Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2.

Coronaviruses (CoVs) are positive-stranded RNA viruses with short clubs on their edges. CoVs are pathogenic viruses that infect several animals and plant organisms, as well as humans (lethal respiratory dysfunctions). A noval strain of CoV has been reported and named as SARS-CoV-2. Numerous COVID-19 cases were being reported all over the World. COVID-19 and has a high mortality rate. In the present study, immunoinformatics techniques were utilized to predict the antigenic epitopes against 3C like protein. B-cell epitopes and Cytotoxic T-lymphocyte (CTL) were designed computationally against SARS-CoV-2. Multiple Sequence Alignment (MSA) of seven complete strains (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2) was performed to elucidate the binding domain and interacting residues. MHC-I binding epitopes were evaluated by analyzing the binding affinity of the top-ranked peptides having HLA molecule. By utilizing the docked complexes of CTL epitopes with antigenic sites, the binding relationship and affinity of top-ranked predicted peptides with the MHC-I HLA protein were investigated. The molecular docking analyses were conducted on the ZINC database library and twelve compounds having least binding energy were scrutinized. In conclusion, twelve CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL) were identified against SARS-CoV-2.

1. Introduction

The viral parentage is used to inhibit a range of diseases and even serves as a tool for the study for previously unknown pathogens. Numerous novel viruses possessing cytotoxic properties have been discovered, including those that do not replicate in cell culture and those that induce cytotoxicity (CPE) [1]. Coronaviridae viruses are RNA-encapsulated viruses belongs to the Coronavirus (CoV) family. RNA is of 27 to 32 kb in length [2]. CoV are respiratory viruses found in rats, bats, turkeys, humans, and other animals [3].

On January 19, 2020, the Chinese Center for Disease Surveillance released a novel CoV, published with the consent of the Centers for Disease Control and Prevention [4]. SARS-CoV is the fourth type of coronavirus. SARS-CoV-2 is considered as the third pandemic of this century and was discovered in a significant number of patients at the Seafood market of Hunan, China on December 31st, 2019 [4, 5]. SARS-CoV-2 symptoms include fever, cough, and trouble breathing similar to other coronaviruses including the acute respiratory syndrome coronavirus and the Middle East respiratory coronavirus [3].

11th of Janary, 2020, several Chinese institutions submitted SARS-CoV-2 sequences to GSAID database. Thousands of people have died as a consequence of SARS-CoV-2 [6]. The signs and symptoms were sever including dry cough, fever, leukopenia, and shortness of breath [7]. The old age patients are less likely to contract SARS-CoV infection and the mortality rate is about 10% [8, 9].

SARS-CoV-2 was first transmitted via direct human contact [10]. Research of human serology found a link between bat coronavirus proteins and SARS-CoV-2. About 89% of the genome was shared with two SARS-CoV-2 variants (C45 and Z21). 75% of the amino acids in the SARS-CoV-2 spike protein are similar to SARS-CoV proteins. Infections with SARS-CoV-2 were found in 7.3% of medical and non-medical workers, implying a 7.0% absolute risk (95% confidence interval for risk difference). The nursing staff was the most infected (WHO) individuals [7].

Our present research aims to utilize immunoinformatics approaches to anticipate and identify the possible B and T cell epitopes for vaccine development against SARS-CoV-2. Furthermore, to identify particular peptides from CoV proteome that may bind to the major histocompatibility complex (MHC), which is one of the most important phases in the vaccine development process.

2. Research methodology

2.1 Sequence retrieval

The amino acid sequence of SARS-CoV-2 protease inhibitor (PDB 6M2N) was retrieved from Protein Data Bank (PDB) [11, 12] and X-ray crystallographic structure of a selected protein (306 residues) was reteived having resolution of 2.20 Å. ProtParam was used to evaluate the biochemical properties of the target protein [13].

2.2 Alignment of multiple sequences

Seven different genomes from CoV family were retrived through NCBI [14]. Multiple Sequence Alignment (MSA) was performed by utilizing Clustal Omega [15-17]. MSA was carried out for the whole genomes of selected CoV genomes such as SARS-CoV-2 = NC_045512.2, SARS-CoV = NC_004718.3, MERS-CoV = NC_019843.3, HCoV-NL63 = NC_005831.2, HCoV-229E = NC_002645.1, HCoV-OC43 = NC_006213.1, HCoV-HKU1 = NC_006577.2 [18-21]. WebLogo3 was used to evaluate the conserved residues among the selected genomes and target protein [16, 17].

2.3 Conformational and linear B-cell epitopes prediction

B-lymphocytes are divided into two cell types as a result of interactions between the B-cell epitope and anti-B-lymphocyte as memory cells and plasma antibody secretors.

The surface accessibility and hydrophilic nature of B-cell epitopes are significant for B-cell epitopes [22] by accessing the Immune Epitope Database and Analyses Resource (IEDB), as stated by Parker Hydrophilicity Prediction (PHP), Karplus and Schulz Flexibility Prediction [23], Kolaskar and Tongaonkar antigenicity scale, and Emini surface accessibility prediction [24]. Three different methods were used in conjunction with the Ellipro to predict IEDB analysis resource conformational B-cell epitopes, including nearby clustering of residues based on the protrusion index (PI), approximating the protein shape, and the prediction of IEDB analysis resource conformational B-cell epitopes [24], the criteria of the least possible score of 0.5 and maximum distance of 6 were followed [25].

2.4 Prediction of cytotoxic effects of epitopes on T-Lymphocytes (CTL)

To evaluate CTL epitope predictions, the NetCTL.1.2 server was utilized [4]. The features of NetCTL.1.2 was set as super type A1 including C-terminal cleavage weight 0.15, TAP transport efficiency weight 0.05, and epitope prediction thresholds. The C-terminal cleavage weight was 0.15, the TAP transport efficiency weight was 0.05, and the epitope prediction threshold was 0.75. MHC molecules are antigens, and CTLs are activated by the surface of these molecules. The server was used to combine the antigen processing (TAP) transport efficiency transporter, proteasome C-terminal cleavage, and Class I prediction into one analytics system. FASTA sequences from the selected species were utilized to analyze the human leucocyte antigen (HLA) alleles and the length of the polypeptide sequence. TAP transportation efficiency was assessed by using a weight matrix and T- epitome forecasts, and an artificial neural network was utilized to forecast proteasome C-terminal division and MHC Class-I binding [4].

2.5 Coverage of world population

The IEDB server was utilized to perform worldwide population coverage analyses. Ten different epitopes and the area-country-ethnicity combination were employed for population coverage analyses in the global population inquiry. CTL epitopes were employed against particular allele sets to cover the selected populations. Japan, China, Italy, Iran, and other countries with high COVID-19 mortality rates were selected for global population coverage analyses [26].

2.6 Molecular docking analyses of MHC protein complex peptide

SARS-CoV-2 predicted epitopes were analyzed by utilizing molecular docking analyses for target proteins that include antigens residues. The 3D structures of the proposed peptides were predicted by using PEP-FOLD 3 [27] and 100 simulation runs were conducted in various conformations changes. sOPEP energy ratings were used to assess compliance models clustered with PEP-FOLD3 [28]. The peptides having highest values throughout the screening procedure were subsequently submitted for docking analyses with MHC class I binding molecules by PatchDock [29]. All the unnecessary receptor atoms from all the docked complexes were removed for reliable reults and categorized all the remaining complexes by using geometric complementary. FireDock was employed to further refine the selected docked complexes [30]. The docked complexes were set to decrease the number of scoring errors while simultaneously increase the flexibility of the docking experiments [31]. To determine and evaluate the binding affinity and hydrogen bonding interactions of the docked complexes, PyMOL, Discovery Studio, and UCSF Chimera 1.14 were used for interactional analyses [32].

2.7 Molecular docking analyses

For simulated scanning and molecular docking analyses, the library of FDA-approved compounds was selected for virtual screening. ZINC database was used to retrieve the 2122 FDA approaved compunds and was minimized by utilizing CHemDraw and UCSF Chimera [33]. A nonstructural coronavirus protein (PDB 6M2N) was reteived that plays significant role for replication of SARS-CoV-2. PyRx [33], AutoDock, and AutoDock Vina [34] were used to perform molecular docking analyses [34]. Root-mean-square deviation (RMSD) values were used to scrutinize the suitable docked complexes. The drug-like physical and chemical properties were assessed by using admetSAR and ADMETlab. UCSF Chimera and Discovery Studio were used to investigate and visualize the interacting residues [35, 36].

3. Results and discussion

The genome of SARS CoV-2 was organized into 14 ORF (Open Read Frames) encoding 27 proteins and a projected RNA molecule of approximately 29,900 nucleotides with a positive-stranded RNA (Fig 1). The majority of the genome contains ORF1a and ORF1b coding for sixteen distinct non-structural proteins within the replica complex, however few have alternative and essential function models (nsp1-nsp16) [37]. The remaining one-third genome has four ORFs (spike S, envelope E, membrane M, nucleocapsid N) and ten extra proteins (ORF3a, ORF3b, ORF6, ORF7a, ORF7b, ORF8a, ORF8b, ORF9b, ORF9c, ORF10). Some ORFs overlap and also present in larger ORF (Fig 1). At each end of the genome, the 5′ UTR and 3′ UTR (non-coding or unconventional regions) were present. The UTRs are approximately of 230 bases long and play essential regulatory roles [38]. The viruses cause cold in humans including CoVs and CoVs are also responsible for high mortality rate. SARS-CoV and MERS-CoV were identified in animal sources [39]. 31st December 2019, a novel strain of CoV was identified named as SARS-CoV-2. The conclusive reason of pandemic and consequences remain unclear due to the constantly change of environment around the disease [40].

Fig 1

Decoding of coronavirus genome.

The CoV genome consists of a 5′′ end, a leading sequence, replicase protein, spikes, envelope, membrane, nucleocapsid, and 3′UTR Poly-A-tail end.

To construct a epitope vaccine, immunoinformatics approaches were used to anticipate the relevant antigen epitopes of the target protein [40]. The goal of current effort was to predict peptide vaccines by using immunoinformatics approaches to recognize CTL epitopes [41]. Immunoinformatics analysescan identify several vaccine candidates with promising preclinical results using computational techniques [42].

CTL epitopes have been discovered to develop a peptide vaccine against HLA-B protein. The epitope-based vaccines were being used to target SARS-CoV-2 structural proteins, and CTL epitopes of the target proteins were anticipated to boost the immune response of host [39]. A non-structural protein (PDB 6M2N) was selected for epitope based vaccine design as it has significant rle in the replication of SARS-CoV-2. Vaxijen and Allergen F.P. were used to evaluate the antigenicity and allergenicity of CTL epitope.

In China, population coverage estimations of predicted epitopes showed 16.08 MHC class I coverage with an average hit of 0.48. Numerous epitopes were predicted and top ranked twelve epitopes were selected for further experiments (Tables 1 and 2). The molecular docking analyses were performed against all the selected top ranked 12 peptides to evaluate the effective binding site (Tables S1 and 2).

Table 1

Predicted CTL epitopes and predicted amino acid residues from the SARS-CoV-2.

Sr. #	Residue Number	Peptide sequence	TAP transport efficiency	C-terminal cleavage Affinity	Rescale binding affinity	Predicted MHC binding affinity
1	195	GTDTTITVN	-1.5380	0.0525	0.6485	0.1527
2	253	LSAQTGIAV	0.2310	0.0941	0.6054	0.1426
3	46	SEDMLNPNY	2.6760	0.8406	0.6489	0.1528
4	261	VLDMCASLK	0.5240	0.7881	0.5933	0.1397
5	23	GTTTLNGLW	0.6410	0.4256	0.5566	0.1311
6	93	TANPKTPKY	2.7230	0.9755	0.7118	0.1676
7	24	TTTLNGLWL	0.8460	0.2161	0.5489	0.1293
8	223	FTTTLNDFN	-1.3360	0.0225	0.5426	0.1278
9	256	QTGIAVLDM	0.2870	0.9157	0.5388	0.1269
10	153	DYDCVSFCY	2.7060	0.9722	0.8905	0.2097
11	286	LLEDEFTPF	2.5680	0.9503	0.4807	0.1132
12	246	HVDILGPLS	-2.5130	0.0349	0.4726	0.1113
13	242	LTQDHVDIL	0.7600	0.3794	0.4711	0.1109
14	110	QTFSVLACY	2.9980	0.9725	1.1146	0.2625
15	225	TTLNDFNLV	0.3000	0.9195	0.4694	0.1106
16	185	FVDRQTAQA	-0.8130	0.7828	0.4593	0.1082
17	231	NLVAMKYNY	2.9540	0.8757	0.4555	0.1073
18	146	GSVGFNIDY	2.8570	0.9565	1.3211	0.3112
19	44	CTSEDMLNP	0.0470	0.0243	0.4555	0.1073
20	198	TTITVNVLA	-0.6480	0.0571	0.4275	0.1007
21	118	YNGSPSGVY	2.5820	0.9564	0.4243	0.0999
22	201	TVNVLAWLY	2.9570	0.8852	2.6559	0.6255
23	157	VSFCYMHHM	0.5120	0.9507	0.0996	0.0996
24	81	SMQNCVLKL	1.0740	0.9581	0.4042	0.0952
25	174	GTDLEGNFY	2.7020	0.6229	3.3669	0.7930

Table 2

Top-ranked selected discontinuous epitopes, interacting residues, and scores predicted discontinuous epitopes.

Sr. No.	Residues	Number of residues	Score
1	A:G11, A:K12, A:G15, A:C16, A:D33, A:D34, A:R40, A:C44, A:T45, A:S46, A:E47, A:D48, A:M49, A:L50, A:N51, A:P52, A:N53, A:Y54, A:E55, A:D56, A:L57, A:L58, A:I59, A:R60, A:K61, A:S62, A:N63, A:H64, A:N65, A:Q69, A:A70, A:G71, A:N72, A:V73, A:Q74, A:L75, A:R76, A:V77, A:I78, A:G79, A:H80, A:S81, A:M82, A:K90, A:V91, A:D92, A:T93, A:A94, A:N95, A:P96, A:K97, A:T98, A:P99, A:K100, A:N133, A:D153, A:Y154, A:D155, A:C156, A:G183, A:P184, A:F185, A:V186, A:R188, A:Q189, A:T190, A:A191, A:Q192, A:A193, A:A194, A:G195, A:T196, A:D197	73	0.714
2	A:S1, A:G2, A:F3, , , A:N214, A:D216, A:T304, A:W218, A:L282 A:L220, A:N221, A:R222, A:E270, A:F223, A:T224, A:S301, A:T225, A:T226, A:L227, A:N228, A:D229, A:F230, A:N231, A:L232, A:V233, A:G278, A:A234, A:M235, A:K236, A:Y237, A:N238, A:Y239, A:P241, A:L242, A:T243, A:Q244, A:D245, A:V247, A:D248, A:G251, A:P252, A:S254, A:A255, A:Q256, A:T257, A:G258, A:T198, A:I259, A:T199 A:A260, A:V261, A:V212A:L262, A:D263, A:A266, A:G215, A:S267, A:K269, A:L271, A:I213, A:L272, A:Q273, A:N274, A:G275, A:M276, A:N277, A:R279, A:T280, A:I281, , A:G283, A:R217, A:S284, A:A285, A:L286, A:G302, A:F219, A:V303, A:F305, A:Q306	78	0.711
3	A: C22, A: G23, A: T24	3	0.685

3.1 Analyses of SARS-CoV-2 surface accessibility

The surface accessibility of the predicted peptides were observed >1.0 dipects that the selected peptides were located on the surface. The predicted peptides were observed on the basis of y-axis, and the most likely predicted peptides of SARS-CoV-2 were selected for surface probability (y-axis) and sequence position (x-axis) analyses. The highest score of the predicted peptides were observed 0.911, ranges from 301 to 306 amino acids with the sequence SGVTFQ, while the lowermost value was observed 0.508, rangs from 1 to 5 amino acids with the sequence SGFRK (S1 Fig).

3.2 Surface flexibility of SARS-CoV-2 protein

The atomic vibrational motions in the protein structure determined by B-factor and temperature were calculated and analyzed by using Karplus and Schulz flexibility method. The stability and organization of the structure were determined by the B-factor values [23]. The B-factor values determine the quality of the model however a lower B-factor value suggests s reliable model and higher B-factor value depicts less organized and poorly ordered structures [24]. With the heptapeptide sequences of MESLVPG and SSDVLVN, the lowest and highest flexibility scores were observed as 0.999 and 1.0 respectively [24].

3.3 Prediction of SARS-CoV-2 Parker Hydrophilicity

Parker hydrophilicity scale method was used to evaluate the hydrophilicity of the predicted peptides by using reversed-phase HPLC on a C18 column to estimate the peptide retention durations. The association between antigenic sites and hydrophilic regions was calculated through immunological analyses. The hydrophilicity of each predicted peptide was observed through a hydrophilicity graph, with y-axis measuring hydrophilicity and x-axis showed the residual positions, to assess the hydrophilicity of SARS-CoV-2-predicted peptides (Fig 2). Parker’s hydrophilicity forecast has a maximum hydrophilicity score.

Fig 2

The probability of a residue surface influenced by accessibility to surfaces, surface flexibility, Parker’s hydrophilicity, and antigenicity predictions of non-structural protein (PDB 6M2N) determined by IEDB Parker hydrophilicity prediction has a maximum score of 5.329, ranging from 92 to 98, with the heptapeptide sequence 92 DTANPKT 48, and a minimum value of -4.257, ranging from 204 to 210, with the VLAWLYA sequence of the peptide.

3.4 Antigenicity prediction for SARS-CoV-2 using Kolaskar and Tongaonkar

At locations 85 and 91 of the protein sequence 85 CVLKLKV 91 was determined 1.22 and showed highest antigenicity, while NGMNGRT had 0.84 as lowest antigenicity at positions 274 to 280.

3.5 SARS-CoV-2 structure-based epitope prediction

Antigenicity, epitope prediction, accessibility, and flexibility in the 3D structure were also evaluated to overcome the errors [43]. The protein-antibody interactions were also investigated for all the selected epitopes and top ranked 3 SARS-CoV-2 conformational epitopes having >0.7 score were further evaluated. The proportion of atoms across the molecular substance and the antibody binding was determined for the target protein by using pI (isoelectric point value) score [44] and 5.95 score after titration was observed. The score range between 0.685 and 0.714 was observed along with names, lengths, and positions of the residues, as well as the scores of the three-member conformational epitope prediction panel.

3.6 Molecular docking analyses

Top ranked 25 CTL epitopes were identified and comparative molecular docking analyses were conducted. The binding affinity and global energy ranges from -23.45 to -32.5 kcal/mol and -27.73 to -67.09 kcal/mol respectively were observed against all the selected CTL epitopes (Table 3). Interestingly, it was observed that all the 25 CTL epitopes were docked at similar binding residues. Top ranked eight docked complexes were evaluated (Fig 3), and similar residues (TYR9, LEU5, ILE7, HIS41, MET49, ASN142, HIS164, ARG188, and GLN189) were observed.

Table 3

Summary of designed peptides MHC class I HLA-B interactions against SARS-CoV-2.

Sr. No.	Peptide	Global energy (kcal/mol)	Attractive energy (kcal/mol)	H-bond energy (kcal/mol)	Peptidase-MHC pair	Bond distance (Å)	Conserved residues
1	GTDLEGNFY	-36.48	-24.10	-0.24	LEU 4 N MET 49.A CEASP 3 OD2 SER 46.A OGGLU 5 N MET 49.A CGGLU 5 CG LEU 50.A CGGLU 5 OE2 LEU 50.A CD1GLU 5 N MET 49.A CBLEU 4 C MET 49.A CB	3.145 3.788 4.041 3.642 3.873 3.883	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
2	TVNVLAWLY	-67.09	-28.10	-2.77	VAL 2 C GLN 189.A OVAL 2 O GLN 189.A CBVAL 4 CG1 GLU 166.A CAVAL 2 CG2 ALA 191.A CATHR 1 CB GLN 192.A OLEU 8 CD2 HIS 164.A OTHR 1 OG1 ALA 191.A CA	3.387 3.659 4.127 4.131 3.672 3.674 3.726	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
3	GSVGFNIDY	-40.56	-23.81	-2.75	VAL 3 CG1 LEU 141.A CPHE 5 CE1 ASN 142.A ND2ASP 8 CG ASP 187.A CGVAL 3 CG1 ASN 142.A ND2VAL 3 CG1 GLU 166.A OE1ASN 6 CA CYS 44.A OILE 7 CD ASN 142.A OD1	3.695	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
4	QTFSVLACY	-45.92	-31.22	-2.17	SER 4 CB PRO 293.A CAALA 7 CA THR 111.A OSER 4 CB PHE 294.A CGTYR 9 CZ SER 158.A OGGLN 1 CD PRO 252.A CDCYS 8 N ASN 151.A OD1TYR 9 CE1 ASP 153.A O	4.127 4.127 4.127 3.871 3.453 3.882 3.456 3.580	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
5	DYDCVSFCY	-56.19	-27.17	-1.73	TYR 9 O2 ILE 249.A OPHE 7 CD1 PHE 294.A CZASP 3 OD2 SER 158.A NPHE 7 CZ PHE 294.A CE1CYS 4 SG VAL 104.A CBASP 1 O SER 158.A OGPHE 7 CA PHE 294.A CD1	3.204 3.894 3.441 3.901 4.031 3.264 4.028	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
6	TANPKTPKY	-43.59	-28.20	-4.13	THR 6 CG2 ILE 249.A CG2LYS 5 NZ ILE 249.A CD1PRO 7 CB ASN 151.A ND2PRO 7 CG PHE 294.A CD1ALA 2 N VAL 104.A CG1PRO 4 N GLN 110.A NE2TYR 9 N PHE 294.A CD2	4.105 3.882 3.886 4.011 3.904 3.676 3.800	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
7	SEDMLNPNY	-34.88	-25.96	-2.40	TYR 9 CD2 GLY 143.A CATYR 9 CZ ASN 142.A ND2LEU 5 C GLU 166.A CPRO 7 O MET 165.A NTYR 9 CA MET 165.A CAPRO 7 C MET 165.A CGTYR 9 O2 MET 165.A CA	3.978 3.600 3.577 3.417 4.120 3.871 3.698	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
8	GTDTTITVN	-27.73	-23.12	-2.60	ASP 3 CA PHE 294.A CZTHR 4 CG2 PHE 294.A CZTHR 4 OG1 ASN 151.A CBILE 6 CD PRO 293.A CGVAL 8 CG2 THR 243.A OG1ASN 9 CB ASP 245.A OD2ASN 9 C THR 243.A CB	4.013 4.015 3.726 4.147 3.727 3.690 3.885	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
9	LSAQTGIAV	-39.43	-18.89	-3.80	ALA 3 O MET 165.A CAALA 3 O MET 165.A CBGLN 4 CD GLN 192.A CDGLN 4 N MET 165.A CBSER 2 CB GLN 189.A CBGLN 4 OE1 GLN 189.A OALA 3 C GLU 166.A N	3.675 3.677 3.598 3.906 4.147 3.230 3.644	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
10	VLDMCASLK	-49.11	-23.03	-0.91	LEU 8 N HIS 41.A CE1VAL 1 CG2 GLN 189.A CBCYS 5 C MET 49.A CECYS 5 O ASN 142.A ND2LYS 9 C GLY 143.A CAASP 3 CB GLU 166.A NLYS 9 CA ASN 142.A CG	3.755 4.124 3.867 3.441 3.873 3.913 3.884	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
11	GTTTLNGLW	-44.31	-20.50	-0.64	THR 4 CG2 GLN 110.A CBTHR 2 CA GLY 109.A NGLY 1 O GLN 110.A CGTHR 2 N PRO 108.A CGLY 1 O GLN 107.A CGASN 6 C PHE 294.A CGTHR 2 C GLY 109.A CA	4.112 4.112 3.670 3.631 3.688 3.614 3.888	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
12	TTTLNGLWL	-57.54	-26.76	-1.84	THR 2 OG1 PHE 294.A CATHR 3 CB PHE 294.A CBTHR 2 CA PHE 294.A CD2THR 3 OG1 PRO 293.A CTHR 2 OG1 THR 292.A CG2THR 2 CB THR 292.A CG2LEU 7 CD1 ILE 249.A CD1	3.655 4.079 3.979 3.450 3.725 4.150 4.153	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
13	FTTTLNDFN	-43.52	-23.56	-1.63	PHE 1 CZ PRO 108.A CPHE 1 CG GLU 240.A OE2THR 3 OG1 HIS 246.A CGPHE 1 CD1 GLU 240.A CGASP 7 CB ASP 245.A CGLEU 5 CG GLN 107.A CDLEU 5 CA GLN 107.A CG	3.648 3.323 3.405 3.978 4.117 3.861 4.154	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
14	QTGIAVLDM	-44.24	-20.49	-2.81	THR 2 CA THR 292.A OG1GLN 1 CA THR 292.A CG2VAL 6 CB ASN 151.A CBMET 9 CG VAL 104.A CG1GLN 1 NE2 HIS 246.A ND1THR 2 CG2 PRO 293.A NILE 4 CG2 GLN 110.A NE2	3.655 4.093 4.102 4.118 3.653 3.896 3.912	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
15	LLEDEFTPF	-41.71	-23.91	-2.83	GLU 3 CD ASP 245.A CGGLU 3 OE1 ASP 245.A CAASP 4 C GLN 107.A CBASP 4 O GLN 107.A OE1LEU 2 CB ILE 249.A CG1GLU 3 CG THR 243.A OG1LEU 1 CA ILE 249.A CD1	4.127 3.670 3.866 3.219 4.145 3.730 4.153	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
16	HVDILGPLS	-40.51	-16.69	-1.31	PRO 7 CG GLN 110.A CGVAL 2 CG2 PHE 294.A CE2ILE 4 CA GLN 110.A NE2VAL 2 CG2 PHE 294.A CD2PRO 7 CD THR 292.A CBPRO 7 CA THR 292.A CASER 9 N ILE 249.A CB	4.052 3.939 3.855 3.996 4.122 4.129 3.911	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
17	LTQDHVDIL	-40.26	-25.73	-1.13	ILE 8 CG1 SER 46.A OGGLN 3 NE2 ASN 142.A CILE 8 CG2 SER 46.A CAGLN 3 CB HIS 41.A ND1ASP 4 OD2 HIS 41.A CGASP 4 OD2 HIS 41.A CBGLN 3 NE2 CYS 145.A SG	3.697 3.610 4.128 3.893 3.405 3.692 3.809	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
18	TTLNDFNLV	-54.90	-25.46	-1.23	ASN 4 CB ALA 191.A NASN 4 CA GLN 189.A OASP 5 OD2 GLN 192.A NE2PHE 6 CD1 GLU 166.A CBASP 5 OD1 THR 190.A OLEU 8 C PRO 168.A CAASN 4 CB THR 190.A C	3.880 3.662 3.431 4.016 3.220 3.871 3.872	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
19	FVDRQTAQA	-43.60	-24.43	-4.09	THR 6 N GLN 110.A OE1GLN 5 CA GLN 110.A CDALA 9 O2 HIS 246.A NE2ALA 9 C PRO 108.A OALA 9 CB GLY 109.A NGLN 5 CB GLN 110.A CBGLN 5 NE2 PHE 294.A CD1	3.424 3.855 3.432 3.403 3.907 4.149 3.798	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
20	NLVAMKYNY	-42.71	-21.84	-3.68	TYR 9 OH HIS 246.A CD2TYR 9 CA ASP 245.A CGTYR 9 CE2 ASP 245.A CATYR 9 O2 ILE 249.A CBTYR 9 CA ASP 248.A CBLYS 6 O ASP 245.A OD2TYR 9 CB ILE 249.A CG1	3.520 4.063 3.952 3.644 4.106 3.190 4.138	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
21	CTSEDMLNP	-43.85	-32.50	-6.39	THR 2 CA GLU 166.A OTHR 2 CA GLU 166.A CBASN 8 ND2 HIS 41.A CGGLU 4 CD MET 165.A CALEU 7 CD2 THR 26.A OCYS 1 CA MET 165.A CBMET 6 CG ASN 142.A CG	3.667 4.134 3.625 4.136 3.690 4.154 3.887	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
22	TTITVNVLA	-42.29	-20.11	-2.27	ALA 9 O2 ASN 142.A CGALA 9 O1 ASN 142.A ND2VAL 5 CG2 SER 46.A CAALA 9 O2 ASN 142.A CAVAL 5 CB SER 46.A OGALA 9 CB ASN 142.A CGVAL 5 CG1 MET 49.A CG	3.026 3.088 3.807 3.558 3.630 3.814 4.130	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
23	YNGSPSGVY	-57.72	-25.02	-2.35	SER 6 C GLU 166.A OTYR 1 OH MET 49.A CBGLY 7 N GLU 166.A CGTYR 1 OH MET 49.A CAGLY 7 CA GLU 166.A NASN 2 CB ASN 142.A CGSER 6 C GLU 166.A OE1	3.380 3.698 3.879 3.703 3.884 3.868 3.426	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
24	VSFCYMHHM	-54.36	-30.86	-4.07	SER 2 OG VAL 202.A CG1HIS 8 CB GLN 110.A CDSER 2 OG ILE 249.A CG2TYR 5 CB GLN 110.A NE2TYR 5 OH PHE 294.A CD1PHE 3 CZ ASP 245.A CGVAL 1 C HIS 246.A CD2	3.721 3.871 3.722 3.903 3.604 4.031 3.764	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189
25	SMQNCVLKL	-45.36	-24.55	-3.44	VAL 6 CG2 ASN 142.A OD1MET 2 C ASN 142.A ND2VAL 6 CG2 ASN 142.A CBSER 1 CB GLU 166.A CGGLN 3 NE2 ASP 187.A OSER 1 CB GLU 166.A CDGLN 3 OE1 MET 165.A CE	3.639 3.598 4.115 4.129 3.430 4.134 3.697	TYR9LEU5ILE7HIS41MET49ASN142HIS164ARG188GLN189

Fig 3

A: Peptide-MHC class I HLA-B binding interacting residues of four top-ranked peptides and their sequences presented in different colors. A: Peptide-MHC class I HLA-B binding interacting residues of eight top-ranked peptides and their sequences presented in different colors. Conserved residues are already shown in Table 3.

3.7 Population coverage analyses

Epitopes linked with particular HLA alleles were evaluated to scrutinize MHC class I and MHC class II epitopes. MHC class I and MHC class II epitopes were found to be related to 58.73% and 18.06% of the world’s population, respectively. MHC class I epitope coverage was observed higher in the Italian and Chinese populations as 100% (S1 File).

3.8 Multiple Sequence Alignment (MSA)

The genomes of seven coronaviruses were retrived for MSA alignment to elucidate the conserved region among all. It was observed that the binding domain has conserved region in all of the selected coronavirus strains (S2 and S3 Files).

3.9 Virtual screening and comparative docking

The selected peptides have a substantial anti-SARS-CoV-2 value as per followed methodology [4]. Virtual screening was performed against non structural protein of SARS-CoV-2 (PDB 6M2N) and FDA approaved library from ZINC database was utilized for virtual screening purpose. Comparative molecular docking anayses were performed against the complete FDA approaved library. All the docked complexes were evalued on the basis of least binding energy, contacts, pharmacological characteristics and efficient binding affinity and top ranked 100 compounds were further evaluated. All the docked complexes bound at the similar binding residues and top ranked twelve comounds (ZINC3816514, ZINC3932831, ZINC72318121, ZINC43207238, ZINC1548097, ZINC3995811, ZINC3830405, ZINC3816292, ZINC100016058, ZINC3920266, ZINC16052277 and ZINC84441937)) (Table 4) showed the most promosing results among all (Figs 4 and 5). All the docked complexes showed interactions at similar site and HIS41, CYS44, MET49, ASN142, MET165, GLU166, ARG188 and GLN192 residues were conserved (S2 and S3 Tables).

Table 4

Drug like properties and comparative molecular docking analyses of selected compounds scrutinize through virtual screening.

Sr No.	Ligands	Binding Affinity (Kcal/mol)	RMSD Value	Molecular Weight (g/mol)	H-bond acceptor	H-Bond Doner	Rotable Bond	Interacting Residues	Density	A logp Value	Acute oral toxicity(Kg/mol)	Pfizer	Lipinski Rule	GSK
1	ZINC3816514	-8.9	1.738	500.48	3	2	5	HIS41 CYS44 MET49 TYR54 CYS145 GLU166 ASP187 ARG188 GLN 192	1.077	5.73	3.288	NO	Yes	NO
2	ZINC3932831	-8.3	0.487	528.54	2	2	2	THR24 THR25 HIS41 ASN142 GLY143 CYS145 MET165 THR190 ALA191	1.072	6.58	3.743	NO	NO	NO
3	ZINC72318121	-7.9	1.836	506.61	8	1	7	HIS41 CYS44 MET49 PRO52 GLN189	1	4.94	2.927	Yes	Yes	No
4	ZINC43207238	-7.8	0.912	444.52	6	4	5	HIS41 CYS44 MET49 ASN142 MET165 PRO168 THR190 ALA191 GLN192	1.017	2.97	2.901	Yes	Yes	Yes
5	ZINC1548097	-7.7	1.784	426.49	6	0	8	HIS41 MET49 MET165 PRO168 GLN192	0.988	4.83	2.492	No	Yes ✓Rejected	No
6	ZINC3995811	-7.5	0.575	606.78	3	2	14	MET165 PRO168 THR190 GLN192	1.019	8.68	2.964	No	No ✓Rejected	No
7	ZINC3830405	-7.5	1.591	454.52	12	2	7	MET49 TYR54 GLU166 LEU167 PRO168	1.214	-0.64	1.589	No	Yes ✓Rejected	No
8	ZINC3816292	-7.5	1.781	426.86	5	3	6	THR25 HIS41 SER46 MET49 MET165 ARG188	1.051	4.07	2.287	Yes	Yes ✓Rejected	No
9	ZINC100016058	-7.4	1.02	602.68	6	2	11	HIS41 CYS44 MET49 ASN142 MET165 GLU166 ARG188 GLN192	1.031	7.33	3.286	Yes	No ✓Rejected	No
10	ZINC3920266	-7.3	1.371	497.50	10	5	3	HIS41 MET49 TYR54 ASN142 MET165 GLU166 ASP187	1.032	1.02	3.704	Yes	Yes ✓Rejected	No
11	ZINC16052277	-7.2	1.345	444.44	9	7	2	HIS41 CYS44 MET49 CYS145 HIS164 MET165 GLU166	1.042	0.70	2.191	Yes	Yes ✓Rejected	No
12	ZINC84441937	-7.1	1.568	444.44	9	6	2	HIS41 MET49 TYR54 ASN142 HIS164 MET165 ASP187	1.042	-0.21	2.537	Yes	Yes ✓Rejected	No

Fig 4

A: Peptide-MHC Class-I binding interacting residues of four top-ranked peptides and their sequences represented on the top of each residue. B: Binding interacting residues of eight peptides and their sequences present in different colors. Fig i, ii and iii showed interaction of docked complexes, 2D interaction of compounds and Ramachandran plot for the interaction respectively. FDA approaved compounds present as ZINC3816514 (Dark-blue), ZINC3932831 (Dark-brown), ZINC72318121 (Violet), ZINC43207238 (Yellow), ZINC1548097 (Green), ZINC3995811 (Light-green), ZINC3830405 (Slate-blue), ZINC3816292 (Dim-grey), ZINC100016058 (Orange), ZINC3920266 (Red), ZINC16052277 (Purple) and ZINC84441937 (Pink). Conserved residues are shown in Table 3.

Fig 5

Twelve top-ranked FDA approaved Library from ZINC database.

The selected protein plays a significant role in the replication of SARS-CoV-2. The scrutinized compounds have the ability to inhibit the target protein based on extensive in silico analyses. The scrutinized compounds followed the lipinky’s rule of five and better oral bioavailability. The scrutinized compounds showed the solibility in water at 25°C. The binding sites and maximum binding affinity of all selected compounds have prmosing results (S2 and S3 Tables).

There is an urgent need of an effective cure for coronaviruses. SARS-CoV-2 pandemic became a medical emergency in all over the globe [45]. Vaccine development is of significant interest to peptide inhibitors [46]. The peptide targets include lower toxicity, lower side effects and faster action than traditional medicinal products based on ligands. Immunoinformatics methodologies help scientists to minimise the laboratory load, less expedient and cost-effective [47]. There have been major advances in in silico drug design over the last decade [48-51]. A large number of biological difficulties were tackled by the use of different bioinformatics approaches [52-54]. The epitopes of non structural protein (PDB 6M2N) were designed and CTL epitopes were also predicted against SARS-CoV-2. The binding affinities for the predicted peptides for MHC-I were further evaluated through comparative molecular docking analyses. Eight peptides showed effective MHC-I (HLA-B) interactions. Based on the global energy value, twelve peptides have been selected with the greatest antigenicity and binding affinities (S2 Table).

4. Conclusion

The goal of current effort was to elucidate the efficient peptide based inhibitors against SARS-CoV-2 non-structural protein. The predicted epitopes were designed followed by comparative molecular docking studies against MHC-I. Moreover, the interactional studies of the scrutinized docked complexes were analyzed. In conclusion, 12 epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSAQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL), were predicted having potential targets as peptide vaccine against SARS-CoV-2.

The probability of a residue surface influenced by accessibility to surfaces.

(DOCX)

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Predicted CTL epitopes and predicted amino acid residues from the SARS-CoV-2, Table 2.

(DOCX)

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Fig 5.

(XLSX)

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(CSV)

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Population coverage.

(PDF)

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Multiple sequence alignment.

(DOCX)

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Phylogenetic tree.

(PDF)

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