Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): Immunoinformatics approach.

Recently, a novel coronavirus (SARS-COV-2) emerged which is responsible for the recent outbreak in Wuhan, China. Genetically, it is closely related to SARS-CoV and MERS-CoV. The situation is getting worse and worse, therefore, there is an urgent need for designing a suitable peptide vaccine component against the SARS-COV-2. Here, we characterized spike glycoprotein to obtain immunogenic epitopes. Next, we chose 13 Major Histocompatibility Complex-(MHC) I and 3 MHC-II epitopes, having antigenic properties. These epitopes are usually linked to specific linkers to build vaccine components and molecularly dock on toll-like receptor-5 to get binding affinity. Therefore, to provide a fast immunogenic profile of these epitopes, we performed immunoinformatics analysis so that the rapid development of the vaccine might bring this disastrous situation to the end earlier.

INTRODUCTION

At the end of 2019, a novel coronavirus (SARS‐COV‐2) was identified as the cause of a cluster of pneumonia cases in Wuhan, a city in the Hubei province of China. It has a positive‐sense single‐stranded RNA as their genetic component and shares genome similarity with SARS‐CoV and bat coronavirus, , 79.5% and 96% respectively. Phylogenetically, it belongs to the family Coronaviridae, order Nidovirales and is a β‐coronavirus of 2B group.

Regarding epidemiology, human‐to‐human transmission of the virus through the sneezes, cough, and respiratory droplets has been confirmed, yet the zoonotic nature has not been confirmed. , , Epidemiologic investigation in Wuhan, China identified an initial association with a seafood market where most patients had worked or visited. However, as the outbreak progressed, several confirmed cases were reported sporadically all over the world, showing the pandemic nature of the disease named as COVID‐19. At last, on 30 January 2020, the World Health Organization (WHO) declared this outbreak a public health emergency of international concern. According to the situation report 35 (reported by 24 February 2020) of WHO, in China, 77 262 confirmed cases were reported, of which 2595 cases were with deaths. Moreover, outside of China, 2069 confirmed cases were reported in 29 other countries (https://www.who.int/docs/default‐source/coronaviruse/situation‐reports/20200224‐sitrep‐35‐covid‐19.pdf?sfvrsn=1ac4218d_2).

Therefore, as the situation was getting worse and worse, the need for designing a suitable peptide vaccine component against the SARS‐COV‐2 was growing. Our work was to find suitable epitopes, which can generate enough immune response against the SARS‐COV‐2 infection. Using immunoinformatics, we could recognize and characterize potential B and T‐cell epitopes for the generation of the epitopic vaccine against SARS‐COV‐2. Specifically, the spike glycoprotein of SARS‐COV‐2 is considered as the target because it forms a characteristic crown of the virus and protrudes from the viral envelope. So, the protein sequence of spike glycoprotein was explored thoroughly using multiple immunoinformatic‐based servers and software, to identify various epitopes for an effective vaccine.

MATERIALS AND METHODS

Collection of targeted protein sequence

The amino acid sequence of the targeted protein on SARS‐COV‐2 was collected from the National Centre for Biotechnological Information (NCBI) database. The protein sequence is very crucial for identifying the potential epitopes of the targeted protein.

Identification of B‐cell epitopes

In this subsection, we used the Immune Epitope Database (IEDB) to identify linear B‐cell epitopes using the incorporated BepiPred 2.0 prediction module. , We provided the FASTA sequence of the targeted protein as an input considering all default parameters.

Identification of T‐cell epitopes and antigenicity analysis

T‐cell epitopes having the binding affinity towards MHC‐I and MHC‐II alleles were selected to boost up both cytotoxic T‐cell and helper T‐cell mediated immune response. We adopted two servers which are ProPred‐I and ProPred server to the selection of MHC‐I and MHC‐II binding epitopes respectively within preidentified B‐cell epitopic region. , The selected epitopes were submitted to the VaxiJen v.2.0 server applying a virus as a target field with the given threshold value of 0.4 for analyzing the antigenic propensity.

Vaccine construction, modeling, and validation

With the help of a specific peptide linker, we fused the antigenic epitopes to construct an effectual vaccine component. Later, the vaccine component was modeled in the SPARKS‐X server. An adjuvant was also added with the vaccine component to accelerate the adaptive immune responses. The vaccine model passed through two different servers ProSA‐web and PROCHECK—in a subsequent manner for evaluating the structural accuracy of the model. ,

Molecular docking analysis

Molecular docking is the most promising part of the modern drug‐discovery method. Here, in this study, we adopted PatchDock (Beta 1.3 Version) docking server to receptor‐ligand docking. PatchDock server analyzes the molecular docking between the vaccine component and the toll‐like receptor (TLR)‐5. The generated Protein Data Bank (PDB) file of the protein‐peptide docking complex was visualized in PyMOL software v.2.3.

RESULT

Spike glycoprotein of SARS‐COV‐2, retrieved from the NCBI has the GenBank accession ID: QHR63290.1. This spike glycoprotein has 1282‐long amino acid sequences and this sequence was downloaded in a FASTA format to carry out the further process.

We obtained a total of 34 sequential linear B‐cell epitopes of varying lengths from the IEDB server within spike glycoprotein of SARS‐COV‐2. Those B‐cell epitopes were placed into Table 1 based on their positional value, sequence, and length. In Figure 1 the yellow‐colored peaks represent the epitopic region, while the green‐colored slopes, represent the nonepitopic region.

Table 1

List of linear B‐cell epitopes along with their sequence, position, and length

Serial no.	Start	End	Sequence	Length
1	22	46	SQCVNLTTRTQLPPAYTNSFTRGVY	25
2	68	90	FSNVTWFHAIHVSGTNGTKRFDN	23
3	106	107	KS	2
4	147	163	DPFLGVYYHKNNKSWME	17
5	186	198	MDLEGKQGNFKNL	13
6	215	230	KHTPINLVRDLPQGFS	16
7	259	269	TPGDSSSGWTA	11
8	302	305	LDPL	4
9	313	331	KSFTVEKGIYQTSNFRVQP	19
10	338	372	FPNITNLCPFGEVFNATRFASVYAWNRKRISNCVA	35
11	378	402	YNSASFSTFKCYGVSPTKLNDLCFT	25
12	413	435	GDEVRQIAPGQTGKIADYNYKLP	23
13	449	510	NLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTN	62
14	525	545	ELLHAPATVCGPKKSTNLVKN	21
15	564	571	SNKKFLPF	8
16	589	592	QTLE	4
17	611	615	TNTSN	5
18	625	641	NCTEVPVAIHADQLTPT	17
19	643	653	RVYSTGSNVFQ	11
20	665	675	VNNSYECDIPI	11
21	681	699	ASYQTQTNSPRRARSVASQ	19
22	704	719	YTMSLGAENSVAYSNN	16
23	757	757	E	1
24	782	788	EQDKNTQ	7
25	795	809	KQIYKTPPIKDFGGF	15
26	816	823	PDPSKPSK	8
27	837	851	LADAGFIKQYGDCLG	15
28	997	1001	EAEVQ	5
29	1044	1052	GQSKRVDFC	9
30	1116	1127	RNFYEPQIITTD	12
31	1142	1181	VNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGI	40
32	1212	1215	LGKY	4
33	1261	1276	SCCKFDEDDSEPVLKG	16
34	1278	1278	K	1

Figure 1

Graphical representation of linear B‐cell epitopes within the spike glycoprotein of SARS‐COV‐2

We identified 29 MHC‐I epitopes and 8 MHC‐II epitopes, which fall within the preidentified B‐cell epitopic region. Among them, 13 MHC‐I epitopes and 3 MHC‐II epitopes had the antigenic propensity, according to the VaxiJen v.2.0 server analysis. The MHC‐I and MHC‐II epitopes are listed in Tables 2 and 3 with encountering MHC alleles and antigenic scores.

Table 2

List of epitopes with encountering MHC‐I alleles, positional value, and VaxiJen antigenic score

Serial no.	Epitopic sequence	MHC‐I alleles	Position	Antigenicity
1	SQCVNLTTR	HLA‐A*3101	22‐30	1.5476 (Probable Antigen).
		HLA‐A*3302
		HLA‐A68.1
		HLA‐A20 Cattle
		HLA‐B*2705
		MHC‐Db revised
2	YTNSFTRGV	HLA‐A2	37‐45	−0.6177 (Probable Nonantigen).
		HLA‐A*0201
		HLA‐A2.1
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
		HLA‐B*5801
		HLA‐B61
3	GVYYHKNNK	HLA‐A*1101	151‐159	0.8264 (Probable Antigen).
		HLA‐A3
		HLA‐A*3101
		HLA‐A68.1
		HLA‐B*2705
4	GKQGNFKNL	HLA‐A2	190‐198	1.0607 (Probable Antigen).
		HLA‐A20 Cattle
		HLA‐B*3902
		HLA‐Cw*0301
		MHC‐Db
		MHC‐Db revised
		MHC‐Dd
		MHC‐Kb
5	TPINLVRDL	HLA‐A24	217‐225	0.3862 (Probable Nonantigen).
		HLA‐B14
		HLA‐B*3501
		HLA‐B*3801
		HLA‐B*3901
		HLA‐B*3902
		HLA‐B40
		HLA‐B*4403
		HLA‐B*5101
		HLA‐B*5102
		HLA‐B*5103
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
		HLA‐B60
		HLA‐B7
		HLA‐B*0702
		HLA‐B8
		HLA‐Cw*0301
		HLA‐Cw*0401
		HLA‐Cw*0602
		HLA‐Cw*0702
		MHC‐Kd
		MHC‐Ld
6	GIYQTSNFR	HLA‐A*1101	320‐328	0.5380 (Probable Antigen).
		HLA‐A3
		HLA‐A*3101
		HLA‐A*3302
		HLA‐A68.1
		HLA‐A20 Cattle
		HLA‐B*2705
7	NLCPFGEVF	HLA‐A1	343‐351	0.1999 (Probable Nonantigen).
		HLA‐A3
		HLA‐A2.1
		HLA‐B*2702
		HLA‐B*5201
		HLA‐B*5801
		HLA‐B62
		MHC‐Ld
8	FASVYAWNR	HLA‐A*3101	356‐364	0.0713 (Probable Nonantigen).
		HLA‐A*3102
		HLA‐A68.1
		HLA‐A20 Cattle
		HLA‐B*5301
		HLA‐B*5401
9	ASFSTFKCY	HLA‐A1	381‐389	0.2795 (Probable Nonantigen).
		HLA‐B*2702
		HLA‐B*3501
		HLA‐B*4403
		HLA‐B*5401
		HLA‐B*51
		HLA‐B*5801
		HLA‐Cw*0702
		MHC‐Ld
10	VSPTKLNDL	HLA‐A24	391‐399	1.4610 (Probable Antigen).
		HLA‐A2.1
		HLA‐B*3501
		HLA‐B*3902
		HLA‐B*51
		HLA‐B*5801
		HLA‐B60
		HLA‐B7
		HLA‐B8
		HLA‐Cw*0401
		HLA‐Cw*0602
		MHC‐Dd
		MHC‐Kb
		MHC‐Ld
11	KIADYNYKL	HLA‐A2	426‐434	1.6639 (Probable Antigen).
		HLA‐A*0201
		HLA‐A*0205
		HLA‐A24
		HLA‐A3
		HLA‐A*3101
		HLA‐A2.1
		HLA‐B*2705
		HLA‐B*3501
		HLA‐B*3801
		HLA‐B*3902
		HLA‐B7
		HLA‐Cw*0401
12	KVGGNYNYL	HLA‐A*0201	453‐461	0.5994 (Probable Antigen).
		HLA‐A*0205
		HLA‐A24
		HLA‐A68.1
		HLA‐B*2705
		HLA‐B*3501
		HLA‐B*3801
		HLA‐B*3902
		HLA‐B7
		HLA‐B*0702
		HLA‐Cw*0301
		MHC‐Db
		MHC‐Db revised
		MHC‐Kb
13	RLFRKSNLK	HLA‐A2	463‐471	−0.2829 (Probable Nonantigen).
		HLA‐A*1101
		HLA‐A3
		HLA‐A*3101
		HLA‐A68.1
		HLA‐A20 Cattle
		HLA‐B*2705
14	FERDISTEI	HLA‐B*3701	473‐481	−0.7442 (Probable Nonantigen).
		HLA‐B40
		HLA‐B*4403
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
		HLA‐B60
		HLA‐B61
		MHC‐Kk
15	EGFNCYFPL	HLA‐A2	493‐501	0.5453 (Probable Antigen).
		HLA‐B14
		HLA‐B*3902
		HLA‐B40
		HLA‐B*5101
		HLA‐B*5102
		HLA‐B*5103
		HLA‐B*5401
		HLA‐B60
		HLA‐B7
		HLA‐Cw*0301
		MHC‐Dd
		MHC‐Kb
16	ELLHAPATV	HLA‐A2	525‐533	0.2109 (Probable Nonantigen).
		HLA‐A*0201
		HLA‐A2.1
		HLA‐B*5103
		HLA‐B62
17	GPKKSTNLV	HLA‐B*3501	535‐543	0.6828 (Probable Antigen).
		HLA‐B*5101
		HLA‐B*5102
		HLA‐B*5103
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
		HLA‐B61
		HLA‐B7
		HLA‐B*0702
		HLA‐B8
		HLA‐Cw*0401
		MHC‐Ld
18	TEVPVAIHA	HLA‐B*3701	627‐635	0.2687 (Probable Nonantigen).
		HLA‐B40
		HLA‐B*4403
		HLA‐B60
		HLA‐B61
		MHC‐Kk
19	RVYSTGSNV	HLA‐A2	643‐651	0.2636 (Probable Nonantigen).
		HLA‐A*0201
		HLA‐A*0205
		HLA‐A2.1
		HLA‐B*2702
		HLA‐B*2705
		HLA‐B*5102
		HLA‐B*5103
		HLA‐B*5201
		HLA‐B*5401
		HLA‐B*0702
20	NSYECDIPI	HLA‐B*2702	667‐675	0.2216 (Probable Nonantigen).
		HLA‐B*3501
		HLA‐B*5101
		HLA‐B*5102
		HLA‐B*5103
		HLA‐B*5401
		HLA‐B*5801
		MHC‐Db revised
		MHC‐Kk
21	SPRRARSVA	HLA‐B*3501	689‐697	0.7729 (Probable Antigen).
		HLA‐B*5101
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
		HLA‐B7
		HLA‐B*0702
		HLA‐B8
		MHC‐Ld
22	LGAENSVAY	HLA‐B*3501	707‐715	0.4173 (Probable Antigen).
		HLA‐B*4403
		HLA‐B*51
		HLA‐B62
		HLA‐Cw*0702
		MHC‐Dd
23	KQIYKTPPI	HLA‐A2	795‐803	0.2705 (Probable Nonantigen).
		HLA‐A*0201
		HLA‐A*0205
		HLA‐B*2702
		HLA‐B*2705
		HLA‐B*5102
		HLA‐B*5201
		HLA‐B62
		HLA‐B*0702
		MHC‐Dd
		MHC‐Kd
24	FIKQYGDCL	HLA‐A2.1	842‐850	−0.4436 (Probable Nonantigen).
		HLA‐B*3501
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
		HLA‐B7
		HLA‐B8
25	RNFYEPQII	HLA‐B*2702	1116‐1124	0.3282 (Probable Nonantigen).
		HLA‐B*2705
		HLA‐B*5102
		HLA‐B*5201
		HLA‐B*5401
26	VNNTVYDPL	HLA‐A24	1142‐1150	0.2397 (Probable Nonantigen).
		HLA‐B*3701
		HLA‐B*3902
		HLA‐B*5301
		HLA‐B*51
		HLA‐B60
		HLA‐B7
		HLA‐Cw*0301
		MHC‐Kb
27	ELDSFKEEL	HLA‐A2	1153‐1161	−0.6805 (Probable Nonantigen).
		HLA‐A3
		HLA‐A2.1
		HLA‐B*3801
		HLA‐B*3902
		HLA‐Cw*0401
		HLA‐Cw*0602
28	FKNHTSPDV	HLA‐A2	1165‐1173	0.4846 (Probable Antigen).
		HLA‐A20 Cattle
		HLA‐A2.1
		HLA‐B*5301
		HLA‐B*5401
		HLA‐B*51
29	DEDDSEPVL	HLA‐B*3701	1266‐1274	0.5104 (Probable Antigen).
		HLA‐B40
		HLA‐B*4403
		HLA‐B60
		HLA‐B61
		MHC‐Kk

Table 3

List showing the epitopes with encountering MHC‐II alleles, positional value and VaxiJen antigenic score

Serial no.	Sequence	Alleles	Position	VaxiJen score
1	IHVSGTNGT	DRB1_0306	77‐85	0.8621 (Probable Antigen).
		DRB1_0307
		DRB1_0308
		DRB1_0311
		DRB1_0401
		DRB1_0404
		DRB1_0410
		DRB1_0421
		DRB1_0423
		DRB1_0426
2	VYYHKNNKS	DRB1_0306	152‐160	0.4510 (Probable Antigen).
		DRB1_0307
		DRB1_0308
		DRB1_0311
		DRB1_0401
		DRB1_0402
		DRB1_0404
		DRB1_0405
		DRB1_0408
		DRB1_0410
		DRB1_0421
		DRB1_0423
		DRB1_0426
		DRB1_1102
		DRB1_1114
		DRB1_1120
		DRB1_1121
		DRB1_1322
		DRB1_1323
		DRB1_1327
		DRB1_1328
		DRB1_1501
		DRB1_1506
3	LVRDLPQGF	DRB1_0301	221‐229	0.1234 (Probable Nonantigen).
		DRB1_0305
		DRB1_0306
		DRB1_0307
		DRB1_0308
		DRB1_0309
		DRB1_0311
		DRB1_0421
		DRB1_0426
		DRB1_1107
4	VFNATRFAS	DRB1_0301	350‐358	0.1739 (Probable Nonantigen).
		DRB1_0305
		DRB1_0309
		DRB1_0802
		DRB1_0804
		DRB1_0813
		DRB1_1101
		DRB1_1102
		DRB1_1104
		DRB1_1106
		DRB1_1107
		DRB1_1114
		DRB1_1120
		DRB1_1121
		DRB1_1301
		DRB1_1302
		DRB1_1304
		DRB1_1307
		DRB1_1311
		DRB1_1322
		DRB1_1323
		DRB1_1327
		DRB1_1328
		DRB1_1501
		DRB1_1506
5	YRLFRKSNL	DRB1_0101	462‐470	0.0522 (Probable Nonantigen).
		DRB1_0305
		DRB1_0309
		DRB1_0405
		DRB1_0408
		DRB1_0701
		DRB1_0703
		DRB1_0801
		DRB1_0802
		DRB1_0804
		DRB1_0806
		DRB1_0813
		DRB1_0817
		DRB1_1101
		DRB1_1102
		DRB1_1114
		DRB1_1120
		DRB1_1121
		DRB1_1128
		DRB1_1301
		DRB1_1302
		DRB1_1304
		DRB1_1305
		DRB1_1307
		DRB1_1321
		DRB1_1322
		DRB1_1323
		DRB1_1327
		DRB1_1328
		DRB1_1501
		DRB1_1502
		DRB1_1506
6	FERDISTEI	DRB1_0305	473‐481	−0.7442 (Probable Nonantigen).
		DRB1_0401
		DRB1_0426
		DRB1_0309
		DRB1_0421
		DRB1_0701
		DRB1_0703
7	YQTQTNSPR	DRB1_0421	683‐691	−0.1787 (Probable Nonantigen).
		DRB1_0401
		DRB1_0405
		DRB1_0408
		DRB1_0426
8	FKNHTSPDV	DRB1_0101	1166‐1174	0.4846 (Probable Antigen).
		DRB1_0309
		DRB1_0401
		DRB1_0405
		DRB1_0421
		DRB1_0426
		DRB1_0701
		DRB1_0703
		DRB1_1114
		DRB1_1120
		DRB1_1302
		DRB1_1323
		DRB1_1502

In this study, we linked the 13 MHC‐I and 3 MHC‐II antigenic epitopes with (EAAAK)3 linker peptide to construct a vaccine component. This linker peptide was easily fused with the virus coat protein and increased stability as well as folding of the vaccine component. The predicted structure of the vaccine component is shown in Figure 2. It has 90.0%, 7.1%, 1.6%, and 1.3% residues in most favored, additionally allowed, generously allowed and disallowed regions respectively within PROCHECK as the validation server to generate Ramachandran plot. Using the ProSA server, the “Z” score was −3.82 and most of the residues had negative energy value as shown in Figure 3. Results from both servers indicate the model is in a good quality. ,

Figure 2

Tertiary structural model of construct vaccine component

Figure 3

Different molecular characterization of vaccine model. (a) All atoms at Ramachandran plot, (b) “Z” score plot of vaccine model in ProSA server, and (c) all residue energy plot

The PatchDock server provided 20 docking complexes, and among them, we selected only the docking complex with the highest negative atomic contact energy (ACE) value for analysis. The ACE value of the docking complex was −259.62, which indicates spontaneous reactivity between the vaccine component and TLR‐5. As proper protein‐protein docking regulates the cellular functions, the docking between the vaccine component and TLR‐5 will activate immune cascades for destroying the viral antigens. The selected docking complex is shown in Figures 4 and 5, along with molecular surface interaction as well as some bonding interactions.

Figure 4

Docking complex exhibiting the surface interaction between vaccine component (cyan color) and toll‐like receptor‐5 (green color)

Figure 5

Docking complex exhibiting the bonding interaction between vaccine component and toll‐like receptor‐5

DISCUSSION

The SARS‐COV‐2, the causative pathogen for respiratory distress syndrome, led more than 10 000 people to infection all over the world, even several to death. After first identified in Wuhan, Hubei province of China, the COVID‐19 disease spread unchecked which finally became a global threat. Scientists from all over the world are struggling to find a solution to this evil outbreak.

In our present study, we attempted to find out various B‐cell and T‐cell epitopes against SARS‐COV‐2, using the immunoinformatics, as quick identification of B‐cell and T‐cell epitopes is crucial for designing of vaccine component against this disease. The spike glycoprotein was analyzed for B‐cell epitope identification in the IEDB server, and 34 linear B‐cell epitopes were identified as a result. Subsequently, the sequence was also analyzed in ProPred‐I and ProPred servers for the identification of the T‐cell epitope that can combine with MHC‐I and MHC‐II molecules. Fortunately, we found 29 epitopes against MHC‐I and 8 epitopes against MHC‐II that can be possibly used for vaccine. Unfortunately, antigenic characterization in VaxiJen v.2.0 discarded 16 MHC‐I epitopes out of 29 and 5 out of 8 MHC‐II epitopes as these seemed to be nonantigenic in nature. Nevertheless, we converted the antigenic epitopes into a single vaccine component, using (EAAAK)3 peptide linker.

Later, the vaccine component was modeled in the SPARK‐X server and validated in PROCHECK and ProSA. A total of 90% of nonglycine and nonproline residues presented within the most favored region, while the “Z” score of the model was −3.82. These results from both servers indicate the model is in good quality. Molecular docking between vaccine component and TLR‐5 showed significant ACE value, which indicates spontaneous reactivity within the receptor‐ligand complex.

All the observations of our present work depict the effectiveness of selected epitopes within the spike glycoprotein of SARS‐COV‐2. These epitopes can be used to make an immunogenic multi‐epitopic peptide vaccine against SARS‐COV‐2.

CONCLUSION

Present immunoinformatic analysis pointed out 13 MHC‐I and 3 MHC‐II epitopes within the spike glycoprotein of SARS‐COV‐2. These epitopes are the ideal candidate to formulate a multi‐epitopic peptide vaccine, not only because of being selected from the linear B‐cell epitopic region but also because of their antigenic property was confirmed. Moreover, the molecular docking of vaccine components with the TLR‐5 proves the significance and effectiveness of these epitopes as an ideal vaccine candidate against SARS‐COV‐2. However, these immunoinformatic analyses require several in vitro and in vivo validations before formulating the vaccine to resist COVID‐19.