Study of Dipeptidil Peptidase 4 Inhibitors based on Molecular Docking

Experiments

A. A. Saraiva

2,3 a

, J. N. Soares

2 b

, Nator Junior C. Costa

2 c

, Jos

e Vigno M. Sousa

1,2 d

N. M. Fonseca Ferreira

4,5 e

, Antonio Valente

3,6 f

and Salviano Soares

7 g

University Brazil, S

ao Paulo, Brazil

UESPI-University of State Piau

ı, Piripiri, Brazil

School of Science and Technology, University of Tr

as-os-Montes and Alto Douro, Vila Real, Portugal

Department of Electrical Engineering, Institute of Engineering of Coimbra, Polytechnic Institute, Coimbra, Portugal

Knowledge Engineering and Decision-Support Research Center (GECAD) of the Institute of Engineering,

Polytechnic Institute of Porto, Porto, Portugal

NESC-TEC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, 378, 4200 - 465, Porto, Portugal

University of Tr

as-os-Montes and Alto Douro, Vila Real, Portugal

Keywords:

Drugs, Diabetes, Molecular Docking.

Abstract:

The lack of physical activity and poor nutrition triggers various diseases, among them is diabetes. In this

context, several researches seek ways that can mitigate these diseases to provide a better quality of life for

people. Therefore, the present work aims to analyze the possible inhibitors of the enzyme Dipeptidil Peptidase

4 that hypotheses will be stipulated for the creation of new drugs through molecular docking techniques,

that is, a computational simulation of combinations of drugs of the family of gliptins with other antidiabetics

(metformin, glyburide and cucurbitacin). Among the results, it was observed that the antidiabetic cucurbitacin

combined with the gliptines obtained greater energy during the process.

1 INTRODUCTION

There is great concern about several diseases that have

been genetically changing over time, among them

is diabetes, one of the most studied in the scientiﬁc

world. According to (Vignesh and Amalarethinam,

2017), in 2011 there were 347 million diabetics and

by 2030 that number will be approximately 552 mil-

lion people. Being that its main characteristic is the

high level of glucose in the blood motivated as a re-

sult of defects in the action of the insulin produced by

the pancreas. According to the author, insulin aims to

allow the entry of glucose into the body’s cells, from

which it will be used for various cellular activities,

however, the lack of insulin or malformation of its

https://orcid.org/0000-0002-3960-697X

https://orcid.org/0000-0002-0586-4786

https://orcid.org/0000-0001-5636-424X

https://orcid.org/0000-0002-5164-360X

https://orcid.org/0000-0002-2204-6339

https://orcid.org/0000-0002-5798-1298

https://orcid.org/0000-0001-5862-5706

function in the human system leads to accumulation

of glucose in the body. blood, thus triggering what is

called hyperglycemia.

This disease is a worldwide concern, as more and

more people, due to their sedentary habits, poor diet

and the growing number of obese, end up acquiring it

(Nachabe et al., 2017). As a result, there are several

studies in the literature that seek to elucidate some

solutions to mitigate the damages caused by diabetes.

The pharmaceutical industry perfects efﬁcient

methods for creating new medicines. In this context

there are molecular docking techniques that perform

computational simulations to better predict the adjust-

ment orientation of a ligand to a receptor. The ligand

are molecules produced by cells that interact as a puz-

zle with their receptor as Figure 1. Already the recep-

tor is the target protein in which it is desired to per-

form the interaction between the parts to verify com-

patibility information.

With the docking, one can characterize the behav-

ior of small molecules in connection with the target

proteins. Therefore, when using this virtual tech-

322

Saraiva, A., Soares, J., Costa, N., Sousa, J., Ferreira, N., Valente, A. and Soares, S.

Study of Dipeptidil Peptidase 4 Inhibitors based on Molecular Docking Experiments.

DOI: 10.5220/0007692203220330

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 322-330

ISBN: 978-989-758-353-7

Source: Own ilustrated.

Figure 1: Illustration of the Binder with its Receptor

Molecule.

nique, it will be possible to propose structural hy-

potheses of how the ligands are connected to their tar-

gets.

In this context, the present work aims to use com-

putational simulation in different substances in order

to present solutions that may soften the causes of di-

abetes, that is, the purpose of this research is look for

the relation of gliptine families to then create hypothe-

ses of new drugs that will be combined with other an-

tidiabetics. With the study and results presented in

this research, hypotheses can be formulated and tested

in future lab work.

The target protein of this work is dipeptidyl pep-

tidase 4 (DPP4) whose enzyme is directly bound to

type 2 diabetes mellitus because it acts directly on the

degradation of incretins (Richter et al., 2008). Incretin

works in the body regulating glucose levels, acting

on the pancreatic secretion of insulin. According to

studies presented throughout this work, the inhibition

of DPP4 enzyme may have the function of regulating

glucose control in the body.

An approach used for studies on diabetes mellitus

is related to dipeptidyl peptidase 4 inhibitors (Richter

et al., 2008). In her study, she presented analyzes of

the use of two drugs that are already studied in the

treatment of type 2 diabetes mellitus, Sitagliptin and

Vildagliptin.

Both are drugs that are part of the gliptins fam-

ily. Gliptins is a class of oral hypoglycemic agents

used in the treatment of type 2 diabetes mellitus. Five

different types of gliptins (Sitagliptin, Vildagliptin,

Saxagliptin, Linagliptin and Alogliptin) have been

marketed (Biftu et al., 2014), and in this work four

of them will be approached Sitagliptin, Vildagliptin,

Saxagliptin and Linagliptin.

Section 2 will perform a dosing of DPP4 inhibitors

and antidiabetic agents used in this work and section 3

will explain the molecular docking technique. In sec-

tion 5, we present the molecular docking simulations

of the drugs of the family gliptins together with the

DPP4 protein, and later the afﬁnity table obtained and

the joining of three molecules individually with each

result will be displayed. However, we tried to present

the best results through the afﬁnity table between the

drugs and the molecules selected for the study, then

we will point out a hypothesis to be developed in the

future by the drugs.

2 DIPEPTIDYL PEPTIDASE 4

INHIBITORS

DPP4 is an enzyme that degrades incretin, a substance

responsible for regulating glucose metabolism (Johari

et al., 2011). In this section, the relationship be-

tween the Gliptines family and the DPP4 enzyme will

be elucidated, thus performing a molecular docking,

showing the characteristics of each drug and exempli-

fying its role as a DPP4 inhibitor.

There are studies that show the efﬁciency of

gliptins as the work of (Davidson et al., 2008), who

presented new therapeutic treatments to treat type 2

diabetes. Or as in the work of (Nauck et al., 2017) in

which it conducts a study on the addition of a DPP4

inhibitor, Sitagliptin, to explore whether the addition

of this drug to pre-existing liraglutide therapy alters

glycemic levels after a meal. In the study by (Berger

et al., 2018) comparisons were made between DPP4

inhibitors for the treatment of type 2 diabetes. Ani-

mal experiments aimed at ﬁnding a direct relationship

between enzyme inhibition in plasma and glucose re-

duction were done.

2.1 Sitagliptin

Sitagliptin, as exempliﬁed in Figure 2, is one of the

inhibitors of DPP4 because it is blocked by this drug,

thus causing the elimination of insulin hormone when

blood sugar levels decrease (Rosenstock et al., 2006).

Sitagliptin Phosphate and Hydrochloride act as sup-

porters of physical activity and dietary practices in

order to control the glycemia of patients with type 2

diabetes mellitus, beneﬁting people who are sensitive

to high insulin levels.

2.2 Vildagliptin

Vidagliptin is a drug that acts on the alpha and beta

cells of the pancreas and just like Sitagliptin prevents

the proliferation of DPP4. According to (Berger et al.,

Study of Dipeptidil Peptidase 4 Inhibitors based on Molecular Docking Experiments

323

Source:PubChem. (PubChem, 2005)

Figure 2: Sitagliptin.

Source:PubChem. (PubChem, 2005)

Figure 3: Vildagliptin.

2018) to Vidaglipline shown in Figure 3, by inhibiting

DPP4, incretin gradually increases its efﬁcacy.

2.3 Saxagliptin

Saxagliptin shown in Figure 4 aims to control blood

sugar after meals as well as between meals. This drug

also helps increase insulin in the body, thereby atten-

uating the production of sugar by the liver after meals.

Source:PubChem. (PubChem, 2005)

Figure 4: Saxagliptin.

2.4 Linagliptin

Linagliptin also inhibits the enzyme DPP4, since it

prevents the breakdown of incretins, GLP-1 and GIP,

hormones that help the pancreas to produce insulin

when a high level of glucose in the blood is detected,

and Linagliptin also reduces another hormone called

Glicacon, this hormone produced by the pancreas that

increases blood glucose. When DPP4 inhibits the

drug Linagliptin, GLP-1, responsible for releasing in-

sulin according to the needs of the body, acts longer.

Figure 5 shows how this drug is structured.

Source:PubChem. (PubChem, 2005)

Figure 5: Linagliptin.

Source:PubChem. (PubChem, 2005)

Figure 6: Metformin.

3 ANTIDIABETIC AGENTS

The drugs of the gliptins families are strong candi-

dates for DPP4 inhibitors, thus leading to improve-

ments in patients with diabetes. Therefore, when

making combinations with these medicines can obtain

possible positive results for treatments. One of these

drugs, in which there are already studies of combi-

nations, is Metformin, in which it is already used in

several studies and in this research will play the role

of parameter for the tests performed that will be com-

pared with the following substances: glyburide and

cucurbitacin.

The glyburide substance is very viable to make

combinations with the gliptins. In the studies of (Mar-

bury et al., 1999) the efﬁcacy of this drug in the ﬁght

against diabetes is already evident.

Finally, cucurbitacin, a substance extracted from

plants of the Cucurbitacese family, in which it has

several oxidants, was used. This substance has

high hyperglycemic power as evidence the work of

(Alarcon-Aguilar et al., 2002). Their study explored

the hyperglycemic effect of cucurbitacease.

3.1 Metformin

Metformin is a suitable substance to act as an initial

therapy in patients with type 2 diabetes mellitus. This

BIOINFORMATICS 2019 - 10th International Conference on Bioinformatics Models, Methods and Algorithms

324

drug helps improve glycemic control by enabling in-

sulin sensitivity and decreasing intestinal absorption

of glucose. This drug is structured according to Fig-

ure 6.

3.1.1 Glyburide

It acts as a strong antihyperglycemic agent that can be

used in the treatment of non-insulin dependent dia-

betes mellitus. This substance when used in conjunc-

tion with adequate diets and physical exercises can

help to lower blood sugar levels, thus avoiding vari-

ous types of problems caused by increased glucose.

Source:PubChem. (PubChem, 2005)

Figure 7: Glyburide.

3.2 Cucurbitacin

Cucurbitacin is a highly oxygenated triterpene sub-

stance found free or glycosylated, extracted from

plants of the family Cucurbitaceae such as pumpkins,

cucumbers and gourds. This substance has aroused

the interest of several researchers to present high lev-

els of toxicity acting as an antitumor, antiinﬂamma-

tory, antifertilizer, phage repellent among others.

Source:PubChem. (PubChem, 2005)

Figure 8: Cucurbitacin.

4 MOLECULAR DOCKING

According to (Ferreira et al., 2015), molecular dock-

ing is a versatile computational technique for the

study of biological macromolecules, this technique

studies the production of drugs based on molecular

structures where they are simulated through numeri-

cal interactions by algorithms. The main objective of

this technique is the matching of these molecules for

identiﬁcation and characterization of the binding sites

in the target proteins, generating a table of evaluation

of the interaction potential as shown in Figure 9.

Figure 9: Example of Afﬁnity Result between Binder and

Receptor.

According to (Ferreira et al., 2015), The software

associates two main components: search algorithm

and score function, in which the algorithm is respon-

sible for the search for possible combinations in the

connections and the score demonstrates the best bind-

ing results obtained during the procedure. The algo-

rithms allow the exploration of several angles, both

rotational and translational and conformational of the

ligand in the target protein.

In Figure 9, a result table after the molecular dock

in the autodock vina software is exempliﬁed. This ta-

ble shows the nine best binding results from the linker

to the receiver, where the ﬁrst column shows the se-

quence of the numbered results and the second col-

umn shows the binding afﬁnity in kcal / mol, repre-

senting the highest energy. In the next columns, two

variants of RMSD metrics are provided: rmsd / lb

(lower limit of RMSD) and rmsd / ub (upper limit

of RMSD). The rmsd / ub combines each atom in a

conformation with itself in the other conformation,

ignoring any symmetry, since rmsd / lb is deﬁned as

follows: rmsd / lb (c1, c2) = max (rmsd ’(c1, c2) ,

rmsd ’(c2, c1)).

Study of Dipeptidil Peptidase 4 Inhibitors based on Molecular Docking Experiments

325

5 METHODOLOGY OF WORK

In the simulations developed, the following conﬁgu-

rations were used: core i7 fourth generation, 12 GB

ram, nvidia p6000 video card, HD Sdd 240 gb.

An important step for the accomplishment of the

experiment is the obtaining of the biological macro-

molecules in the format supported by the software that

will be used in the work. There are several databases

that provide the chemical compounds, these same

compounds are converted into tri-dimensional molec-

ular structures and made available in databases such

as PubChem, ChemSPider, Zinc, RCSB.org among

others.

The molecules presented as ligands in this work

were extracted from Pub-Chem, a database with a

large diversity of molecules maintained by the Na-

tional Center for Biotechnology Information. The

DPP4 protein was obtained from the work of (Hira-

matsu et al., 2003) in which it is made available in the

RCSB database PDB.

In order to perform the process and visualiza-

tion of the molecular docking results, we used the

autodock vina software (to perform the docking ex-

periments and the simulation of the best ﬁt between

the binder and the protein), Mgltools (conversion of

molecules formats) and oPyMol (for visualization of

results).

6 RESULTS

The ﬁrst part of the experiment consists of carry-

ing out molecular docking simulations using ﬁrst

the gliptins (Sitagliptin, Vildagliptin, Saxagliptin and

Linagliptin) molecules presented in section 2 together

with the dipeptidyl peptidase 4 target protein. the

combination with other antidiabetics and then observe

the behavior of the other molecules.

All the connection models obtained are divided

into ﬁles for multimodal visualization in three-

dimensional format. For the analysis of the experi-

ments we need a function to predict the binding afﬁn-

ity, there are several computational ways to obtain

them with different sofwares, each based on their cal-

culations.

For the experiments we used the autodock vina

where from the obtained results we will explore the

afﬁnity (kcal / mol). In this software the results that

released more energy (represented by the results of

smaller value) are the best binder ﬁttings in the re-

ceiver. (Shityakov and F

orster, 2014) explains that the

lower the value presented in the autodock vina simu-

lations, the more signiﬁcant it will present to the con-

nection found, where the values for the afﬁnity in the

molecular docking process are favorable only when

they are represented negatively, that is, the more neg-

ative the value obtained , the better the interaction.

Table 1 shows the best binding afﬁnity results ob-

tained in the docking process. It was observed that

the afﬁnity of Linagliptin with DPP4 generated the

highest energy during the process, releasing a value

of -9.3 kcal / mol, higher than the others, which ob-

tained values between -7.6 to -7.9, both being drugs

used as inhibitors of DPP4.

Table 1: Gliptine afﬁnity table with DPP4.

Ligante Receptor Aff

(a) Sitagliptin DPP4 -7.6

(b) Vildagliptin DPP4 -7.3

(d) Linagliptin DPP4 -9.3

Aff = Afﬁnity(kcal/mol).

In Figure 10, we ﬁnd the simulation results in the

three-dimensional view, in which the DPP4 enzyme is

represented in green color and the others (drugs) are

highlighted in a red circle. This image represents the

best allocation of gliptins in the molecule.

6.1 Mixing Gliptines with Metformin

The following sections aim to explore the results ob-

tained from the combinations of the gliptins presented

in table 1 with the subtances previously presented,

aiming at the behavior (afﬁnity in kcal / mol) of an-

tidiabetic agents in docking with DPP4. The ﬁrst is

Metformin, which according to the study by (David-

son et al., 2008) is a good candidate to effectively in-

hibit DPP4. In table 2 it was observed that all combi-

nations obtained the same afﬁnity values with DPP4.

Table 2: Gliptines afﬁnity table with Metformin.

Ligantes Receptor Aff)

(a) Sitagliptin + Metformin DPP4 -5.4

(b) Vildagliptin + Metformin DPP4 -5.4

(d) Linagliptin + Metformin DPP4 -5.4

Aff = Afﬁnity(kcal/mol).

In the three-dimensional simulations presented in

Figure 11, it was observed that the ideal ﬁt of Met-

formin is very close to the gliptins presented previ-

ously, however, they generate less energy in this pro-

cess, but due to their increasing research advances this

combination can be very useful for treatments of dia-

betes.

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(a) Sitagliptin (b) Vildagliptin

Figure 10: Afﬁnity Result of Gliptines with DPP4.

6.2 Gliptines Mixture with Glyburide

The next antidiabetic combined with glipins was gly-

buride. It is observed in Table 3 that the combination

Vildagliptin + glyburide was the one that generated

the highest energy, standing out from the others, thus

showing, that this combination has a greater afﬁnity

with DPP4 than the others.

Table 3: Gliptines afﬁnity table with Glyburide.

Ligantes Receptor Aff)

(a) Sitagliptin + Glyburide DPP4 -6.6

(b) Vildagliptin + Glyburide DPP4 -8.6

(d) Linagliptin + Glyburide DPP4 -6.8

Aff = Afﬁnity(kcal/mol).

6.3 Mixture of Gliptin with

Cucurbitacin

The last experiment was carried out with Cucur-

bitacin, a substance extracted from pumpkin plants,

gourds and cucumbers, its vegetables are usually con-

sumed in the day to day, which does not diminish its

importance in this study. The combinations of cu-

curbitacin with the selected glipitins generated afﬁn-

ity values between -8.0 and -8.7 as shown in table 4.

Among the antidiabetics selected for the research, this

was the one that provided the highest gerander energy

in combination with the glipins at the DPP4 receptor,

showing high afﬁnity in this combination.

Table 4: Afﬁnity Table of Glucines with Cucurbitacin.

Ligantes Receptor Aff

(a) Sitagliptin + Cucurbitacin DPP4 -8.0

(b) Vildagliptin + Cucurbitacin DPP4 -8.3

(d) Linagliptin + Cucurbitacin DPP4 -8.7

Aff = Afﬁnity(kcal/mol).

7 CONCLUSIONS

With the obtained results, it could be observed that

all the co-creations used had afﬁnity with the DPP4

enzyme, thus resulting in options that can be used in

clinical laboratory studies to create an effective antidi-

abetic. However, some combinations stood out more

than others regarding the energy release during the

Study of Dipeptidil Peptidase 4 Inhibitors based on Molecular Docking Experiments

327

(a) Sitagliptin + Metformin (b) Vildagliptin + Metformin

Figure 11: Afﬁnity Result of Gliptines with Metformin.

docking process, thus creating better hypotheses for

future laboratory tests.

In table 5, the three best results were highlighted

based on the combinations that released more energy.

It may be noted that among the most signiﬁcant re-

sults two are combinations made with cucurbitacin.

Table 5: Best Results Based on Energy Release.

Ligantes Receptor Aff

Vildagliptin + Glyburide DPP4 -8.6

Vildagliptin +Cucurbitacin DPP4 -8.3

Linagliptin + Cucurbitacin DPP4 -8.7

Aff = Afﬁnity(kcal/mol).

The combinations of gliptines with cucurtbitacin

were those that obtained less oscillations and, conse-

quently, better results, being in a scale of energy be-

tween 8.0 to 8.7. Already the combinations with Gly-

buride obtained scale of -6.1 to -8.6, where its com-

bination with Vildagliptin had the best result. Finally,

Metformin was the one that achieved the worst energy

release indices, in which all combinations were -5.4.

In this work, new hypotheses of efﬁcient combina-

tion for the creation of drugs that act as antidiabetics,

that is, substances that can inhibit the effects of type 2

diabetes, which affects a large part of the world pop-

ulation, have been presented through the molecular

docking technique. But for studies like these to meet

the needs of the population it is crucial that these re-

searches go hand in hand with laboratory tests to thus

prove the effectiveness of the simulations.

In this work, we hypothesized new drugs for the

creation of efﬁcient inhibitors of the DPP4 protein,

thus being able to carry out these experiments in lab-

oratories and to verify efﬁcacy. Also look for con-

troversial effects that can negatively affect these mix-

tures in humans.

Search for other combinations of other antidia-

betic agents and perform tests for future comparisons

with these drugs.

ACKNOWLEDGMENTS

This work is ﬁnanced by National Funds through the

FCT - Fundac¸

ao para a Ci

encia e a Tecnologia (Por-

tuguese Foundation for Science and Technology) as

part of project UID/EEA/00760/2019.

BIOINFORMATICS 2019 - 10th International Conference on Bioinformatics Models, Methods and Algorithms

328

(a) Sitagliptin + Glyburide (b) Vildagliptin + Glyburide

Figure 12: Afﬁnity Result of Gliptines with Glyburide.

(a) Sitagliptin + Cucurbitacin (b) Vildagliptin +Cucurbitacin

Figure 13: Afﬁnity Result Glucines with Cucurbitacin.

Study of Dipeptidil Peptidase 4 Inhibitors based on Molecular Docking Experiments

329

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