The secondary structure was predicted using the SOPMA server. The server predicts protein secondary structure from amino acid sequences using the self-optimized prediction approach29.

The AlphaFold server was used to predict the tertiary structure of the vaccine construct30. AlphaFold uses innovative neural networks in combination with the primary amino acid sequence and alignment sequence of homologs to predict the tertiary structure of a particular protein.

Thornton’s approach and a residue clustering algorithm on the IEDB database are both used by the ElliPro server to predict conformational epitopes on the 3D structure. According to Ponomarenko et al.31, ElliPro offers a framework for predicting tertiary structure models from amino sequences.

The quality of the 3D structure of the chosen model, protein structure refinement, protein interaction prediction, GPCR applications, and water position prediction were all improved using the GalaxyWEB refine server32. By recreating and repackaging protein side chains using dynamic simulation techniques, this server improves 3D protein architectures, facilitating the production of proteins with appropriate structural relaxation.

Thereafter, structural analysis using Ramachandran and Z-score analysis was used to validate the vaccine’s structure33, 34. PROCHECK and ProSA-web were used. The Ramachandran plot, which displays the proportion of the amino acids present in the preferred, permitted, and forbidden zones, is produced by the PROCHECK server. ProSA-web performs mathematical analysis on the entire protein structure quality score and presents values and energy.

To adapt the vaccine for expression in the Escherichia coli strain K-12 sub-strain MG1655 chosen as the expression host, codon optimization was performed using JCAT35. This is crucial for maximizing protein expression during the recombinant vaccine’s cloning process. Using the SnapGene v5.1.7 program, the chimeric model was virtually cloned into an appropriate expression vector, E. coli K12 pET-28a (+)36.

A computational procedure called molecular docking is used to predict the affinities of the ligand and receptor to create stable complexes. Using the HDOCK server, the final vaccine design was docked against toll-like receptor (TLR) 7 and 837.

Table 1

Genome Segment	Protein	Antigenicity
L	Polymerase	Non-antigen
M	Glycoprotein	Antigen
	Nucleocapsid	Non-antigen

Table 2

Genome Segment	Protein	Protein Segment	Position
L	Polymerase	1-2091	Inside
M	Glycoprotein	1-16	Signal peptide
		17-584	Outside
		585-603	TMHelix
		604-675	Inside
		676-685	TMHelix
		686-1159	Outside
		1160-1178	TMHelix
		1179-1197	Inside
	Nonstructural	1-265	Inside
	Nucleocapsid	1-245	Inside

Table 3

Peptide	Antigenicity	Allergen city
DSLREEEMPE	Antigenic	Non- allergic
VYLDKLDLKTEENLLPD	Antigenic	Non- allergic
SYWTGSFSPKCLSSRRCHLV	Antigenic	Non- allergic
SKLKTKMKGVCEVGVQALKK	Antigenic	Non- allergic

Table 4

Peptides	Antigenicity score	Instability status	Toxicity status	Allergenicity status
SLKKGSYPL	1.3734	Stable	Non- toxic	Non-allergic
KIKTVSSEL	1.4693	Stable	Non- toxic	Non-allergic
GNPCMKEKL	0.0854	Stable	Non- toxic	Non-allergic
TMAGIAMTV	1.9979	Stable	Non- toxic	Non-allergic
NCIDWVHKL	0.6161	Stable	Non- toxic	Non-allergic
RAPNLVSYK	0.1302	Stable	Non- toxic	Non-Allergic
RQMTGASLK	1.7603	Stable	Non- toxic	Non-allergic
EVVPFAVFK	0.4533	Stable	Non- toxic	Non-allergic
IQVSGVWKK	1.5642	Stable	Non- toxic	Non-allergic
SAHGNPCMK	0.9450	Stable	Non- toxic	Non-allergic
RVADNINQV	0.5002	Stable	Non- toxic	Non-allergic
VVFAEDPHL	1.4568	Stable	Non- toxic	Non-allergic
ITSTGTGTL	0.0479	Stable	Non- toxic	Non-allergic
LSAKPIQRV	0.1587	Stable	Non- toxic	Non-allergic
FKNSKKVYL	1.5282	Stable	Non- toxic	Non-allergic
LKIAPRKVL	0.6219	Stable	Non- toxic	Non-allergic
VQADLTLMF	1.8723	Stable	Non- toxic	Non-allergic
HKGQYKGTM	0.6591	Stable	Non- toxic	Non-allergic
TGFKISSAV	1.6721	Stable	Non- toxic	Non-allergic
TKDQCQILH	0.5748	Stable	Non- toxic	Non-allergic
VQADLTLM	1.8470	Stable	Non- toxic	Non-allergic
GDIGVHMA	0.2486	Stable	Non- toxic	Non-allergic
TKDQCQIL	0.4858	Stable	Non- toxic	Non-allergic
LEKGKFPL	1.6611	Stable	Non- toxic	Non-allergic
RETMAGIA	0.3986	Stable	Non- toxic	Non-allergic
LKIAPRKV	0.2515	Stable	Non- toxic	Non-allergic
RQMTGASL	1.5040	Stable	Non- toxic	Non-allergic

Table 5

Peptides	Antigenicity status	Allergenicity status	Toxicity status	Stability status
QSAHYLNNDGKMASV	Antigenic	Non-allergic	Non-toxic	Stable
FLKIKTVSSELSCRE	Antigenic	Non-allergic	Non-toxic	Stable
VLKCLKIAPRKVLNP	Antigenic	Non-allergic	Non-toxic	Stable

Table 6

No.	Predicted discontinuous epitope	Number of residues	Score
1.	A:V152, A:L153, A:N154, A:P155, A:G156, A:P157, A:G158, A:P159, A:G160, A:S161, A:K162, A:L163, A:T165, A:K166, A:K168	15	0.773
2.	A:K194, A:T195, A:E196, A:E197, A:N198, A:L199, A:L200, A:P201, A:D202	9	0.733
3.	A:D1, A:F2, A:A3, A:S4, A:C5, A:H6, A:T7, A:N8, A:G9, A:G10, A:I11, A:C12, A:L13, A:P14, A:N15, A:R16, A:C17, A:P18, A:G19, A:H20, A:G25, A:I26, A:C27, A:F28, A:R29, A:P30, A:R31, A:V32, A:K33, A:C34	30	0.711
4.	A:K180, A:G181, A:P182, A:G183, A:P184, A:G185, A:V186, A:Y187	8	0.697
5.	​​A:P117, A:G118, A:P119, A:G120, A:F121, A:E135, A:G136, A:P137, A:G138, A:P139, A:G140, A:V141, A:L142, A:K143, A:C144, A:L145, A:K146	17	0.669
6.	A:G81, A:T82, A:G83, A:T84, A:L85, A:A86, A:A87, A:Y88, A:R89, A:Q90, A:M91, A:T92, A:G93, A:A94, A:S95, A:K97, A:A98	17	0.639
7.	A:K125, A:T126, A:V127, A:S128, A:S129, A:E130	6	0.633

Table 7

Toll-like Receptor	Docking score	Confidence Score	Ligand rmsd (Å)
TLR-7	-278.26	0.9286	46.14
TLR-8	-324.28	0.9703	94.62

Table 1 shows the antigenicity of selected proteins. The glycoprotein (M) sequences were seen to be the most antigenic of all the RVFV proteins.

The development of subunit multi-epitope vaccines requires in-depth knowledge of transmembrane topology. To develop subunit multi-epitope vaccines, epitopes located in the extra-cytoplasmic segments are crucial for efficacy38. In this study, the TMHMM tool was used to analyze the topology of the different genome segments. The transmembrane topology of the selected proteins is presented in Table 2. A large part of the glycoproteins was in the extra-cytoplasmic portion, indicating their usability in designing a vaccine against RVFV.

A total of 60 RVFV M segment sequences from cattle, sheep, and goats were obtained from the NCBI. The sequences were obtained from a total of eight countries, including Kenya, Zimbabwe, South Africa, Egypt, Madagascar, and Namibia.

Figure 1

The multiple sequence alignment showed large overlapping and conserved portions (Figure 1).

Epitopes with between 10 and 20 amino acids that cut across at least two of the servers used (BepiPred, ABCpred, and SVMTriP) were selected. The physicochemical properties of the selected epitopes were then assessed for suitability in a vaccine construct. The results are presented in Table 3.

A total of 130 strongly binding CTL peptides were predicted for the selected BoLA alleles. These peptides were selected based on their binding score or ability. Strongly binding CTL peptides were examined for their stability index, toxicity, allergenicity, and antigenicity. Table 4 lists the 27 epitopes from the glycoprotein that passed these tests. However, not all the 27 made the final construct.

A total of 97 peptides demonstrated strong binding for the selected MHCII alleles and were further assessed to induce interleukin-4 and interleukin-10 cytokines. After being evaluated for their physicochemical characteristics, epitopes that showed the potential to stimulate the production of the aforementioned cytokines were also tested for antigenicity, allergenicity, and toxicity. Table 5 shows the HTL epitopes that scaled through the screening processes.

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

The vaccine primary construct comprised a Bos taurus-specific beta defensin adjuvant, six CTL peptides, three HTL peptides, and two B-cell-based peptides. The adjuvant was attached to the first CTL peptide using the EAAAK linker and to subsequent CTL peptides using the AAY linker. The rest of the primary construct was linked using GPGPG linkers. Our final vaccine construct was a 202-amino-acid-long primary sequence (Figure 2).

The antigenicity index for a chimeric model with 0.4 points was determined using VaxiJen v2.0 server output and found to be quite high (0.6926). The multi-epitope protein construct was also anticipated to be dispersible upon overexpression using Protein Sol. The 202-amino-acid protein had a theoretical isoelectric point (Pl) of 9.44 and a relative molar mass (Mr) of 21.4 kDa as per the ExPasy Protparam web server. The sequence also had 26 cations (Arg + Lys) and 12 anions (Asp + Glu) residues. The assembled sequence was also shown to be sturdy, with a stability index of 20.37. The multi-epitope had a DIVIDEND score of −0.121 along with an acyclic index of 81.19, which indicated that it was hydrophilic (Figure 3) and thermo-stable.

The JCAT server, reverse translation, and codon optimization (ECAI) optimizations for the adaptation of E. coli strain K-12 sub-strain MG1655 were performed³⁵. The improved GC content after codon optimization was 53.3% at a Codon Adaptation Index (CAI) of 1.0. To successfully integrate the RVF construct into the E. coli expression system, a high GC content is a sign of stability. The restriction enzymes Xho1 and EcoR1 were utilized to clone the back-translated nucleotide into the E. coli expression system. Effective insertion of the segment into the pET28a (+) vector resulted in the production of a 6096 bp clone. The in silico cloning procedure of the vaccine in an Escherichia coli pET28a (+) vector is presented in Figure 4.

The SOPMA server was used to analyze the secondary structure of the intended protein, and the findings were as follows: alpha helix = 31.55%, random coil = 44.92%, beta turn = 5.35%, and extended strand = 18.18%. The tertiary structure is presented in Figure 5a.

The PROCHECK assessment of the stoichiometric quality of the structure revealed the residue and overall structural computation. Ramachandran plot analysis indicated that 95.7 % of the protein remains were located in their preferred regions (Figure 5 c). Furthermore, results from ProSA, which predicts implied oversights of a 3D model, protein, predicted a negative score of −4.85, which is a favorable indicator of model quality (Figure 5 b).

The ElliPro server predicted an aggregate of 91 conformational epitopes from 187 amino acid residues on the vaccine tertiary construct31. Moreover, the Jmol viewer allows for the uptake and foresight of immune epitopes in a given protein sequence or structure. Conformational epitopes with protrusion index scores greater than 0.7 are shown in Figure 5 a-c.

HDOCK predicted 10 models docking between the vaccine construct and TLR7. The rankings were based on the Docking Score, Confidence Score, and Ligand RMSD (Å) (Figure 7 and Table 7).

The server C-ImmSim simulates immune findings indicated in a system of effective immune systems and an extended duration of the vaccine candidate. The vaccine design, as shown in Figure 8, produced a strong and consistent main and secondary immune response. The main immune response was defined by the level of the IgM antibodies after 4­–5 days of antigenic exposure. The basic immune response was distinguished by enhanced B-cell generation and increased IgM and IgG1 + IgG2 expression and IgM + IgG antibodies. The advanced vaccine construct not only evoked a strong B-cell increase but also led to a multitude of memory B-cells. Specific interleukin concentration, a high level of IFN-γ for a long interval, CD4 activity, HTL count, and B-lymphocyte count were revealed.