|Year : 2020 | Volume
| Issue : 4 | Page : 330-336
In Silico modeling and docking study of potential helicase (nonstructural proteins) inhibitors of novel coronavirus 2019 (severe acute respiratory syndrome coronavirus 2)
School of Biotechnology, Gangadhar Meher University, Sambalpur, Odisha, India
|Date of Submission||07-Aug-2020|
|Date of Acceptance||30-Sep-2020|
|Date of Web Publication||30-Dec-2020|
Dr. Raghunath Satpathy
School of Biotechnology, Gangadhar Meher University, AmrutaVihar, Sambalpur - 786 004, Odisha
Source of Support: None, Conflict of Interest: None
Background: Currently, the helicase enzyme of novel severe acute respiratory syndrome coronavirus 2019 has been proposed as a potential drug target. This work envisages predicting the three dimensional (3D) structure of helicase (nonstructural protein 13) and screen for the novel inhibitor molecules. Methods: For this purpose, the sequence information of helicase enzyme was obtained from NCBI, and 3D model was predicted using I TASSER server followed by model validation. The helicase enzyme sequence was then used to search for the potential inhibitors in the Drug Bank database. The search resulted eight numbers of probable drug molecules against the receptor. To confirm the binding affinity of the drug molecules, further molecular docking study was conducted using AutoDock Vina software. Results: From the docking result, it was obtained that, among all eight numbers, only the molecule remdesivir shows more binding affinity to the nucleoside triphosphate binding site of helicase enzyme and further confirmed by analysis of amino acid interaction profile. Conclusion: In the present study, it was predicted that, the drug molecule remdesivir can be suitably used as a helicase inhibitor in case of novel severe acute respiratory syndrome coronavirus 2019.
Keywords: Binding affinity, docking, drug molecule, helicase, molecular modeling, nonstructural proteins 13, severe acute respiratory syndrome coronavirus 2, structure prediction
|How to cite this article:|
Satpathy R. In Silico modeling and docking study of potential helicase (nonstructural proteins) inhibitors of novel coronavirus 2019 (severe acute respiratory syndrome coronavirus 2). Biomed Biotechnol Res J 2020;4:330-6
|How to cite this URL:|
Satpathy R. In Silico modeling and docking study of potential helicase (nonstructural proteins) inhibitors of novel coronavirus 2019 (severe acute respiratory syndrome coronavirus 2). Biomed Biotechnol Res J [serial online] 2020 [cited 2022 Jan 27];4:330-6. Available from: https://www.bmbtrj.org/text.asp?2020/4/4/330/305635
| Introduction|| |
In December 2019, some of citizens of Wuhan city in China suffered from respiratory disease by the novel coronavirus (CoV) (severe acute respiratory syndrome CoV-2 [SARS-CoV-2]) infection. Soon after, this became the center of origin of the novel CoV disease with the acronym COVID-19, designated by the World Health Organization (WHO). Afterward, the disease outbreak continues to spread quickly across the globe in a very short period. Due to its severe infection rate, on January 30, 2020, the WHO declared COVID-19 as the public health emergency of international concern followed by a worldwide pandemic declaration on March 11, 2020. As of May 5, 2020, it has spread to 215 countries. The major cause of SARS-CoV -2 infection is due to the contact between person-to-person and that leads to transmission of the disease.,,,, The novel coronavirus is spherical and enveloped in nature and exhibit variation in shape and size with a diameter range from 60 to 140 nm. This contains a positive (+)-sense, single-stranded RNA genome of a ~26 kilobase size. As the part of basic genomic feature, this consists of a single-stranded RNA that contains multiple open reading frames (ORFs) that encodes for 16 nonstructural proteins (Nsps) and four structural proteins such as spike (S), envelope (E), membrane (M), and nucleocapsid (N). About two-third of genomic RNA is located in the first ORF (ORF1a/b) that helps in the translation of two polyproteins (PPs), viz., pp1a and pp1ab at the 5' end. Further, the subsequent proteolytic cleavages of PPs generate 16 Nsp. After processing of PP by protease, it produces 16 NSPs. Out of this, the NSP13 shows helicase activity, that is, having an important role in replication and transcription.,,
Currently, there is no licensed drug or vaccine available for SARS-CoV-2, although several clinical trials are in progress to test for the possible therapies., The recent research confirmed about the Nsp 13 helicase enzyme of the virus can be used as major target for which novel drug molecule can be designed. This enzyme prefers adenosine triphosphate (ATP), deoxyadenosine triphosphate, and deoxycytidine triphosphate as substrates; it also hydrolyzed all nucleoside triphosphates (NTPs).,,, The mechanism of action of helicase (Nsp 13) enzyme of SARS-CoV catalyzes the NTP-dependent unwinding reaction from 5' 3' direction that coverts the duplex RNA into single strands. Furthermore, it has been reported from extensive research that many of the small molecular weight compounds have the potential to inhibit the ATP-binding site followed by NTPase activity. In addition to this, helicase enzyme (Nsp13) has been seen to be conserved among other related viral strains. Therefore, this enzyme can be treated as a promising target for the development of new therapeutic molecules that will act against SARS-CoV-2 infection.,,,
No low resolution (<2 angstrom level) crystallographic structure structural data are currently available for SARS-CoV-2 helicase (Nsp 13) proteins in the Protein Databank (PDB) at the beginning of this study. Therefore, computer-based three-dimensional (3D) structure prediction can be the alternative sophisticated method for getting insight into the functional aspects of the molecule. In addition to this, molecular docking technique can be potentially used to predict the binding affinity and binding confirmation (pose) of the ligand molecule to the receptor.,,
| Materials and methods|| |
For the present work, the protein sequence of the SARS-CoV-2 helicase was obtained from NCBI server (https://www.ncbi.nlm.nih.gov/protein/YP_009725308.1). The 3D structure was predicted using the ITASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). I TASSER is a reliable automatic protein structure prediction server that works on the principle of protein threading, in which the structure of the template from the PDB is first recognized and multiple threading process is executed. Then, the 3D structural models are constructed by the assembly of fragments in an iterative manner followed by knowledge-based functional annotation. The modeled 3D structure obtained was further evaluated using the SAVES server (https://servicesn.mbi.ucla.edu/SAVES/). This server contains a number of structure evaluating tools used for the validation purpose. Then, the functional domain of the helicase protein was computed from the Prosite scan server (https://prosite.expasy.org/). To obtain the probable drug molecules for the target helicase, the amino acid sequence of the enzyme was searched in the DrugBank (https://www.drugbank.ca/structures/search/bonds/sequence) using the default search parameter. Further, the molecular docking was performed using the predicted 3D model of SARS-CoV2 helicase along with the molecules obtained from the DrugBank search. The molecular docking software tool such as AutoDock Vina was used. AutoDock Vina is a user-friendly, freely available docking tool. During the course of development of AutoDock Vina, a number of stochastic approaches have been used including genetic algorithms, particle swarm optimization, and simulated annealing followed by various local optimization procedures.,
| Results and Discussion|| |
Molecular modeling study
To predict the 3D structure of the helicase, the following amino acid sequences of length 601 amino acids were retrieved from the NCBI server as described in the above section.
>YP_009725308.1 helicase [severe acute respiratory syndrome coronavirus 2]
AV G A C V L C N S Q T S L R C G A C I R R P F L
C C K C C Y D H V I S T S H K L V L S V N P Y V
C N A P G C D V T D V T Q L Y L G G M S Y Y C
K S H K P P I S F P L C A N G Q V F G LY K N T C
V G S D N V T D F N A I AT C D W T N A G D Y I L
A N T C T E R L K L F A A E T L K AT E E T F K L
S Y G I A T V R E V L S D R E L H L S W E V G K
P R P P L N R N Y V F T G Y RV T K N S K V Q I G
E Y T F E K G D Y G D AV V Y R G T T T Y K L N
V G D Y F V LT S H T V M P L S A P T LV P Q E H
Y V R I T G LY P T L N I S D E F S S N VA N Y Q K
V G M Q K Y S T L Q G P P G T G K S H F A I G L A
LY Y P S A R I V Y T A C S H A AV D A L C E K A
L K Y L P I D K C S R I I PA R A RV E C F D K F K
V N S T L E Q Y V F C T V N A L P E T T A D I V V
F D E I S M AT N Y D L S V V N A R L R A K H Y
V Y I G D P A Q L P A P R T L L T K G T L E P E Y
F N S V C R L M K T I G P D M F L G T C R R C P
A E I V D T V S A LV Y D N K L K A H K D K S A Q
C F K M F Y K G V I T H D V S S A I N R P Q I G V V
R E F LT R N PAW R K AV F I S P Y N S Q N AVA
S K I L G L P T Q T V D S S Q G S E Y D Y V I F T Q T T
E TA H S C N V N R F N VA I T R A K V G I L C I M S
Further, the functional domains of the protein were predicted by feeding the sequence information to the Prosite server and it identified 2 major domains. The first domain was coronaviridae zinc-binding (CV_ZBD) domain that ranges the amino acid position from 1 to 84. Similarly, the other major domain was obtained as PSRV_HELICASE (+) RNA virus helicase core domain. The helicase domain ranges from 257 to 601 amino acids and contains three basic features as predicted by Prosite. The amino acid from 257 to 438 contains (+) RNA virus helicase ATP-binding regions, 282–289 is NTP-binding region, and 439–601 is the (+) RNA virus helicase C-terminal region [Figure 1].
|Figure 1: The three-dimensional model of helicase (nonstructural proteins 13) protein with predicted functional binding site and domains (shown in color codes)|
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Computational mode of modeling of 3D structure provides a basic way to investigate the structural features and domain details. In this study, the ITASSER server is used for 3D structure prediction was observed as an reliable and valuable tool and the same been used by many researchers.,, After structure prediction, it is essential to validate the model. In this work, the helicase (Nsp 13) model was validated by three different methods. The Ramachandran plot generated by PROCHECK program indicates the distribution of ϕ and ψ angle as well as the distribution of non-glycine, non-proline residues present in the 3D structure. In this case, altogether 99.4% of the residues were in favored and allowed regions were obtained [Figure 2]. Similarly, the verify 3D server computes the compatibility of an 3D atomic model with respects to its amino acid sequence based on the distribution of the amino acid with respect to its location and environment. In this case, the average score above the threshold shows its reliability [Figure 3]. The structure validation tool ERRAT computes the non-bonded interactions between different atom types and plots the value in the form of the error function versus position. In this case, the overall quality factor was obtained as 94.407, which indicates the reliability of the structure [Figure 4].
|Figure 2: Ramachandran plot of the model details obtained from PROCHECK program|
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Searching of drug molecule from DrugBank
The amino acid sequence information of the selected SARS -CoV-2 helicase enzyme was used for searching in the DrugBank (described in the previous section).While searching for the potential drug molecule the default search parameters was used and it resulted eight potential drug candidate shown in [Table 1] and [Figure 5].
|Table 1: Drug molecules obtained from the DrugBank by target searching option|
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|Figure 5: Structure of the selected drug molecules obtained from the DrugBank search with their IUPAC name|
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Molecular docking study
As described in the Materials and Methods section, the molecular docking study was performed by taking two different docking software and the results are represented in [Table 2]. For the molecular docking purpose, energy minimized receptor molecule and crystallographic structures of the ligands were used, except for the molecule Gs-441524. The molecule Gs-441524 was drawn and energy minimized by Marvin Sketch tool (https://chemaxon.com/products/marvin). AutoDock Vina is an important and popular docking platform that was found reliable by many researchers., During the docking experiment by AutoDock Vina, Gasteiger charge is added to the ligand molecules and polar hydrogens were added to the receptor molecule. The grid box was created around the helicase enzyme model with maximum dimension for the better binding option for the ligands. The exhaustiveness was set as 8.
|Table 2: Docking result of selected drug molecules and Nsp 13 helicase model|
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After the docking simulation is over, the amino acids of the receptor molecule that interact with the ligand remdesivir was analyzed. It was observed that majority of the amino acid residues interacts to the ligand falls within the PSRV_HELICASE core domain as described in the above section [Figure 6] and [Figure 7].
|Figure 6: Binding poses of the ligands [as per the serial number of Table 2] and the nucleoside triphosphate.binding sites (highlighted in red)|
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|Figure 7: The ligand remdesivir (green stick) binding residues (amino acids residues number from 284 to 289 are par t of nucleoside triphosphate binding site)|
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Helicase (Nsp 13) in case of the SARS-CoV-2 has been used as a potential target for the development of novel drug molecule against this deadliest disease. Moreover, the basis of selecting as the target is the mechanism of action of the enzyme as well as the pathogenesis. In addition to that, the Middle East respiratory syndrome novel CoV helicase enzymes also share some degree of homology to Sars CoV-2 helicase enzymes. Therefore some of the researchers assume that, the inhibitor of the former enzyme might be effective against the later one., General strategies to design specific and selective drugs for the treatment of viral infections targeting helicase could be by the inhibition of NTPase activity. As the inhibitor would interfere with ATP binding, it will limit the energy required for the unwinding reaction. Furthermore, the mechanism of the drug molecule binds to the viral helicase has been described by Tanner et al. in 2005 and Habtemariam et al. in 2020., As per their report, the helicase (Nsp13) activity can be inhibited by targeting the suitable drug molecules that bind to ATP-binding thereby inhibiting the NTPase activity. So ultimately this lead to blocking of the virus replication. The effective helicase inhibitors as proposed by them are molecules like benzotriazole, imidazole, imidazodiazepine, phenothiazine, quinoline, anthracycline, triphenylmethane, tropolone, pyrrole, acridone, small peptide, and bananin derivatives. Khater and Das proposed the novel viral replication inhibitor ivermectin can be used effectively against SARS-CoV-2, based on the fact that the helicase domain shares homology to other viruses. Similarly, the research by Balasubramaniam and Reis has shown that the drug molecule elbasvir can be used as the inhibitor for both helicase and proteases. Chen et al. solved the cryo-electron microscopic structure of SARS-CoV-2 and suggested about both the molecules helicases (Nsp13) and proteases are essential for virus replication and hence can be used as the potential target. Borgio et al. proposed about the two approved drug molecules, viz., vapreotide and atazanavir, as a potential helicase inhibitor by molecular docking analysis, that are used to treat HIV-related diarrhea and HIV infection, respectively. One of the recent reports also states about the bismuth salt can be effectively used to inhibit both the NTPase and RNA helicase activities in case of SARS-CoV-2. Similarly, the molecular docking simulation study by Rowaiye et al. stated about the binding affinity of triphenylmethane toward the helicase enzyme of SARS-CoV-2. The recent experimental binding study of 4-(dimethylamino) benzoic acid suggests about its effective protease inhibitory action. Similar computational research work that involves 3D structure prediction followed by docking methods has been successfully developed and implemented by many researchers for discovery of potential inhibitors against other diseases. Recently, many advance version of computational prediction methods are being used for secondary and tertiary structure of proteins lead to prediction of the function ., Taira et al. in 2017 used docking method for the screening and prediction of some potential molecules as antituberculosis agents. Dhorajiwala et al. in 2019 implemented in silico docking study in some phytochemicals of Salix subserrata and onion to evaluate their potential against anti-rabies viral disease. In the present case, the considered drug molecule remdesivir was initially developed for the treatment of hepatitis C virus and then it was also tested against Ebola Marburg viral disease, however proved to be ineffective in its action. Recently, this drug molecule has shown its antiviral activity against SARS-CoV-2 and has been authorized for emergency use in many countries. To avoid possible side effects of remdesivir, many types of derivatives of this molecule are being tested for its effectiveness.,, The mechanism of action of the remdesivir drug molecule resides in its structure. Since the structure comprises of a nucleotide (adenosine) analog, hence it is capable itself for incorporation into the newly synthesized viral RNA and ultimately causes the premature replication termination., Hence, in this work, the effectiveness of the drug molecule remdesivir has been exploited for its use as a suitable inhibitor of helicase enzyme activity and the binding potential was quantified by molecular docking. Some of the molecules selected for the study show high affinity in comparison to remdesivir. However, considering the binding position (pose) of the molecules nearby the NTP binding site of the helicase enzyme, confirms the functional aspects of remdisivir only [Table 2] and [Figure 6]. Subsequently, performance of the molecular docking is necessary between several derivatives of remdesivir drug with the helicase enzyme in order to identify suitable effective remdesivir derivatives. Further molecular dynamics simulation can be performed to analyze the dynamic behavior of the amino acid residues involve in the inhibition mechanism of the enzyme.
| Conclusion|| |
The pandemic caused by SARS-CoV-2 and observing its severity and increasing trend in the infection rate creates an alarming situation in terms of global health emergency in recent times. However, no such effective vaccine or licensed drug molecule is available to treat these pathogenic coronaviruses. Therefore, the discovery and development of novel antiviral drug molecules in an urgent priority is desirable. In this work, the 3D protein structure of the target enzyme helicase was predicted and the reliability of the structure was checked. All total of eight possible numbers of drug molecules were obtained from DrugBank target search option by taking the helicase enzyme sequence. The binding site was predicted by performing molecular docking process using AutoDock Vina software. It was observed that the molecule remdesivir shows the binding affinity to the SARS-CoV-2 helicase (Nsp13) protein at its NTP-binding residues. The molecule remdesivir was previously reported as the effective virus replication inhibitor and this study also supports the fact by quantifying its helicase inhibitory potential. As a continuation to this study, molecular docking simulation of the helicase enzyme with several derivatives of remdesivir is necessary to screen for a suitable molecule. In addition to this, molecular dynamics simulation can be performed to understand more about the affinity and dynamic behavior the amino acids interact with the drug molecule. However, this is a computational prediction; hence, experimental validation is necessary to confirm the result.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2]