MOLECULAR DESIGNING AND DOCKING STUDIES ON SOME …

www.wjpps.com Vol 9, Issue 5, 2020.

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Sherin et al. World Journal of Pharmacy and Pharmaceutical Sciences

MOLECULAR DESIGNING AND DOCKING STUDIES ON SOME

NOVEL 5-ARYL 1,3,4-THIADIAZOLE DERIVATIVES AS INSULYSIN

INHIBITORS

Sherin Abdul Hameed*1, Joyamma Varkey

2 and Jayasekhar Parameswaran Nair

3

1College of Pharmaceutical Sciences, Government Medical College, Thiruvananthapuram.

2College of Pharmaceutical Sciences, Government Medical College, Alappuzha.

3College of Pharmacy, National University of Science and Technology, Sultanate of Oman.

ABSTRACT

Diabetes mellitus (DM) is a leading non-communicable disease which

affects more than100 million people worldwide and is considered as

one of leading diseases which causes death in the world. Type-2

diabetes mellitus (T2DM) is a chronic metabolic disorder that results

from defects in both insulin secretion and insulin action. Insulin

degrading enzyme (IDE, Insulysin), a structurally distinctive zinc

metallopeptidase, is a best characterized mediator of insulin

catabolism. IDE deficit increases the abundance and signalling of

pancreatic hormones insulin, amylin and glucagon. Therefore, agents

that inhibit the activity of IDE would be a viable approach towards

treatment of T2DM. In the present study, a series of 5-aryl-1,3,4

-thiadiazole derivatives were designed and subjected to in silico screening for drug likeness

and desired molecular properties compared with probe ML345. Those analogues having best

molecular parameters and good PASS score are selected for molecular docking studies using

the docking software Discovery studio 2018. Only 12 of them showed significant interaction

with the target protein. PA33 and PA34 have good docking score and more hydrogen

bonding interactions with active site residues of Insulysin protein target (PDB ID: 2G47 and

3E4A). From the docking studies, the best pose for the compounds were obtained with least

energy value. With all these results, it can be hypothesized that these compounds can be

considered as potential inhibitors of Insulysin (IDE) that could hold therapeutic value for the

treatment of diabetes.

WORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES

SJIF Impact Factor 7.632

Volume 9, Issue 5, 1726-1743 Research Article ISSN 2278 – 4357

Article Received on

19 March 2020,

Revised on 07 April 2020,

Accepted on 27 April 2020

DOI: 10.20959/wjpps20205-16155

*Corresponding Author

Asst. Prof. Sherin Abdul

Hameed

College of Pharmaceutical

Sciences, Government

Medical College,

Thiruvananthapuram.


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KEYWORDS: Insulysin, insulin degrading enzyme, 1,3,4-thiadiazole, diabetes mellitus,

molecular docking.

INTRODUCTION

Diabetes mellitus is a diverse and complicated disorder that is characterized by persistent

hyperglycemia. Type 2 diabetes affects approximately 200 million people worldwide,

including more than a quarter of elderly living in developed countries. It is also defined as a

condition in which the pancreas no longer produce enough insulin or cells stop responding to

insulin that is produced, so that glucose in the blood cannot be absorbed in to the cells of the

body. When not properly controlled, complications include neuropathy, coronary artery

disease, and renal failure may have resulted. Diabetes is a major cause of blindness, kidney

failure, heart attacks, stroke and lower limb amputation. In 2016, an estimated 1.6 million

deaths were directly caused by diabetes. WHO estimates that diabetes was the seventh

leading cause of death in 2016.[1]

Several drugs are approved for diabetes treatment; however,

their use is associated with side effects and lack of efficacy in attenuating the development of

long-term complications. [2]

Hypoglycemic medication is used to lower the blood sugar level

in the body or to treat other severe symptoms of diabetes mellitus. These medications can be

categorized into insulin and insulin preparations, which are used only parenterally, and

hypoglycemic medicine, which can be administered orally.[3], [4]

Global prevalence of diabetes

and its rising frequency makes it a key area of research in drug discovery programs.

Insulin-degrading enzyme (IDE) is an allosteric Zn+2

metalloprotease involved in the

degradation of many peptides including amyloid-b, and insulin that play key roles in

Alzheimer‟s disease (AD) and type 2 diabetes mellitus (T2DM), respectively. It is the key

enzyme responsible for the cellular proteolysis of insulin. Like insulin, IDE is structurally

distinctive, consisting of two bowl-shaped halves connected by a flexible linker that can

switch between „„open‟‟ and „„closed‟‟ states. In its closed state, IDE completely encapsulates

its substrates within an unusually large internal cavity that appears remarkably well-adapted

to accommodate insulin.[5]

IDE deficit increases the abundance and signalling of the

pancreatic hormones insulin, amylin, and glucagon. Increased insulin improves glucose

tolerance, and increased amylin levels slow postprandial gastric emptying. [6]

In principle, it

should be possible to enhance insulin signalling within cells by inhibiting IDE-mediated

insulin catabolism. A viable approach towards the treatment of T2DM is thus evolved by

designing IDE inhibitors.


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It is known that heterocyclic scaffolds possess a leading role in designing novel class of

chemotypes as drug candidates. Thiadiazole is a significant heterocyclic nucleus with two

carbon atoms, two hydrogens, two nitrogens and one sulphur. The molecular structural

formula is C2H2N2S. They occur in four isomeric forms viz 1,2,3-thiadiazole; 1,2,5-

thiadiazole and 1,3,4-thiadiazole. [7]

1, 3, 4 - Thiadiazoles have diversified biocidal activities

probably by virtue of a toxophoric – N=C-S- Grouping. Strong aromaticity of the ring system

gives greater stability and possess low toxicity for higher vertebrates. The sulfur atom of the

thiadiazole imparts improved liposolubility, and the mesoionic nature of thiadiazoles makes

these compounds better able to cross cellular membranes.[8]

Literature survey revealed that

various thiadiazoles have resulted in many potential drugs and are known to exhibit a broad

spectrum of pharmacological properties. 1,3,4-thiadiazole and its derivatives possesses wide

range of therapeutic activities like antimicrobial[9]

antifungal,[10]

antitubercular,[11], [12]

anti-

inflammatory,[13]

diuretics, antiulcer, antidiabetic,[14]

anticonvulsant,[15]

anticancer[16]

anti-

leshmanial, carbonic anhydrase inhibitors, antidepressant, antioxidant, radio-protective, and

antiviral activities.[17]

Datar and Deokule proposed a series of thiadiazolimines exhibiting

antidiabetic activity via peroxisome proliferator-activated receptor gamma (PPARgamma)

inhibition. An isothiazole derivative, ML345 (5-Fluoro-2- (2-morpholino-5-

(morpholinosulfonyl) phenyl) benzo[d] isothiazol-3(2H)-one) is an IDE inhibitor molecular

probe, which targets a specific cysteine residue (Cys819) in IDE. So far, modification of the

thiadiazole ring have proven highly effective with improved potency and lesser toxicity.

The application of computational technology during drug discovery and development offers

considerable potential for reducing the number of experimental studies required for

compound selection and development and for improving the success rate. Computational

methods are required because the amount of biological data has increased and manual

screening against such data requires much time and human resources.[18]

In silico approaches

are being used today in drug discovery to assess the ADME properties of compounds at the

early stages of discovery and development. ACD Lab Chemsketch, PASS (Prediction of

Activity Spectra for Substances), Osiris property calculator, Pre-ADMET prediction,

Molinspiration and SwissADME are some of the software which help pharmaceutical

scientists to select the best candidates for development as well as to reject those with a low

probability of success.[19]


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The virtual screening approach for docking small molecules into a known protein structure is

a powerful tool for drug design that has become an integral part of the drug discovery process

in recent years.[20]

In silico virtual screening has become a reliable, cost effective and time-

saving technique that is complementary to in vitro screening for the discovery and

optimization of potent lead and hit compounds. There are two broad categories of screening

techniques, the ligand-based virtual screening and receptor-based virtual screening, to select

candidate compounds that are likely to interact favourably with the target binding sites from a

chemical database.[21]

The three-dimensional structure of protein or protein-ligand complex is

helpful in lead identification using molecular modelling. Discovery studio, AUTODOCK,

Glide, and SwissDOCK are some of the software providing docking analysis. Discovery

Studio is a suite of software for simulating small molecule and macromolecule systems. It is

developed and distributed by Dassault Systemes BIOVIA (formerly Accelrys). Quantitative

structure-activity relationship (QSAR), pharmacophore and biological assays can be helpful

to optimize and design new leads.

Literature survey prompted us to propose some compounds with 1,3,4-thiadazole as core

molecule with an expectation of antidiabetic activity by inhibition of insulin catabolism. In

our present study, a series of 5-aryl-1,3,4-thiadiazole derivatives were designed and screened

and compared with insulysin inhibitor probe ML345, for their drug liking, molecular and

pharmacological parameters towards insulin degrading enzyme. Biological activity, selected

pharmacokinetic and toxicity properties were predicted using different software. Docking

studies were included to find the affinity and interaction of proposed derivatives with the

protein target. Figure 1 shows the schematic representation of insulin degrading enzyme.

Figure 1.

https://en.wikipedia.org/wiki/Software

https://en.wikipedia.org/wiki/Small_molecule

https://en.wikipedia.org/wiki/Macromolecule

https://www.3dsbiovia.com/


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MATERIAL AND METHODS

ACD Lab Chemsketch 2018.2.5. All derivatives were drawn using this software. molecular

parameters like parachor, index of refraction, surface tension, log p, molecular weight, molar

refractivity, molar volume density and polarizability.[22]

PASS (Prediction of Activity Spectra for Substances) provides simultaneous predictions of

many types of biological activity based on the structure of organic compounds.[23,24,25]

Structures of the title compounds were drawn through Chem Sketch software, submitted to

the PASS online program and predicted the possible mechanisms of action as well as

biological activities.

Osiris property calculator[26]

is chemical structural database that calculates various

molecular parameters like clogp, solubility, Molecular weight, Drug likeness. Molinspiration

offers SMILES and SDfile conversion, RO5 compliance, normalization of molecules,

generation of tautomers, molecule fragmentation, calculation of various molecular parameters

and bioactivity towards different receptors needed in QSAR, molecular modelling and drug

design.[27,28]

Pre-ADMET prediction software provided toxicological properties and ADME parameters.

Docking Analysis[29]

of the selected targets with synthetic ligands were analyzed using the

docking software Biovia Discovery studio 2018. Library docking is performed for identifying

the binding affinity with the targets using CHARMM as force field. The data regarding the

protein targets PDB ID: 2G47 and 3E4A were obtained from Protein Data Bank (PDB). The

list of software used for in silico screening in this study is given in table 1. ML345[30]

is the

insulysin inhibitor probe of which molecular parameters were compared.


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Table 1: List of software used.

Software Usage

PASS Online To predict the insulysin inhibitory action of proposed moiety

ACD Lab Chemsketch 10.00 3-D drawing, optimizing and calculating various physiochemical

descriptors of proposed molecules

Molinspiration To calculate log P values, Lipinski‟s rule of five and drug likeness

Open Babel 2.3.2l To interconvert file formats and data for the analysis of docking results

Osiris Property Explorer To predict ADME and toxicity

PreADMET Prediction

To predict permeability for Caco-2 cell, MDCK cell and BBB(blood-

brain barrier), HIA(human intestinal absorption), skin permeability and

plasma protein binding

PreADMET Toxicity Prediction To predict toxicological properties from chemical structures, such as

mutagenicity and carcinogenicity

Biovia Discovery studio 2018 To analyse Docking interactions of proposed derivatives.

RESULTS AND DISCUSSION

A series of 35 different derivatives of 5-aryl-1,3,4-thiadiazole moiety were drawn using ACD

Lab Chemsketch 2018.2.5. These structures were screened for the prediction of bioactivity

for insulysin (IDE) inhibitory action using PASS online software. Out of 35 derivatives, only

16 showed significant insulysin inhibitory action with PASSonline score (Pa-Pi >0.5).

Chemical structure and bioactivity score of each one are given in table 2.

Table 2: PASS Prediction results of Insulysin (IDE) inhibitory action of 5-aryl-1,3,4-

thiadiazole derivatives.

Code Structure (Pa-Pi) Code Structure (Pa-Pi)

PA01 N

S

N

N

0.614 PA19 N

S

N

NN

O

0.491

PA02 N

S

N

N

N

0.544 PA20 ON

S

N

NN

O

0.491

PA03

N

S

N

N

NH

NIL PA21

N

S

NN

N

0.465

PA04 N

S

N

N

NH

0.165 PA22

O

N

S

NN

N

0.557

PA05

N

S

N

N

S

NIL PA23

O

N

S

NN

N O

0.465

http://preadmet.bmdrc.org/preadmet/query/query3.php

http://preadmet.bmdrc.org/preadmet/query/query4.php


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PA06 N

S

N

N

O

0.628 PA24 O N

S

N

N

CH3

CH3

0.604

PA07 N

S

N

N

NIL PA25 O

N

S

N

N

0.451

PA08 N

N

S

N

N

0.605 PA26

O

N

S

N

N

NCH3

CH3

0.715

PA09 N

S

N

NNH

NIL PA27 O

N

S

NN

Cl

Cl

0.496

PA10 N

S

N

NNH

0.373 PA28

O

N

S

N

N

OO

CH3

CH3

O

CH3

0.651

PA11 N

S

N

N

S

0.311 PA29

O

N

S

N

N

OH

0.709

PA12 N

S

N

N

O

0.674 PA30 O

N

S

N

N

N+

O–

O

0.423

PA13 O

N

S

N

N

0.260 PA31

N N

SN

N

S

N

NH2NH2

0.263

PA14 O

N

S

N

N

N

0.622 PA32

N

S

N

NH

0.544

PA15

O

N

S

N

N

O

0.567 PA33 N

NN

O

S

O

0.683

PA16

O

N

S

N

N

NH

0.336 PA34

OH

N

NN

O

S

O

0.674


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PA17 O

N

S

N

N

S

0.097 PA35 ON

NN

O

S

O

0.641

PA18

O

N

S

N

N

NH

S

O

O

0.184 ML345 O

F

S N

S

O

O

N ONO

0.936

Molecular properties of derivatives having a PASSonline value (Pa-Pi) > 0.5 were calculated

using Molinspiration online property calculation toolkit. Among the 35 derivatives, only 16

showed good bioactivity score as insulin degrading enzyme inhibitor. The standard values of

molecular properties were based on Lipinskis rule of 5 which stated as molecular weight less

than 500 Dalton, Log p values less than 5, not more than 10 H bond acceptors and not more

than 5 H bond donors. All the derivatives were found to obey Lipinski‟s rule of 5 (RO5)

which is evaluated by Molinspiration software. Some selected pharmacokinetic properties,

which are of high importance to drug discovery process were predicted using Chemsketch v

12.0. Molecular Formula, number of atoms Molar Refractivity, Molar Volume, Parachor,

Surface Tension and Polarizability of 16 selected compounds were detailed in table 3. Molar

Refractivity is an important descriptor in the study of molecular modeling, which relates the

dispersive forces of London present in the drug-receptor interaction. It is found that the

derivatives PA33 and PA34 having high molar refractivity than others and was near to the

value of ML345.To improve the predictions of druglikeness, the rules have spawned many

extensions like Ghose filter and Veber rule. Ghose filter rule stated that compounds with

Partition coefficient log P in (−0.4 to +5.6) range, Molar refractivity from 40 to 130,

Molecular weight from 180 to 480 and Number of atoms from 20 to 70 (includes H-bond

donors [e.g. OHs and NHs] and H-bond acceptors [e.g. Ns and Os]) give more druglikeness

character. While Veber rule stated that for a compound predicted to have good oral

bioavailability then the rotatable bond count should be less than or equal to 10 and polar

surface area should be less than or equal to 140. All of the proposed compounds and insulysin

inhibitor probe ML345 have the said parameters within the range. Lipophilicity affects drug

absorption, bioavailability, hydrophobic drug-receptor interactions, metabolism of molecules,

as well as their toxicity. All the proposed molecules have log p greater than that of ML345

but less than 5. The number of rotatable bonds indicated that all derivatives are flexible. All

derivatives have enough rotatable bonds comparable to that of ML345. TPSA is a very useful


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parameter for the prediction of drug transport properties and is defined as a sum of surfaces

of polar atoms (usually oxygens, nitrogens and attached hydrogens) in a molecule. PSA is a

commonly used medicinal chemistry metric for the optimization of a drug's ability to

permeate cells. Molecules with a polar surface area of greater than 140 angstroms squared

tend to be poor at permeating cell membranes. All the proposed derivatives have TPSA value

less than140 angstroms. This parameter has been shown to correlate very well with the

human intestinal absorption, Caco-2 monolayer‟s permeability, and blood brain barrier

penetration. Percentage of absorption (% ABS) was estimated using the equation: %

ABS=109−(0.345×TPSA), according to Zhao et al. Table 4 represents a calculated

percentage of absorption (% ABS), topological polar surface area (TPSA) and Lipinski

parameters of the compounds.

Table 3: Molecular descriptors of selected 5-aryl-1,3,4-Thiadiazole derivatives using

Chemsketch software.

Code Molecular

Formula

No of

atoms

Mol. Ref

Cm3

Parachor

Cm3

Surface tension

Dyne /cm

Polarizability

10-24

cm3

ML345 C21H22FN3O5S2 54 119.18± 0.4 908.5± 6.0 62.0 ± 3.0 47.24± 0.5

PA01 C15H11N3S 30 81.00 ± 0.5 577.8± 8.0 50.1 ± 7.0 32.11± 0.5

PA02 C14H10N4S 29 79.44 ± 0.5 559.0 ±8.0 54.4 ± 7.0 31.49± 0.5

PA06 C13H9N3OS 27 73.17 ± 0.5 519.8± 8.0 54.2 ± 7.0 29.00± 0.5

PA08 C14H10N4S 29 79.44 ± 0.5 559.0± 8.0 54.4 ± 7.0 31.49± 0.5

PA12 C13H9N3OS 27 73.17 ± 0.5 519.8± 8.0 54.2 ± 7.0 29.00± 0.5

PA14 C12H8N4OS 26 71.61 ± 0.5 500.9± 8.0 59.8 ± 7.0 28.39± 0.5

PA15 C11H7N3O2S 24 65.34 ± 0.5 461.7 ±7.0 60.1 ± 7.0 25.90± 0.5

PA22 C13H8N4OS 27 74.57 ± 0.5 499.1± 8.0 68.0 ± 7.0 29.56± 0.5

PA24 C11H11N3OS 27 64.48 ± 0.5 441.9± 8.0 53.2 ± 7.0 25.56± 0.5

PA26 C15H14N4OS 35 85.97 ± 0.5 616.1± 8.0 49.1 ± 7.0 34.08± 0.5

PA28 C16H15N3O4S 39 90.61 ± 0.5 670.6± 8.0 46.6 ± 7.0 35.92± 0.5

PA29 C13H9N3O2S 28 74.02 ± 0.5 525.5± 8.0 60.0 ± 7.0 29.34± 0.5

PA32 C14H11N3S 29 74.78 ± 0.3 545.4 ±8.0 59.1 ± 3.0 29.64± 0.5

PA33 C19H13N3O2S 38 99.66 ± 0.5 715.8± 8.0 52.4 ± 7.0 39.51 ± 0.5

PA34 C19H13N3O3S 39 100.51±0.5 721.5± 8.0 56.4 ± 7.0 39.84 ± 0.5

PA35 C17H11N3O3S 35 91.83 ± 0.5 657.8± 8.0 56.1 ± 7.0 36.40 ± 0.5

https://en.wikipedia.org/wiki/Medicinal_chemistry

https://en.wikipedia.org/wiki/Angstrom


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Table 4: Molecular parameters predicted via Molinspiration online property calculation

toolkit.

Insulin degrading enzyme is a type of protease enzyme. The bioactivity score of proposed

derivatives on different receptors were shown in table 5. It is found that derivatives like

PA32, PA33, PA34, PA35 have more protease inhibiting activity compared to others with

PA33 and PA34 have its maximum value which also supports the docking results. The

preADMET server was used to calculate parameters such as human intestinal absorption,

cellular permeability Caco-2 in vitro, cell permeability Maden Darby Canine Kidney

(MDCK), skin permeability, plasma protein binding, and penetration of the blood brain

barrier, carcinogenicity and mutagenicity were listed in table 6 and table 7 while aqueous

solubility, drug likeness score and drug score were calculated used Osiris property explorer

online and were given in table 8.

Code Mol. Wt Log P nHAc nHDo nrot b TPSA %ABS

ML345 479.5 1.78 8 0 4 81.09 81.02

PA01 265.33 4.11 3 0 3 38.15 95.8

PA02 266.32 2.82 4 0 3 51.04 91.4

PA06 255.30 3.36 4 0 3 51.29 91.3

PA08 266.32 2.82 4 0 3 51.04 91.4

PA12 255.30 3.25 4 0 3 51.29 91.3

PA14 256.28 1.96 5 0 3 64.18 86.9

PA15 245.26 2.50 5 0 3 64.43 86.8

PA22 268.29 3.34 5 0 2 56.23 89.6

PA24 233.29 3.80 4 0 1 43.33 94.1

PA26 298.36 3.35 5 0 4 54.53 90.2

PA28 345.37 2.88 7 0 6 78.99 81.8

PA29 271.29 3.19 5 1 3 71.52 84.3

PA32 253.32 4.56 3 1 3 37.81 96.0

PA33 347.39 4.93 5 0 5 64.43 86.8

PA34 363.39 4.69 6 1 5 84.66 79.8

PA35 337.35 4.00 6 0 5 77.57 82.2


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Table 5: Molinspiration bioactivity score.

Code GPCR

ligand

Ion channel

modulator

Kinase

inhibitor

Nuclear

receptor

Protease

inhibitor

Enzyme

inhibitor

ML345 -0.04 -0.39 -0.10 -0.47 -0.26 0.02

PA01 -0.92 -1.16 -0.46 -0.85 -1.03 -0.44

PA02 -0.86 -1.08 -0.30 -0.89 -0.99 -0.35

PA06 -1.07 -1.33 -0.86 -1.17 -1.31 -0.66

PA08 -0.87 -1.08 -0.29 -0.90 -1.00 -0.37

PA12 -1.17 -1.45 -0.94 -1.34 -1.48 -0.67

PA14 -1.10 -1.36 -0.77 -1.38 -1.44 -0.57

PA15 -1.29 -1.54 -1.28 -1.61 -1.64 -0.81

PA22 -0.69 -0.71 -0.53 -1.34 -1.20 -0.56

PA24 -1.05 -1.18 -1.43 -1.50 -1.89 -0.49

PA26 -0.89 -1.28 -0.64 -1.00 -1.18 -0.56

PA28 -0.81 -1.20 -0.59 -0.95 -1.03 -0.53

PA29 -1.03 -1.46 -0.83 -1.09 -1.28 -0.58

PA32 -0.67 -0.82 -0.59 -0.95 -0.84 -0.28

PA33 -0.56 -0.97 -0.83 -1.09 -0.80 -0.38

PA34 -0.53 -0.94 -0.16 -0.70 -0.77 -0.35

PA35 -0.66 -1.18 -0.63 -0.65 -0.90 -0.43

Table 6: Pharmacokinetic parameters obtained from PreADMET Prediction software.

Code PPB BBB HIA% Caco2 MDCK SP

ML345 93.16 0.18 99.79 21.70 0.201 -3.91

PA01 95.99 3.22 98.26 34.89 197.26 -2.76

PA02 90.39 0.52 99.08 33.20 191.34 -3.36

PA06 97.04 1.56 98.76 32.01 188.15 -3.43

PA08 91.39 0.52 99.08 32.90 191.34 -3.36

PA12 95.86 1.56 98.76 31.50 239.62 -3.42

PA14 87.31 0.21 96.99 27.40 246.66 -3.95

PA15 86.90 0.64 95.20 29.95 209.74 -3.89

PA22 89.80 1.69 99.09 46.60 149.30 -3.77

PA24 100 2.43 98.51 49.82 292.40 -3.58

PA26 91.86 1.21 99.12 36.53 20.81 -3.47

PA28 90.68 0.43 96.99 27.18 2.93 -3.94

PA29 96.00 0.81 95.57 10.17 78.69 -3.66

PA32 91.82 1.45 95.96 38.48 199.34 -2.91

PA33 94.72 1.98 99.43 28.60 92.33 -2.86

PA34 93.71 0.85 97.81 27.42 91.94 -3.05

PA35 94.69 1.02 98.41 26.83 150.64 -3.43


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Table 7: Toxicological parameters.

Code Rat Mouse Ames Test hERG Inhibition

ML345 Negative Negative Mutagen Medium Risk

PA01 Negative Negative Mutagen Medium Risk


PA06 Positive Negative Mutagen Medium Risk






PA24 Positive Positive Mutagen Low Risk






PA34 Positive Positive Mutagen Medium Risk


Table 8: Drug score values predicted using Osiris property explorer.

Code C log P Aqueous solubility TPSA Drug-likeness Drug score

ML345 1.65 -4.47 113.0 2.25 0.36

PA01 3.70 -3.66 66.38 3.24 0.76

PA02 2.70 -2.87 79.27 3.04 0.86

PA06 2.89 -3.34 79.52 3.16 0.83

PA08 2.70 -2.87 79.27 2.90 0.86

PA12 2.93 -3.01 79.52 2.97 0.84

PA14 1.93 -2.22 92.41 2.83 0.90

PA15 2.12 -2.69 92.66 2.92 0.89

PA22 2.60 -2.78 84.46 3.35 0.70

PA24 2.64 -4.26 66.40 4.20 0.78

PA26 2.82 -3.05 82.76 2.20 0.49

PA28 2.72 -3.07 107.2 6.30 0.83

PA29 2.58 -2.72 99.75 3.02 0.87

PA32 3.86 -3.70 66.05 1.70 0.71

PA33 3.83 -4.59 92.66 1.70 0.61

PA34 3.48 -4.30 112.8 3.06 0.68

PA35 3.02 -4.28 105.8 2.77 0.43

Among the 16 ligands, only 12 have showed docking score more than 60. Since the insulin

degrading enzyme acquires a big cavity appearance, the dimensions of derivatives PA01,

PA02, PA08 and PA32 may be less to have interaction with the active site residues of the

selected protein. Docking studies were done with two protein targets (PDB ID: 2G47, 3E4A).

The 12 ligands were docked at the active site residues of PDB ID: 2G47. Active site residues


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of the selected target were Asn139, Glu 189, Arg824, and Tyr831. The ligands such as PA24,

PA29, PA33, and PA34 showed better interaction at critical residues at Arg 824. PA33 and

PA34 have good docking score and more hydrogen bonding interaction with the active sites.

(Table 9)

Table 9: Docking score of derivatives with PDB ID: 2G47.

SL:No. Code Docking score Interacting Residue of PDB ID: 2G47

1 PA06 73.5505 No H Bond Interaction

2 PA12 102.195 His108

3 PA14 101.281 His108 whb:Gln111

4 PA15 67.9659 Asn139

5 PA22 94.7679 No H Bond Interaction

6 PA24 87.8602 Arg824,Thr825

7 PA26 79.0343 Whb:Ser138, Glu182

8 PA28 93.0204 Thr825,WHB: Ser138,Glu182,Ser128,Glu124

9 PA29 76.691 Arg824, Thr825

10 PA33 105.05 Arg824, Thr825

11 PA34 110.147 Arg824, Thr825

12 PA35 95.8804 His112,whb: Glu189,Ser138,Gln111

The 12 ligands were docked against protein target PDB ID: 3E4A also. Asn139, Glu 189,

Arg824, and Tyr831 are the active site residues. The proposed derivatives such as PA15,

PA33 and PA34 showed exact binding interaction at the critical residue Arg824. From the

docking result with PDB ID: 3E4A, the derivative PA34 have a dock score of about 112.241

with three H- bonds interaction against Insulin degrading enzyme (Table 10).

Table 10: Docking score of derivatives with PDB ID: 3E4A.

SL:no. Name of the ligand Docking score Interacting Residue of PDB ID: 3E4A

1 PA06 77.3707 Gly136 whb: Ser128,Ser138

2 PA12 96.9876 His108,Asn139, whb:Gln111,Thr142

3 PA14 96.3918 His108,Asn139 whb:Gln111,Thr142

4 PA15 74.3331 Arg824 whb:Ser128

5 PA22 95.5868 whb:Ser138, Arg824

6 PA24 89.694 Ser128

7 PA26 81.7984 Whb: Gly136,Glu124,Ser128

8 PA28 80.782 Whb:Glu817,Ser128, Ser138

9 PA29 82.2773 Gly136, Whb:Ser128, Ser138

10 PA33 107.399 Arg824,Thr825

11 PA34 112.241 Arg824,Thr825,Tyr831

12 PA35 105.166 Gln111,whb:Glu189,Gln111,Ser128

From the docking studies, it is found that the derivatives PA34 and PA33 have shown

maximum docking score with both protein targets. The bioactivity score for protease


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inhibition is maximum for PA33 and PA34. Both of them have 5-furyl 1,3,4-thiadiazole

moiety linked with 1-(furan-2-yl)-3-phenylprop-2-en-1-one (chalcone). Two furyl ring and

one phenyl ring present in their structure may contribute much to their interaction with the

proteins. Both of them are having similar structural dimensions of that of ML345 with a bond

length (between two extreme atoms in 3d optimised structure) more than 13 angstroms. The

insulysin probe ML345 is also having bulk structure which would help to occupy the big

cavity of insulysin for effective blocking. It might help them to have more surface area to

interact more with the Insulin Degrading Enzyme. The completely wrapping of the molecules

PA33 and PA34 by amino acid residues at the active site pocket region of PDB ID 2G47

were displayed in Figure 2 (a, b), 3 (a, b) while that of PDB ID 3E4A in figure 4 (a, b), and 5

(a, b).

(a) (b)

Figure 2: 2G47 interaction with PA33

(a) (b)

Figure 3: 2G47 interaction with PA34.


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(a) (b)

Figure 4: 3E4A interaction with PA33.

(a) (b)

Figure 5: 3E4A interaction with PA34.

CONCLUSION

Using computational and bioinformatics tools, the study identified, designed, and proposed

twelve novel lead molecules from among 35 different derivatives and were compared with

insulysin inhibitor probe ML345. Docking analysis, molecular parameters and bioactivity

score states that PA33 (1-(furan-2-yl)-N-[5-(furan-2-yl)-1,3,4-thiadiazol-2-yl]-3-phenylprop-

2-en-1-imine) and PA34 (3-(furan-2-yl)-3-{[5-(furan-2-yl)-1,3,4-thiadiazol-2-yl] imino}

prop-1-en-1-yl] phenol) these analogues can be considered as the most probable Insulysin

Inhibitors which can be used for the treatment of diabetes mellitus by reducing our

dependency on insulin. Thus this paper proves to be significant for further research work on

the bioactive thiadiazole ring as antidiabetic agent. Further wet lab assessment of these

derivatives has to be performed to establish their insulin mimicking activity.

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this

paper.


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ACKNOWLEDGEMENT

The authors are thankful to the authorities of all software used in this study for providing free

access and also to Department of Computational Biology & Bioinformatics, University of

Kerala for performing Docking Analysis.

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https://europepmc.org/search?query=AUTH:%22Michael%20H%20Abraham%22

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https://europepmc.org/search?query=AUTH:%22Gordon%20Beck%22

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