Epitope-based Vaccine Design from Alpha and Beta Variant of
SARS-CoV-2: An Immunoinformatics Approach
Hendyco Pratama1, Nur Imaniati Sumantri1*, Siti Fauziyah Rahman1, Viol Dhea Kharisma2,3,
Arif Nur Muhammad Ansori4,5,6
1Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia.
2Computational Virology Research Unit, Division of Molecular Biology and Genetics,
Generasi Biologi Indonesia Foundation, Gresik, Indonesia.
3Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia.
4Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia.
5European Virus Bioinformatics Center, Jena, Germany.
6Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Dehradun, India.
*Corresponding Author E-mail: nur.imaniati@ui.ac.id
ABSTRACT:
Coronavirus disease 2019, also known as COVID-19, is a respiratory disease. Symptoms of COVID-19 include fever, dry cough, inflammation of the throat area, loss of smell, and even breathing difficulty. COVID-19 is caused by SARS-CoV-2 infection, a virus that is a member of the coronavirus family. The SARS-CoV-2 structure consists of S (spike), M (membrane), E (envelope), and N (nucleocapsid) protein. Two SARS-CoV-2 variants, namely alpha (B.1.1.7) and beta (B.1.351) variants are considered a variant of concern (VoC) due to their increased infectivity. It has been reported that the vaccine's efficacy against these two variants decreased. The purpose of this study is to compare epitopes from S and N proteins of alpha and beta variants to find the most suitable vaccine candidate through reverse vaccinology. In this study, physicochemical properties, antigenicity, and epitope prediction, as well as molecular docking of the epitope and B cell receptor, 5IFH, were done. The result suggested that the epitope from S protein was more suitable as a vaccine candidate. S protein epitope has a lower global energy value which means that it can bind to 5IFH more spontaneously compared to N protein epitopes. The most suitable vaccine candidate for the alpha variant is Pep_B, with a global energy value of -48.77 kcal/mol, and Pep_F, for the beta variant, with a global energy value of -61.61 kcal/mol. These results would recommend the epitopes to be used in further COVID-19 vaccine development.
KEYWORDS: COVID-19, S Protein, N Protein, immunoinformatic, reverse vaccinology.
INTRODUCTION:
Coronavirus disease 2019, also known as COVID-19, is a human respiratory disease caused by SARS-CoV-2 infection1. SARS-CoV-2 was first discovered in Wuhan, China, in December 20192,3. The first case of COVID-19 outside of China was reported on 13 January 2020 in Thailand. As of now, COVID-19 has spread globally and reached a total of more than 4 million reported cases, with more than one hundred thousand deaths.
SARS-CoV-2 is a species of coronavirus which belongs to the genus betacoronavirus4,5. Multiple studies have reported that this virus has a similarity with SARS-CoV, the virus responsible for the severe acute respiratory syndrome (SARS) in China, 2002, and MERS-CoV, the virus responsible for Middle East Respiratory Syndrome (MERS) back in 2012 at Middle East countries6. SARS-CoV-2 also has a similar structure as SARS-CoV and MERS-CoV. It consists of four main structures, the spike or S protein, the nucleocapsid or N protein, the envelope or E protein, and the membrane or M protein5,6.
The S protein is mainly responsible for viral entry. The S1 subunit of the S protein contains the receptor binding site (RBD). The binding of the RBD with angiotensin-converting enzyme 2, ACE2, marks the start of the viral entry7. Furthermore, most of the mutations that SARS-CoV-2 underwent occurs in the S protein. Meanwhile, the N protein is considered more stable and conserved compared to the other protein. The amino acid of the N protein has a homology of about 90% and underwent fewer mutations than the S protein8.
Multiple variants of SARS-CoV-2 have been reported, and World Health Organization (WHO) categorized said variants into a variant of interest (VoI), a variant of concern (VoC), and a variant of high consequences (VOHC). The most dangerous among these variants are the VoHC, and the least dangerous is VoI, but there has not been any variant considered as VoHC. Meanwhile, two of the variants belonging to VoC are alpha and beta variants9. These two variants are characterized by their increased transmissibility, more dangerous symptoms, and their abilities to evade the immune response. Vaccines' efficacy and efficiency have also been reported to decrease up to 30% against these two variants10.
Studies regarding SARS-CoV-2 vaccine design through in silico method have been conducted multiple times, but most of them use the wild-type isolates of SARS-CoV-2. Enayatkahni et al. conducted a multi-epitope vaccine design derived from SARS-CoV-2 N protein, M (membrane) protein, and ORF3a (open reading frame)11. Jena et al. also conducted a study on a multi-epitope vaccine design derived from SARS-CoV-2 S, M, and N protein12. Gupta et al. designed a vaccine from SARS-CoV-2 S protein and reported that the vaccine could induce a high cell-mediated immune response13,. Sarkar et al. designed a multi-epitope vaccine that was reported to be effective against SARS-COV-2 infection from the S, M, and N proteins as well as ORF3a14. Studies regarding a variant-specific vaccine are very limited.
Due to these reasons, this research was conducted to design an epitope-based vaccine for alpha and beta variants of SARS-CoV-2 through in silico method using an immunoinformatic approach. The vaccine candidates predicted in this study were derived from the S (spike) and N (nucleocapsid) protein of SARS-CoV-2. The S protein was chosen because of its nature of this protein which is known as the very first part of SARS-CoV-2 to bind with angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 receptor in the human body14,15. Not only that, but most of the mutation also found in these two variants occurs in the S protein16. On the other hand, N protein was chosen because of its high antigenicity as well as its ability to induce a strong and long-lasting immune response17.
MATERIALS AND METHODS:
Phylogenetic Analysis:
Phylogenetic analysis for the alpha and beta variants of Indonesian SARS-CoV-2 isolate, EPI ISL 6827364 and EPI ISL 3138807, was conducted using MEGA X software18. The phylogenetic analysis was done between isolates' proteins with the other 50 isolates acquired from the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov) database.
Protein Sequence Acquisition:
The S and N protein of each variant was acquired through NCBI database19. The accession code for the alpha variant S protein is QWA53375.2, alpha variant N protein is QYC97632.1, beta variant S protein is QWW93436.1, and beta variant N protein is QWW93444.1.
3D Modelling of the Protein:
Models of the proteins were acquired with SWISS-MODEL20. The basic parameter settings are used to model the protein.
Antigenicity and Physicochemical Properties Prediction:
The antigenicity of all proteins was predicted using Vaxijen v2.0 with its default parameter21. Physicochemical properties of each protein were predicted using ExPasy's online tool ProtParam22. The physicochemical properties predicted include the number of amino acids, molecular weight, theoretical pI, instability index, aliphatic index, and Grand Average of Hydropathicity (GRAVY).
B Cell Epitope Prediction:
The epitope of each protein was predicted using the IEDB webserver (http://tools.iedb.org/) with the Bepipred linear epitope prediction 2.0 method23.
Antigenicity, Allergenicity, and Toxicity Prediction of Epitopes:
After acquiring the epitopes of each protein from IEDB, their antigenicity, allergenicity, and toxicity were analyzed. Antigenicity of the epitopes was predicted with Vaxijen v2.0, the allergenicity of the epitopes was predicted with AllerTOP v2.024, and toxicity with ToxinPred25. The epitopes that will be continued to the next step are the ones that are antigenic, non-allergenic, and non-toxic.
Molecular Docking:
The epitopes selected in the previous step were used for molecular docking. The molecular docking was done between the B cell receptor and the epitopes. First, the epitopes were modeled with PEP-FOLD325, and then the B cell receptor (5IFH)26,27 were acquired from RCSB PDB. The web servers Patchdock28,29 and Firedock30,31 were used for the molecular docking process and visualization was performed using PyMol v2.4.032.
RESULTS AND DISCUSSION:
In this study, the genetic variation of S and N protein of SARS-CoV-2 Alpha and Beta were investigated through phylogenetic analysis. The phylogenetic analysis shows the evolutionary relationship of the isolates depicted with the branches formed. 50 unique isolates from all around the world, including the UK, USA, Italy, Kenya, Philippines, Hongkong, Saudi Arabia, New Zealand, and Germany, are involved in each phylogenetic analysis. As the virus spreads from individual to individual, it undergoes a series of mutations in viral genes that encodes ORF1ab, ORF3a, N, and S proteins. Among these mutations, the mutations that occur in the S protein are considered important since the S protein of SARS-CoV-2 plays a crucial role in the first step of viral mutations33.
Figure 1 shows the phylogenetic tree for the SARS-CoV-2 Alpha variant for both the S and N protein isolates acquired in Indonesia. The figure compares the similarity between the Indonesian isolates to other sequences gathered from NCBI. As the figure suggests, the S protein of the isolate is close to the isolates gathered from the United Kingdom, United States of America, Italy, and Kenya. But out of all isolates, the S protein of the Indonesian isolate, EPI ISL 6827364, has a striking resemblance with the isolate OU228723.1, which was gathered from England, United Kingdom. On the other hand, the N protein of said isolate has a closer resemblance to isolate OU155876.1, which was gathered from Germany.
Figure 1. Phylogenetic tree of S (left) and N (right) protein of SARS-CoV-2 Alpha variant Indonesian isolates aligned with 50 other isolates.
Figure 2 shows the phylogenetic tree for the SARS-CoV-2 Beta variant for both the S and N protein isolates acquired in Indonesia. As the figure suggests, the S protein of the isolate is close to the isolates gathered from the United Kingdom, United States of America, Germany, Saudi Arabia, Philippines, New Zealand, and Hongkong. But out of all isolates, the S protein of Indonesian isolate, EPI ISL 3138807, has a striking resemblance with the isolate OK104999.1, which was gathered from Texas, United States of America, and the N protein has a closer resemblance to isolate OK006997.1 which was gathered from Georgia, United States of America.
Figure 2. Phylogenetic tree of S (left) and N (right) protein of SARS-CoV-2 Beta variant Indonesian isolates aligned with 50 other isolates.
The sequences gathered from NCBI have their own respective accession code. The accession code for S protein of alpha variant is QWA53375.2, N protein of alpha variant is QYC97632.1, S protein of beta variant is QWW93436.1, and N protein of beta variant is QWW93444.1. These four proteins will be modeled with SWISS-MODEL. Figure 3 shows the model acquired for each protein.
Figure 3. 3D model of (A) S protein of alpha variant, (B) N protein of beta variant, (C) S protein of alpha variant, and (D) N protein of beta variant.
The phylogenetic analysis could show the migratory histories, founder events, and sample size34,35,36. According to the research, the isolates collected in Indonesia are largely comparable to those collected in the United States of America and the United Kingdom. The SARS-CoV-2 Alpha variant was initially reported in the United Kingdom in November 202037,34. The close resemblance of the S protein of EPI ISL 6827364 with OU228723.1 suggests that the S protein of the Indonesian isolates underwent similar mutations to the isolate from the United Kingdom or that EPI ISL 6827364 originated from the United Kingdom. The N protein of said isolates is closer to OU155876.1 from Germany, but nonetheless, the alignment shows that it still closely resembles OD994453.1 from the United Kingdom. Hence, it is still probable that the isolates in Indonesia originated from the United Kingdom.
The phylogenetic analysis for EPI ISL 3138807, Indonesian isolates of SARS-CoV-2 Beta variant, shows that it is mostly similar to the isolates from the United States of America and the United Kingdom. Both the S and N proteins have a strong similarity with the isolates gathered from the United States of America, OK104999.1 and OK006997.1, respectively. On the contrary, the first reported beta variant was from South Africa37,34. This might be caused by the submitted samples from the United States of America, and the United Kingdom outnumbered the samples from South Africa. The other possibility is that after a series of migrations, the isolates from Indonesia might undergo mutations closer to the beta variant found in the USA rather than South Africa.
The antigenicity and physicochemical properties of all proteins were predicted. Table I shows the results of the predictions.
Table 1 Physicochemical Properties and Antigenicity Prediction of all Protein
|
|
Alpha Variant |
Beta Variant |
||
|
|
S Protein QWA53375.2 |
N Protein QYC97632.1 |
S Protein QWW93436.1 |
N Protein QWW93444.1 |
|
Number of Amino Acid (aa) |
1,270 |
419 |
1,270 |
419 |
|
Molecular Weight (Da) |
140,872.20 |
45,695.92 |
140,817.12 |
45,637.75 |
|
Theoretical pI |
6.35 |
10.09 |
6.64 |
10.07 |
|
Instability Index |
32.82 |
53.80 |
33.19 |
55.09 |
|
Aliphatic Index |
84.95 |
53.46 |
84.10 |
53.46 |
|
Grand Average of Hydropathicity (GRAVY) |
-0.074 |
-0.947 |
-0.075 |
-0.959 |
|
Antigenicity |
0.4742 |
0.4882 |
0.4657 |
0.5074 |
The value of the alpha and beta variants does not have a big difference in all parameters. An instability index below 40 determines the protein's stability, while more than 40 means it is unstable35,36. A negative value of GRAVY indicates that the protein is hydrophilic, and a positive value indicates hydrophobicity36,38. Antigenicity above 0,4 indicates antigenic properties. Based on the prediction, the S protein of both variants is stable, hydrophilic, and antigenic, while the N protein of both variants is unstable, hydrophilic, and antigenic.
From the physicochemical properties, S protein from both variants has 1,270 amino acids, and N protein in both variants has 419 amino acids. The molecular weight of both proteins from both variants also has a similar value. The molecular weight of the S protein of the alpha variant is 140,872.20 Da, and the beta variant is 140,817.12 Da. Meanwhile, the molecular weight of the N protein of the alpha variant is 45,695.92 Da, and the N protein of the beta variant is 45,637.75 Da. The molecular weight is proportional to the number of amino acids present in the proteins and could be predicted from the amino acid inside the proteins38. The mutation caused a slight change in the amino acid inside the proteins, which could cause a small difference in the molecular weight value.
Theoretical pI or isoelectric point shows a pH where the protein has a zero net charge39. When the protein is in a solution with pH below its pI, the protein will have a negative net charge. The same is applied when a protein is in a solution with pH above its pI, and the protein will have a positive net charge. A pI above 7 shows that the protein is basic and below 7 shows that the protein is acidic40. The isoelectric point of both proteins from both variants shows that S proteins were acidic and N proteins were basic, with the value of 6.35 for S protein of alpha variant, 6.64 for S protein of beta variant, 10.09 for N protein of alpha variant, and 10.07 for N protein of beta variant. The isoelectric point also indicates that S protein will precipitate in an acidic solution and N protein will precipitate in a basic solution41,42.
The instability index indicates the stability of a protein. The more stable a protein is, the harder they become denatured. The S protein of both variants is stable, while the N protein of both variants is unstable. The aliphatic index shows the relative volume of proteins occupied by the aliphatic amino acid chain, including alanine, isoleucine, leucine, proline, and valine. The higher the aliphatic index means that the protein is more hydrophobic and thermally stable41. The S protein of both variants has a higher aliphatic index value compared to the N protein. This indicates that S proteins are more hydrophobic and thermally stable than N proteins.
The grand average of hydropathicity (GRAVY) shows whether a protein is hydrophobic or hydrophilic. It is calculated based on the hydropathy value of the amino acid divided length of the sequence36,37. All proteins, S protein of both variants and N proteins of both variants, are hydrophilic since they all have a negative value of GRAVY. But the N proteins are more hydrophilic than the S proteins. Antigenicity shows how reactive the epitope is to the antibody molecule. It shows whether the epitope will be recognized as an antigen or not by the immune system43. All proteins, S proteins of both variants, and N proteins of both variants are antigenic, although N proteins are more antigenic than S proteins.
The B Cell epitope of all proteins will be predicted with IEDB. Figure 4 shows the number of epitopes that each protein has. S protein of alpha variant has 35 epitopes, N protein of alpha variants has 11 epitopes, S protein of beta variant has 36 epitopes, and N protein of beta variant has 11 epitopes. The yellow part of the graphs in Figure 4 indicates the part of the protein that is an epitope, while the green part indicates the part of the protein that is not an epitope.
Figure 4. IEDB epitope prediction for (A) S protein of alpha variant, (B) N protein of beta variant, (C) S protein of beta variant, and (D) N protein of beta variant.
The antigenicity, allergenicity, and toxicity of the epitopes acquired from the step before will be predicted for a further selection of the epitopes. Table II shows the remaining epitopes after the selection. After the selection, each protein had only two epitopes left and was named Pep_A to Pep_H.
Table 2: Physicochemical Properties and Antigenicity of Selected Epitopes
|
Antigenicity |
Allergenicity |
Toxicity |
Label |
|
|
Alpha Variant |
||||
|
S Protein |
||||
|
TPINLVRDLPQGFSA |
Antigen |
Non-allergen |
Non-toxic |
Pep_A |
|
LTPGDSSSGWTA |
Antigen |
Non-allergen |
Non-toxic |
Pep_B |
|
N Protein |
||||
|
QHGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS |
Antigen |
Non-allergen |
Non-toxic |
Pep_C |
|
DAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDD |
Antigen |
Non-allergen |
Non-toxic |
Pep_D |
|
Beta Variant |
||||
|
S Protein |
|
|
|
|
|
YLTPGDSSSGWTA |
Antigen |
Non-allergen |
Non-toxic |
Pep_E |
|
LPDPSKPSKRS |
Antigen |
Non-allergen |
Non-toxic |
Pep_F |
|
N Protein |
|
|
|
|
|
HGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS |
Antigen |
Non-allergen |
Non-toxic |
Pep_G |
|
DAYKTFPPTEPKKDKKKKADETQALPQRQKKQQTVTLLPAADLDD |
Antigen |
Non-allergen |
Non-toxic |
Pep_H |
Molecular docking was done between Pep_A to Pep_H with a model of an antigen-binding fragment of B cell receptor acquired from RCSB PDB, 5IFH. Fig. 5. shows where each of the epitopes from alpha variant bonds with 5IFH. While Fig. 6. shows the bond of the epitopes from beta variants. Pep_A formed six bonds with 5IFH (SER-153, ILE-154, SER-168, GLN-170, SER-174, and GLN-207), and Pep_B formed seven bonds (with LYS-105, THR-111, THR-166, PRO-167, LYS-169, SER-174, and TYR-181), Pep_C formed two bonds (VAL-149 and THR-164), Pep_E formed three bonds (ASP-152, SER-153, and THR-166), Pep_F formed eight bonds (GLY-40, ASP-84, THR-111, ASP-152, SER-153, GLN-170, PHE-172, and SER-174), Pep_G formed four bonds (SER-25, GLN-41, GLN-108, and SER-171), and Pep_D, as well as Pep_H, formed no bond at all.
Figure 5. A bond formed between (A) Pep_A, (B) Pep_B, (C) Pep_C, and (D) Pep_D with 5IFH.
Figure 6: A bond formed between (A) Pep_E, (B) Pep_F, (C) Pep_G, and (D) Pep_H with 5IFH.
The global energy of each pair is calculated using the firedock webserver. Table III shows the global energy of each epitope. Global energy value shows Gibbs free energy, where it is preferable to have a low value42,43,44. Negative Gibbs free energy, which indicates that a process is exogenic and does not require external energy to occur, is in accordance with the thermodynamic law. In other words, negative Gibbs free energy indicates that a reaction could happen spontaneously45,46,47,48,49,50,51,52. The molecular docking result shows that the epitope acquired the lowest global energy from the S proteins, namely Pep_B with -48.77 kcal/mol for the alpha variant and Pep_F with -61.61 kcal/mol for the beta variant. Overall, the global energy from the beta variant has lower global energy. This means that the beta variant is more likely to bind with the B cell receptor.
Table 3 Global energy value of all epitopes.
|
Global Energy (kcal/mol) |
|
|
Varian alpha |
|
|
Protein S |
|
|
Pep_A |
-27.28 |
|
Pep_B |
-48.77 |
|
Protein N |
|
|
Pep_C |
-11.27 |
|
Pep_D |
-7.38 |
|
Varian beta |
|
|
Protein S |
|
|
Pep_E |
-39.62 |
|
Pep_F |
-61.61 |
|
Protein N |
|
|
Pep_G |
-30.74 |
|
Pep_H |
-25.43 |
Based on the physicochemical properties, both S and N proteins from both variants are hydrophilic and antigenic. But N protein is more hydrophilic and antigenic compared to S proteins. Since N proteins are more hydrophilic, it means that N proteins are more soluble than S proteins. Studies have shown that a soluble antigen has an advantage in inducing immune response45,46,47. A higher number of immune complexes (ICs) as well as IgG have been reported to be formed in response to soluble antigens. Not only that, but N proteins also have a higher antigenicity than S proteins which mean N protein could induce immune response easily compared to S proteins. Hence, from the physicochemical properties, N proteins are more suitable to be made as a vaccine candidate.
Some studies have shown that the N protein of SARS-CoV-2 is a vaccine candidate with a lot of potentials. N protein has highly immunogenic traits, conserved sequence, as well as expressed a lot in the infection period47,48. A large number of N protein-specific IgG has also been reported in SARS-CoV-2 patients48,49. A study on multi-epitope vaccine design with SARS-COV-2 N protein shows that the vaccine developed could elicit humoral and cell-mediated immune response49,53,54,55,56.
Although the physicochemical properties indicate that the N protein is a better vaccine candidate due to its more antigenic and hydrophilic traits, the difference between S and N proteins is insignificant. Furthermore, the molecular docking results show that the epitopes from S protein interact more with B cell receptors than those from N protein. The global energy of the S protein epitopes is also lower than the N protein epitopes. This indicates that S protein epitopes could better bind with B cell receptors than N protein epitopes. Hence B cell will also be activated, and immunity will be formed faster.
A study reported that most vaccines use part of the SARS-CoV-2 S proteins. Pfizer and Moderna use mRNA of SARS-CoV-2 S protein, and AstraZeneca uses a whole SARS-CoV-2 S protein with adenovirus vector ChAdOx150,51. It is also reported in previous studies on SARS-CoV that N protein as a vaccine target could not provide immunity and even caused a higher pneumonia rate.
CONCLUSION:
All in all, the research conducted shows that alpha and beta variant does not have that much of a difference in terms of their physicochemical properties, but molecular docking result shows that the beta variant could bind better with B cell receptor. N protein, although not much, is more antigenic and hydrophilic than S protein in both variants. Despite that, the S protein epitope could bind better with B cell receptors than N protein epitopes, indicated by the difference in global energy value where S protein epitopes have lower global energy. Hence, the most suitable candidate for vaccine design is Pep_B (LTPGDSSSGWTA) for the alpha variant and Pep_D (LPDPSKPSKRS) for the beta variant.
ACKNOWLEDGEMENT:
We gratefully acknowledge the funding from Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, Republic of Indonesia through Penelitian Dasar Unggulan Perguruan Tinggi 2022 No. NKB-846/UN2.RST/HKP.05.00/2022.
CONFLICT OF INTEREST:
The authors declare no conflict of interest.
REFERENCES:
1. Utami AT, Budiarti RPN. Honey as Miracle Therapy for Covid-19: Literature Study. Bali Medical Journal. 2022; 11(3): 1207–1211. DOI: 10.15562/bmj.v11i3.3542
2. Lee KW, Gew LT, Siau CS, Peh SC, Chia YC, Yacob S, Chan NN, Seow VK, Ooi PB. COVID-19 vaccine hesitancy and its associated factors in Malaysia. PLoS One. 2022; 17(9): e0266925. doi: 10.1371/journal.pone.0266925.
3. Ansori ANM, Kharisma VD, Fadholly A, Tacharina MR, Antonius Y, Parikesit AA. Severe Acute Respiratory Syndrome Coronavirus-2 Emergence and Its Treatment with Alternative Medicines: A Review. Research Journal of Pharmacy and Technology. 2021; 14(10):5551-7. DOI: 10.52711/0974-360X.2021.00967
4. Ansori AN, Kharisma VD, Parikesit AA, Dian FA, Probojati RT, Rebezov M, Scherbakov P, Burkov P, Zhdanova G, Mikhalev A, Antonius Y, Pratama MRF, Sumantri NI, Sucipto TH, Zainul R. Bioactive Compounds from Mangosteen (Garcinia mangostana L.) as an Antiviral Agent via Dual Inhibitor Mechanism against SARS-CoV- 2: An In Silico Approach. Phcog J. 2022; 14(1): 85-90. DOI: 10.5530/pj.2022.14.12
5. Reviono, Muhammad F, Maharestri KZ, Hanif I, Sukmagautama C, Apriningsih H, Hananto AZA, Harsini. Efficacy of therapeutic plasma exchange and convalescent plasma therapy in moderate-to-severe COVID-19 patients. Bali Medical Journal. 2022; 11(3): 1369–1374. DOI: 10.15562/bmj.v11i3.3493
6. Rahman FF, Haris F. COVID-19 emergency response in Southeast Asian region: A bibliographic analysis using VOSviewer software. Bali Medical Journal. 2022; 11(3): 1656–1659. DOI: 10.15562/bmj.v11i3.3758
7. Kharisma VD, Agatha A, Ansori ANM, Widyananda MH, Rizky WC, Dings TGA, Derkho M, Lykasova I, Antonius Y, Rosadi I, Zainul R. Herbal combination from Moringa oleifera Lam. and Curcuma longa L. as SARS-CoV-2 antiviral via dual inhibitor pathway: A viroinformatics approach. J Pharm Pharmacogn Res. 2022; 10(1): 138-146. DOI: 10.56499/jppres21.1174_10.1.138
8. Aldino M, Maulani R, Probojati R, Dhea Karisma V, Ansori ANM, Parikesit AA. Potential Vaccine Targets for COVID-19 and Phylogenetic Analysis Based on the Nucleocapsid Phosphoprotein of Indonesian SARS-CoV-2 Isolates. Indonesian Journal of Pharmacy. 2021; 32(3): 328-337. DOI: 10.22146/ijp.1497
9. Wong CY, Tham JS, Foo CN, Leng Ng F, Shahar S, Zahary MN, Ismail MN, Tan CS, Hoh BP, Kumar SV, Lim YM. Factors influencing COVID-19 vaccination intention among university students: A cross-sectional study in Malaysia. Biosafety and Health. 2022.
10. Antonius Y, Ongko J, Hardjo PH. Identification of potential activity of volatile compounds derived from pogostemon Cablin benth as antiviral of SARS-CoV-2. Int J App Pharm. 2023; 15(1): 93-97.
11. Hsieh PP, Kristian H, Permana AAJM, Wongsodiharjo M, Nugraheni PA, Charisti P, Diarsvitri W. The clinical pictures of COVID-19 pediatric patients in dr. R. Soedarsono Regional General Hospital, Pasuruan, East Java, Indonesia. Bali Medical Journal. 2022; 11(1): 460–465. DOI: 10.15562/bmj.v11i1.3046
12. Jena M, Kumar V, Kancharla S, et al. Reverse vaccinology approach towards the in-silico multi-epitope vaccine development against SARS-CoV-2. F1000. Research. 2021; 10: 1-14. doi.org/10.12688/f1000research.36371.1.
13. Gupta E, et al. Identification of potential vaccine candidates against SARS-CoV-2.JMIR Bioinform Biotech.2020;35(2):26-37.doi.org/ 10.2196/32401
14. Sarkar B, Ullah MA, Johora FT, et al. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2). Immunobiology. 2020; 225(3): 151996-.doi.org/10.1016/j.imbio.2020.151955
15. Walls AC, Park YJ, Tortorici MA, et al. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Gylcoprotein.Cell. 2020; 181(2): 281-292.doi.org/10.1016/j.cell.2020.02.058
16. Public Health England. Investigation of SARS-CoV-2 Variants of Concern in England: Technical Briefing 6.2020
17. Ahmed SF, Quadeer AA, and McKay MR. Preliminary identification of potential vaccine targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies. Viruses. 2020; 12(3). doi.org/10.3390/v12030254.
18. Tamura K, Stecher G, and Kumar S. MEGA11: Molecular Evolutionary Genetics Analysis version 11. Molecular Biology and Evolution. 2021; 38: 3022-3027.doi.org/10.1093/molbev/msab120
19. National Center for Biotechnology Information (NCBI). Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information. 1988. https://www.ncbi.nlm.nih.gov/
20. Waterhouse A, Bertoni M, Biepert S, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018; 46. doi.org/10.1093/nar/gky427
21. Khuluq H, Yusuf PA, Perwitasari DA. A bibliometric analysis of coronavirus disease (COVID-19) mortality rate. Bali Medical Journal. 2022; 11(2): 579–586. DOI: 10.15562/bmj.v11i2.3423
22. Gasteiger E, Hoogland CG, Gattiker A, et al. Protein Identification and Analysis Tools on the ExPaSy Server. John M. Walker: The Proteomics Protocols Handbook. 2005. doi.org/10.1385/1-59259-890-0:571
23. Fadholly A, Ansori ANM, Kharisma VD, Rahmahani J, Tacharina MR. Immunobioinformatics of Rabies Virus in Various Countries of Asia: Glycoprotein Gene. Res J Pharm Technol. 2021; 14(2): 883-886. doi: 10.5958/0974-360X.2021.00157.8
24. Dimitrov I, Flower DR, and Doytchinova I. AllerTOP - a server for in silico prediction of allergens. BMC Bioinformatics. 2013; 26: 631-634.doi.org/10.1186/1471-2105-14-S6-S4
25. Lamiable A, et al.PEP-FOLD3: Faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res.2016;44. doi.org/10.1093/nar/gkw329
26. Minici C, et al. Crystal Structure of the BCR Fab fragment from subset #2 case P11475.2017.doi.org/10.2210/pdb5IFH/pdb
27. Micini C, et al. Distinct homotypic B-Cell receptor interactions shape the outcome of chronic lymphocytic leukaemia.Nat Commun.2017;8(15746).doi.org/10.1038/ncomms15746
28. Duhovny D, Nussinov R, and Wolfson HJ. Efficient Unbound Docking of Rigid Molecules. Proceedings of the 2nd Workshop on Algorithms in Bioinformatics (WABI). 2002.doi.org/10.1007/3-540-45784-4_14
29. Duhovny D, Inbar Y, and Nussinov R. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucl. Acids. Res. 2005; 33.doi.org/10.1093/nar/gki481
30. Andrusier N, Nussinov R, and Wolfson HJ.FireDock: Fast Interaction Refinement.Proteins. 2007; 69(1): 139-159.doi.org/10.1002/prot.21495
31. Mashiach E, Schneidman-Duhovny D, Andrusier N, et al. FireDock: a web server for fast interaction refinement in molecular docking. Nucleic Acids Res. 2008; 36.doi.org/10.1093/nar/gkn186
32. Schrödinger, L., DeLano, W. PyMol. 2020. http://www.pymol.org/pymol
33. Zhao N, et al. Mutations and Phylogenetic Analyses of SARS-CoV-2 Among Imported COVID-19 From Abroad in Nanjing, China. Front. Microbiol.2022.doi.org/10.3389/fmicb.2022.851323
34. Forster P. et al. Phylogenetic network analysis of SARS-CoV-2 genomes. PNAS.2020;117(17):9241-9243. doi.org/10.1073/pnas.2004999117
35. O'Toole A, et al. Tracking the internation spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2 [version 1; peer review: 3 approved]. wellcome open res.2021;6(121).doi.org/10.12688/wellcomeopenres/16661.1
36. Guruprasad K, Reddy BVB, and Pandit MW.Correlation between stability of a protein and its dipeptide composition: A novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng. Des. Sel. 1990; 4(2): 155-161.doi.org/10.1093/protein/4.2.155.
37. Chang KY and Yang JR. Analysis and Prediction of Highly Effective Antiviral Peptides Based on Random Forests. PLoS One. 2013; 8(8). doi.org/10.1371/journal.pone.0070166.
38. Guan Y, Zhu Q, Huang D, et al. An equation to estimate the difference between theoretically predicted and SDS PAGE-displayed molecular weights for an acidic peptide. Sci. Rep. 2015; 5(13370): 1-11.doi.org/10.1038/srep13370.
39. Heffron J and Mayer BK. Virus Isoelectric Point Estimation: Theories and Methods. Appl. Environ. Microbiol. 2021; 87(3): 1-17.doi.org/10.1128/AEM.02319-20.
40. Moldoveanu SC and David V. Properties of Analytes and Matrices Determining HPLC Selection. Selection of the HPLC Method in Chemical Analysis. 2017.doi.org/10.1016/B978-0-12-803684-6.00005-6
41. Novák P and Havlíček V. Protein Extraction and Precipitation. Proteomic Profiling and Analytical Chemistry.2016; 2: 51-62.doi.org/https://doi.org/10.1016/B978-0-444-63688-1.00004-5
42. Panda S and Chandra G. Physicochemical characterization and functional analysis of some snake venom toxin proteins and related non-toxin proteins of other chordates. Bioinformation. 2012; 8(18): 891-896.doi.org/10.6026/97320630008891.
43. Zhang J and Tao A. Antigenicity, Immunogenicity, Allergenicity. Allergy Bioinformatics. 2015.doi.org/10.1007/978-94-017-7444-4_11
44. Ikpeazu OV, Otuokere IE, and Igwe KK. Computational Characterization of the Binding Energy and Interactions between Trimethoprim and Dihydrofolate Reductases of Candida albicans, Staphylococcus aureus and Thermotoga Maritima. Acta Scientific Pharmaceutical Sciences. 2017; 1(3): 26-30.
45. Hasegawa M. Thermodynamic Basis for Phase Diagrams. Treatise on Process Metallurgy2013;1: 527-556. doi.org/10.1016/B978-0-08-096986-2.00003-5
46. Ferreira VP and Cortes C. The Complement System. Ref. Modul. Biomed. Sci. 2021. doi.org/10.1016/B978-0-12-818731-9.00056-2.
47. Mancardi D and Daëron M. Fc Receptors in Immune Responses. Ref. Modul. Biomed. Sci.2014. doi.org/10.1016/b978-0-12-801238-3.00119-7.
48. Dutta NK, Mazumdar K, and Gordy JT. The Nucleocapsid Protein of SARS–CoV-2: a Target for Vaccine Development. J. Virol. 2020; 94(13). doi.org/10.1128/jvi.00647-20.
49. Bai Z, Cao Y, Liu W, et al. The SARS-CoV-2 Nucleocapsid Protein and Its Role in Viral Structure, Biological Functions, and a Potential Target for Drug or Vaccine Mitigation. Viruses. 2021; 13(1115): 1-13. doi.org/10.3390/v13061115
50. Chia LY, Kumar PV, Maki MAA, Ravichandran G, Thilagar S. A Review: The Antiviral Activity of Cyclic Peptides. Int J Pept Res Ther. 2023; 29(1): 7. doi: 10.1007/s10989-022-10478-y.
51. Wijaya RM, Hafidzhah MA, Kharisma VD, Ansori ANM, Parikesit AP. COVID-19 In Silico Drug with Zingiber officinale Natural Product Compound Library Targeting the Mpro Protein. Makara J Sci. 2021; 25(3): 5. DOI: 10.7454/mss.v25i3.1244
52. Nugrahaningsih DAA, Purnomo E, Siswanto, Reviono, Yasmina A, Prenggono MD, Fajari NM, Rudiansyah M, Harsini, Syarif RA, Sholikhah EN. Features of COVID-19 adult patients and the treatment in Indonesia: a retrospective cohort study. Bali Medical Journal. 2022; 11(1): 528–539. DOI: 10.15562/bmj.v11i1.2810
53. Hanardi DDYP, Rochmawati E. Tracing management and epidemiological characteristics of close contact COVID-19 in primary health care. Bali Medical Journal. 2022; 11(3): 1614–1619. DOI: 10.15562/bmj.v11i3.3705
54. Ansori ANM, Kharishma VD, Muttaqin SS, Antonius Y, Parikesit AA. Genetic Variant of SARS-CoV-2 Isolates in Indonesia: Spike Glycoprotein Gene. J Pure Appl Microbiol. 2020; 14: 971-978. DOI: 10.22207/JPAM.14.SPL1.35
55. Kharisma VD, Ansori ANM. Construction of Epitope-Based Peptide Vaccine Against SARS-CoV-2: Immunoinformatics Study. J Pure Appl Microbiol. 2020; 14: 999-1005. DOI: 10.22207/JPAM.14.SPL1.38
56. Santosa D, Sofro MAU, Retnaningsih R, Pangarsa EA, Setiawan B, Farhanah N, Kholis FN, Handoyo T, Rahardjo S, Nindhita LR, Puspitasari I, Naibaho RM, Kharismasari R, Kartiyani I, Yunarvika V, Rizky D, Suhartono S. Convalescent plasma as an adjunctive treatment for severe and critically ill COVID-19. Bali Medical Journal. 2021; 10(3): 851–859. DOI: 10.15562/bmj.v10i3.2590
Received on 26.10.2022 Modified on 24.12.2022
Accepted on 21.02.2023 © RJPT All right reserved
Research J. Pharm. and Tech 2023; 16(10):4617-4625.
DOI: 10.52711/0974-360X.2023.00752