Chikungunya Virus Inhibitor Study based on Molecular Docking

Experiments

A. A. Saraiva

2,6 a

, Soares Jeferson

1 b

, Castro Miranda

1 c

, Jose Vigno Moura Sousa

1 d

N. M. Fonseca Ferreira

3,4 e

, J. E. S. Batista Neto

, Salviano Soares

3 f

and Antonio Valente

2,5 g

UESPI - University of State Piaui, Piripiri, Brazil

University of Tr

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

Coimbra Polytechnic - ISEC, Coimbra, Portugal

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

Polytechnic Institute of Porto, Portugal

INESC-TEC Technology and Science, Porto, Portugal

University of S

ao Paulo, S

ao Carlos, Brazil

Keywords:

Chikungunya, Molecular, Docking.

Abstract:

Chikungunya virus disease transmitted by the sting of the mosquito ’Aedes aegypti’ presenting an epidemic in

some regions. In order to have an early diagnosis and the best treatment technique, it establishes the study of

inhibitors for laboratory elaboration of a drug from molecular docking. As a result you have a better chance

of using Suramin followed by Silibin.

1 INTRODUCTION

Numerous factors inﬂuence the proliferation of the

Aedes Aegypti mosquito such as standing water and

street litter. This spread of the mosquito is worri-

some because it is the cause of numerous diseases

such as Dengue and Chikungunya (CHIKV). The cur-

rent context is very apprehensive, because according

to (Weaver et al., 2012). So far there is no antiviral

treatment for virus infection and no vaccine licensed

to totally inhibit it.

According to (Monath, 2018). There are sev-

eral clinical and antiviral studies under development

to combat Aedes Aegypti transmitted diseases and

may provide further speciﬁcations for the ﬁght against

CHIKV in the future.

The structure used to represent the CHIKV virus

was obtained from the work of (Voss et al., 2010), that

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

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

https://orcid.org/0000-0002-7751-9455

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

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

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

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

by X-ray crystallography a model of glycoproteins

E1 and E2 represented in 3D format was obtained as

shown in the ﬁgure. 1 .

Figure 1: Chikungunya E1 E2 Envelope Glycoproteins.

Therefore, the present work aims to employ com-

puter simulation in different substances in order to

present the most efﬁcient inhibitor of Chikungunya

virus. The objective of this research is to establish the

relationship of the selected molecules with the virus

and then to hypothesize new drugs through molecular

docking techniques.

Molecular docking is important to predicting the

best instruction aiming to adjust a linker to a protein,

200

Saraiva, A., Jeferson, S., Miranda, C., Sousa, J., Ferreira, N., Neto, J., Soares, S. and Valente, A.

Chikungunya Virus Inhibitor Study based on Molecular Docking Experiments.

DOI: 10.5220/0009118602000205

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS, pages 200-205

ISBN: 978-989-758-398-8; ISSN: 2184-4305

also characterizing the behavior of small molecules in

connection with the target proteins. Using this virtual

technique, it will be possible to propose structural hy-

potheses of how ligands are connected to their targets

and whether they are possible inhibitors of the virus

in question.

In this context, obtaining Chikungunya protein

was obtained through the work of (Voss et al., 2010)

and ligands through cubichem database.

Research on drugs to inhibit the Chikungunya

virus is relevant to both the scientiﬁc and social

spheres. Since most of the population has had Aedes

Aegypti mosquito-related diseases, but so far there is

no vaccination or drug that can inhibit the spread of

the virus in an individual’s body.

The paper is divided into ﬁve sections, section 2

deals with possible virus inhibitors, while section 3

emphasizes the computational technique used in the

work, Molecular Docking. In section 4 we describe

the methodology of the work, in section 5 the results

obtained through the simulations and ﬁnally the con-

clusion.

2 CHIKV VIRUS INIBITORS

According to (Monath, 2018) The evolution and

spread of the virus is a worldwide concern and the

vaccine is the main aspect that can alleviate epi-

demics. Given this context, there are several options

for virus inhibitors to analyze in different ways. In

this work Andrographolide, Epigallocatechin gallate,

Harringtonine, Silibinin and Suramin were used.Such

inhibitors will be classiﬁed below.

Andrographolide is a labdane diterpene produced

by the plant Andrographis paniculata, which has a

wide range of therapeutic applications such as anti-

inﬂammatory and platelet aggregation activities and

potential antineoplastic properties. (Gupta et al.,

2018) employs Andrographolide as an important

bioactive with anti-inﬂammatory properties, in its

work the compounds found in this substance reduce

inﬂammation in various diseases. In research con-

ducted (Gupta et al., 2018),showed the effect of in

vitro research of andrographolide in action on the

treatment of CHIKV virus, these experiments yielded

encouraging results.

Being a compound obtained from green tea, epi-

gallocatechon gallate (EGCG) has inhibitory effects

on various viruses. (Weber et al., 2015) described

it as an antiviral compound for a variety of viruses,

although the exact mechanism of the inhibitory ef-

fects are not yet understood, it was included in this

research as a candidate for a future drug capable of

Figure 2: Andrographolide 3D Structure. Source: Pub-

Chem. (PubChem, 2005).

Figure 3: Epigallocatechin gallate 3D structure. Source:

PubChem. (PubChem, 2005).

ﬁghting the Chikugunya virus shown in the ﬁgure.

3. (Raekiansyah et al., 2018) reported how difﬁcult

it is to develop an effective and safe vaccine to com-

bat dengue mosquito-borne diseases. In this work, we

investigated the combination of EGCG treatment with

suramin drug, this context increased chikungunya in-

hibition.

Harrigtonine is a substance of the alkaloid family,

where they are derived mainly from plants, but may

also be derived from fungi, bacteria and even animals.

(Kaur et al., 2013) conducted a study on Harringto-

nine’s action in inhibiting CHIK cell replication and

subsequently conﬁrmed its effectiveness against the

virus. In this study the results indicated that harring-

tonine acts in the post-initial stage of CHIKV replica-

tion and strongly interferes with the viral protein syn-

thesis process. Given this, Harrigtonine is a strong

candidate for research on efﬁcient inhibitors of the

CHIKV virus. In the ﬁgure 6 Harrigtonine molecule

in 3D format obtained from the Pubchem database is

presented.

A compound of the ﬂavonoid family, silibinin

is used to treat a variety of diseases such as hep-

atitis, liver cirrhosis, and chemical ﬁg-leaf injury.

(Lani et al., 2015) carried out research with different

ﬂavonoid types among them silibinin, where it pre-

sented promising results for CHIVK virus inhibition.

Suramin is a polyanionic compound with an un-

known mechanism of action. It is used parenterally in

the treatment of African trypanosomiasis and clinical

hypotheses have been created to be used as a CHIVK

Chikungunya Virus Inhibitor Study based on Molecular Docking Experiments

201

Figure 4: Harringtonine 3D Structure. Source:PubChem.

(PubChem, 2005).

Figure 5: Estrutura 3D Silibinin. Source: PubChem. (Pub-

Chem, 2005).

inhibitor. According to (Albulescu et al., 2015) In in

vitro experiments suramin proved to be an antipara-

sitic drug, which obtained satisfactory results in the

inhibition and replication of CHIKV and other al-

phaviruses.

Figure 6: Suramin 3D structure. Source: RCSB PDB.

(Berman et al., 2000).

3 MOLECULAR DOCKING

(Ferreira et al., 2015),says molecular docking is a ver-

satile computational technique for the study of biolog-

ical macromolecules, this technique studies the pro-

duction of drugs based on molecular structures where

they are simulated through numerical interactions by

algorithms, where the objective is to predict the bound

conformations and binding afﬁnity. between receptor

and ligand.

Docking can be deﬁned as a ”key-lock” problem,

which has the purpose of predicting the modes of in-

teraction between two molecules knowing only their

isolated three-dimensional structures.

Figure 7: Illustration of the ligand with its receptor

molecule.

Figure 8: Ligand-Receiver Afﬁnity Result Example.

Ligands are molecules produced by cells that in-

teract like a puzzle with its receptor as shown in Fig-

ure 7. The receptor, on the other hand, is the target

protein in which the interaction between the parts to

verify compatibility information is desired.

The practice of this coupling method is for identi-

ﬁcation and characterization of the binding sites in the

target proteins, generating evaluation values of the in-

teraction potential between the target and the ligand.

In the ﬁgure 8 There is an example of a table show-

ing the results of a simulation performed in autodock

vina, which is showing afﬁnity values.

Also according to (Ferreira et al., 2015), The soft-

ware combines two main components: search algo-

rithm and the score function, in which the algorithm

is responsible for searching for possible combinations

in the links and the score demonstrates the best bind-

ing results obtained during the procedure. The algo-

rithms allow the exploration of various angles, both

rotational, translational and conformational of the lig-

and in the target protein.

In the image 8, a result table is exempliﬁed af-

BIOINFORMATICS 2020 - 11th International Conference on Bioinformatics Models, Methods and Algorithms

202

ter the molecular dock on the software autodock

vina.This table shows the nine best ligand-to-receptor

nesting results, 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: the rmsd/lb

(lower limit of RMSD) an the rmsd/ub (upper limit

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

conformation with itself in the other conformation,

ignoring any symmetry, whereas rmsd/lb is deﬁned

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

rmsd’(c2, c1)).

4 METHODOLOGY OF WORK

The methodology has two main objectives: prediction

of conformation, and binding afﬁnity.

In the simulations developed in this work the fol-

lowing conﬁgurations were used: core i7 fourth gen-

eration, 12 GB ram, nvidia p6000 video card, HD Sdd

240 gb.

The molecules presented as ligands in this work

were extracted from PubChem, a highly diverse

database of molecules maintained by the National

Center for Biotechnology Information. The receivers

were obtained through the work of (Voss et al., 2010),

where the chikungunya virus glycoproteic structure is

available in the RCSB PDB Prontein data bank.

The experiment consists in performing the molec-

ular docking simulations using the molecules pre-

sented in the section 3, together with the target pro-

tein (CHIKV virus). From the results of these exper-

iments, we seek to investigate which ligand had the

best protein afﬁnity to stipulate a better candidate for

chikungunya inhibitor.

Importantly, before the molecular docking pro-

cess, the protein goes through a mapping phase of the

region where the software must perform the ligand-

protein docking simulations. (Vina, 2010) Called this

function AutoGrid where you can manually choose

the best ligand coupling location, this process will

avoid unnecessary processing effort as the region size

to try to couple is smaller. As shown 9The entire pro-

tein area was used, in which the parameters shown

in the image were selected for precise ligand ﬁtting

at the best protein site to obtain the best quadrant in

common with all ligands.

The coordinates, that is, the parts of the proteins

where the ligand will dock, are presented numerically

in the image. 10.

To perform the molecular docking process was

used autodock vina (performing simulations with re-

Figure 9: Grid Box Example on Chikungunya Virus

Molecule.

Figure 10: Grid Box Coordinates at Chikungunya Virus

Molecule.

ceptor and ligands), Mgltools (format conversion of

molecules) and PyMol (for visualization of results).

5 RESULTS

On the table 1, The best results of molecular docking

among ligands are shown. The results that released

the most energy (represented by the lowest value re-

sults) are the best ligand-receptor ﬁttings. (Shityakov

and F

orster, 2014) explains that the lower the value

presented more signiﬁcance it will present to the bind-

ing found, in which the afﬁnity values in the molecu-

lar docking process are favorable only when they are

negatively represented. That is, the more negative the

value obtained, the better the interaction.

According to table has suramin as a result of lower

value. And as a demonstration of your connection you

Chikungunya Virus Inhibitor Study based on Molecular Docking Experiments

203

Figure 11: Suramin + Chivk.

Table 1: Afﬁnity Table Of CHUCK Ligands.

Ligante Afﬁnity(kcal/mol)

(a) Andrographolide -7.9

(b) Epigallocatechin gallate -9.1

(d) Silibinin -9.4

(e) Suramin -12.7

have the image 11 which represents the Suramin lig-

and on the protein. In green the main target, the chiku-

gunya virus and in blue the suramin ligand circled in

red.

Another interesting factor to note is the quad-

rant where the active molecules (Andrographolide,

Epigallocatechin gallate, Harringtonine, Silibinin and

Suramin) had the highest binding value. In the ﬁg-

ure 13, the best connecting regions are presented,

in which the molecules are coupled, and from these

strands can test other possible inhibitors in the quad-

rant obtained. Since it was observed that in this quad-

rant the melecules obtained higher binding value. Af-

ter this phase, one can propose the place where the

protein will release the most energy and thus deﬁne

the guidelines for future inhibitors.

Figure 12: Best Results Coordinates.

Figure 13: Best ligand Results.

6 CONCLUSION

Computational receptor-ligand docking methodolo-

gies are very important tools in the intelligent plan-

ning of new drugs. The applied methodology al-

lowed comparing the inhibitors for chikungunya virus

through molecular docking techniques, where the in-

hibitor that obtained the best prediction results of con-

formation and binding afﬁnity in this simulation was

Suramin followed by Silibin.

The conclusion is observed in the values presented

in the table 1 que The best result of all ligands was

suramin, where it presented -12.7 protein afﬁnity, the

binding models obtained are divided into ﬁles for

multimodal visualization in three-dimensional format

to observe where it best ﬁt the protein.

As a continuation of this work consists in in vitro

simulations of the combination of these inhibitors

with other substances. It can also, as a suggestion of

future work, consider the use of other ligands in the

coordinates presented in this work, to verify an opti-

mization.

ACKNOWLEDGMENTS

The elaboration of this work would not have been

possible without the collaboration of the Engineering

and Decision Support Research Center (GECAD) of

the Institute of Engineering, Polytechnic Institute of

Porto, Portugal and FAPEMA.

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