T027 · Kinase similarity: Ligand profile

Note: This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects.

Authors:

Aim of this talktorial

The aim of this talktorial is to investigate kinase similarity through ligand profiling data. In the context of drug design, the following assumption is often made: if a compound was tested active on two different kinases, it is suspected that these two kinases may have some degree of similarity. We will use this assumption in this talktorial. The concept of kinase promiscuity is also covered.

Contents in Theory

  • Kinase dataset

  • Bioactivity data

  • Kinase similarity descriptor: Ligand profile

    • Kinase similarity

    • Kinase promiscuity

  • From similarity matrix to distance matrix

Contents in Practical

  • Define the kinases of interest

  • Retrieve the data

  • Preprocess the data

    • Hit or non-hit

  • Kinase promiscuity

  • Kinase similarity

    • Visualize similarity as kinase matrix

    • Save kinase similarity matrix

  • Kinase distance matrix

    • Save kinase distance matrix

References

Theory

Kinase dataset

We use the kinase selection as defined in Talktorial T023.

Bioactivity data

In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known ChEMBL database and the query focuses on human kinases. Luckily, a curated version of ChEMBL29 is already freely available through the Openkinome organization, see https://github.com/openkinome/kinodata. For more details on querying the ChEMBL database, please refer to Talktorial T001.

In drug design, it is common to binarize the activity of a compound against a target of interest as a “hit” or “non-hit”. Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater than the cutoff, the compound is labeled as active (hit), and inactive (non-hit) otherwise.

Manning tree with number of ChEMBL activities per kinase (KinMap)

Figure 1: Number of ChEMBL29 bioactivities per kinase - as collected in kinodata - mapped onto the Manning kinome tree using KinMap. The figure has been generated in Talktorial T023.

Kinase similarity descriptor: Ligand profile

As a measure of similarity, we use ligand profiling data in this talktorial.

Kinase similarity

We use the following metric as similarity between two kinases \(K_i\) and \(K_j\) :

\[\text{similarity}(K_i, K_j) = \frac{\#\text{ of compounds that were tested active on both } K_i \text{ and } K_j} {\#\text{ of compounds that were tested on both } K_i \text{ and } K_j}.\]

Assuming that only one compound was tested on two kinases, and that the compound was tested as active for one and inactive for the other, then the similarity between these two kinases would be zero.

Kinase promiscuity

Computing the similarity between a kinase and itself may be interpreted as kinase promiscuity, where the similarity described above would therefore represent the fraction of active compounds over all tested compounds.

From similarity matrix to distance matrix

As discussed in Talktorial T024, we convert the similarity matrix to a distance matrix.

Practical

[1]:
from pathlib import Path
from collections import Counter

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import IPythonConsole
[2]:
HERE = Path(_dh[-1])
DATA = HERE / "data"

Define the kinases of interest

Let’s load the kinase selection as defined in Talktorial T023.

[3]:
kinase_selection_df = pd.read_csv(
    HERE
    / "../T023_what_is_a_kinase/\
data/kinase_selection.csv"
)
kinase_selection_df
# NBVAL_CHECK_OUTPUT
[3]:
kinase kinase_klifs uniprot_id group full_kinase_name
0 EGFR EGFR P00533 TK Epidermal growth factor receptor
1 ErbB2 ErbB2 P04626 TK Erythroblastic leukemia viral oncogene homolog 2
2 PI3K p110a P42336 Atypical Phosphatidylinositol-3-kinase
3 VEGFR2 KDR P35968 TK Vascular endothelial growth factor receptor 2
4 BRAF BRAF P15056 TKL Rapidly accelerated fibrosarcoma isoform B
5 CDK2 CDK2 P24941 CMGC Cyclic-dependent kinase 2
6 LCK LCK P06239 TK Lymphocyte-specific protein tyrosine kinase
7 MET MET P08581 TK Mesenchymal-epithelial transition factor
8 p38a p38a Q16539 CMGC p38 mitogen activated protein kinase alpha

Retrieve the data

We retrieve a pre-curated version of a kinase subset of ChEMBL29 freely available at Openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3.

[4]:
path = "https://github.com/openkinome/kinodata/releases/download/\
v0.3/activities-chembl29_v0.3.zip"
# Load data and reset index so that it starts from 0
data = pd.read_csv(path, index_col=0).reset_index(drop=True)
print(f"Current shape of data: {data.shape}")
data.head()
# NBVAL_CHECK_OUTPUT
Current shape of data: (190634, 16)
[4]:
activities.activity_id assays.chembl_id target_dictionary.chembl_id molecule_dictionary.chembl_id molecule_dictionary.max_phase activities.standard_type activities.standard_value activities.standard_units compound_structures.canonical_smiles compound_structures.standard_inchi component_sequences.sequence assays.confidence_score docs.chembl_id docs.year docs.authors UniprotID
0 16291323 CHEMBL3705523 CHEMBL2973 CHEMBL3666724 0 pIC50 14.096910 nM CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2... MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL... 9 CHEMBL3639077 2014.0 NaN O75116
1 16264754 CHEMBL3705523 CHEMBL2973 CHEMBL3666728 0 pIC50 14.000000 nM CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2... MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL... 9 CHEMBL3639077 2014.0 NaN O75116
2 16306943 CHEMBL3705523 CHEMBL2973 CHEMBL1968705 0 pIC50 14.000000 nM CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2... MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL... 9 CHEMBL3639077 2014.0 NaN O75116
3 16340050 CHEMBL3705523 CHEMBL2973 CHEMBL1997433 0 pIC50 13.958607 nM CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1... MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL... 9 CHEMBL3639077 2014.0 NaN O75116
4 16287186 CHEMBL3705523 CHEMBL2973 CHEMBL3666721 0 pIC50 13.920819 nM CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2... MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL... 9 CHEMBL3639077 2014.0 NaN O75116

Preprocess the data

We look at the type of activity and the associated units.

[5]:
print(
    f"Activities: {sorted(set(data['activities.standard_type']))}\n"
    f"Units: {set(data['activities.standard_units'])}"
)
Activities: ['pIC50', 'pKd', 'pKi']
Units: {'nM'}

Let’s keep the entries which have pIC50 values only.

[6]:
data = data[data["activities.standard_type"] == "pIC50"]
[7]:
data.columns
# NBVAL_CHECK_OUTPUT
[7]:
Index(['activities.activity_id', 'assays.chembl_id',
       'target_dictionary.chembl_id', 'molecule_dictionary.chembl_id',
       'molecule_dictionary.max_phase', 'activities.standard_type',
       'activities.standard_value', 'activities.standard_units',
       'compound_structures.canonical_smiles',
       'compound_structures.standard_inchi', 'component_sequences.sequence',
       'assays.confidence_score', 'docs.chembl_id', 'docs.year',
       'docs.authors', 'UniprotID'],
      dtype='object')

The DataFrame contains many columns that won’t be necessary for the rest of the notebook which are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound (compound_structures.canonical_smiles), the measured activity (activities.standard_value) and the UniProt ID of the kinase (UniprotID). These columns are renamed for readability.

[8]:
data = data[["compound_structures.canonical_smiles", "activities.standard_value", "UniprotID"]]
data = data.rename(
    columns={
        "compound_structures.canonical_smiles": "smiles",
        "activities.standard_value": "pIC50",
    }
)
[9]:
print(f"Current shape of data: {data.shape}")
data.head()
# NBVAL_CHECK_OUTPUT
Current shape of data: (160857, 3)
[9]:
smiles pIC50 UniprotID
0 CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... 14.096910 O75116
1 CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... 14.000000 O75116
2 CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... 14.000000 O75116
3 CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... 13.958607 O75116
4 CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O... 13.920819 O75116

NA values are dropped.

[10]:
data = data.dropna()
print(f"Current shape of data: {data.shape}")
# NBVAL_CHECK_OUTPUT
Current shape of data: (160703, 3)

We only keep the data for the query kinases:

[11]:
data = data[data["UniprotID"].isin(kinase_selection_df["uniprot_id"])]
print(f"Current shape of data: {data.shape}")
data.head()
# NBVAL_CHECK_OUTPUT
Current shape of data: (33427, 3)
[11]:
smiles pIC50 UniprotID
58 Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1 11.522879 P00533
98 CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1 11.221849 P00533
99 CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC 11.221849 P00533
140 CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1 11.096910 P00533
141 Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1 11.096910 P00533

Let’s look at EGFR data (which corresponds to the first row in the kinase selection DataFrame):

[12]:
egfr_data = data[data["UniprotID"] == kinase_selection_df["uniprot_id"][0]]

Some compounds have been tested several times against EGFR, as shown below.

[13]:
measured_compounds = Counter(egfr_data["smiles"])
measured_compounds.most_common()[0:5]
# NBVAL_CHECK_OUTPUT
[13]:
[('COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 39),
 ('C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 27),
 ('C=CC(=O)Nc1cc(Nc2nccc(-c3cn(C)c4ccccc34)n2)c(OC)cc1N(C)CCN(C)C', 15),
 ('C=CC(=O)Nc1cccc(Oc2nc(Nc3ccc(N4CCN(C)CC4)cc3OC)ncc2Cl)c1', 11),
 ('CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1', 8)]

We have a look at those compounds.

[14]:
mols = []
for entry in measured_compounds.most_common()[0:5]:
    mols.append(Chem.MolFromSmiles(entry[0]))
Draw.MolsToGridImage(mols, molsPerRow=5)
[14]:
../_images/talktorials_T027_kinase_similarity_ligand_profile_43_0.png

In this example, the first molecule is gefitinib, a known FDA-approved drug against EGFR.

As a simple workaround — since we prefer to have one activity value per compound-kinase pair — we keep the value for which the compound has the best activity value, i.e., the highest pIC50 value.

[15]:
data = data.groupby(["UniprotID", "smiles"])["pIC50"].max().reset_index()
data.head()
# NBVAL_CHECK_OUTPUT
[15]:
UniprotID smiles pIC50
0 P00533 Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(... 5.336488
1 P00533 Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c... 5.996539
2 P00533 Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1... 8.397940
3 P00533 Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c... 7.207608
4 P00533 Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c... 8.420216

Hit or non-hit

Finally, we binarize the pIC50 values to obtain hit or non-hit using a cutoff. We use a pIC50 cutoff of \(6.3\), similarly to the cutoff used in Molecules (2021), 26(3), 629.

[16]:
cutoff = 6.3
[17]:
def binarize_pic50(pic50_value, threshold):
    """
    Binarizes a scalar value given a threshold.

    Parameters
    ----------
    pic50_value : float
        The measurement pIC50 value of a kinase-ligand pair.
    threshold : float
        The cutoff to determine activity.

    Returns
    -------
    int
        1 if the pIC50 value is above the threshold, which indicates activity.
        0 otherwise.
    """
    if pic50_value >= threshold:
        return 1
    else:
        return 0
[18]:
data["activity_binary"] = data["pIC50"].apply(binarize_pic50, args=(cutoff,))
[19]:
print(f"Current shape of data: {data.shape}")
data.head()
# NBVAL_CHECK_OUTPUT
Current shape of data: (32916, 4)
[19]:
UniprotID smiles pIC50 activity_binary
0 P00533 Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(... 5.336488 0
1 P00533 Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c... 5.996539 0
2 P00533 Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1... 8.397940 1
3 P00533 Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c... 7.207608 1
4 P00533 Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c... 8.420216 1

Kinase promiscuity

We now look at the kinase promiscuity.

For a given kinase, three values are computed:

  1. the total number of measured compounds against the given kinase,

  2. the number of active compounds against the kinase, and

  3. the fraction of active compounds, i.e., the ratio of active compounds over the total number of measured compounds per kinase.

[20]:
def kinase_to_activity_numbers(uniprot_id, activity_df):
    """
    Retrieve the three values for a given kinase.

    Parameters
    ----------
    uniprot_id : str
        The UniProt ID of the kinase of interest, e.g. "P00533" for "EGFR".
    activity_df : pd.DataFrame
        The dataframe with activity values for kinases.

    Returns
    -------
    tuple : (int, int, float)
        The three metrics:
        1. The total number of measured compounds against the kinase.
        2. The number of active compounds against the kinase.
        3. The fraction of active compounds against the kinase.
    """
    kinase_data = activity_df[activity_df["UniprotID"] == uniprot_id]
    total_measured_compounds = len(kinase_data)
    active_compounds = len(kinase_data[kinase_data["activity_binary"] == 1])
    if total_measured_compounds > 0:
        fraction = active_compounds / total_measured_compounds
    else:
        print("No compounds were measured for this kinase.")
        fraction = np.nan
    return (total_measured_compounds, active_compounds, fraction)

Let’s see what information we get for EGFR (which corresponds to the first row in the DataFrame):

[21]:
uniprot_id = kinase_selection_df["uniprot_id"][0]
print(f"{kinase_selection_df['kinase'][0]} ({uniprot_id}):")
egfr_metrics = kinase_to_activity_numbers(uniprot_id, data)
print(
    f"{'Total number of measured compounds:' : <40}"
    f"{egfr_metrics[0]} \n"
    f"{'Number of active compounds:' : <40}"
    f"{egfr_metrics[1]} \n"
    f"{'Fraction of active compounds:' : <40}"
    f"{egfr_metrics[2]:.2f} \n"
)
# NBVAL_CHECK_OUTPUT
EGFR (P00533):
Total number of measured compounds:     5965
Number of active compounds:             3635
Fraction of active compounds:           0.61

Let’s create a table from these values for all kinases:

[22]:
def promiscuity_table(kinase_selection, activity_df):
    """
    Create a table with all three values for all kinases.

    Parameters
    ----------
    kinase_selection : pd.DataFrame
        The DataFrame for the chosen kinases.
    activity_df : pd.DataFrame
        The DataFrame with activity values for kinases.

    Returns
    -------
    promiscuity_table : pd.DataFrame
        A DataFrame with the kinases as rows and values as columns.
    """
    promiscuity_table = pd.DataFrame(
        index=kinase_selection["kinase_klifs"], columns=["total", "actives", "fraction"]
    )
    promiscuity_table.index.name = None
    promiscuity_table.columns.name = None

    for name, uniprot_id in zip(kinase_selection["kinase_klifs"], kinase_selection["uniprot_id"]):
        values = kinase_to_activity_numbers(uniprot_id, activity_df)
        promiscuity_table.loc[name] = values
    return promiscuity_table
[23]:
kinase_promiscuity_df = promiscuity_table(kinase_selection_df, data)
kinase_promiscuity_df
# NBVAL_CHECK_OUTPUT
[23]:
total actives fraction
EGFR 5965 3635 0.609388
ErbB2 1700 1031 0.606471
p110a 4393 2827 0.643524
KDR 7641 5328 0.697291
BRAF 3688 2992 0.81128
CDK2 1500 815 0.543333
LCK 1560 935 0.599359
MET 2832 2248 0.793785
p38a 3637 2778 0.763816

Let’s beautify the table:

[24]:
kinase_promiscuity_df.style.format("{:.3f}", subset=["fraction"]).background_gradient(
    cmap="Purples", subset=["fraction"]
).highlight_min(color="yellow", axis=None, subset=["fraction"]).highlight_max(
    color="red", subset=["fraction"]
)
[24]:
  total actives fraction
EGFR 5965 3635 0.609
ErbB2 1700 1031 0.606
p110a 4393 2827 0.644
KDR 7641 5328 0.697
BRAF 3688 2992 0.811
CDK2 1500 815 0.543
LCK 1560 935 0.599
MET 2832 2248 0.794
p38a 3637 2778 0.764

From the table, we notice that CDK2 is the least (in yellow) and BRAF the most (in red) promiscuous kinase.

Kinase similarity

We now investigate how we can use the similarity measure discussed in the Theory part to compare kinases.

[25]:
def similarity_ligand_profile(uniprot_id1, uniprot_id2, activity_df):
    """
    Compute the similarity between two kinases using ligand profiling data.

    Parameters
    ----------
    uniprot_id1 : str
        UniProt ID of first kinase of interest.
    uniprot_id2 : str
        UniProt ID of second kinase of interest.
    activity_df :  pd.DataFrame
        The DataFrame with activity values for kinases.

    Returns
    -------
    tuple : (int, int, float)
        The three metrics:
        1. The total number of measured compounds against both kinases.
        2. The number of active compounds against both kinases.
        3. The metric for kinase similarity,
                i.e. number of active compounds on both kinases
                over number of measured compounds on both kinases.
    """
    if uniprot_id1 == uniprot_id2:
        return kinase_to_activity_numbers(uniprot_id1, activity_df)
    else:
        # Data for the two kinases only
        reduced_data = activity_df[activity_df["UniprotID"].isin([uniprot_id1, uniprot_id2])]

        # Look at active compounds only
        active_entries = reduced_data[reduced_data["activity_binary"] == 1]
        # Group by compounds
        compounds = active_entries.groupby("smiles").size()
        # Look at the number of active compounds measured on both kinases
        active_compounds_on_both = compounds[compounds == 2].shape[0]

        # Look at all tested compounds
        compounds = reduced_data.groupby("smiles").size()
        # Look at the number of compounds measured on both kinases
        measured_compounds_on_both = compounds[compounds == 2].shape[0]

        if measured_compounds_on_both > 0:
            fraction = active_compounds_on_both / measured_compounds_on_both
        else:
            print(
                f"No compounds were measured on both kinases, "
                f"namely {uniprot_id1} and {uniprot_id2}."
            )
            fraction = np.nan
            measured_compounds_on_both = np.nan
            active_compounds_on_both = np.nan
        return (measured_compounds_on_both, active_compounds_on_both, fraction)

Let’s look at the values and similarity between EGFR and MET (which corresponds to the first and seventh row in the DataFrame).

[26]:
similarity_egfr_met = similarity_ligand_profile(
    kinase_selection_df["uniprot_id"][0], kinase_selection_df["uniprot_id"][7], data
)
print(
    f"Values for EGFR and MET: \n\n"
    f"{'Total number of measured compounds:' : <50}"
    f"{similarity_egfr_met[0]} \n"
    f"{'Number of active compounds:' : <50}"
    f"{similarity_egfr_met[1]} \n"
    f"Fraction of active compounds or \n"
    f"{'ligand profile similarity between EGFR and MET:' : <50}"
    f"{similarity_egfr_met[2]:.2f} \n"
)
# NBVAL_CHECK_OUTPUT
Values for EGFR and MET:

Total number of measured compounds:               92
Number of active compounds:                       21
Fraction of active compounds or
ligand profile similarity between EGFR and MET:   0.23

Visualize similarity as kinase matrix

Let’s first look at the non-reduced fraction of number of active compound against total number of compounds to have an idea of the counts.

[27]:
kinase_counts_matrix = pd.DataFrame(
    index=kinase_selection_df.kinase_klifs, columns=kinase_selection_df.kinase_klifs
)
kinase_counts_matrix.index.name = None
kinase_counts_matrix.columns.name = None

for i, (uniprot_id1, klifs_name1) in enumerate(
    zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)
):
    for j, (uniprot_id2, klifs_name2) in enumerate(
        zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)
    ):
        total, actives, _ = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)
        integer_ratio = f"{actives}/{total}"
        kinase_counts_matrix[klifs_name1][klifs_name2] = integer_ratio
kinase_counts_matrix
# NBVAL_CHECK_OUTPUT
No compounds were measured on both kinases, namely P04626 and P42336.
No compounds were measured on both kinases, namely P42336 and P04626.
[27]:
EGFR ErbB2 p110a KDR BRAF CDK2 LCK MET p38a
EGFR 3635/5965 662/1170 13/179 313/893 27/59 5/40 31/126 21/92 18/52
ErbB2 662/1170 1031/1700 nan/nan 72/180 4/16 4/27 5/33 1/28 2/16
p110a 13/179 nan/nan 2827/4393 32/174 1/3 4/12 0/3 0/1 0/5
KDR 313/893 72/180 32/174 5328/7641 199/262 71/115 179/413 184/340 63/122
BRAF 27/59 4/16 1/3 199/262 2992/3688 1/13 22/40 3/26 29/41
CDK2 5/40 4/27 4/12 71/115 1/13 815/1500 2/18 2/22 1/9
LCK 31/126 5/33 0/3 179/413 22/40 2/18 935/1560 17/63 69/138
MET 21/92 1/28 0/1 184/340 3/26 2/22 17/63 2248/2832 1/20
p38a 18/52 2/16 0/5 63/122 29/41 1/9 69/138 1/20 2778/3637

Note that the total number of tested compounds as well as the number of active compounds on two kinases vary largely.

  • For the p110a-ErbB2 pair, there are none.

  • For p110a and [BRAF, CDK2, LCK, MET and p38a], there are less than \(13\) commonly tested compounds.

  • In contrast, the EGFR-ErbB2 pair has \(1170\) commonly tested compounds, of which \(662\) were active on both.

Now let’s look at the similarity, in this case, the reduced fraction:

[28]:
kinase_similarity_matrix = np.zeros((len(kinase_selection_df), len(kinase_selection_df)))
for i, uniprot_id1 in enumerate(kinase_selection_df.uniprot_id):
    for j, uniprot_id2 in enumerate(kinase_selection_df.uniprot_id):
        kinase_similarity_matrix[i, j] = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)[
            2
        ]
No compounds were measured on both kinases, namely P04626 and P42336.
No compounds were measured on both kinases, namely P42336 and P04626.
[29]:
kinase_similarity_matrix_df = pd.DataFrame(
    data=kinase_similarity_matrix,
    index=kinase_selection_df.kinase_klifs,
    columns=kinase_selection_df.kinase_klifs,
)
kinase_similarity_matrix_df.index.name = None
kinase_similarity_matrix_df.columns.name = None
kinase_similarity_matrix_df
# NBVAL_CHECK_OUTPUT
[29]:
EGFR ErbB2 p110a KDR BRAF CDK2 LCK MET p38a
EGFR 0.609388 0.565812 0.072626 0.350504 0.457627 0.125000 0.246032 0.228261 0.346154
ErbB2 0.565812 0.606471 NaN 0.400000 0.250000 0.148148 0.151515 0.035714 0.125000
p110a 0.072626 NaN 0.643524 0.183908 0.333333 0.333333 0.000000 0.000000 0.000000
KDR 0.350504 0.400000 0.183908 0.697291 0.759542 0.617391 0.433414 0.541176 0.516393
BRAF 0.457627 0.250000 0.333333 0.759542 0.811280 0.076923 0.550000 0.115385 0.707317
CDK2 0.125000 0.148148 0.333333 0.617391 0.076923 0.543333 0.111111 0.090909 0.111111
LCK 0.246032 0.151515 0.000000 0.433414 0.550000 0.111111 0.599359 0.269841 0.500000
MET 0.228261 0.035714 0.000000 0.541176 0.115385 0.090909 0.269841 0.793785 0.050000
p38a 0.346154 0.125000 0.000000 0.516393 0.707317 0.111111 0.500000 0.050000 0.763816
[30]:
# Show matrix with background gradient
cm = sns.light_palette("green", as_cmap=True)
kinase_similarity_matrix_df.style.background_gradient(cmap=cm).format("{:.3f}")
[30]:
  EGFR ErbB2 p110a KDR BRAF CDK2 LCK MET p38a
EGFR 0.609 0.566 0.073 0.351 0.458 0.125 0.246 0.228 0.346
ErbB2 0.566 0.606 nan 0.400 0.250 0.148 0.152 0.036 0.125
p110a 0.073 nan 0.644 0.184 0.333 0.333 0.000 0.000 0.000
KDR 0.351 0.400 0.184 0.697 0.760 0.617 0.433 0.541 0.516
BRAF 0.458 0.250 0.333 0.760 0.811 0.077 0.550 0.115 0.707
CDK2 0.125 0.148 0.333 0.617 0.077 0.543 0.111 0.091 0.111
LCK 0.246 0.152 0.000 0.433 0.550 0.111 0.599 0.270 0.500
MET 0.228 0.036 0.000 0.541 0.115 0.091 0.270 0.794 0.050
p38a 0.346 0.125 0.000 0.516 0.707 0.111 0.500 0.050 0.764

Note that the diagonal contains the previously discussed promiscuity values.

As mentioned above, no compounds were measured on both ErbB2 and p110a and therefore creates a np.nan entry which can be problematic for algorithmic reason.

As a simple workaround, we will fill the NA values with zero.

[31]:
kinase_similarity_matrix_df = kinase_similarity_matrix_df.fillna(0)

kinase_similarity_matrix_df.style.background_gradient(cmap=cm).format("{:.3f}")
[31]:
  EGFR ErbB2 p110a KDR BRAF CDK2 LCK MET p38a
EGFR 0.609 0.566 0.073 0.351 0.458 0.125 0.246 0.228 0.346
ErbB2 0.566 0.606 0.000 0.400 0.250 0.148 0.152 0.036 0.125
p110a 0.073 0.000 0.644 0.184 0.333 0.333 0.000 0.000 0.000
KDR 0.351 0.400 0.184 0.697 0.760 0.617 0.433 0.541 0.516
BRAF 0.458 0.250 0.333 0.760 0.811 0.077 0.550 0.115 0.707
CDK2 0.125 0.148 0.333 0.617 0.077 0.543 0.111 0.091 0.111
LCK 0.246 0.152 0.000 0.433 0.550 0.111 0.599 0.270 0.500
MET 0.228 0.036 0.000 0.541 0.115 0.091 0.270 0.794 0.050
p38a 0.346 0.125 0.000 0.516 0.707 0.111 0.500 0.050 0.764

Save kinase similarity matrix

[32]:
kinase_similarity_matrix_df.to_csv(DATA / "kinase_similarity_matrix.csv")

Kinase distance matrix

The similarity matrix \(SM\) is converted to a pseudo-distance matrix (all entries of the similarity matrix are between \(0\) and \(1\)):

[33]:
print(
    f"The values of the similarity matrix lie between: "
    f"{kinase_similarity_matrix_df.min().min():.2f}"
    f" and {kinase_similarity_matrix_df.max().max():.2f}"
)
# NBVAL_CHECK_OUTPUT
The values of the similarity matrix lie between: 0.00 and 0.81
[34]:
kinase_distance_matrix_df = 1 - kinase_similarity_matrix_df

Finally, we set the diagonal values to \(0\) and we obtain the kinase distance matrix:

[35]:
np.fill_diagonal(kinase_distance_matrix_df.values, 0)
[36]:
kinase_distance_matrix_df.style.background_gradient(cmap=cm).format("{:.3f}")
[36]:
  EGFR ErbB2 p110a KDR BRAF CDK2 LCK MET p38a
EGFR 0.000 0.434 0.927 0.649 0.542 0.875 0.754 0.772 0.654
ErbB2 0.434 0.000 1.000 0.600 0.750 0.852 0.848 0.964 0.875
p110a 0.927 1.000 0.000 0.816 0.667 0.667 1.000 1.000 1.000
KDR 0.649 0.600 0.816 0.000 0.240 0.383 0.567 0.459 0.484
BRAF 0.542 0.750 0.667 0.240 0.000 0.923 0.450 0.885 0.293
CDK2 0.875 0.852 0.667 0.383 0.923 0.000 0.889 0.909 0.889
LCK 0.754 0.848 1.000 0.567 0.450 0.889 0.000 0.730 0.500
MET 0.772 0.964 1.000 0.459 0.885 0.909 0.730 0.000 0.950
p38a 0.654 0.875 1.000 0.484 0.293 0.889 0.500 0.950 0.000

Save kinase distance matrix

[37]:
kinase_distance_matrix_df.to_csv(DATA / "kinase_distance_matrix.csv")

Discussion

In this talktorial, we investigate how activity data can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can be quantified using the ratio of active compounds over measured compounds.

When working with these data, we have to keep in mind that some kinases have much higher coverage with respect to the number of compounds that were tested against them, leading to an imbalance in information content. This cannot be inferred from the calculated fraction. For example, the pairs EGFR-KDR and EGFR-p38a both have a profile similarity of \(0.35\). However, the first was calculated based on \(893\) tested compounds, whereas the latter on \(52\) only.

The kinase distance matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial.

Quiz

  1. Is there an optimal way to deal with multiple kinase-ligand measurements?

  2. Can promiscuity be fairly compared between two kinases if one has been tested against many compounds whereas the other only against very few?

  3. Using the similarity described in this talktorial, what does it mean that two kinases have a similarity of \(0\), as is the case for p110a and LCK?