CATHEPSIN K INHIBITORS - Manikandan.A, T. Jayalakshmi ... · 1Manikandan.A, 2T. Jayalakshmi...

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CATHEPSIN K INHIBITORS -

PHARMACOPHORE MODELLING AND DOCKING ANALYSIS

1Manikandan.A,

2T. Jayalakshmi

1Assistant Professor,

2 Associate Professor

Dept. of Genetic Engineering

BIHER, BIST, Bharath University

Chennai- 600073.

[email protected]

ABSTRACT

Cathepsin K (CatK),an established drug target for Osteoporosis, has been reported to be

up regulated in atherosclerotic lesions. Due to its proteolytic activity CatK may influence

the atherosclerotic lesion composition and stability. In the current study features necessary

for CatK binding affinity. The predictive power of the generated pharmacophore model

was validated by using test compounds. These validated models were compared to the

structure based models in order to identify ligand receptor interactions essential for

receptor activation.

INTRODUCTION

The primary goal of bioinformatics is to increase our understanding of biological processes.

What sets it apart from other approaches, however, is its focus on developing and applying

computationally intensive techniques (e.g., data mining, and machine learning algorithms) to

achieve this goal. [1-4]

Osteoporosis is a debilitating disease that is caused by an imbalance between bone

matrix resorption and bone remodeling. Cathepsin K, which is selectively and highly

expressed in osteoclasts, is a lysosomal cysteine protease of the papain superfamily that has

high homology to Cathepsins S and L.1,2 Studies using cathepsin K antisense3 and cathepsin

K deficient mice4 have shown that the proteinase is primarily[6-9] involved in osteoclastic

bone resorption. Cathepsin K inhibitors therefore are regarded as a potential therapy for the

treatment of bone loss, such as osteoporosis.5 One significant consideration in the design of

cathepsin K inhibitors is selectivity for its highly homologous lysosomal cysteine proteases,

cathepsins L and S.

International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 1589-1602ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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Interestingly, cathepsin K is the most abundant cysteine protease expressed in osteoclasts and

capable of degrading type I collagen, the major component of bone matrix. The finding of

cathepsin K deficiency in pycnodysostosis, an[10-12] osteopetrotic disorder characterized by

decreased bone resorption, further underscores the importance of cathepsin K as a potential

target for developing agents to treat osteoporosis and other disorders characterized by

increased bone resorption.5

Pharmacophore modeling and 3D database searching are now recognized as integral

components of lead discovery and lead optimization, and the continuing need for improved

Pharmacophore-based tools has driven the development of PHASE. By employing a novel,

tree-based partitioning algorithm, PHASE exhaustively identifies spatial arrangements of

functional groups that are common and essential to the biologic activity of a set of high

affinity ligands. Pharmacophore model consists of a[13-15] collection of features necessary

for the biological activity of the ligands arranged in 3D space the common ones being

hydrogen-bond acceptor, hydrogen-bond donor and hydrophobic features.

Hydrogen bond donors are defined as vectors from the donor atom of ligand to the

corresponding acceptor atom in the receptor. Hydrogen bond acceptors are analogously

defined. Hydrophobic features are located at the centroids of the hydrophobic atoms. In

modern computational chemistry, pharmacophore are used to define the essential features of

one or more molecules with the same biological activity. New compounds may have

beneficial effects at different doses, they may be taken up more readily by different tissues,

and they may be produced more efficiently. In additional, new compounds may not be

covered by existing parents.[16-19] Typical pharmacophore features are for where a molecule

is hydrophobic aromatic, a hydrogen bond acceptor, a hydrogen bond donor, a cation or an

anion. Therefore docking is useful for predicting both the strength and type of signal

produced.

Docking is frequently used to predict the binding orientation of small molecule drug

candidates to their protein targets in order to in turn predict the affinity and activity of the

small molecule. Hence docking plays an important role in the rational design of drugs.[1]

Cathepsin K is a cysteine protease that plays an essential role in osteoclast function and in the

degradation of protein components of the bone matrix by cleaving proteins such as collagen

type I, collagen type II and osteonectin. Cathepsin K therefore plays a role in bone

remodeling and resorption in diseases such as osteoporosis, [20-26]osteolytic bone metastasis

and rheumatoid arthritis. We examined cathepsin K in the serum of 100 patients with active

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longstanding rheumatoid arthritis. We found increased levels of cathepsin K compared with a

healthy control group and found a significant correlation with radiological destruction,

measured by the Larsen score. Inhibition of cathepsin K may therefore be a new target for

preventing bone erosion and joint destruction in rheumatoid arthritis. [27-29]However,

further studies have to be performed to prove that Cathepsin K is a valuable parameter for

bone metabolism in patients with early rheumatoid arthritis.[9]

MATERIALS AND METHODS:

SOFTWARES: cerius2, catalyst

CERIUS2:

The advanced drug discovery technologies in Cerius2 are being integrated into the

Discovery Studio research environment— a comprehensive suite of modeling and simulation

solutions for life science researchers. Within Discovery Studio, Cerius2 functionality will be

seamlessly integrated with many other premier application modules that perform such tasks

as protein modeling, simulations, and receptor-ligand interactions.[10]

CATALYST:

The objective is to develop an automated method to generate ideal Pharmacophore

using known inhibitors of Cathepsin K in hypothesis module in Catalyst software. [11]

METHODOLOGY FOR PHARMACOPHORE:

Molecules are sketched using the software cerius2 from the scaffolds and minimized. Now

these molecules are loaded in to catalyst and conformers are generated. These compounds are

separated in to training set and test set based on their activity values. If the activity value is

high then it is a low active molecule. If it is low it is a high active molecule. Molecules are

sketched based on these scaffolds. And hypothesis is generated by HIPHOP and HYPOGEN

in catalyst workbench.

HIP HOP HYPOTHESIS:

The objective is to identify and enumerate all possible pharmacophore configurations

that are common to the training set. The implementation of this is to perform an exhaustive


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search starting with the simplest pharmacophore configuration i.e., all possible combinations

of two features pharmacophore. Once all two features are exhausted, it then moves to the

three features combinations.

The process continues until Hiphop can no longer generate common pharmacophore

combinations.

Analysis:

The molecule with highest activity were entered (ten high active molecules were

entered). Then the features were chosen according to the prior knowledge. After hypothesis

was generated, the rank file and the features are shown in the log and analyzed. All direct hits

were obtained from hypothesis. [30-36]The features which are important for the high active

molecules are hydrogen bond acceptor (H), ring aromatic (R) and hydrophobic. The first

hypothesis generated was the best pharmacophore and a best fit of four hits was found for the

highest active molecule. The distance features of the first hypothesis of the pharmacophore

were considered for evaluation of the refined hypothesis.

SIGNIFICANCE OF HYPOGEN:

In hypothesis generation, the structure and activity correlation in the `training set were

rigorously examined. Hypogen identifies features that were common to the active compounds

but excluded from the inactive compounds with in conformationally allowable regions of

space. It further estimates the activity of each training set compound using regression

parameter.[38-41] The parameters were computed by the regression analysis using the

relationship of the geometric fit. The fit function does not only check if the features are

mapped or not. It also contains distance terms which measures the distance that separates the

features on the molecules from centroid to the hypothesis feature.

The generated pharmacophore model should be statistically significant, should predict

activity of molecules accurately and should identify active compounds from the database.

COST ANALYSIS:

The Hypogen module in catalyst performs two important theoretical cost calculations that

determine the success of any pharmacophore hypothesis.

Fixed cost: It represents the simplest model that fits all data perfectly.

Null cost: It represents the highest cost of pharmacophore with no features and

estimates the activity to be the average of the activity data of the training set

molecules.


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A meaningful pharmacophore hypothesis may result when the difference between null and

cost values is larger. A value of 40-60 bits for a pharmacophore hypothesis may include that

it has 75-90% probability of correlating the data.[42-45] The total cost (Pharmacophore cost)

of any hypothesis should be equal to the fixed cost to provide any useful model.

Two other parameters that also determine the quality of any hypothesis with possible

values are, the configuration cost, entropy cost or error cost, which depends on the

complexity of the pharmacophore hypothesis. Error cost should be less than 17, error cost

dependent on the root mean square deviation between the estimated and actual activities of

the training set. The best pharmacophore model has the highest cost difference, lowest

RSMD and best correlation coefficient.

METHODOLOGY OF DOCKING:

From the literature, compounds were selected with variation in their activity (based on IC50

values). These compounds were sketched, minimized (UNIVERSAL Force Field) and saved

in .Msi file in Cerius2. The receptor was downloaded from PDB (Protein Data Bank) and

saved in .pdb. In Cerius2, the active site was found by using the crystal ligand present in the

receptor (2AUK). Then the molecules were docked and various conformations were

obtained.

4. RESULTS AND DISCUSSION:

CATALYST RESULTS:

.


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Figure 3: Hypogen log file

Fig 4: Graph showing Correlation between True Activity and Predicted Activity of the

Training Set molecules

Fig5: Graph showing correlation between least activity and predicted activity of test set

molecules.

Statistical Analysis Of Result:

Here the total cost of the first hypothesis is 79.5386 and the total cost of null

hypothesis is 85.9735. Now the correlation is 0.904016 which is acceptable.[25-29]

The max-fit score corresponds to the highly active and stable molecule, which is in

the fourth hypothesis and is characterized by the highest scoring and max-fit of, yielded the


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relevant information about the pharmacophore element of the studied compound. According

to the results, hypothesis one has been chosen to represent “Pharmacophoric model”.

Hypogen generated for nineteen alternative pharmacophores of the 25 training set

molecules in the study, the cost of the null hypothesis for all the hypothesis and the fixed cost

of run were with a cost difference.

All the hypothesis showed total cost close to the cost of the fixed hypothesis and

having large difference with no correlation cost. As mentioned the configuration cost value

must be less than 17 for a good pharmacophore and accordingly was obtained.

.

FIGURE 6: Pharmacophore mapping with the highest active molecule

This model consists of special features like hydrogen bond acceptor,[22-27] donor,

hydrophobic aliphatic, ring aromatic. Activities were estimated for all the compounds based

on the best ranking Pharmacophore.

Figure 7: Pharmacophore Mapping with least active molecule in test set.

ACTIVITY OF THE TRAINING SET MOLECULES:

The activity of the (19) molecules considered as the test molecules were predicted

according to the hypothesis generated by the Hypogen pharmacophore. The molecules


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considered had a wide range of activity starting from some high active, moderately active and

least active molecules. [13-17]The correlation between the activity and predicted activity was

also given by the graph with correlation of (0.904022)

Table 5: Training set molecules in hypotheses work bench.

.

Inference:

The work presented in this study shows how chemical features of set of compounds along

with their activities ranging over several order of magnitude can be used to generate

pharmacophore hypothesis that can be successfully predict the activity.[7-14] The models

were not only predictive with in the same series of compounds but also different classes of

diverse compounds also effectively mapped on to the features important for activity. A highly

predictive Pharmacophore was generated based on 20 training set compounds, which consists

of hydrophobic, hydrogen donor and ring aromatic.


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DOCKING RESULTS BY USING CERIUS 2

Figure 8: Docked conformation of highest active molecule in dataset. Dotted lines indicates

Hydrogen bonds

Figure 9: Electrostatic surface of protein active site with docked conformation of highest

active molecule


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Figure 10: Docked conformation of highest active molecule and its interaction with

active site aminoacids of Cathepsin K

SUMMARY AND CONCLUSIONS:

Pharmacophore studies:

Ideal Pharmacophore model was generated by using known inhibitors of Cathepsin K, to

identify the key features of Cathepsin K. Best Pharmacophore consists of 2 Hydrogen bond

acceptor and 2 hydrophobic features with a correlation of 0.904. My future study is to

identify a novel inhibitor using virtual screening studies using this pharmacophore model as a

query.

5.2 Docking studies were carried out by Ligand fit.[40-45]

Docking studies were performed to reveal the interactions of Ligand and active site. Docking

studies reveals that interaction HIS162 is having hydrogen bond with Ligand.

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