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. 2025 Nov 6;13(3):172. doi: 10.1007/s40203-025-00467-6

Design of chimera vaccine against cutavirus using vaccinomics and immunoinformatics approaches

Syed Luqman Ali 1,✉,#, Awais Ali 1,#, Asifullah Khan 1,✉
PMCID: PMC12592613  PMID: 41211464

Abstract

Cutavirus (CuV) is a newly discovered human parvovirus, which gained tremendous interest due to its association with cutaneous lymphoma T cells. CuV transmission primarily occur via fecal–oral route and identified as a causal agent of diarrhea. The current study based on prediction of novel multi-epitopic potential vaccine models against CuV by predicted by selecting highly antigenic proteins using reverse vaccinology approaches. A top-ranked T and B cell epitopes from lead vaccine candidate proteins were selected and linked via immunogenic adjuvant peptides and linker sequences to design model vaccine. The models were assessed in terms of physicochemical properties including antigenicity, non-allergenicity, and their binding affinities to TLR4 immune receptors. The in-silico restriction and cloning analysis were pursued to check the expression capacity of model vaccine. Several vaccine models were designed from lead epitopes of CuV vaccine candidates’ proteins along with combination of immune-enhancer adjuvants and linkers. Physicochemical and immunoinformatics analyses prioritized a V2 model construct as top-ranked. The molecular docking and molecular dynamic simulation analyses ensured the V2 construct molecular stability and strong interaction with TLR4 immune receptor with binding affinity of − 41.27 kcal/mol. The normal mode analysis (NMA) predicted improved flexibility, proper molecular mobility, and decreased protein deformability of immune complex for the generation of powerful immune response. Molecular dynamics simulation confirmed the structural stability of the V2–TLR4 complex with minimal RMSD fluctuations and sustained hydrogen bonding. Principal component analysis (PCA) revealed limited conformational shifts, indicating a stable dynamic behavior of the vaccine–receptor complex. These results support the robust interaction and stability of the designed vaccine, reinforcing its potential for effective immune activation. Immune simulation predicted a strong primary and memory immune response with elevated levels of IgG, IFN-γ, and T-cell activity. Codon optimization yielded a CAI score of 0.98 and GC content of 53.1%, indicating efficient expression in E. coli. The multi-epitope vaccine construct prioritized in the current study is effectively bind with immune receptors and elicit potent immune response against CuV.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00467-6.

Keywords: Cutavirus (CuV), Parvovirus, Immunoinformatics, Multi-epitope-based vaccine, Reverse vaccinology, PCA analysis

Introduction

Cutavirus (CuV) is a novel parvovirus discovered in 2016. The genome size of this small, non-enveloped viruses are about 4.5 kilo-base pairs (kb) (Fu 2017; Phan and Nagaro 2020) belongs to the genus Protoparvovirus. In silico analysis of existing metagenomics databases led to the discovery of CuV DNA in the epidermal biopsies of two people with lymphoma involving cutaneous T-cells (CTCL) (Kreuter et al. 2018; Phan et al. 2016). Previously, CuV DNA was discovered in the feces of Brazilian children infected with diarrhea. Polymerase chain reaction (PCR) studies verified CuV DNA in two of the fifteenth French CTCL patients but found no evidence of CuV in twenty-six other cutaneous symptoms with three normal skin tissues. However, according to the recent studies CuV shows highest genetic similarity to bufaviruses found in humans (Väisänen et al. 2019). Despite their rare nature, bufaviruses are common sources of infections in human waste. The virus enrichment methods identified CuV strain as a malignant form of cutaneous melanoma and understanding the variety of different types tissues that contain CuV for the better knowledge of the human virome. Recently, the development and understanding of genomics, proteomics and infections, the epitope-based designing is a promising new method for the development of vaccines against viruses (Ali and Luqman Ali 2025). Furthermore, epitopes are discrete segments of a protein's sequence that trigger highly specific immune responses. (Firbas et al. 2006; Staneková and Varečková 2010; He et al. 2015) In addition, recent research also demonstrated the efficacy of epitope-based vaccinations in eliciting potent immune responses and protection against a variety of antigens. The use of in silico methods for the development of epitope-based vaccine has cut down on development time and expenses.

Despite being a recently discovered parvovirus, CuV has been increasingly detected in both healthy and diseased skin tissues, as well as in fecal samples of children with diarrhea, suggesting widespread but underreported transmission. Importantly, its association with cutaneous T-cell lymphoma (CTCL) has raised concerns about its oncogenic potential. Currently, there are no approved vaccines or antiviral therapies targeting CuV, highlighting the urgent need for prophylactic strategies to curb its potential public health impact.

Given CuV’s small genome and its conserved structural proteins particularly capsid-associated proteins that are typically exposed to the host immune system these antigens represent promising targets for a multi-epitope vaccine aimed at stimulating robust T and B cell-mediated immunity.

The fundamental objective of this study was to design T-cell and B-cell multi-epitope-based peptide vaccines utilizing publically available sequences of protein through various in silico approaches. The results of present investigation suggest several potential epitope-based vaccine approaches to develop effective vaccinations against CuV.

Materials and methods

The multi-epitopic peptide-based vaccination design flow-chart as shown in Fig. 1. In addition, an effective peptide-based multi-epitopic vaccination against CuV was designed using different immunoinformatics technologies.

Fig. 1.

Fig. 1

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Process flow diagram for the planned vaccination

Proteome retrieval and purification

Cutavirus proteome data (NCBI Taxonomy ID: 1867125) was retrieved and verified from the National Centre for Biotechnology Information (NCBI) database and the Virus Pathogen Resource (ViPR) (Geer et al. 2010). Submitted to NCBI on July 15, 2023, the CuV protein sequences, formatted in FASTA, underwent filtration using CD-hit to achieve 80% sequence similarity and eliminate redundancy. NCBI BLASTp assessed sequence homology to human proteins (identity < 40%, coverage > 70%, e-value < 10−4, bit score < 100) (Mahram and Herbordt 2015). Allergenicity was determined using the AllerTop online server (Dimitrov et al. 2013), antigenicity with a parameter of 0.4 in VaxiJen (Doytchinova and Flower 2007), and toxicity via Toxinpred 3.0 (Manzoor et al. 2023; Rathore et al. 2023).

Prediction, filtering, and overlapping of MHCI, MHCII, and B-cell epitopes

MHCI and MHCII epitopes were predicted using two independent tools from the IEDB (Immune Epitope Database) for higher accuracy. The “Recommended” and “netmhccons” methods were utilized for MHCI epitope prediction, while for MHCII, “Recommended” and “nn_align” were employed, both predicting nine and 15 amino acid epitopes respectively (Hoof et al. 2009). MHC-I and MHC-II epitope predictions were carried out using the NetMHCIpan 4.1 EL and NetMHCIIpan 4.1 EL methods, which are the current recommended tools on the IEDB Analysis Resource (accessed June 12–15, 2025). These tools provide enhanced accuracy by incorporating eluted ligand data into their predictions. Only epitopes with IC50 < 100 nM and percentile rank < 1.0 were selected for further analysis. B-cell epitopes of size 16 were predicted by ABCpred, with a threshold score of 0.51 for selection (Ali et al. 2025). Initially, epitopes with IC50 less than 100 nM and percentile rank less than 1.0 were chosen, but subsequent analysis revealed MHCI epitopes with IC50 less than 80, refining the selection criteria. Epitope overlap analysis was performed to select common MHCI and MHCII epitopes and those shared with B-cell epitopes, ensuring concurrent activation of MHCI/MHCII and B cells (Nielsen et al. 2007). The final epitopes were chosen based on their ability to induce immune responses across MHCI and B cells, and MHCII and B cells. The MHCI immunogenicity tool was then used to predict epitopes with an immunogenicity score greater than 1.0, filtering out the rest.

Analyzing clusters, conserving epitopes and enhancing population coverage

To isolate a single highly similar epitope with minimal amino acid differences, we conducted clustering analysis using the IEDB tool “Epitope Cluster Analysis” to remove redundant or similar epitopes (Zhang et al. 2008). Although vaccines aim to confer protection against all strains of a pathogen, genetic variations can lead to new variants. To evaluate epitope coverage across CuV strains, we employed the IEDB tool “Epitope Conservancy Analysis” along with complete reference genomes from the NCBI database (Sharmin and Islam 2014). This tool aligns epitopes with specific lineage proteins, computing similarity percentages. Genetic diversity at Major Histocompatibility Complex (MHC) loci presents challenges for global vaccine development due to varying MHC molecule frequencies among ethnic groups (Khatrawi et al. 2025). To gauge vaccine feasibility and efficacy, we used the IEDB population coverage analysis tool, estimating the percentage of individuals likely to mount an immune response to selected epitopes in specific regions (Bui et al. 2006). We ensured a comprehensive assessment of potential population coverage by evaluating all relevant alleles used in epitope prediction for each chosen epitope. The referenced alleles are present in the Supplementary file Table S1 and Table S2.

Construction of the primary structure of the immunogenic and evaluation of its characteristics

Selected epitopes were joined using linker peptides such as AAY (for CTL epitopes) (Kavoosi et al. 2007) and GPGPG (for HTL and B-cell epitopes), which facilitate proper folding, antigen processing, and enhanced presentation by the host immune system. These linkers have been widely used in multi-epitope vaccine design to maintain epitope integrity and improve immunogenicity. These linkers, pivotal for protein folding and stabilization, aid in antigenic presentation and have proven efficacy in prior multi-epitope vaccine investigations, emphasizing their value in this study. The adjuvants incorporated into the vaccine constructs include the HBHA protein, β-defensin, the 50S ribosomal protein L7/L12, and conserved HBHA-derived peptide sequences, all of which have been previously reported to enhance immune responses in epitope-based vaccine designs. EAAAK linkers were employed to join adjuvant sequences to the N-terminus of vaccination constructions (Ali et al. 2023), HEYGAEALERAG or GGGS linkers were used to connect the selected epitopes. To promote contact within the N-terminus of each Construct and its epitope, the EAAAK connection was used as a stiff insert. The HEYGAEALERAG ah GGGS flex linkers were created using the technique proposed by Solanki & Tiwari (2018) to improve the flu vaccine's efficacy in terms of its ability to elicit an immune response, as well as its efficacy in preventing disease.

In ExPASy web-server (https://www.expasy.org/) the ProtParam server (https://web.expasy.org/protparam/) was used to analyze the vaccine design's physicochemical properties (Ali et al. 2024). The potential immunogenicity with the multi-epitope vaccine designs was examined by immunogenicity analysis. However, evaluation for the safety profiles of developing vaccine was confirmed by the AllerTOP2.0 webserver (https://www.ddg-pharmfac.net/AllerTOP/) used to estimate their probable allergenicity (Dimitrov et al. 2013). The VaxiJen v2.0 server was used to ascertain their antigenicity, with a cutoff value of 0.4 or greater (Doytchinova and Flower 2007). Finally, the ToxinPred webserver (https://webs.iiitd.edu.in/raghava/toxinpred/) was used to conduct toxicity evaluation on the developing vaccine (Rathore et al. 2023).

Exploring the secondary and tertiary architecture of a multi-epitope vaccine model

The 3D tertiary structure of the vaccine construct was predicted using SWISS-MODEL (https://swissmodel.expasy.org/), followed by refinement using DeepRefiner. The refined models were validated using ProSA-web for Z-score evaluation, ERRAT for structural quality, and PROCHECK (via the SAVES v6.0 server) for Ramachandran plot analysis (McGuffin et al. 2000). Following validation, secondary structure features such as α-helices, β-strands, and coils were derived from the 3D models using PDBsum (http://www.ebi.ac.uk/pdbsum/), with further validation via PSIPRED (http://bioinf.cs.ucl.ac.uk/psipred/) for comparison (Wiederstein and Sippl 2007).

Multi-epitope vaccine docks with TLR4 receptor

We evaluated the vaccine's interaction with the innate immune system by employing the HDOCK web tool for molecular docking between the vaccine and TLR4 receptor (PDBID: 3FXI) (Ciesielska et al. 2021). Toll-like receptor 4 was retrieved from RCSB, with non-TLR structures removed. The highest-scoring protein–protein docking model from HDOCK was validated using HADDOCK version 2.4 (Port et al. 2023). Furthermore, HawkDock assessed the binding free energy of the docked complex via the Molecular Mechanics/Generalized Born Surface Area method, as detailed by Weng et al. (2019). The final docking structure was visualized using PayMOL software (Liu et al. 2023a).

Simulation of the immune response of the multi-epitope vaccine

The C-ImmSim server simulated the immune response to multi-epitope vaccination accurately, administering three doses with a four-week interval (Castiglione and Bernaschi 2004). In C-ImmSim, 1 simulation step corresponds to 8 h of real time. To simulate three vaccine doses given at 4-week intervals, we selected simulation steps 1, 84, and 168, corresponding to day 0, day 28, and day 56, respectively (Joshi et al. 2023). An additional long-term simulation step at 1060 (approximately 354 days post-vaccination) was included to assess immune memory formation. These settings were based on established protocols, IFNepitope (Dhanushkumar et al. 2024), IL17eScan, and IL6pred tools (Dhall et al. 2023), crucial for long-term response and Th1/Th17 polarization. These tools employ deep-learning-based algorithms to predict inducing or non-inducing epitopes.

Normal mode analysis and molecular dynamic simulation

Simulation is the most promising linking analysis. However, the examination was conducted using MD simulations to investigate the CuV-V2-TLR4-complex. Energy minimization, disease modelling, and protein flexibility assessment were performed with the use of the iMODS web server (https://imods.iqfr.csic.es/), Normal Mode analyses (NMA) in the internal (dihedral) measurements of monomers are harnessed by iMODS to precisely reassemble the collective functional motions demonstrated by organic macromolecules (López-Blanco et al. 2014).

iMODS is use to locate specific transition states between two structurally related macromolecules. This cutting-edge server provides a more accurate visualization of the complex main dynamics innate to macromolecules and provides an accurate idea about conformational variants, identifying elastic network possibilities and employing a wide range of coarse-grained tiny designs for model settlement. These methods also probe the structural dynamics of attached protein assemblies that link other proteins and ligands, revealing crucial biological facts including eigenvalue, B-factor (mobility characteristics), deformation, autocorrelation maps, deviation and other elastic network information (Khan et al. 2019).

Utilizing the GROMACS 2021.5 software package with the AMBER14SB force field, protein and ligand molecular dynamics simulations were conducted under periodic boundary conditions using the TIP3P water model. Cl ions were introduced to neutralize system charges. The simulation workflow comprised energy minimization, followed by NVT and NPT equilibrium stages, and production dynamics. Energy minimization was carried out for the entire solvated system, including the vaccine–TLR4 complex, water molecules, and ions, using 5000 steps of the steepest descent algorithm to eliminate steric clashes and achieve a stable starting structure for molecular dynamics simulation. Subsequently, a 50,000-step NVT ensemble simulation was conducted at 298 K with a 2 fs time step. This was followed by a 50,000-step NPT ensemble simulation under the same conditions. The final stage involved a 200 ns NPT ensemble simulation with a 2 fs time step. Trajectory analyses were performed using GROMACS built-in tools, including RMSD, RMSF, radius of gyration, and hydrogen bond calculations, to evaluate the structural stability and dynamics of the vaccine–TLR4 complex (Spoel et al. 2005).

PCA analysis

Principal component analysis (PCA) was executed within molecular dynamics (MD) simulations to determine the principle components (PCs) as eigenvectors directing motion and eigenvalues quantifying residual motion. Gromacs 2021.5 facilitated the PCA analysis on complexes formed by multi-epitope vaccines with TLR-4, projecting PC1 and PC2 onto a two-dimensional space (Liu et al. 2023b).

Codon optimization and in silico cloning

The codon use of the finalized vaccine design (V2) was meticulously cloned and optimized for maximal expression in the E. coli system (Chen 2012). The Java Codon Adaptation Tool (JCat) tool was used to translate the vaccination concept backward and change the codons (Grote et al. 2005). Codon optimization of the final vaccine construct was performed using the Java Codon Adaptation Tool (JCat) (http://www.jcat.de/) to enhance expression in Escherichia coli strain K12. During optimization, three restriction enzyme sites BamHI, HindIII, and EcoRI were excluded to prevent cleavage within the sequence. The optimized sequence yielded a Codon Adaptation Index (CAI) of 0.98 and a GC content of 53.1%, indicating high potential for expression in E. coli.

The enhanced vaccine design was integrated into an E. coli expression system, utilizing Snapgene software v6.2.1 for precise cloning (Biotech, 2020). The necessary components for the E. coli expression system, including the pET28a-TIAL1 plasmid, were obtained from the trusted Addgene repository (https://www.addgene.org/), ensuring the availability of the suitable vector for vaccine construct expression and analysis.

Results

Prediction, filtering, and analysis of CTL, HTL, and B-cell epitopes

The CuV strain's proteome from diverse global strains was retrieved from NCBI in FASTA format. CD-hit was employed to reduce redundancy, and human blast analysis revealed four unique proteins (Capsid protein VP1, Non-structural protein NS1, Protein X, and Protein Y) (Huang et al. 2010). These proteins were then assessed for allergenicity, antigenicity, and toxicity to pinpoint ideal candidates with elevated antigenicity and devoid of allergens and toxicity for epitope prediction.

Prediction of epitopes for B-cells and T-cells

This investigation examined six prioritized proteins to identify potential epitopes for constructing chimeric vaccines against CuV. The IEDB service was used to identify T-cell (MHC-I and MHC-II) epitopes, setting the IC50 ≥ 100 nM. Furthermore, overlapping B-cell epitopes were identified with a specificity of 75% using ABCpred scores above 0.8. Furthermore, three shared core epitopes were isolated across all the proteins for future vaccine design. A total of 18 regions were prioritized based on parameters involving IFN-positivity, solid antigenicity, non-toxicity, and non-allergic responses (Table S1).

The purpose of screening was to identify candidate epitopes that could stimulate host IFN as well as humoral and cellular- immune responses. Ethnicity demographics study for CuV across different countries. The IEDB results revealed intriguingly high population-coverage of similar epitopes, especially in several CuV-affected nations like Central Africa, Central America, East Asia, Europe, and the rest of the world (Table 1) AQN78783.1, AMS35112.1, WET53092.1, WET53087.1, AQN78783.1 and AMS35092.1 are the NCBI accession ID of each fasta used for epitope prediction and (Fig. 2) (Oyarzun and Kobe 2015).

Table 1.

Different Ethnic Groups' MHC Coverage as a Whole

Region AQN78783.1 AMS35112.1 WET53092.1 WET53087.1 AQN78783.1 AMS35092.1
MHC-1 Epitopes MHC-2 Epitopes MHC-1 Epitopes MHC-2 Epitopes MHC-1 Epitopes MHC-2 Epitopes MHC-1 Epitopes MHC-2 Epitopes MHC-1 Epitopes MHC-2 Epitopes MHC-1 Epitopes MHC-2 Epitopes
World 94.54% 71.88% 95.12% 73.11% 97.97% 71.88% 97.97% 71.88% 94.54% 71.88% 94.54% 71.88%
Central Africa 85.25% 49.02% 87.98% 49.22% 91.08% 49.02% 91.08% 49.02% 85.25% 49.02% 85.25% 49.02%
Central America 2.19% 29.86% 2.19% 50.84% 4.91% 29.86% 4.91% 29.86% 2.19% 29.86% 2.19% 29.86%
East Africa 86.19% 53.35% 87.59% 53.35% 92.49% 53.35% 92.49% 53.35% 86.19% 53.35% 86.19% 53.35%
East Asia 95.54% 73.63% 96.16% 76.99% 97.44% 73.63% 97.44% 73.63% 95.54% 73.63% 95.54% 73.63%
Europe 96.51% 75.87% 96.94% 76.08% 98.87% 75.87% 98.87% 75.87% 96.51% 75.87% 96.51% 75.87%
North Africa 93.69% 61.73% 94.55% 62.12% 96.57% 61.73% 96.57% 61.73% 93.69% 61.73% 93.69% 61.73%
North America 95.40% 78.65% 95.99% 79.67% 98.34% 74.63% 98.34% 78.65% 95.40% 78.65% 95.40% 78.65%
Northeast Asia 85.40% 51.23% 85.85% 52.58% 95.10% 51.23% 95.10% 51.23% 85.40% 51.23% 85.40% 51.23%
Oceania 87.60% 51.30% 88.40% 52.46% 95.35% 51.30% 95.35% 51.30% 87.60% 51.30% 87.60% 51.30%
South Africa 95.62% 7.65% 95.62% 7.65% 98.20% 7.65% 98.20% 7.65% 95.62% 7.65% 95.62% 7.65%
South America 89.90% 41.65% 91.00% 56.79% 92.82% 41.65% 92.82% 41.65% 89.90% 41.65% 89.90% 41.65%
South Asia 88.29% 65.03% 89.04% 65.46% 95.29% 65.03% 95.29% 65.03% 88.29% 65.03% 88.29% 65.03%
Southeast Asia 85.51% 50.14% 86.82% 50.58% 94.47% 50.14% 94.47% 50.14% 85.51% 50.14% 85.51% 50.14%
Southwest Asia 85.49% 36.92% 86.81% 37.09% 91.58% 36.92% 91.58% 36.92% 85.49% 36.92% 85.49% 36.92%
West Africa 87.23% 57.03% 88.18% 58.37% 93.46% 57.03% 93.46% 57.03% 87.23% 57.03% 87.23% 57.03%
West Indies 82.71% 60.98% 82.88% 59.37% 93.90% 60.98% 93.90% 60.98% 82.71% 60.98% 82.71% 60.98%
Average 84.53 53.88 85.36 56.4 89.87 52.33 89.87 53.88 84.53 53.88 84.53 53.88
Standard deviation 21.06 17.59 21.21 17.06 21.37 16.97 21.37 17.59 21.06 17.59 21.06 17.59
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Fig. 2.

Fig. 2

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A Comparing National Ethnic Structures with Global Country-Level Diversity. B MHC epitope population density in disease hotspots around in different ethnicity using the IEDB Database

Multi-epitope vaccine constructions

The creation of chimeric vaccines requires the careful matching of various primary epitopes. The HEYGAEALERAG and GGGS linkers were utilized to ensure the structural integrity and functional safety of each epitope. These vaccine complexes maintained their 3D structure when the epitopes were linked with linker. The immunogenic response was also enhanced by fusing the epitopes to one of four adjuvants including the HBHA proteins (De Maio et al. 2019), beta-defensing (Kim et al. 2018), 50S ribosomal protein (Ban et al. 1999), L7/L12 adjuvants (homologous HBHA peptide sequences) (Tabynov et al. 2014). EAAAK linkers in the linking process enhanced the immunological response. Furthermore, the inclusion of PADRE protein sequences in the vaccine constructs aimed to address the challenges posed by HLA DR difference across diverse populations. Previous research has demonstrated that the induction of PADRE molecules with vaccine formulations can increase immune protection and lethal T cell (CTL) responses. Furthermore, all of the four vaccine prototypes were determined to be safe and effective based on the analysis of their immunological properties. Antigenicity values > 0.5 were employed by the ANTIGENpro servers to deduce considerable immunity by tenfold cross-validation and examination of safety aspects for the antigenic sequences. VaxiJen 2.0 scores in the 0.44–0.47 range were greater than the typical virus parameters, providing more evidence of the antigenic capacity of viruses.

ProtParam website determined the molecular weights of the vaccine constructs between 35 and 48 kDa. c. Computed GRAVY ratings ranged from − 0.108 to − 0.325 indicating the hydrophilic nature of the vaccine constructs, the most plausible range for the theoretical value of pI was found 5.09–9.05 (Aiman et al. 2023). Additionally, the aliphatic index, an indicator of thermal stability, varied from 64.50 to 76.29. The various constructs-maintained stability over a wide temperature range, with anticipated stability index values ranging from 30.24 to 41.53 as shown in Table 2. The physicochemical features of the constructions were also remaining intact after the addition of various adjuvants. Several research indicate that the vaccine types may be effective in eliciting a strong immune response in humans. Therefore, further experiments and investigations are need to confirm the validity of the results.

Table 2.

Servers for assessing vaccines physiochemical properties: ProtParam and JCAT

Vaccine constructs AminoAcids Molecular
weight
(kDa)		 Instability
index		 Theoretical PI Grand average
of hydropathicity
(GRAVY)		 GC content CAI Aliphatic index
Con#1 adjuvant = HBHA adjuvant 452 48,141.36

37.96

protein as stable

 5.18 − 0.327 53 1.0 73.58
Con#2 adjuvant = Beta definsin adjuvant 338 35,673.91

36.05

protein as stable

 9.05 − 0.297 51 1.0 64.50
Con#3 adjuvant = HBHA conserved adjuvant 443 47,023.14

41.53

protein as unstable

 5.09 − 0.301 52 1.0 75.71
Con#4 adjuvant = Ribosomal protein adjuvant 423 43,953.20

30.24

protein as stable

 5.19 − 0.108 50 1.0 76.29
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Secondary and tertiary structure prediction and validation

Computational approaches were used to evaluate the structural properties of the vaccine constructions by predicting a secondary structure. The position-specific scores matrices (PSSM) to predict the number of transdermal helices, domains and folds in a drug's outer membrane (Fig. 3A).

Fig. 3.

Fig. 3

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The prediction and validation of secondary structure of CuV.V2. (A), the -helices are shown in pink, -strands are shown in yellow, and coil structures are shown in grey, all of which indicate different parts of the protein's secondary structure by using the PSIPRED 4.0. B The Swiss Models are used for constructing the 3D structure for a multi-epitope vaccination using a homology building approach. C DeepRefiner online Tool to fine-tune the 3D CuV-V2 structure, The Swiss Model constructed a 3D model for the multi-epitope vaccine using the homology modeling strategy. D The Z-score was calculated as -4.52 utilizing ProSA-web. Seventy percent of the residues of the plot fall into the preferred area, twenty-eight percent into the permitted area, and less than one percent into the forbidden area, as shown by a Ramachandran plot analysis

The investigation of the molecular relationships between a vaccination and immune receptor proteins requires a stable and functional three-dimensional structure. We utilized homology modelling strategies and implemented via the Swiss Model service (Fig. 3B) to predict the structure of developing vaccine (Fig. 3C). However, the models were also subjected to structural validation examination and refined by utilizing the DeepRefiner online tools (Fig. 3C). In addition, Ramachandran plots revealed that 87.4% of V1 (HBHA) residues, 97.4% of V2 (beta-defensin construct) residues, 87.6% of V3 (HBHA conserved) residues, and 82.8% of V4 (50S ribosomal) residues were inside the plots' chosen area (Fig. 3D). The PDB of the vaccine are available in the supplementary file.

Furthermore, the enhanced vaccine constructs demonstrated a superb overall quality factor of 95% and 100%, using the ERRAT tools. In addition, the ProSA-web server was used to analyze the vaccine designs, yielding a Z score between − 4.52 and − 1.08. The 3D structure of developing vaccine was confirmed by RAMPAGE, ERRAT, and ProSA-web examinations and predicted high-quality tertiary structures of the developing vaccine models (Table 3).

Table 3.

Three-dimensional structural justification is (Ra Mach and plot preferred region), and ProSA-Web Server for vaccine designing

Vaccine Construct Constructs PROCHECK ERRAT ProSA (Z-score)
CuV-V1 V1 87.4% 96.9325 − 2.23
CuV-V2 V2 97.4% 93.75 − 4.52
CuV-V3 V3 87.6% 96.3235 − 1.08
CuV-V4 V4 82.8% 99 − 4.11
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Molecular docking and NMA analysis

The advance techniques of molecular docking were used to determine the best possible match between the vaccination and the receptor molecules using HDOCK and HADDOCK, the vaccine designs were docked with the human TLR4 receptor on the cell surface utilizing HADDOCK. The best ten docking results were then optimized using the HawkDock. HawkDock allows for the quick and precise refinement of complex structures to deal with the difficulties of protein flexibility during vaccine and receptor interactions. In the docking interaction of the Vaccine receptors and TLR4 receptor, the CuV-V2 construct shows the lowest energy as shown in Table 4. To further analyze these interactions, a schematic illustration of interactions was obtained through PDBsum (Fig. 4).

Table 4.

The molecular docking energy of vaccine constructs and TLR4 receptor

Vaccine construct TLR4(3FXI)
CuV-V1 − 5429.33
CuV-V2 − 6077.81
CuV-V3 − 5166.25
CuV-V4 − 4905.97
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Fig. 4.

Fig. 4

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A The plot of visualized analysis and the interacting amino acid residues of the docking complex. The chain A refers to TLR4 receptor and the chain B refers to vaccine. B B-factor of Cuta-V2-TLR4 C Eigen value of Cuta-V2- TLR4 complex

The V2 component with beta defense adjuvant demonstrated the most favorable bonding potential (− 41.27 kcal/mol) comparable to the TLR4 receptor. A higher eigenvalue (1.304551e-05) CuV-V2-TLR4 was shown to be a stable pairing, which corresponds to a lower B-factor representing the typical RMSD in the relationship between Lpc ratings and the ability equivalent to the NMA of docked facilities, representing a stronger docking affinity with TLR4 receptor (Fig. 5B, C). However, all the tested vaccine-receptor combinations had equal docking scores. Furthermore, the docking experiments showed that the designed vaccine had a high binding affinity for TLR4 receptor.

Fig. 5.

Fig. 5

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Results of iMODS server-based molecular dynamics simulations of the CuV-V2 vaccine-TLR4 complex.: A Stiffer areas (grey) in the elastic network model, B indicating correlated (red), unrelated (white), or oppositely correlated (cyan) residue motions, variances C represented by colored bars indicating individual (purple) or cumulative (grey) variances, and D deformability

Normal Mode Analysis (NMA) of the CuV-V2–TLR4 complex provided insights into its dynamic stability and flexibility. The deformability plot (Fig. 5A) revealed localized peaks between residues 450–480 and 580–610, indicating flexible loop regions likely involved in receptor interaction. In contrast, the core residues (200–400) exhibited low deformation, reflecting rigid structural stability essential for stable complex formation. The covariance matrix (Fig. 5B) displayed regions of correlated motions (red) predominantly within the V2 construct, while anti-correlated (green) and uncorrelated (white) motions were scattered across the TLR4 binding interface. These patterns suggest coordinated movement in flexible loops and stable anchoring in the complex core. The eigenvalue associated with the first normal mode was 1.30 × 10⁻5, indicating a low-energy requirement for structural displacement, further supporting the dynamic stability of the complex. The variance graph (Fig. 5C) showed an inverse relationship between eigenvalues and variance, confirming that lower eigenvalues correspond to higher mobility. The elastic network model (Fig. 5D) highlighted stiff (dark gray) regions in the complex core, and flexible (light gray) regions at the N- and C-termini of the V2 construct, consistent with the deformability findings. NMA results for the CuV-V2–TLR4 complex revealed localized deformation peaks between residues 420–460 and 580–610, suggesting moderate flexibility in these regions, likely corresponding to surface-exposed loops. Most of the core residues (residues 200–400) showed minimal deformability, indicating structural rigidity essential for stable complex formation. The B-factor plot confirmed these observations, with higher mobility at the N- and C-termini of the vaccine construct. The calculated eigenvalue of 1.30 × 10⁻5 indicates low energy requirements for conformational changes, signifying favorable stability of the complex. Additionally, the elastic network map showed a dense cluster of stiffer interactions (dark grey) in the core, supporting compactness and structural resilience.

Molecular dynamic simulation

MD simulation was carried out on the CuV-V2–TLR4 receptor complex using GROMACS v2022.1 to analyze the structural stability and dynamics of the vaccine–receptor interaction. The complex was solvated in a triclinic water box (TIP3P), neutralized with Na⁺/Cl⁻ ions, and energy minimized using 5000 steps of steepest descent. The simulation ran for 100 ns, with trajectory analyses performed using built-in GROMACS tools. Employing RMSD, RMSF, and Rg analyses, we gained nuanced insights into the construct's innate flexibility and robust stability. RMSD plot (Fig. 6A) demonstrates the vaccine-receptor complex's resilience, revealing initial deviations that gracefully stabilized post 5 ns, ensuring a consistently stable structure with deviations below 0.2 nm. The RMSF plot (Fig. 6B) portrayed minimal fluctuations in atomic positions, emphasizing the steadfastness of amino acid residues throughout the simulation. Delving into GROMACS energies, a comprehensive view of the system's thermodynamic stability and intricate intermolecular interactions were depicted (Fig. 6C). The Rg plot (Fig. 6D) unfolded a compelling narrative, highlighting minimal deviation and a flat curve, affirming the compactness and resilience of the complex structure. In essence, these holistic molecular dynamics simulations underscored the structural flexibility and stability of the vaccine-receptor complex, i.e. promising to elicit effective immune responses. The radius of gyration (Rg) of the CuV-V2–TLR4 complex remained stable around 2.1 nm, indicating consistent structural compactness. The directional components Rgx, Rgy, and Rgz showed minimal fluctuation, suggesting the complex maintained isotropic stability without significant distortion along any axis.

Fig. 6.

Fig. 6

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A The RMSD plot showed that the CuV-V2–TLR4 complex reached equilibrium at ~ 0.25 nm after 10 ns and remained stable throughout the 100 ns simulation. In comparison, individual simulations of CuV-V2 and TLR4 showed higher RMSD fluctuations (~ 0.35 nm and ~ 0.30 nm, respectively), indicating that binding enhances structural stability. B The RMSF plot revealed that residues 450–470 (CuV-V2) and 620–640 (TLR4) exhibited higher flexibility (~ 0.4–0.5 nm), suggesting these regions are likely involved in dynamic interactions. Core regions remained stable with RMSF < 0.2 nm. C The potential energy profile of the docked complex stabilized around − 1.35 × 106 kJ/mol, with minor fluctuations, indicating energetically favorable interactions. D The radius of gyration (Rg) of the complex remained stable at ~ 2.1 nm, whereas CuV-V2 alone showed slightly higher Rg (~ 2.3 nm), implying increased compactness upon binding

PCA analysis

Protein dynamics, analyzed via Principal Component Analysis (PCA) (Zhou et al. 2009), reveal collective trajectory motions during MD simulations. The graph in Fig. 7A plots eigenvalues against eigenvector index for the first 20 modes of motion, illustrating stability with fluctuations in hyperspace eigenvectors. Higher eigenvalues correspond to greater mobility regulation in the target protein, particularly evident in the top five eigenvectors with larger eigenvalues (20.3–61.3%). Changes are visualized in three principal components (PC1, PC2, and PC3), with PC1 exhibiting the largest variability (20.28%), PC2 showing moderate variability (12.94%), and PC3 indicating the least variability (9.08%). PC3, with its compact structure compared to PC1 and PC2, suggests a more stabilized protein–ligand binding complex. Clustering in the PC subspace reveals conformational variations, where blue signifies significant mobility, white indicates intermediate movement, and red suggests less flexibility (Laczkó et al. 2020). The high pairwise cross-correlation coefficient values in the cross-correlation map (Fig. 7B) indicate significant correlation between the selected ligand and the target protein, with magenta representing anti-correlated residues (− 0.4) and cyan indicating correlated residues (> 0.8). Notably, a large number of pairwise correlated residues were observed between the target protein (AIM2) and the selected ligand.

Fig. 7.

Fig. 7

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In A, a Principal Component Analysis (PCA) eigenvalue is depicted against the percentage of variance, with distinct sections illustrating variations in PC1, PC2, and PC3, amounting to 26.35%, 10.7%, and 26.35%, respectively. B Presents a dynamic cross-correlation map of the complex where does colors represent positive and negative correlations among residues, respectively

Immune simulation prediction

The immune simulation analysis showed that the prioritized construct (CuV-V2) elicited a robust and dynamic immune response over the three simulated doses. After the first dose (day 1), a primary immune response was observed, marked by a peak in IgM levels (~ 800 AU), along with an initial increase in naïve B-cell populations (Fig. 8A, C). Following the second (day 28) and third (day 56) doses, secondary and tertiary immune responses showed a significant rise in IgG1 + IgG2 antibodies (~ 1200 AU), IgM + IgG levels (~ 1500 AU), and memory B-cell populations, indicating effective isotype switching and immunological memory development (Fig. 8C, I). These responses corresponded with a rapid and sustained decline in antigen levels, which dropped sharply after each dose and remained near baseline following the final dose (Fig. 8B). Cytokine analysis revealed high levels of IFN-γ (~ 900 AU) and IL-2 (~ 600 AU) post-immunization, suggesting a strong Th1-type immune response (Fig. 8A). T-cell dynamics showed that both cytotoxic T-cells (CTLs) and helper T-cells (Th cells) peaked around day 10 post-dose, followed by the formation of memory T-cell populations, which increased progressively after each exposure (Fig. 8D, E). Additionally, macrophages, dendritic cells, and natural killer (NK) cells maintained elevated levels throughout the simulation period, indicating sustained innate immune activation (Fig. 8F–H). These findings suggest that the CuV-V2 vaccine construct can induce a strong, multi-layered immune response with effective antigen clearance, cytokine activation, and development of immunological memory, supporting its potential efficacy for in vivo evaluation.

Fig. 8.

Fig. 8

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The results of a C-ImmSim Server-based in silico immunity simulation was performed to evaluate the immunological capability of the proposed CuV-V2 hybrid peptide vaccine. A There is a rise in concentrations. B Following repeated antigen exposure, B-cell populations keep expanding. C B-cell populations show growth whereas antigen levels show a decline. D, E Repeated antigen exposure results in an increase in the populations of T-cytotoxic and T-helper cells. F–H During the immunization period, the population of dendritic cells, macrophages, or natural killing cells gradually rises. I The simulation shows that immunoglobulin antibodies are elevated and antigen levels are decreased

Codon optimization and in silico cloning

In order to maximize the expression potential of the proposed vaccine designs, substantial codon optimization was performed using the JCat web server. In addition, vaccine constructs could be expanded to obtain larger expression levels in E. coli expression system by reverse translating peptide sequences into DNA sequences. The analysis of the modified sequences revealed that cDNA had adaption index (CAI) value of 1, indicating an excellent level of compatibility with the host cell. Furthermore, the optimized sequences had a mean GC content of about 53%, which is considerably within the range to induce robust production of the hypothesized vaccine (Table 2).

SnapGene was utilized for heterologous cloning and expression in the E. coli system, however the recombinant sequence was created by inserting the V2 vaccine construct and the optimized codon sequence in the pET28a (plus) plasmid vector (Fig. 9). This approach improves the chances of a successful clone and subsequent manufacture of the vaccine design, which in turn improves the vaccine's potential efficacy and utility.

Fig. 9.

Fig. 9

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CuV-V2 vaccine expression in E. coli pET28a (+) was achieved by in silico restriction cloning

Discussion

Newly developing CuV infections can now be treated using a novel therapeutic approach. The vaccine development become more advanced due to the widespread availability of proteomic and genomic data and reverse vaccinology approaches (Fu 2017). However, the cutting-edge bioinformatics techniques shown substantial improvement over more traditional methods. The epitope-based vaccinations hold great promise as a new treatment strategy for the creation of effective, potential and secure vaccines. The approach of specific immune response was achieved by designing the epitope-based vaccines however, multi-epitope based vaccines further enhanced immunity against an antigen. In addition, this approach also reduces immune response against unwanted epitopes. Similarly, the innovation of immunoinformatics techniques to build novel multi-epitope CuV vaccine designs can enhance immune response to eradicate the CuV infection (Väisänen et al. 2019). Furthermore, in the present approach T-cell and B-cell epitopes were isolated by selectively studying six inner membranes and exogenous CuV proteins for antigenic behavior, non-allergenic qualities, and non-toxic nature (Kreuter et al. 2018).

The inclusion of MHC-I regions in CuV vaccine development ensures permanent immunity by identifying and destroying viruses and infected cells, in addition, MHC-II epitopes induce cytotoxic and humoral immune reactions. The immunity response developed by both the MCH-I and MHC-II are crucial for adaptive immunity. These epitopes elicit CD4+ helper T-cell response, which in turn generates CD8+ T-cell memories and activates B-cells. We built vaccine candidates with four different adjuvant protein sequences and immune enhancers that combine MHC-I and MHC-II epitopes that overlap with B-cell epitopes. These multi-epitope construction eradicate CuV infection as a vaccine candidate due to their high antigenic properties scores, as measured by ANTIGENpro or Vaxijen v2.0 (Magnan et al. 2010). However, the immuno-informatics approaches revealed that the present vaccine is non-allergic and non- toxic in nature. The immuno-informatics approaches and structural data enable us in the understanding of interactions between antigens & receptor molecules. The 3D structure prediction of the vaccine constructions was examined by the innovative computational methods and further analyzed by the spot on the Ramachandran scatter plot. The high quality of the predicted structures in the multi-epitope vaccine constructions was further confirmed by validation using ProSA-web with ERRAT predictions (Wiederstein and Sippl 2007). Importantly, the molecular weights with the cut off value of 30 kDa of vaccine indicating to be an effective vaccine.

Physicochemical studies of the developing vaccine showed excellent solubility and extraordinary stability after expression. The overexpression of a recombinant peptide in an E. coli cloning system is essential for functional and biochemical research (Chen 2012). In addition, the developing vaccine structures exhibit good stability during expression showed by the projected instability scores indicating, their efficacy as a vaccine candidate. It is critical to validate their immune-reactivity using serological investigation to verify their efficacy. (Gori et al. 2013). The E. coli expression method was used for recombinant peptide cloning and development. Herein, a molecular docking study was conducted to validate the interaction of vaccine with immune receptor, TLR4. These receptors are integral parts of the innate defenses system. TLR4 is a key player in immune cell activation and the initiation of adaptive immunological responses. Previous studies showed that TLR4 is essential for the generation of inflammatory cytokines after recognizing viral peptide.

The results of the molecular docking study demonstrated high binding affinities between planned vaccine structures and the receptor active sites of the target protein. It showed that the proposed designed vaccine has the potential to produce reliable immune responses. In addition, docking poses, atomic interactions, and binding liberated energy were carefully analyzed to determine that CuV-V2 is the most stable and effective vaccine candidate. The V2 construct demonstrated the highest antigenicity (0.78), strong binding to TLR4 (− 6077.81 kcal/mol), and favorable immune simulation profiles, indicating robust potential to elicit both humoral and cellular immune responses. These findings are consistent with similar epitope-based vaccines targeting TLR4, such as those developed for Salmonella enterica (Laczkó et al. 2020) and SARS-CoV-2 (Ali et al. 2024), where strong TLR4 engagement correlated with enhanced immune responses. Additionally, MD simulation results confirmed stability of the V2–TLR4 complex, with low RMSD fluctuations and sustained hydrogen bond interactions, supporting its structural integrity over time. However, both the immunological and MD simulation investigations also confirmed the application of CuV2 as a suitable vaccine candidate. Simulation results showed a robust immune response after repeated antigen exposure, correlating well with observations of native cellular immunogenic responses. Importantly, our findings demonstrated the development of adaptive immunity characterized by the long-term active maintenance of B- and T memory cells over a protracted period, of several months. Furthermore, the levels of Immunoglobulin g (Ig) synthesis and the presence of both T-cytotoxic and T-helper cells were also found high. The first boost up vaccination elicited strong humoral responses which was measured by high levels of IFN-gamma and IL-2 (Zhou et al. 2009). However, cloned specific research showed potential variations in immune responses utilizing the Simpson index D. Research evidence studies showed that, both in vitro and in vivo, the long period immunogenic response is necessary to ensure the efficacy of a vaccine. MD Simulation studies showed strong interactions between CuV-V2 andTLR4in a biological system. These simulations studies confirmed the stability of the multi-epitope vaccine complex by showing strong interactions between the vaccines with the immunological receptor. Therefore, the vaccine construct elicits signals for highly expressing genes involved in immunological responses. Laczkó et al. (2020). Furthermore, in addition to effective and economic immunoinformatics methods used in silico research are a, informative to expedite and supplement laboratory investigations However, some more validation using in vitro immunology assays and preclinical studies are necessary to establish the efficacy and challenge protection for our vaccine candidate. Furthermore, immunoinformatics approaches can be further enhanced by improving prediction methods, accuracy in databases to combat different viral infections through vaccines However, in vitro and in vivo investigations and confirmation are required for the safety and efficacy of our proposed vaccine design (Li et al. 2021).

Conclusions

Cutavirus (CuV), the newest human parvovirus, garners increasing attention due to its potential association with cutaneous T-cell lymphoma. Detected in normal skin, CuV's prevalence, infection levels, and genetic variations in the general population remain poorly understood. With no available vaccines, our study proposes a vaccine containing 18 epitopes derived from reverse vaccinology predictions and previous experimental studies, aiming to elicit long-term immune responses involving cytotoxic and helper B- and T-cell epitopes, potentially inducing Th1 and Th17 patterns along with IFN-γ, TNF-α, IL17, and IL6 cytokines. CuV-V2 demonstrates high antigenicity, non-toxicity, non-allergenicity, and dissimilarity to human proteins. Further in vitro and in vivo studies are necessary to validate the recombinant protein's immunogenicity in humans.

Server’s Accessible link

  1. VaxiJen v2.0 for antigenicity prediction

    Website: https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html

  2. AllerTOP v2.0 for allergenicity prediction

    Website: https://www.ddg-pharmfac.net/AllerTOP/

  3. ToxinPred 3.0 for toxicity prediction

    Website: https://webs.iiitd.edu.in/raghava/toxinpred/

  4. IEDB Analysis Resource for epitope prediction and population coverage

    Website: https://www.iedb.org/

  5. ABCpred for B-cell epitope prediction

    Website: https://webs.iiitd.edu.in/raghava/abcpred/

  6. PSIPRED 4.0 for secondary structure prediction

    Website: http://bioinf.cs.ucl.ac.uk/psipred/

  7. SWISS-MODEL for 3D structure modeling

    Website: https://swissmodel.expasy.org/

  8. ProSA-web for model quality assessment

    Website: https://prosa.services.came.sbg.ac.at/prosa.php

  9. RAMPAGE for Ramachandran plot analysis

    Website: http://mordred.bioc.cam.ac.uk/~rapper/rampage.php

  10. HDOCK for molecular docking

    Website: http://hdock.phys.hust.edu.cn/

  11. HawkDock for binding free energy calculation

    Website: http://www.cadd.zju.edu.cn/hawkdock/

  12. iMODS for normal mode analysis

    Website: http://imods.iqfr.csic.es/

  13. C-ImmSim for immune simulation

    Website: http://150.146.2.1/C-IMMSIM/index.php

  14. JCat for codon optimization

    Website: http://www.jcat.de/

  15. SnapGene (offline software) for in silico cloning

    Website: https://www.snapgene.com/

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (16.1KB, docx)
Supplementary Material 2 (34.8KB, pdb)
Supplementary Material 3 (81.5KB, pdb)
Supplementary Material 4 (132.2KB, pdb)
Supplementary Material 5 (147.1KB, pdb)

Acknowledgements

We are thankful to Abdul Wali Khan University Mardan for providing the best environment for research.

Author contribution

Syed Luqman Ali and Awais Ali Project design, evaluated the data, literature Reviewing, writing main script and data clarification transcribed the manuscript and organized the figures, validation, literature Reviewing and Writing and data analysis. Asifullah khan: Supervision, validation and revising.

Funding

Not received.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Syed Luqman Ali and Awais Ali contributed equally to this work.

Contributor Information

Syed Luqman Ali, Email: syedluqmanali5@gmail.com, Email: luqmanali@awkum.edu.pk.

Asifullah Khan, Email: asif@awkum.edu.pk.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (16.1KB, docx)
Supplementary Material 2 (34.8KB, pdb)
Supplementary Material 3 (81.5KB, pdb)
Supplementary Material 4 (132.2KB, pdb)
Supplementary Material 5 (147.1KB, pdb)

Data Availability Statement

No datasets were generated or analysed during the current study.

Articles from In Silico Pharmacology are provided here courtesy of Springer

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