalist else None
for row in range(1,9)
]
for col in range(1,13)
]
return welllist
In [33]:
file_ABL_GEF= "data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml"
file_name = os.path.splitext(file_ABL_GEF)[0]
root = etree.parse(file_ABL_GEF)
Sections = root.xpath("/*/Section")
much = len(Sections)
print "****The xml file " + file_ABL_GEF + " has %s data sections:****" % much
for sect in Sections:
print sect.attrib['Name']
In [40]:
#Just going to work with topread for now
TopRead = root.xpath("/*/Section")[0]
welllist = get_wells_from_section(TopRead)
AblGef_dataframe = pd.DataFrame(welllist, columns = ['A - Abl','B - Buffer','C - Abl','D - Buffer', 'E - Abl','F - Buffer','G - Abl','H - Buffer'])
#AN ERROR FOR 'OVERS' COMES UP UNLESS THE NEXT LINE IS HERE
#THE MAX VALUE IS TAKEN FROM THE MAX VALUE FOR THE ABL GEF IMA DATA
#dataframe_rep = AblGef_dataframe.replace({'OVER':'64060.0'})
AblGef_dataframe
Out[40]:
In [41]:
#dataframe_rep[['fluorescence']] = dataframe_rep[['fluorescence']].astype('float')
dataframe_rep = dataframe_rep.astype('float')
sns.set_palette("Paired", 10)
sns.set_context("notebook", rc={"lines.linewidth": 2.5})
dataframe_rep.plot(figsize=(6, 4), title=file_name)
plt.xlim(-0.5,11.5);
In [42]:
file_ABL_GEF_IMA= "data/Abl Gef Ima gain 120 bw1020 2016-01-19 16-22-45_plate_1.xml"
file_name = os.path.splitext(file_ABL_GEF_IMA)[0]
root = etree.parse(file_ABL_GEF_IMA)
Sections = root.xpath("/*/Section")
much = len(Sections)
print "****The xml file " + file_ABL_GEF_IMA + " has %s data sections:****" % much
for sect in Sections:
print sect.attrib['Name']
In [43]:
#Just going to work with topread for now
TopRead = root.xpath("/*/Section")[0]
welllist = get_wells_from_section(TopRead)
AblGefIma_dataframe = pd.DataFrame(welllist, columns = ['A - Abl','B - Buffer','C - Abl','D - Buffer', 'E - Abl','F - Buffer','G - Abl','H - Buffer'])
sns.set_palette("Paired", 10)
sns.set_context("notebook", rc={"lines.linewidth": 2.5})
AblGefIma_dataframe = AblGefIma_dataframe.astype('float')
AblGefIma_dataframe.plot(figsize=(6, 4), title=file_name)
plt.xlim(-0.5,11.5)
Out[43]:
In [44]:
AblGefIma_dataframe.values.max()
Out[44]:
In [45]:
plt.plot(AblGefIma_dataframe[:].values, 'r');
plt.plot(AblGef_dataframe[:].values, 'k');
plt.text(8,60000,'Gefitinib (ABL)',fontsize=15)
plt.text(8,55000,'Imatinib + Gefitinib (ABL)',fontsize=15,color='red')
plt.savefig('Abl_Gef_Ima_Jan2016_repeat.png')
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