Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches

Abstract

An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.

Keywords: Deep learning; Epitopes; Molecular docking; Reverse vaccinology; SARS-CoV-2; Vaccine-designing.

Figure 1. Schematic diagram of SARS-CoV-2 showing its basic component proteins along with its receptor binding site, angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease (TMPRSS2).

The virus consists of a spherical membrane (shown in white and grey) which constitutes membrane protein (shown in orange), spike protein (shown in red), hemagglutinin esterase (shown in blue), and envelope small membrane protein (shown in yellow). The spike protein binds to the ACE2 receptor of the host cell after being activated by the proteolytic cleavage activity of TMPRSS2.

Figure 2. Flow chart depicting the multi-epitope subunit vaccine development against SARS-CoV-2.

Figure 3. Schematic diagram of multi-epitope vaccine peptide.

It is a 32 (insert amino acid number) amino acid long sequence having Beta-defensin as an adjuvant (light canary yellow) which is connected to the highest antigenic CTL epitope sequence (pink) through EAAAK linker (white). The CTL epitopes are linked to each other by GGGS linkers (grayish cyan), and to the highest antigenic HTL epitope (light blue) by AAY linkers (very soft yellow). Next, the HTL epitopes are linked to each other through AAY linkers, and to the highest antigenic B Epitope (vivid yellow) through KK linkers (pale violet). The B epitopes are linked to each other using the KK linkers as well.

Figure 4. Representation of protein-peptide docked complex of top four MHC class-1 epitopes sequences.

(A) KIADYNYKL, (B) VVVLSFELL, (C) TLDSKTQSL and (D) GKQGNFKNL, shown in golden yellow) in association with the HLA-A*02 allele using HPEPDOC. The epitopes have a binding affinityof −205.89, −157.77, −150.05 and −178.48 respectively with HLA_A*02.

Figure 5. Graphical representation of secondary structure features of proposed subunit vaccine sequence using the PSIPRED tool.

Figure 6. Tertiary structure modeling, refinement and validation.

(A) The final 3D model of multi epitope vaccine chimeric protein generated via homology modelling on I-TASSER, (B) Refined model obtained via ModRefiner, (C) The refined 3D structure generated by 3DRefine (D) Ramachandran Plot Analysis signifying 57.0%, 38.9% and 4.0% of protein residues in favoured, allowed and disallowed (outlier) regions respectively, (E) ProSA-web, giving a Z-Score of −4.4.

Figure 7. TLR-8 and vaccine construct V1 docked complex.

(A) Docked complex of TLR-8 with the chimeric vaccine construct. (B) Docking complex generated via ClusPro server illustrating binding affinity between TLR-8 and vaccine component. The lowest energy of −1,277.5 kcal/mol was achieved for this model (complex 2). (C) Docking complex generated via HDOCK server which predicted the binding energy as −330.04 for protein and ligand.

Figure 8. Molecular dynamics simulation study of protein-ligand complex representing.

(A) Potential energy variations (B) Pressure variations plot shows that the average pressure is −2.44361 bar during 100 ps (C) Density variations; plot shows that the average density is 1,028.87 kg/m3 during 100 ps (D) Radius of gyration (E) Root mean square deviation of the docked complex backbone for the time duration of 50 ns. (F) Solvent accessible surface area of the docked complex.

Figure 9. Immune simulations of the chimeric protein vaccine.

(A) Production of Immunoglobulins in response to successive antigen injections (different coloured peaks corresponding to different sub-classes of immunoglobulins and antigen represented by black vertical lines). (B) Changes observed in B-cell population (C) T-helper cells per state (resting state denotes the cells not presented with antigen while anergic state denotes cells showing tolerance to antigens due to repeated exposure). (D) Changes in T-cytotoxic cell population after administration of vaccine construct V1.

Figure 10. Codon adaptation and in silico cloning of the chimeric protein.

(A) Codon adaptation result of vaccine construct V1 predicted by JCat tool predicting that the optimized codon sequence has a length of 978 nucleotides and its CAI (codon adaptation index) was predicted to be 1.0, with an average of 41.21% GC for the adapted sequence. (B) Final protein in-silico restriction cloning into pET28a (+) vector. Here, the red portion represents the gene sequence of the designed vaccine, and the black portion denotes the backbone of the vector. The DNA sequence is inserted into the MCS region of the cloning vector.

Figure 11. Evaluation of vaccine construct against other SARS-CoV-2 variants.

(A) Schematic representation of SARS-CoV-2 Surface glycoprotein with different colours denoting different domains along with location of shortlisted BCL, HTL and CTL epitope; NTD: N-terminal domain, RBD: Receptor binding domain, RBM: Receptor binding motif, SD1/2 subdomain 1 and 2, FP: Fusion peptide, HR1: heptad repeat 1, CH: central helix, CD; connector domain, HR2: Heptad repeat 2, TM: transmembrane domain, CT: cytoplasmic tail, S1 and S2 cleavage site are protease cleavage site. DLCFTNVY: BCL epitope, KIADYNYKL: CTL epitope, VKNKCVNFN: HTL epitope. (B) Multiple sequence alignment of different SARS-CoV-2 variants done by CLC Main workbench, showing conserved epitopes (i) BCL epitope, (ii) CTL epitope and (iii) HTL epitope. (C) Phylogenetic tree of 19 SARS-CoV-2 variants. Pango lineage number has been added in the description of variants in the figure.