key: cord-1016628-sbmbaxm4 authors: Khan, Md.Tahsin; Islam, Md. Jahirul; Parihar, Arpana; Islam, Rahatul; Jerin, Tarhima Jahan; Dhote, Rupali; Ali, Md Ackas; Laura, Fariha Khan; Halim, Mohammad A. title: Immunoinformatics and Molecular Modeling Approach to Design Universal Multi-Epitope Vaccine for SARS-CoV-2 date: 2021-04-21 journal: Inform Med Unlocked DOI: 10.1016/j.imu.2021.100578 sha: 965dc20670b5b7cf1cf01f3fd589d3dc3c7dee7d doc_id: 1016628 cord_uid: sbmbaxm4 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmittable and pathogenic human coronavirus that caused a pandemic situation of acute respiratory syndrome, called COVID-19, which has posed a significant threat to global health security. The aim of the present study is to computationally design an effective peptide-based multi-epitope vaccine (MEV) against SARS-CoV-2. The overall model quality of the vaccine candidate, immunogenicity, allergenicity, and physiochemical analysis have been conducted and validated. Molecular dynamics studies confirmed the stability of the candidate vaccine. The docked complexes during the simulation revealed that a strong and stable binding interactions of MEV with human and mice toll-like receptors (TLR), TLR3 and TLR4. Finally, candidate vaccine codons have been optimized for their in silico cloning in E.coli expression system, to confirm increased expression. The proposed MEV can be a potential candidate against SARS-CoV-2, but experimental validation is needed to ensure its safety and immunogenicity status. clinical trial or are ready to start the trial process by fulfilling preliminary evaluation protocols. the immunoinformatics approach to accelerate the race over safe vaccine development, but they 98 were limited to either single protein [14] ; [15] or very few proteins of SARS-CoV-2 [16] ; [17] . 99 This study aims to design a multi-epitope vaccine against SARS-CoV-2 that efficiently elicits 100 both innate and adaptive immune responses into the host body and thereby provides maximum 101 protection. Therefore, 13 out of 21 structural and non-structural proteins (spike, nucleocapsid, 102 membrane, envelope, endo RNAse, nsp4, nsp9, nsp6, orf3a, orf6, orf7a, orf8 and orf10) of The bioinformatics resources used in the current study are presented in Supplementary Table S1 . 116 Protein selection: 117 The reference proteins of SARS Corona Virus-2 were retrieved from National Center for 118 Biotechnology Information (NCBI). VaxiJen v2 server was utilized to screen out highly with IEDB class I immunogenicity server for Human HLA class-I binding. Finally, most HLA 143 covering epitopes were selected as final epitopes and subjected to population coverage analysis. MHC class II (CD4+ T cell) epitope screening: 145 The IEDB MHC II binding server was used to predict epitopes from selected antigenic proteins. 146 Firstly, 15-mer HTL epitopes were predicted for Mice alleles (H2-IA b , H2-IA d , H2-IE d ) with a 147 selecting percentile rank <10. Then, the TMHMM, VaxiJen v2.0, AllerTOP v.2.0 and ToxinPred 148 servers were utilized as in the first filtration such as for CTL screening. Secondly, epitopes were 149 screened through the IFNepitope server to select epitopes with the capability to induce IFN-γ 150 production. Finally, epitopes showing positive results were further cross-checked with the IEDB 151 class II immunogenicity server for Human HLA class-II binding. As with CTL screening, most 152 HLA class-II covering epitopes were selected as final HTL epitopes and then subjected to 153 population coverage analysis. Linear and Discontinuous B cell epitope screening: 155 The available webserver used for B cell epitope prediction was not accurate enough, and B-cell 156 epitope prediction is a semi-low accuracy procedure. Hence, in our study we predicted the B cell 157 epitopes through a multi-method approach including (i) the physicochemical-based algorithms 158 (e.g., Bcepred and IEDB-based linear epitope prediction) and (ii) the machine learning based 159 algorithms (e.g., ABCpred, BepiPred, and LBtope). We initially used the ABCpred webserver as 160 a standard for BCL (B cell lymphoma) prediction (14, 16, 18 and 20-mer), and furthermore, 161 other webservers were used to find overlapped epitopes through cross checking. Finally, 162 overlapped consensus epitope were screened through VaxiJen v2.0, AllerTOP v.2.0 and 163 ToxinPred servers for selecting the final linear BCL epitopes. 164 We also predicted the discontinuous epitopes through IEDB Ellipro tools in our study. Structural 165 proteins were used for this analysis. Three dimensional (3D) structures of structural proteins 166 were retrieved from RCSB (Research Collaboratory for Structural Bioinformatics) and I-167 TASSER online server. Finally, shortlisted overlapped consensus epitopes were cross-checked 168 with the IEDB Ellipro webserver to find overlapped discontinuous epitope residues. The secondary structure of the vaccine protein was predicted using the Self-Optimized Prediction were employed for execution of the simulation. A cut-off radius of 8.0 Å was used for the 205 particle-mesh Ewald (PME) method [21] for long-range electrostatic interaction calculations. For Selection of significant protein 223 The NCBI database was used to retrieve amino acid sequences of 21 structural and non-structural 224 proteins. All proteins were evaluated through the VaxiJen server, which produced an overall 225 score for each protein sequence prevailing their antigenicity response. We found an antigenicity 226 J o u r n a l P r e -p r o o f score of more than 0.45 for 13 proteins (nsp4, nsp6, nsp9, endoRNAse, Spike, ORF3a, Envelop, 227 ORF6, ORF7a, Membrane, ORF8, Nucleocapsid, and ORF10 protein), as shown in 228 supplementary Table S2 . Based on antigenic scores, these proteins were chosen for further 229 design of a multi-epitope vaccine. Identification and evaluation of CTL (Cytotoxic T Lymphocytes) epitopes 231 CTL epitopes of structural and non-structural proteins were initially predicted by a two-stage 232 filtration/screening process. First filtration was conducted after CTL selection by the NetCTL1.2 233 server, which predicted CTL epitope of 9-mer in length. After identifying CTL epitopes through 234 NetCTL1.2 server, epitopes were filtered by checking antigenicity, allergenicity, non-toxicity 235 and immunogenicity in the first filtration process. Finally, epitopes were evaluated through best 236 binding affinity with mouse alleles in the second filtration. About 1582 epitopes were predicted Initially, HTL epitopes for 13 proteins were predicted by the IEDB server for mouse MHC-II 242 alleles (H2-IAb, H2-IAd, H2-IEb). Most significant epitopes were selected based on their 243 percentile rank, which should be less than 10. About 703 HTL epitopes were predicted from the 244 IEDB MHC-II server (supplementary File 2). Subsequently, these epitopes were screened by 245 following the CTL-first filtration process. However, a total of 120 unique HTL epitopes were 246 predicted for 9 proteins including nsp4, nsp6, nsp9, spike, membrane, envelope, ORF3a, ORF8, 247 and nucelopcaspsid protein along with their respective mouse alleles. Among these 120, after 251 The most significant linear B-cell epitopes for spike, envelope, membrane, and nucleocapsid 252 proteins were identified by using a multi-method approach, as detailed in the method section. which showed 96% conservancy, as presented in Supplementary File 1, 2 and 3. 286 After Conservancy analysis, we selected the final CTL and HTL epitopes. We selected total 12 287 CTL epitopes from each protein by considering their high antigenic and immunogenic score and 288 multiple types of mice allele and human super type coverage (Supplementary File 1) . 289 Thereafter, we predicted the MHC-I binding allele of each of the 12 CTL epitopes from IEDB 290 server. The cut off value for the prediction was selected as percentile rank ≤ 1, and according to 291 IEDB recommendations, a percentile rank of ≤ 1 was used as the cutoff to predict peptide binders 292 for MHC I and a percentile rank of ≤ 10 was used as the cutoff to predict peptide binders for 293 MHC II [23] . In the case of CD4+ T cell epitope prediction, similarly 9 HTL epitopes were selected from each 295 protein by considering their mice allele percentile rank, high antigenic score and maximum (Figure 1b) . 314 The evaluated physicochemical properties of constructed vaccines are represented in the Table 315 S3. The vaccine construct was found to be basic in nature by identifying the Isoelectric point (PI) The MD simulation optimized homology model of the vaccine protein was evaluated through a 341 Ramachandran plot. The results showed that 93% of residues were placed in most favored 342 regions, 6% in additional allowed regions, and 0.3% in disallowed regions respectively, as 343 predicted by RAMPAGE server (Figure S5c ). Model quality was also checked by ERRAT and 344 the Verify3D server where the quality of vaccine construct was found 86.62% and 87.45%, 345 respectively. The secondary structure of the final vaccine construct was predicted by the SOPMA web server, Overall secondary structure prediction result indicates 37.25% are coiled, 36.85% are beta 351 strands, 18.13% are alpha helix, and 7.77% are beta turn, as shown in Figure S6 . Table 1 . 355 Energy scores attained for human TLR3 and TLR4 were -1400.3 and -1713.7, respectively, and 356 for mouse TLR3 and TLR4 were -1436.6 and -1393.4, respectively. These complexes were 357 subjected to MD simulations to analyze their stability. To evaluate the dynamics of the constructed vaccine complex, MD simulation was performed. The RMSD of alpha-carbon, Rg and RMSF for all amino acid residues of the protein were In this study, we designed a unique multi-epitope vaccine against SARS-CoV-2 by using various 600 Newly discovered coronavirus as the primary cause of severe acute respiratory syndrome A SARS-CoV-2 protein 603 interaction map reveals targets for drug repurposing Structure, Function, and 606 Antigenicity of the SARS-CoV-2 Spike Glycoprotein Structure of the SARS-CoV-2 spike receptor-609 binding domain bound to the ACE2 receptor Epidemiological and clinical 612 characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive 613 study Genomic characterisation and epidemiology of 615 2019 novel coronavirus: implications for virus origins and receptor binding Safety and Efficacy of 618 the BNT162b2 mRNA Covid-19 Vaccine COVID-19 Vaccine | cvdvaccine A Review of SARS-CoV-623 2 and the Ongoing Clinical Trials Post-genomic approaches in drug and vaccine 626 development A universal epitope-based 628 influenza vaccine and its efficacy against H5N1. 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Design an Efficient Multi-Epitope Peptide 827 Vaccine Candidate Against SARS-CoV-2: An in silico Analysis

Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens Computationally Optimized 832 SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype 833 In silico identification of 835 vaccine targets for 2019-nCoV Immunoinformatics characterization of SARS-CoV-2 spike 838 glycoprotein for prioritization of epitope based multivalent peptide vaccine Design of an epitope-based 841 peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach Identification of potential vaccine candidates against SARS-844 CoV-2, a step forward to fight novel coronavirus 2019-nCoV: A reverse vaccinology approach Potential T-cell and B-cell epitopes of 2019-nCoV Immunoinformatics-aided identification of T cell and B cell epitopes in the 849 surface glycoprotein of 2019-nCoV Bioinformatics analysis of epitope-based 852 vaccine design against the novel SARS-CoV-2 Epitope-based peptide vaccines predicted 855 against novel coronavirus disease caused by SARS-CoV-2 Sequence-based 858 prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics 859 predictor identifies immunogenic T cell epitopes Computationally 862 validated SARS-CoV-2 CTL and HTL Multi-Patch vaccines, designed by reverse epitomics 863 approach, show potential to cover large ethnically distributed human population worldwide Multi-epitope vaccine design using an 866 immunoinformatics approach for 2019 novel coronavirus (SARS-CoV-2) Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate 873 against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach We are grateful to our donors who supported to build a computational platform (http://grcbd.org/donate/). The authors like to acknowledge the World Academy of Science (TWAS) to purchase High-Performance Computer for performing molecular dynamics simulation. Highlights: Potential T and B cell epitopes were screened from structural, non-structural and accessory proteins through immunoinformatics approach  An immunogenic multi-subunit vaccine was hypothesized by combining the best immunodominat peptides with TLR agonist and universal PADRE sequence  Molecular Dynamics revealed the stability of vaccine with the Toll-like-Receptors  Epitopes from structural proteins were highly conserved with SARS CoV-1 epitopes J o u r n a l P r e -p r o o f Authors declare no conflict of interests.