key: cord-0897541-2fwz8z8n authors: Kumar, Amit; Kumar, Prateek; Saumya, Kumar Udit; Kapuganti, Shivani K.; Bhardwaj, Taniya; Giri, Rajanish title: Exploring the SARS-CoV-2 structural proteins for multi-epitope vaccine development: an in-silico approach date: 2020-09-09 journal: Expert review of vaccines DOI: 10.1080/14760584.2020.1813576 sha: 25c249a725b7a368b8b462e3441a82763cc924c6 doc_id: 897541 cord_uid: 2fwz8z8n INTRODUCTION: The ongoing life-threatening pandemic of coronavirus disease 2019 (COVID-19) has extensively affected the world. During this global health crisis, it is fundamentally crucial to find strategies to combat SARS-CoV-2. Despite several efforts in this direction and continuing clinical trials, no vaccine has been approved for it yet. METHODS: To find a preventive measure, we have computationally designed a multi-epitopic subunit vaccine using immuno-informatic approaches. RESULTS: The structural proteins of SARS-CoV-2 involved in its survival and pathogenicity were used to predict antigenic epitopes. The antigenic epitopes were capable of eliciting a strong humoral as well as cell-mediated immune response, our predictions suggest. The final vaccine was constructed by joining the all epitopes with specific linkers and to enhance their stability and immunogenicity. The physicochemical property of the vaccine was assessed. The vaccine 3D structure prediction and validation were done and docked with the human TLR-3 receptor. Furthermore, molecular dynamics simulations of the vaccine-TLR-3 receptor complex are employed to assess its dynamic motions and binding stability in-silico. CONCLUSION: Based on this study, we strongly suggest synthesizing this vaccine, which further can be tested in-vitro and in-vivo to check its potency in a cure for COVID-19. Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV -2) is the causative agent for the associated disease named as COVID-19, which had spread throughout the world and declared as a pandemic [1, 2] . SARS-CoV-2 has been demonstrated to have a strong affinity for human respiratory receptors (hACE2), which makes it more vulnerable to humans globally [3] . SARS-coronavirus infections are commonly found to be associated with multiple disorders, such as respiratory, enteric, hepatic, and neurological complications [4] . In general, coronaviruses (CoV) belong to a family Coronaviridae, which consists of enveloped positive-sense, single-stranded RNA viruses [5, 6] . Among the four genera of CoVs, SARS-CoV, MERS-CoV, and SARS-CoV-2 belong to Betacoronaviruses [7] . Inside host cells, SARS-CoV-2 genome is translated into two large polyproteins: replicase polyprotein 1a (pp1a) and 1ab (pp1ab) [8] . The larger pp1ab comprises 16 non-structural proteins (Nsp1-Nsp16), which are responsible for constructing a viral replication-transcription complex [9] . Alongside the viral RNA transcribes subgenomic RNAs, which encode four structural proteins; spike (S), envelope (E), membrane (M), and nucleocapsid (N) and several accessory proteins [10] . While accessory proteins are engaged in host immune suppression, including downregulation of the interferon pathway, structural proteins shape the virion, facilitating genome encapsulation, viral particle assembly, and release [11, 12] . Recently, one health concept is used and hypothesized that the prior exposure of humans with pet animals lowers the adverse effect of SARS-CoV-2. The N and S protein epitopes in comparison with taxonomically related coronaviruses' epitopes share high sequence homology, which has high implications for vaccine designing against SARS-CoV-2 [13, 14] . Additionally, there are repurposing of drugs are also important to cobat this disease [15, 16] . Since the beginning of the 21 st century, two outbreaks of SARS-CoV and MERS-CoV have led the researchers into designing vaccines against SARS coronaviruses. The third outbreak, wherein SARS-CoV-2 infects promiscuously to humans, has given the need for speedy research into developing a broad-range vaccine against these coronaviruses. Evidence shows that humoral and cell-mediated immune response plays a protective role against coronavirus infections, specifically against S and N proteins [17] [18] [19] . Although the effective antibody response is short-lived, the T cell responses are found to provide long-term protection [20, 21] . Similarly, T C cell response against the structural proteins S and N are long-lasting [22] . Previously, immunoinformatics approaches have been used to construct the vaccines against the Zika virus Nipah virus, and Human papillomavirus, where in-vivo studies of the Human papillomavirus showed promising results [23] [24] [25] . Hence, in this study, we have used immunoinformatic approaches to predict highly antigenic epitopes from SARS-CoV-2 structural proteins that would evoke a strong immune response in humans. For this purpose, we have used the structural proteins: Spike, Envelope, and nucleocapsid to predict B-cell, cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) epitopes for construction of vaccine. Thus, the multi-epitopic subunit vaccine would be capable of eliciting humoral as well as cellmediated immune responses. We have also performed the docking and molecular dynamic simulations (MDS) between the vaccine and human Toll-like Receptor-3 (TLR-3) to study their binding stability. The outcome of the present study will result in a novel and immunogenic vaccine, which may be further accessed against SARS-CoV-2. The IEDB tools (http://tools.iedb.org/bcell/) were used to identify the B cell epitope and for verifying antigenicity, BepiPred linear epitope analysis was performed. According to the BepiPred linear epitope prediction method, residues with scores above the threshold (default value 0.35) are predicted to be part of an epitope having 49% sensitivity and 75% specificity [26] . The IEDB server (http://tools.iedb.org/mhcii/) was used for the prediction of MHC II antigenic binders in translated sequences. Top 10 HTL epitopes were sorted based on their percentile rank and IC 50 value. Epitopes with the lowest percentile rank were considered an excellent binding affinity, whereas, peptides with IC 50 values in lower (<50 nM), middle (<500 nM), and higher (<5000 nM) range corresponds to the highest, intermediate, and lowest binding affinity, for the T-cell, respectively [27] . MHC class I binding/CTL epitopes for supertype A3 were predicted for all three proteins by using an online server NetCTL 1.2 (http://www.cbs.dtu.dk/services/NetCTL/) [28] . Default threshold of 0.75 was chosen for vaccine construction in this study (>1.25 corresponds to 54% sensitivity and 99.30% specificity; >1.00 shows 70% sensitivity and 98.5% specificity; >0.90 shows 74% sensitivity and 98% specificity). MHC class I binding and proteasomal cleavage prediction was performed using artificial neural networks. Accordingly, TAP transport efficiency was predicted using a weight matrix. MHC class II binder epitopes/HTL, with the ability to induce cell-mediated immunity, were identified using the IFNepitope server (http://crdd.osdd.net/raghava/ifnepitope/). The prediction was performed by a motif and support vector machine (SVM) hybrid approach [29] . All the positive epitopes able to induce interferon-γ (IFN-γ) were selected for vaccine construct. Selected high-scoring CTLs, high-affinity HTLs, and B-cell epitopes were used to generate the vaccine sequence. The different epitopes were linked together using linkers: CTL linker (AAY), HTL linker (GPGPG), and B epitope linker (KK). To improve the immunogenicity of the vaccine, β-defensin 1 (Uniprot Id: P60022) as an adjuvant was added through an EAAAK linker at the N-terminal of the construct. The servers ANTIGENpro (http://scratch.proteomics.ics.uci.edu/ ) and VaxiJen v2.0 (http://www.ddg-pharmfac.net/vaxijen/ VaxiJen/VaxiJen.html) were used for the antigenicity evaluation of the final vaccine construct [30, 31] . The VaxiJen 2.0 server predicts the antigenicity of the multi-epitope vaccine peptide based on the physicochemical properties of the input protein. Whereas, ANTIGENpro server predicts the antigenicity of the multi-epitopic vaccine based on the protein microarray data analysis of the target organism. The server AllerTOP v2.0 (http://www.ddg-pharmfac.net/ AllerTOP) and AllergenFP were used to predict the allergenicity of multi-epitopic vaccine [32, 33] . AllerTOP v2.0 classifies the vaccine protein sequence by machine learning methods for the classification of allergens. AllergenFP is an alignmentfree online server that uses SVM module to predict the allergenic and non-allergenic nature of proteins. Vaccine construct was accessed for its physicochemical properties using the ProtParam server (http://web.expasy.org/prot param/) that predicts the theoretical pI, instability index, halflife, stability profiling, aliphatic index, and Grand Average of Hydropathy of the sequence [34] . Protein secondary structure was determined using PSIPRED, a web-based freely accessible online server (http://bioinf.cs. ucl.ac.uk/psipred/) [35] which also gives information of contact analysis, fold recognition, structure modeling, function prediction, protein intrinsic disorder prediction and domain prediction of a query sequence [36] . The disorder profile of the vaccine construct was generated using PONDR pool of four predictors (http://original.disprot. org/metapredictor.php) [37] [38] [39] and IUPRED 2A predictor [40] as described in our previous reports [41] [42] [43] . The tertiary structure of the vaccine construct was generated using the I-TASSER web server [44] . It provides an energy minimized model through iterative template-based fragment assembly simulations. The structure was further optimized upon adding hydrogen and missing side-chain atoms followed by minimization in Schrodinger's protein preparation wizard using OPLS 2005 forcefield [45] . This produced a high-quality model for protein-protein docking and molecular simulations. RAMPAGE [Mordred.bioc.cam.ac.uk/~rapper/rampage.php] and PROCHECK [46] web servers were used for evaluation of the vaccine model based on the distribution of amino acids in the Ramachandran plot. PIPER program embedded in the BioLuminate module of Schrodinger for protein-protein docking was implemented for docking of vaccine model and TLR-3 (PDB ID: 20AZ) and TLR-5 (PDB ID: 3J0A) receptors [47, 48] . It is an efficient tool that reduces false positive poses and performs a global search with Fast-Fourier Transform (FFT) approach. Using 1000 conformations of input structures, the top 50 clusters were selected with cluster radius 9 Å, which then centralized, and the outcomes of the docking based on cluster size were evaluated. With the largest cluster size, the docked complex out of 10 complexes was selected for molecular dynamics simulation. The ~86kDa vaccine model in complex with TLR-3 receptor was evaluated for its binding stability in an aqueous environment for 20ns. With a total of 6,66,607 molecules of TIP3P water model, 60 neutralizing Cl − ions, and 0.15 M concentration of salt, the complex was incorporated in a cubic box. Recently updated forcefield CHARMM36 was implemented to generate the topologies of the protein-protein complex. The generated system was minimized for 50,000 steps of the steepest descent algorithm. Further, the equilibration process under NPT and NVT conditions for 1ns was done with Parrinello-Rahman and V-rescale methods for coupling of pressure and temperature, respectively. Lastly, production MD run for all three systems was performed in periodic boundary conditions for 20ns. LINCS algorithm was used for calculating bond parameters. Particle Mesh Ewald (PME) for long-range electrostatics with fourier spacing of 0.16 was used for production MD. An analysis of MD trajectory, Root Mean Square Deviation (RMSD), and Root Mean Square Fluctuation (RMSF) was calculated with gmx rms, and rmsf commands. For the production of final vaccine in large quantity, Java Codon Adaptation Tool (http://www.jcat.de/) was used for codon optimization and attached with expression vector. The optimized codon sequence includes Codon Adaption Index (value 1.0) and %GC content (30-70%), which denotes the high level of protein expression and DNA stability. Finally, restriction cloning was performed using tool SnapGene v3.3.4. The restriction sites NdeI and XhoI were added to the N and C terminals of the optimized complementary DNA sequences followed by their insertion within the pET-21a(+) vector to ensure the polyprotein synthesis within the E coli expression system. HTL epitopes for all structural proteins were predicted using IEDB server for human MHC-II alleles. Among these, top 10 epitopes based on their least percentile rank depicting their high affinity were selected. The final selection of epitopes was made on the basis of overlapping sequences, which were further assessed for their IFN inducing potential. Epitopes with positive IFN inducing potential were finally selected for vaccine construction (Table 1) . For each structural protein, CTL epitopes for supertype A3 were predicted using an online server NetCTL 1.2. A total of 40, 5, and 4 epitopes were predicted for S, N, and E proteins, respectively (Table 2) . Linear B-cell epitopes in all three structural proteins were predicted using another online server BCPREDS. Epitopes selected on the basis of prediction scores were further screened for their non-allergic and antigenicity potential. Two epitopes each for S and N with high antigenicity scores were selected for final vaccine construction are tabulated in Table 3 . However, no B cell epitopes were found for the E protein. The designed construct comprising of 48 CTL epitopes, 4 HTL epitopes, and 4 B-cell epitopes were then joined using linkers. Additionally, in order to improve the immunogenicity of multi-epitope vaccine, the construct was tagged with an adjuvant (β-defensin-TLR-3 agonist) at N terminal region, which enhances the natural immune response. The vaccine must be antigenic and non-allergic in nature and also induce humoral as well as cell-mediated immune responses against the targeted virus. Our vaccine was observed to be antigenic with a respective probability score of 0.566 and 0.845 predicted by VaxiJen v2.0 and ANTIGENPro servers. The construct was also detected as a non-allergen, checked using the AllerTOP v2.0 server. The vaccine construct is composed of 799 amino acids and has a molecular weight of 86.35 kDa. It is basic in nature (theoretical pI 9.70) and has a half-life of 30 hrs in mammalian reticulocytes (in vitro). The instability index is estimated to be 32.21, which denotes the stability of the protein. The aliphatic index (68.42) showed that the protein is thermostable, whereas grand average of hydropathicity is observed to be negative (−0.084), indicating the hydrophilic nature of vaccine. The secondary structure was predicted using PESIPRED 4.0 server. As shown in Figure 1 , the vaccine contains several αhelices together with many the β-strands. Six long helices from residues 3-19, 65-102, 119-142, 433-448, 475-541, and 597-614 with a long β-strands of 14 amino acids from residues 401-414 can be observed. The 799-residue long vaccine construct was modeled using I-TASSER. The lowest energy predicted model was selected for further refinement and docking studies. However, the validation on Ramachandran plot by PROCHECK server suggested that 88% residues of the constructed vaccine model lies in the favored and allowed regions while the rest 12% are a part of generously allowed and disallowed regions (Figure 2(b) ). Similarly, RAMPAGE server also mapped nearly 90.7% residues in favored and allowed regions, while 9.3% residues were present in coordinates portraying the outlier region (Figure 2(a) ). As predicted by secondary structure and disorder-based structure analysis, the tertiary structure of the construct has a high number of unstructured regions. Based on disorder prediction data, it is largely disordered at its terminals. From the PIPER program, with highest cluster size 28, out of 10 complexes ranging till 15, the complex 1, also with greatest minimal local energy, was chosen for further analysis. The vaccine model interacts with the TLR-3 receptor (PDB: 20AZ) through its N-terminal and the middle regions (Figure 3 ). The resultant interacting residues of vaccine-TLR-3 complex are shown in Table 4 . At the interface of docked complex, Asn617 of vaccine is observed to form a H-bond with Asp153 residue of the receptor. Additionally, a few more aromatic H-bonds between Val621, Leu620 residues of vaccine, and His60 of TLR-3 are identified. The complex if further stabilized by a pi-cation interaction between Lys595 of vaccine and Tyr283 of TLR-3 (Table 4 ). Figure 4 depicts a close view of vaccine-TLR-3 interface. Several amino acids (red) of the receptor (green) are found engaged with vaccine residues (blue). Additionally, we have also performed docking with another toll-like receptor (TLR-5) which is known to recognize the flagellin of bacteria. Similar to TLR-3, the vaccine model has shown significant interactions with TLR-5 through its N-terminal as well as residues of middle region where the N-terminal folds in the constructed 1 . PSIPRED graphical result of secondary structure prediction of vaccine is represented by different colors: α-helix (pink), β-sheet (yellow), and coil (gray). EXPERT REVIEW OF VACCINES model, as shown in Figure 5 . Residues such as Tyr5, Glu17 from N-terminal, and Thr511, Lys514 from middle regions of vaccine construct interacts with residues Asp607, Ala674, Cys585, and Glu584 of TLR-5, respectively, through aromatic hydrogen bonds, salt bridge, and hydrogen bonds. To analyze the stability and dynamic motions of docked complex, we performed a 20ns long MD simulation using Gromacs v5.1. The simulation system comprised ~7 lacs atoms in a cubic box with proteins, solvent, and ions. Based on the RMSD data, the complex was found to have an upward trend ranging from ~1 to 2.5 nm till 20ns with small fractions of stability in between ( Figure 6(a) ). However, according to RMSF values depicted in the graph of Figure 6 comparison with the other residues. Overall, the average fluctuation in the residues of the vaccine model was near ~1.2 nm. Moreover, the regions interacting with TLR-3 receptor are not disordered as per our analysis in Figure 1(b) and had stability throughout the simulation. As calculated in visual molecular dynamics, the average H-bonds were found to be 200 in vaccine-TLR-3 complex during MD simulations (Figure 6(c) ). Additionally, disordered regions based on the sequence of vaccine were also predicted. The graph in Figure 6 (d) represents the moderate amount of disorder (~20% calculated as the mean of all predictors) within residues 50-100 and 650-799 near N-and C-terminal regions. The regions interacting with TLR-3 receptor are not disordered as per our analysis in Figure 6 (d) and had stability throughout the simulation. The CAI improved value for codon optimized sequence was 1.0, while GC content obtained for our vaccine construct was 49.56%. Overall, the vaccine sequence lies in a suitable category for its expression in E coli. Finally, the vaccine construct was cloned with restriction sites NdeI and XhoI in the E coli pET-21a(+) vectors. Finally, a cloned construct was generated, having a sequence length of 7767 base pairs (Figure 7 ). SARS-CoV-2 is highly pathogenic, which has caused more than 4 lakh deaths globally until the writing of this manuscript. Particularly among all, Coronaviridae structural proteins S, M, E, and N are the main structural proteins and can be employed for protein/peptide-based vaccines. Different human infecting coronaviruses like OC43, HKU1, and MERS-CoV use a diverse set of lipids and receptors to enter into target cells [49, 50] . In SARS-CoV, the spike glycoprotein interacts with ACE-2 receptor and facilitates the fusion of viral membrane with the lungs membrane [11, 51, 52] . E is an integral membrane protein with N-terminal region, a transmembrane domain (TMD), and a flexible hydrophilic C-terminal tail containing a PZDbinding motif in the last four amino acids [53, 54] . Envelope protein forms pentamer to lead viroporin like structure that exhibits a membrane-destabilizing ion-channel activity on the host membrane [55] . CoV envelope, which plays a vital role during virion assembly, consists mainly of M protein and only a small fraction of E protein [56] . Whereas N protein shows interaction with M protein in lipid membrane of infected cells forming a double-layered vesicular structure in a secreted form that further interacts with host proteins [57, 58] . Multiple vaccines are in clinical trial, but till date, no vaccine is approved against the SARS-CoV-2. In a recent in-silico report, the vaccine model using spike glycoprotein has been constructed but limited to the docking with TLR 5 receptor [59, 60] . It was reported that Human coronaviruses OC43 recognize the MHC class I supertype A3 [61] . Based on these reports, we have chosen the A3 super type for epitope prediction, which will help the vaccine construct to act significantly. According to information obtained from the World Health Organization (WHO), one vaccine is in Phase 2, and two vaccines (DNA and RNA based) are in Phase 12 clinical trial. Moreover, 67 vaccines are in pre-clinical trials [62] . Previously, studies showed that the whole virus vaccine and virus-like particle vaccine against SARS-CoV are able to cure infection but leads to the pulmonary immunopathologic type lung disease [63] . Therefore, it is needed that the vaccine is selected on the basis of multi factors, i.e., antigenicity, immunogenicity, toxicity, allergenicity. In the present study, we have designed the vaccine construct by using immune-informatics approaches. Three structural proteins (spike glycoprotein, nucleocapsid, and envelope) were selected to construct a multi-epitope vaccine, which is capable of eliciting the humoral and cell-mediated immune response. Furthermore, the vaccine is antigenic, non-allergen, nontoxic, immunogenic in nature, and capable of IFN-γ production. The vaccine epitopes were joined with the help of specific linkers to enhance stability and immunogenicity. The vaccine has a molecular weight of 86.35 kDa, basic in nature, and half-life of 30 hrs in mammalian reticulocytes (in-vitro). Moreover, vaccine was found to be highly stable and hydrophilic in nature. Finally, the build vaccine construct was found to be moderately disordered, containing few disordered residues at N-terminal and relatively a vast region at C-terminus. As previously known, the vaccine candidates often have an ample amount of protein intrinsic disorder as they feature in the immune system [64] . Further, we also checked the interaction of vaccine construct through a modeled structure docked with two Toll-like Receptors (TLR-3 and TLR-5) structure. The adjuvant βdefensin 1 showed interaction with TLR-3 receptor and few residues from the same region also showed interaction with TLR-5. Further, the stability of the vaccine-TLR-3 complex was monitored by implementing molecular dynamics simulations till 20ns. Several residues at the N-terminal and middle region of vaccine construct were found to form stable interactions. Finally, the cloned sequence vector was generated in-silico for the expression of the vaccine. Newly emerged SARS-CoV-2 is declared as pandemic and scientists all over the world running a race against the time to find vaccine/drugs against COVID-19. Despite that, many obstacles like allergenicity, immunogenicity, toxicity, etc. limit the progress of vaccine development. 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WHO | Coronavirus disease World Health Organization Immunization with SARS Coronavirus vaccines leads to pulmonary immunopathology on challenge with the SARS virus Insights into the immunological properties of intrinsically disordered malaria proteins using proteome scale predictions The authors would like to thank IIT Mandi for research facilities. RG is thankful to DBT, Govt. of India (BT/11/IYBA/2018/06) to RG. AK is supported from DBT, Govt. of India grant (BT/11/IYBA/2018/06). PK and SK were supported by MHRD-India for their funding. KUS and TB are grateful to the ICMR SRF and DST INSPIRE PhD fellowships, respectively.