3.1 Pre-screening phase
Stenotrophomonas maltophilia possesses 1441 proteomic data in the database of the National Center for Biotechnology Information (NCBI). Among these records, 81 entries encompass the complete proteome of different strains. We have successfully retrieved all 81 entries from the NCBI server, where the sizes of pathogen strains varied from 4.1 Mb to 5 Mb, and the average GC contents ranged from 66.1–67% (Table 1). In addition to providing an overview of horizontal gene transfer and insights into the species' evolution, the pan-genomic analysis of the 81 entries obtained from NCBI has further revealed the core, accessory, and unique gene pools of this bacterial species. Figure 1 depicts the number of genomes encompassing pan-genome families and core genome families. Based on the pan-genome analysis, all 81 strains exhibited 1945 core proteins, and on average, there were 1856 accessory proteins, as shown in Table 1. To gain a better understanding of genome evolution, gene orthology, genome complexity, and the identification of pathogenic and therapeutic sequences, the core genes, which are shared by all species' genomes, are extensively explored. The core genome set refers to the collection of sequences that are shared by all strains, while the pan-genome encompasses all strain genomic sequences.
Table 1
Genome statistics of S. maltophilia strains that were entirely sequenced.
Organism Name | Strain | Size (Mb) | GC% | No. of core genes | No. of accessory genes | No. of unique genes | No. of exclusively absent genes |
Stenotrophomonas maltophilia | 2013-SM24 | 4.53309 | 66.4 | 1945 | 1178 | 0 | 0 |
Stenotrophomonas maltophilia | ACYCa.6E | 4.64618 | 66.4887 | 1945 | 1177 | 0 | 0 |
Stenotrophomonas maltophilia | CYZ | 4.51769 | 66.6 | 1945 | 1998 | 23 | 1 |
Stenotrophomonas maltophilia | ACYCe.8N | 4.63822 | 66.3447 | 1945 | 2282 | 53 | 3 |
Stenotrophomonas maltophilia | ISMMS2 | 4.50972 | 66.4 | 1945 | 2011 | 43 | 0 |
Stenotrophomonas maltophilia | ISMMS2R | 4.50972 | 66.4 | 1945 | 2147 | 70 | 6 |
Stenotrophomonas maltophilia | HW002Y | 4.50894 | 66.5 | 1945 | 2143 | 87 | 13 |
Stenotrophomonas maltophilia | PEG-305 | 4.49551 | 67.2 | 1945 | 1992 | 0 | 0 |
Stenotrophomonas maltophilia | Col1 | 4.45857 | 66.5 | 1945 | 1992 | 0 | 0 |
Stenotrophomonas maltophilia | CPBW01 | 4.44433 | 66.5 | 1945 | 1706 | 348 | 32 |
Stenotrophomonas maltophilia | ACYCd.9D | 4.43537 | 66.5 | 1945 | 2075 | 52 | 3 |
Stenotrophomonas maltophilia | ACYCb.6H | 4.41762 | 66.5 | 1945 | 2374 | 0 | 0 |
Stenotrophomonas maltophilia | ACYCc.3B | 4.41646 | 67 | 1945 | 2235 | 82 | 1 |
Stenotrophomonas maltophilia | ACYCb.1K | 4.41071 | 66.5 | 1945 | 2121 | 64 | 4 |
Stenotrophomonas maltophilia | ZT1 | 4.39147 | 66.5 | 1945 | 2272 | 54 | 1 |
Stenotrophomonas maltophilia | LH-B2 | 4.12196 | 66.8 | 1945 | 2280 | 30 | 4 |
Stenotrophomonas maltophilia | SG.Y2 | 4.12052 | 66.9 | 1945 | 2209 | 41 | 3 |
Stenotrophomonas maltophilia K279a | K279a | 4.85113 | 66.3 | 1945 | 2255 | 58 | 4 |
Stenotrophomonas maltophilia D457 | D457 | 4.76916 | 66.8 | 1945 | 2095 | 29 | 1 |
Stenotrophomonas maltophilia R551-3 | R551-3 | 4.57397 | 66.3 | 1945 | 1967 | 51 | 7 |
Stenotrophomonas maltophilia JV3 | JV3 | 4.54448 | 66.9 | 1945 | 2331 | 160 | 19 |
Stenotrophomonas maltophilia | T50-20 | 4.7777 | 66 | 1945 | 2099 | 18 | 6 |
Stenotrophomonas maltophilia | PEG-173 | 4.76952 | 66.1 | 1945 | 1964 | 44 | 0 |
Stenotrophomonas maltophilia | CSM2 | 4.73905 | 66.6 | 1945 | 1915 | 100 | 3 |
Stenotrophomonas maltophilia | W18 | 4.73843 | 66.1 | 1945 | 2060 | 84 | 1 |
Stenotrophomonas maltophilia | 2013-SM15 | 4.70257 | 66.3 | 1945 | 2161 | 0 | 0 |
Stenotrophomonas maltophilia | sm454 | 4.68544 | 66.3 | 1945 | 2182 | 175 | 4 |
Stenotrophomonas maltophilia | DHHJ | 4.68291 | 66.3 | 1945 | 2024 | 62 | 1 |
Stenotrophomonas maltophilia | Sm53 | 4.68244 | 66.3 | 1945 | 2084 | 21 | 4 |
Stenotrophomonas maltophilia | SKK55 | 4.67545 | 66.3 | 1945 | 1974 | 41 | 1 |
Stenotrophomonas maltophilia | ACYCa.1J | 4.67028 | 66.4 | 1945 | 2300 | 63 | 0 |
Stenotrophomonas maltophilia | OUC_Est10 | 4.66874 | 66.3 | 1945 | 1893 | 64 | 1 |
Stenotrophomonas maltophilia | 454 | 4.66643 | 66.3 | 1945 | 2090 | 71 | 0 |
Stenotrophomonas maltophilia | AA1 | 4.66334 | 67.4 | 1945 | 2311 | 57 | 33 |
Stenotrophomonas maltophilia | NCTC10498 | 4.66135 | 66.4 | 1945 | 2429 | 34 | 3 |
Stenotrophomonas maltophilia | ACYCb.10K | 4.63699 | 66.2 | 1945 | 1893 | 21 | 0 |
Stenotrophomonas maltophilia | JZL8 | 4.63543 | 66.3 | 1945 | 1922 | 102 | 2 |
Stenotrophomonas maltophilia | PEG-68 | 4.63502 | 66.7 | 1945 | 2342 | 107 | 1 |
Stenotrophomonas maltophilia | XL133 | 4.63014 | 66.4 | 1945 | 2193 | 88 | 1 |
Stenotrophomonas maltophilia | FDAARGOS_649 | 4.62588 | 66.5 | 1945 | 2031 | 61 | 1 |
Stenotrophomonas maltophilia | 2013-SM13 | 4.6137 | 67 | 1945 | 2130 | 78 | 2 |
Stenotrophomonas maltophilia | 2013-SM12 | 4.61223 | 67 | 1945 | 2253 | 82 | 0 |
Stenotrophomonas maltophilia | NCTC13014 | 4.59263 | 66.3 | 1945 | 2468 | 0 | 0 |
Stenotrophomonas maltophilia | CF13 | 4.5917 | 66.5 | 1945 | 2049 | 105 | 2 |
Stenotrophomonas maltophilia | ACYCa.2H | 4.58497 | 66.5 | 1945 | 1933 | 26 | 5 |
Stenotrophomonas maltophilia | KMM 349 | 4.5783 | 66.2 | 1945 | 2066 | 10 | 2 |
Stenotrophomonas maltophilia | FDAARGOS_507 | 4.57757 | 66.6 | 1945 | 2055 | 56 | 0 |
Stenotrophomonas maltophilia | 2013-SM4 | 4.57585 | 66.4 | 1945 | 2102 | 32 | 0 |
Stenotrophomonas maltophilia | PSKL2 | 4.57466 | 66.5 | 1945 | 1848 | 39 | 2 |
Stenotrophomonas maltophilia | NCTC10259 | 4.56388 | 66.8 | 1945 | 1921 | 29 | 2 |
Stenotrophomonas maltophilia | X28 | 4.55422 | 66.5 | 1945 | 1962 | 159 | 0 |
Stenotrophomonas maltophilia | PEG-390 | 4.55402 | 66.4 | 1945 | 2024 | 43 | 1 |
Stenotrophomonas maltophilia | MER1 | 4.5473 | 66 | 1945 | 2001 | 139 | 1 |
Stenotrophomonas maltophilia | HT2 | 4.54291 | 66.6 | 1945 | 1854 | 34 | 0 |
Stenotrophomonas maltophilia | U5 | 4.54164 | 66.4 | 1945 | 1877 | 27 | 2 |
Stenotrophomonas maltophilia | CW002SM | 4.95906 | 66.1 | 1945 | 2044 | 24 | 0 |
Stenotrophomonas maltophilia | AB550 | 4.94343 | 66.5 | 1945 | 1989 | 8 | 20 |
Stenotrophomonas maltophilia | SJTH1 | 4.93232 | 65.9 | 1945 | 2092 | 25 | 1 |
Stenotrophomonas maltophilia | NCTC10498 | 4.92865 | 66.3 | 1945 | 2079 | 0 | 0 |
Stenotrophomonas maltophilia | WP1-W18-CRE-01 | 4.92394 | 66.2 | 1945 | 2077 | 0 | 1 |
Stenotrophomonas maltophilia | WGB211 | 4.91368 | 66 | 1945 | 1957 | 48 | 1 |
Stenotrophomonas maltophilia | SJTL3 | 4.891 | 66.3 | 1945 | 1824 | 32 | 8 |
Stenotrophomonas maltophilia | SCAID WND1-2022 (370) | 4.91903 | 66.1663 | 1945 | 2301 | 42 | 3 |
Stenotrophomonas maltophilia | PEG-42 | 4.8548 | 66.1 | 1945 | 2024 | 34 | 4 |
Stenotrophomonas maltophilia | FDAARGOS_325 | 4.85151 | 66.3 | 1945 | 2205 | 77 | 0 |
Stenotrophomonas maltophilia | Stenotrophomonas maltophilia 1800 | 4.83711 | 66.2 | 1945 | 2102 | 167 | 1 |
Stenotrophomonas maltophilia | 142 | 4.83098 | 66.2 | 1945 | 2277 | 22 | 0 |
Stenotrophomonas maltophilia | FDAARGOS_92 | 4.82022 | 66.3 | 1945 | 2274 | 56 | 2 |
Stenotrophomonas maltophilia | FZD2 | 4.81712 | 66.4 | 1945 | 1943 | 21 | 8 |
Stenotrophomonas maltophilia | ISMMS3 | 4.804 | 66.7 | 1945 | 2373 | 0 | 0 |
Stenotrophomonas maltophilia | NEB515 | 4.78552 | 66.4 | 1945 | 1597 | 51 | 3 |
Stenotrophomonas maltophilia | GYH | 4.94921 | 66.345 | 1945 | 2009 | 14 | 5 |
Stenotrophomonas maltophilia | O1 | 4.517 | 66.5 | 1945 | 2156 | 0 | 2 |
Stenotrophomonas maltophilia | SoD9b | 4.41565 | 66.8 | 1945 | 1998 | 45 | 0 |
Stenotrophomonas maltophilia | NCTC10257 | 5.00426 | 66.1 | 1945 | 1589 | 61 | 7 |
Stenotrophomonas maltophilia | SM 866 | 5.08618 | 66 | 1945 | 2468 | 0 | 0 |
Stenotrophomonas maltophilia | NCTC10258 | 4.48112 | 66.6 | 1945 | 1960 | 17 | 0 |
Stenotrophomonas maltophilia | sm-RA9 | 5.00758 | 65.6 | 1945 | 2101 | 14 | 1 |
Stenotrophomonas maltophilia | FDAARGOS_1044 | 5.00427 | 66.1 | 1945 | 2068 | 42 | 0 |
Stenotrophomonas maltophilia | PEG-141 | 5.0023 | 66.1 | 1945 | 2025 | 22 | 1 |
Stenotrophomonas maltophilia | ICU331 | 4.99599 | 66.2 | 1945 | 2293 | 55 | 0 |
3.2 Subtractive Proteomics filters
The core proteins that were obtained were submitted to the VFDB database through the BLASTP search engine in order to determine the virulent proteins. Among the core proteins, a total of 191 pathogenic proteins were identified. The inclusion of virulent proteins in vaccine formulations is highly desirable due to their ability to initiate immune pathways and enhance the elicitation of safe immune responses. Since these proteins are only used in limited areas during vaccine design, their incorporation into vaccines is secure and unlikely to negatively affect human cells. The PSORTb server was then utilized to verify the localization of the selected proteins. The pathogenesis, invasion, and colonization of bacteria within host cells heavily rely on surface proteins. Furthermore, the host immune system can effectively recognize the antigenic epitopes of these proteins and initiate innate immune responses. According to the projections made by the PSORTb server, it is likely that 52 proteins are located within the cytoplasmic membrane, while five are situated in the outer membrane, leaving the remaining proteins to be localized within the cell. Subsequently, the HMMTOP and THMMTOP 2.0 servers were employed to confirm the presence of transmembrane helices in the 57 surface proteins. Only proteins with zero or one transmembrane helix were selected, while the others were discarded. Out of the 57 surface proteins that were filtered out, a total of 22 proteins met the criteria for having transmembrane helices. Conducting research and cloning on membranes with more than two helices poses a significant challenge. This assumption was made due to inadequate protein expression in vitro systems such as E. coli. Additional analysis was conducted on the selected proteins, including their molecular weight, atomic composition, instability index, theoretical isoelectric point (pI), amino acid composition, aliphatic index, grand average of hydropathicity (GRAVY), and anticipated half-life. The instability index was the most important factor in evaluating the physiochemical properties. A threshold value of 40 was set for the instability index. If a protein had an instability index greater than 40, it was considered unstable and excluded from the study. Conversely, proteins with an instability index below 40 were considered stable and subjected to further examination. Based on the instability index criteria, a total of 14 proteins were deemed stable. These 14 proteins were then further investigated for selection based on predictions of their antigenicity, allergenicity, and toxicity. Out of the initial 1945 core proteins, 11 out of the final 14 proteins were anticipated to be antigenic and non-allergenic (Table 2). The top three antigenic proteins were chosen for additional investigation.
Table 2
Different physicochemical analysis of virulent proteins; subcellular localization, Transmembrane helices, instability index, Antigenicity, Allergenicity, and Toxicity.
ID | Subcellular localization | HMMTOP | TMHMM | Instability index | Antigenicity | Allergenicity | Toxicity |
>CORE_REP| Org37_Gene2333# | Cytoplasmic Membrane 10.00 | 1 | 2 | 32.25 | 0.5526 | NON | NON |
>CORE_REP| Org55_Gene2869# | Outer Membrane 10.00 | 1 | 0 | 22.75 | 0.6772 | NON | NON |
>CORE_REP| Org61_Gene2356# | Outer Membrane 10.00 | 0 | 0 | 34.64 | 0.6396 | NON | NON |
>CORE_REP| Org65_Gene1269# | Cytoplasmic Membrane 9.97 | 0 | 0 | 34.5 | 0.6363 | NON | NON |
>CORE_REP| Org43_Gene3823# | Cytoplasmic Membrane 9.99 | 0 | 0 | 32.78 | 0.5401 | NON | NON |
>CORE_REP| Org40_Gene905# | Cytoplasmic Membrane 9.99 | 0 | 0 | 38.62 | 0.5198 | NON | NON |
>CORE_REP| Org70_Gene1716# | Outer Membrane 9.93 | 0 | 0 | 37.17 | 0.5596 | NON | NON |
>CORE_REP| Org60_Gene83# | Outer Membrane 9.93 | 0 | 0 | 37.52 | 0.7839 | NON | NON |
3.3 Epitope prediction and prioritization phase
B and T cell binding epitopes serve a crucial purpose in the field of vaccine development as they indicate specific regions of pathogens that elicit immune responses. The integration of these epitopes into vaccines ensures precise and efficient immune responses, leading to the production of antibodies and memory T cells that can provide persistent protection against specific pathogens. To anticipate the epitopes that activate CD8 + T cells, CD4 + T cells, and B cells, the three most antigenic proteins were submitted to the IEDB server (> CORE_REP|Org55_Gene2869#, >CORE_REP|Org60_Gene83#, >CORE_REP|Org54_Gene653#). The server was accessed to retrieve CD8 + T cell epitopes and CD4 + T cell epitopes based on their respective scores. Subsequently, epitopes with scores greater than 0.5 were obtained from the server for further investigation. The alleles HLA-A*01:01, HLA-A*02:01, HLA-A*02:06, HLA-A*03:01, HLA-A*11:01, HLA-B*07:02, HLA-B*08:01, and HLA-B*15:01 were utilized to predict the MHC-I epitopes. In the case of MHC-II epitopes, the alleles HLA-DRB1*07:01, HLA-DRB1*03:01, HLA-DRB3*01:01, HLA-DRB3*02:02, HLA-DRB4*01:01, and HLA-DRB5*01:01 were employed for analysis. Each B cell epitope generated by the server was collected for further examination. Through the utilization of various immunoinformatic approaches, we identified the most effective vaccine candidate as a water-soluble, non-toxic, non-allergenic, and antigenic protein. This involved the prioritization of epitope selection.
3.4 Multi-epitope Vaccine Construction
Based on the antigenic characteristics they possess, a selection of seven MHC-I epitopes (Table 3), seven MHC-II epitopes (Table 4), and seven B-cell epitopes (Table 5) was created in order to create a combination of multi-epitope vaccine. The epitopes were combined using AAY, GPGPG, and KK linkers, as depicted in Fig. 4, and the resulting peptide was connected to the adjuvant cholera toxin B subunit (Uniprot Id - E9RIX3) using an additional "EAAAK" linker. The constructed vaccine was then subjected to the ProtParam tool on the Expasy server in order to assess its physiochemical attributes. The instability index of the vaccine was determined to be 26.91, which classifies it as stable. Furthermore, the grand average of hydropathicity was calculated to be -0.803, indicating that the vaccine is hydrophilic. More comprehensive information regarding the physiochemical properties of the multi-epitope vaccine can be found in Table 6.
Table 3
Selected CTL epitopes for the development of the vaccine
Allele | CD8 + Epitope | Antigenicity | Allergenicity | Toxicity |
HLA-A*01:01 | SSEKGKLSY | 1.2644 | Non-Allergen | Non-Toxic |
HLA-A*03:01 | RIYYPVPAY | 1.4076 | Non-Allergen | Non-Toxic |
HLA-B*07:02 | EPNWNPLAL | 1.0055 | Non-Allergen | Non-Toxic |
HLA-A*02:06 | KQQERAVNL | 1.0622 | Non-Allergen | Non-Toxic |
HLA-A*03:01 | GLQDAYAKK | 1.2781 | Non-Allergen | Non-Toxic |
HLA-B*15:01 | TLKDRNGGY | 1.0801 | Non-Allergen | Non-Toxic |
HLA-A*01:01 | DSGLDLALY | 1.2433 | Non-Allergen | Non-Toxic |
Table 4
Selected HTL epitopes for the development of the vaccine
Allele | CD4 + Epitope | Antigenicity | Allergenicity | Toxicity |
HLA-DRB5*01:01 | KAEYEKAAAENKTKSDQ | 1.4446 | Non-Allergen | Non-Toxic |
HLA-DRB1*07:01 | TTGESNFDRTTGAGISP | 1.1543 | Non-Allergen | Non-Toxic |
HLA-DRB1*07:01 | GESNFDRTTGAGISP | 0.9414 | Non-Allergen | Non-Toxic |
HLA-DRB3*01:01 | DADLTPDTQLSVGYD | 0.9881 | Non-Allergen | Non-Toxic |
HLA-DRB5*01:01 | SGVQYRVIEAGKGAK | 1.2919 | Non-Allergen | Non-Toxic |
HLA-DRB5*01:01 | GVQYRVIEAGKGAKPTQ | 1.0946 | Non-Allergen | Non-Toxic |
HLA-DRB3*01:01 | VIDADLTPDTQLSVG | 0.9985 | Non-Allergen | Non-Toxic |
Table 5
Selected CTL epitopes for the development of the vaccine; where AN = antigenicity, AL = allergenicity, TOX = toxicity, ES = estimated solubility.
Epitope | AN | AL | TOX | ES |
RAEGYSVRRTSAGTRFDLAPREIPQ | 1.1842 | Non-Allergen | Non-Toxic | GOOD |
DTDGQMDRYNQR | 1.5806 | Non-Allergen | Non-Toxic | GOOD |
DYQHKRANGA | 1.5678 | Non-Allergen | Non-Toxic | GOOD |
DWKSEGEGADRAHKVT | 1.4406 | Non-Allergen | Non-Toxic | GOOD |
NLAELTGRGEQLDIN | 1.4165 | Non-Allergen | Non-Toxic | GOOD |
QKREQGRAQAAKAEYEKAAAENKTKSDQFIAANKAKAGVQSLPS | 0.9070 | Non-Allergen | Non-Toxic | GOOD |
IEAGKGAKPT | 1.2443 | Non-Allergen | Non-Toxic | GOOD |
NLKPKDKTSGNARSG | 2.2945 | Non-Allergen | Non-Toxic | GOOD |
Table 6
Physicochemical characteristics of the developed vaccine.
Characteristics | Result |
Number of amino acids | 523 |
Molecular weight | 56054.94 da |
Theoretical pI | 9.55 |
Total number of negatively charged residues (Asp + Glu) | 57 |
Total number of positively charged residues (Arg + Lys) | 82 |
Chemical formula | C2465H3916N712O769S7 |
Total number of atoms | 7869 |
Estimated half-life (mammalian reticulocytes, in vitro) | 1 hour |
Estimated half-life (yeast, in vivo) | 30 mins |
Estimated half-life (Escherichia coli, in vivo) | > 10 hours |
Instability index | 26.91 |
Aliphatic index | 60.46 |
Grand average of hydropathicity (GRAVY) | -0.803 |
3.5 Secondary and Tertiary Structure Prediction of MEV
The investigation examined the secondary and tertiary configurations of the developed vaccine utilizing sophisticated computational tools. Specifically, the utilization of the PsiPred and Sopma servers allowed for the anticipation of the secondary structure of the immunization. The PsiPred server disclosed that the immunization consists of 40% alpha helix, 10.32% beta-strand, and 49.68% random coil, as demonstrated in the accompanying Fig. 5A. Similarly, the Sopma server anticipated that the immunization is comprised of 33.84% alpha helix, 14.34% beta-strand, and 51.82% random coil, as exhibited in the aforementioned Fig. 5B. Additionally, the Robetta server was employed to anticipate the tertiary structure of the immunization, enabling the visualization of its three-dimensional arrangement, as portrayed in the accompanying Fig. 6. In order to facilitate the visualization of the tertiary structure, the Discovery Studio 2021 software was employed.
3.6 Vaccine refinement and structure validation
The vaccine that was developed underwent analysis using the GalaxyRefine tool, which was employed to enhance the structure of the vaccine. This tool facilitates the correct folding of the MEV and enables the MEV to interact with the immune system more efficiently. The server generated 5 revised models based on various factors such as GDT-HA, RMSD, MolProbity, clash score, poor rotamers, Rama preferred residues percentage, and GALAXY energy. After considering the information provided by the server in Table 7, Model 3 was selected as the refined candidate for the MEV. The refined 3D model of the vaccine was further assessed using the SAVESv6.0 and ProSA-web servers. The analysis of the improved vaccine's Ramachandran plot showed that 92.3% of the residues were in the favorable zone, 5.1% were in allowed regions, and 1.6% were in prohibited regions, as depicted in Fig. 7A. In comparison, the crude model had a Z-score of -8.77 (Fig. 7B). To achieve superior 3D-1D profiling, it is recommended that at least 80% of the amino acids in a model should have a score greater than 0.1. The refined model, in this case, contains 83.75% of residues with an average 3D-1D profiling score higher than 0.1.
Table 7
The GalaxyRefine server's results. Based on the GDT-HA, RMSD, MolProbity, Clash score, Poor rotamers, and Rama favored parameters, Model 3 was picked as the best-refined model.
Sequence Number | AA | Sequence Number | AA | Chi3 | Energy | Sum B-Factors |
167 | TYR | 197 | ALA | -94.93 | 0.21 | 0 |
170 | GLY | 193 | GLN | 87.04 | 0.62 | 0 |
174 | ALA | 190 | ALA | -83.91 | 1.07 | 0 |
112 | VAL | 119 | HIS | -87.62 | 1.31 | 0 |
202 | ALA | 220 | GLU | 85.74 | 1.92 | 0 |
152 | TYR | 162 | ALA | 98.37 | 2.19 | 0 |
3.7 Disulphide Engineering and in-silico codon optimization
One of the primary objectives of protein engineering is to enhance protein stability. A reasonable approach involves augmenting the naturally occurring molecular interactions that stabilize proteins. Disulfide bridges are covalent connections that confer substantial structural stability. Based on the energy derived from Disulphide bonds, we elected to substitute 6 amino acid residues in the mutant version of the vaccine design (Figs. 8A and 8B). Specifically, we exclusively replaced six amino acids with energies lower than 2.20 (Kcal/mol) (Table 8). In the vaccine mutant structure, the yellow-colored stick signifies the altered pair of amino acids (Fig. 8B).
Table 8
Sequence number, Chi value, Energy, and sum B-Factors of amino acid (AA)
Model | GDT-HA | RMSD | MolProbity | Clash score | Poor rotamers | Rama favored |
Initial | 1.000 | 0.000 | 3.391 | 24.1 | 16.4 | 71.3 |
Model 1 | 0.9381 | 0.466 | 2.586 | 24.9 | 1.7 | 88.3 |
Model 2 | 0.9434 | 0.451 | 2.456 | 24.2 | 1.1 | 89.5 |
Model 3 | 0.9561 | 0.443 | 2.442 | 23.6 | 0.9 | 91.2 |
Model 4 | 0.9327 | 0.447 | 2.521 | 24.5 | 1.4 | 88.9 |
Model 5 | 0.9396 | 0.453 | 2.593 | 24.3 | 1.8 | 88.6 |
Subsequently, the reverse translated DNA sequence was inserted into the pET28a(+) vector (Codon Usage modified to Escherichia coli (strain K12). The improved sequence displays a CAI-value of 1, indicating its suitability as a promising vaccination candidate. The improved sequence exhibits a GC content of 50.92415551306565% (GC content should range between 30–70% for optimal vaccine candidates).
3.8 Molecular docking studies
The ClusPro 2.0 server, an advanced computational tool utilized for molecular docking, performed a complex examination of the multi-epitope vaccine and TLR4 (B chain). Consequently, 30 docked complexes with distinct cluster members were generated as a result of this extensive analysis, and the complex with the lowest energy was chosen for further investigation. Among all the docked complexes, Cluster No. 1 demonstrated the most negative score (strongest interaction), with an energy score of -1161.7 kcal/mol. Additionally, the BIOVIA Discovery studio software, a widely employed tool for visualizing intricate 3D interactions, was employed to further clarify the interaction between TLR4 (B chain) and the developed vaccine as depicted in Fig. 9A. The Ligpolot software, an efficient tool for the 2D analysis of residue interactions, was employed as demonstrated in Fig. 9B. Furthermore, Table 9, which includes a comprehensive list of the residues involved in hydrogen bonding along with the bond length, was generated to provide a more comprehensive analysis of the interactions between the vaccine and TLR4 (B chain).
Table 9
List of residues in the docked complex that are involved in hydrogen bonding between the vaccine and TLR4 (chain B).
TLR4 (chain B) | Vaccine | Bond length (A) |
Arg447 | Asp309 | 2.87 |
Thr311 | 2.67 |
2.80 |
Asn417 | Ser317 | 2.86 |
| Asn307 | 2.86 |
Ser445 | Asn307 | 3.03 |
Glu422 | Arg310 | 2.70 |
2.74 |
3.9 Molecular Dynamic Simulation
Molecular dynamics simulations (MDS) have been extensively employed in determining the conformational strength of atoms and molecules by modeling the system at an atomistic scale. With the objective of investigating the stability of a ligand in a targeted protein macromolecule, the MD simulation has been recognized as a remarkable and distinctive approach. This study intended to evaluate the capability of the vaccine to bind to the protein and to the active site cavity of the protein. The findings of the MD simulation have been explicated based on the root means square deviation (RMSD) and root means square fluctuation (RMSF).
The RMSD of a vaccine-receptor complex system is a valuable tool that permits the determination of the average distance caused by a selected atom's displacement over a specified period. Generally, the square root of the mean of squared errors is utilized to ascertain the extent of dissimilarity between two values, specifically the observed value and estimated value. The mean or average value varies from one frame to another, with a permissible range of 1–5 Å or 0.1–0.5 nm. However, a value outside this acceptable range indicates a significant conformational shift in the protein. Consequently, the RMSD of the vaccine candidate (orange) complex structure has been compared with the human Toll-like receptor 4 protein (PDB ID: 4G8A) to observe the changes in order, as depicted in Fig. 10A. The RMSD was found to exhibit minor fluctuations, which were perfectly acceptable in the context of vaccine development.
As a result, the RMSF values of the experimental vaccine-receptor complex were computed in order to analyze the modification in protein structural flexibility, as exemplified in Fig. 10B. The RMSF values provide an indication of the deviation of the position of each atom in the protein from its average position over a specified time period. This analysis is crucial in comprehending the flexibility of the protein in response to the binding of the vaccine candidate to the receptor.
3.10 Immune response simulation
The experimental model of the immune response displayed similarities to the authentic immunological phenomena that are elicited by specific pathogens, as demonstrated in the accompanying Fig. 11. It is worth noting that the secondary and tertiary immune responses were discovered to be more robust compared to the primary immune response (Fig. 9A). The secondary and tertiary responses were characterized by heightened levels of various types of antibodies (such as IgG1 + IgG2, IgM, and IgG + IgM), which were accompanied by a decrease in the antigen burden, thus signifying the development of memory cells and the subsequent enhancement of antigen clearance upon subsequent exposures (Fig. 9A). Additionally, there was an extended lifespan observed in B-cells, cytotoxic T-cells, and helper T-cells, which indicated the switching between immune cells and the formation of IgM memory (Fig. 11B-D). The upregulation of IFN, IL-4, and IL-10 was also evident (Fig. 11E). Interestingly, the percentage (%) and quantity (cells/mm3) of Th0 type immune reaction were discovered to be lower compared to those of the Th1 type reaction (Fig. 11F). During the presentation, it was demonstrated that the movement of macrophages significantly increased, while the movement of dendritic cells was found to be predictable (Fig. 11G-H).