Introduction of protein vaccine candidate based on AP65, AP33, and -actinin proteins against Trichomonas vaginalis … – Parasites & Vectors
                            March 31, 2024
                            Genome extraction of AP33, AP65, and -actinin proteins    
    The sequences of AP33 (accession number: Q65ZG5), AP65    (accession number: Q27093), and -actinin (accession number:    O96524) proteins were extracted from the UniProtKB database.  
    Prediction of B cell epitopes for all three proteins, AP33,    AP65, and -actinin, was done using Bepipred and IEDB servers    (Kolaskar and Tongaonkar) (Additional file 1: Table S1).  
    IEDB and Rankpep databases were also used to predict T cell    epitopes. The allelic group for MHCII alleles DRB1*0101, *0301,    *0401, *0701, *0801, *1101, *1301, *1501, which covers the    genetic background of most humans, was selected. The most    important epitopes with the highest score were selected    (Additional file 2: Table S2).  
    The regions of AP33, AP65, and -actinin proteins with the    highest epitope abundance are considered as the target domain    for vaccine design to select the domains that make up the    vaccine candidate. Finally, nine epitope-rich domains from    these three proteins were selected as vaccine candidates, which    contain a large number of B cell and T cell epitopes    (Table1).  
    By combining selected epitope-rich domains at different    positions using EAAAK, EAAAKEAAAK, and GGGGS linkers, several    protein constructs were designed. The designed constructs were    evaluated based on physicochemical properties, antigenicity,    and secondary and tertiary structure, and finally the most    suitable construct was introduced as a vaccine candidate    (Additional file 3: S3)    (Fig.1a, b).  
            a Schematic diagram of the final construct of            the multiepitope protein. b The tertiary            structure of the designed protein          
    Using the EXPASY ProtParam server (http://expasy.org/tools/protparam.html),    the physical and chemical properties of the designed structures    such as the number of amino acids, molecular weight, PI, number    of charged amino acids, amino acid composition, hydrophobicity,    and hydrophilicity were obtained. The results of this    investigation showed that our designed vaccine candidate    protein finally consisted of 780 amino acids and had a    molecular weight of 85,190.25daltons    (Table2). The    instability index (<40) indicates that the designed protein    has high stability to induce an immunogenic response. The    instability index of our vaccine candidate was 35.8, which    classifies the protein as stable. The aliphatic index of the    recombinant protein was calculated to be 86.04, indicating the    stability of this protein at different temperatures    (Table2).  
    The Vaxijen 2.0 server predicts the designed protein as an    antigen with a threshold score0.4 (score: 0.4983). The    Evaller web server was used to check the allergenicity of the    designed structure. The designed protein was not allergenic.    The solubility of the vaccine candidate was also evaluated    using the Protein-sol server. Our selected protein has a    solubility score of 0.555. Solubility-scaled proteins using the    Protein-sol server that have a score greater than 0.45 indicate    a higher solubility than the average soluble    E.coli protein from the experimental solubility    dataset [45]; therefore, our    designed protein has a high solubility.  
    The GOR software was used to check the second structure of the    designed structures. The amino acids that make up these    recombinant proteins are involved in the formation of random    coils, alpha helixes, and beta strands. The results showed that    out of 780 amino acids, 430 amino acids (55.13%) are alpha    helix, 96 amino acids (12.31%) are extended strands, and 254    amino acids (32.56%) are random coils (Fig.2a). Tertiary    structures were predicted by the I-TASSER server for designed    protein sequences. All structures were validated and the best    structure was selected. Predicted tertiary structures were    evaluated using the MolProbity, ProSA-web, and SAVES servers.    The MolProbity server was used to evaluate the structural    similarity of new proteins to the best-known structures of    similar proteins (http://molprobity.biochem.duke.edu/help/validation_options/summary_table_guide.html).    On MolProbity analysis, the protein structure analysis was    evaluated based on the Clash score and the MolProbity score.    The SAVES server (https://saves.mbi.ucla.edu/)    was also used to check the Ramachandran plot and evaluate the    placement of amino acids in the favored, allowed, and    disallowed regions.  
            Predicting and validating the secondary and tertiary            structure of the vaccine candidate. a Secondary            structure of the designed protein. b Validation            of the tertiary structure of the protein by ProSA-web.            c Validation of the tertiary structure of the            protein by Ramachandran plot          
    On MolProbity evaluation, it was found that the Clash score for    this protein was 2.49 (99% similar to the structures). Also,    the MolProbity score was 2.13 (69% similar to the best    structures). ProSA-web analyzed a 3D model of the vaccine    candidate using an energy plot and Z-score. ProSA-web    analyzed a 3D model of the vaccine candidate using energy plot    and Z-score. The Z-score of the selected protein    was 3.44, which was within the range of native protein    structure (Fig.2b). The    evaluation of the Ramachandran diagram also showed that 97.4%    of the amino acids were in the favored and allowed region and    2.6% were in the nonallowed areas, indicating the appropriate    structure predicted for the protein (Fig.2c).  
    Ellipro servers were used to predict this type of epitope    (Fig.3af). The 3D    structure of the designed vaccine protein used in the Ellipro    server was predicted by the I-TASSER server. The most antigenic    epitopes with a score above 0.5 is presented in    Table3.  
            The most potent vaccine candidate conformational            epitopes designed using the Ellipro server. a            Epitope with score 0.901, b Epitope with score            0.736, c Epitope with score 0.723, d            Epitope with score 0.657, e Epitope with score            0.641, f: Epitope with score 0.596          
    Cluspro 2.0 was used to study the proteinprotein binding    between the designed vaccine candidate with TLR4 and TLR2. To    select the best interaction, the parameters of the weighted    score and number of clusters calculated by Cluspro 2.0 were    evaluated. In addition, hydrogen and hydrophobic bonds between    the vaccine candidate and TLR4 and TLR2 were investigated using    the LIGPLOT tool. Finally, we considered the lowest energy and    the lowest affinity (Kd) obtained from the PRODIGY web server    as essential standards for selecting the strongest complexes.    The results showed that there is a strong interaction between    the vaccine candidate with TLR4 and TLR2    (Table4).    Interactions between TLR2 (Fig.4) and TLR4    (Fig.5) and the    designed vaccine candidate were observed using PyMOL and    LIGPLOT. As shown in Figs.4 and    5, the vaccine    candidate made a strong interaction with the active site of the    receptors, and this binding includes the essential amino acids    Ile319, Phe322, Phe325, Tyr326, Val348, Phe349, and Pro352 for    TLR2 and the amino acids Arg434, Arg380, Lys341, Lys263, and    Gln339 for TLR4.  
            a Graphic representation of the interaction of            the designed vaccine candidate with the TLR2 complex.            b LIGPLOT representation of the amino acids            involved in the interaction between the protein vaccine            candidate and TLR2. *Hydrogen bonds between receptors            (blue) and the protein vaccine candidate (green) and            hydrophobic interactions with receptors (black) and the            protein vaccine candidate (blue) are indicated by dark            green lines          
            a Graphical representation of the interaction of            the designed vaccine candidate with the TLR4 complex.            b LIGPLOT representation of the amino acids            involved in the interaction between the protein vaccine            candidate and TLR4. *Hydrogen bonds between receptors            (blue) and the protein vaccine candidate (green) and            hydrophobic interactions with receptors (black) and the            protein vaccine candidate (blue) are indicated by dark            green lines          
    To verify the stability of the designed protein structure and    proteinreceptor complexes, MD simulation was performed for up    to 100ns. The RMSD parameter is used when analyzing the    results of MD simulations of proteins and complexes to obtain    the degree of movement of the protein or atoms when the ligand    is placed in the active site of the receptor and to evaluate    the stability of the structure, deviation, and conformations of    the protein or complex during the simulation period. A lower    RMSD value indicates more stability and less fluctuations    during the simulation. The analysis of the results related to    the RMSD of the designed protein and the complexes showed that    the designed protein reached stability after about 10ns    and its average RMSD was 0.95nm (Fig.6a). This    stability is maintained during the simulation up to    100ns. Also, proteinTLR2 complexes with an average of    1.7nm are stable during the simulation    (Fig.6a). The    proteinTLR4 complex reached stability after about 40ns    with an average RMSD of 1.1nm, and considering that the    fluctuations during 40100ns are less than 0.3nm,    it can be concluded that the complex has reached stability    (Fig.6a). Another    parameter that has been investigated in the evaluation of MD    simulations is the Rg, which is evaluated the amount of    compression changes during the MD simulation. Rg is defined as    the distribution of a proteins atoms around its axis and is    widely used in the calculation of protein behavior. Therefore,    this variable allows us to analyze the overall dimensions of    the protein, and the more stable the compression of the protein    is during the simulation, it indicates the stability of the    protein and the complexes. As the graph shows, the fluctuations    of the designed protein alone and in interaction with TLR4 and    TLR2 are stable during the simulation (Fig.6b).  
            a RMSD results of the designed protein and            proteinTLR2 and proteinTLR4 complexes in unit time            (ns). b Rg results of the designed protein and            proteinTLR2 and proteinTLR4 complexes per unit time.            c RMSF results of the designed protein in the            noninteracting form and in the interacting form with            TLR2 and TLR4          
    The RMSF of the amino acid residues can be used to evaluate the    motion and flexibility of the structure. In addition, we    decided to perform an RMSF analysis to examine the changes in    the backbone atoms of the designed protein and the proteinTLR4    and proteinTLR2 complexes. In this analysis, the average value    of changes of each residue during the simulation was plotted.    As shown in Fig.6c, the RMSF    values show small fluctuations (less than 0.3nm) for most    amino acids in proteinTLR4 and proteinTLR2 complexes compared    with the designed protein. These results show that the designed    protein becomes more stable in interaction with the immune    system receptors.  
    Snapshots taken at 0, 50, 75, and 100ns intervals to    check the state of the vaccine during the simulation showed    that the structure of the vaccine and the site of interaction    of the vaccine with the receptors were stable during the    simulation (Fig.7ac).  
            Snapshots of 0, 50, 75, and 100ns of MD            simulation of the vaccine candidate and ligandreceptor            complexes. a The vaccine candidate, b            vaccine candidateTLR2, and c vaccine            candidateTLR4 complexes. Brown: 0ns; blue:            50ns; purple: 75ns; light green:            100ns          
    Using covariance matrices of C atoms, PCA calculates the    significant motions of atom pairs associated with vital    biological functions. The first two principal components (PC1    and PC2) of the candidate vaccine, candidate vaccineTLR2 and    candidate vaccineTLR4 complexes were generated by projecting    the trajectories onto their respective eigenvectors.    Figure8 shows the PCA    of the three structures. The plot shows that most of the common    essential subspace was occupied by the vaccine candidateTLR2    and vaccine candidateTLR4 complexes. In the Eigenvector (EV)    plots, the three structures shared a common conformational    subspace. The sampling of both systems demonstrates the    stability of the complexes and the vaccine candidate in the    simulation. In addition, the FELs of the first and second PCA    showed that the vaccine candidate, vaccine candidateTLR2 and    vaccine candidateTLR4 complexes had global energy minima of    7.71, 7.54, and 7.15kJmol1,    respectively (Fig.9). The Gibbs    energy landscape shows the same energy range for all three    structures and it can be argued that the structures have not    undergone sudden drastic changes and are stable. These results    are consistent with the analysis of RMSD, Rg, and RMSF values.  
            Conformational sampling in principal component            analysis. Two-dimensional projection of trajectories            showing conformational sampling of the vaccine            candidate and vaccine candidateTLR2 and vaccine            candidateTLR4 complexes          
            The Gibbs energy landscape plot during 100 ns of            simulation. a The vaccine candidate, b            Vaccine candidate-TLR2, c Vaccine candidate-TLR4            complexes          
    The C-ImmSim server was used to simulate the immune system    response to the designed vaccine candidate.    Figure7 shows the    simulation of the host immune response to the vaccine candidate    protein. Antigen and immunoglobulin parameters, cytokine    production, TH cell population and B cell population were    examined in this evaluation. An increase in IgM levels    indicates the initial host response. In addition, a secondary    response to the designed protein as antigen is indicated by    increased levels of B cell population (Fig.10a), TH cell    population (Fig.10b), and IgG1    and IgG2 (Fig.10c). There    was also a significant increase in the levels of cytokines and    interleukins after immunization, especially interferon-    (Fig.10d).    Interpretation of the results indicates that the vaccine    candidate is capable of stimulating the immune system to    produce cytokines and antibodies against    T.vaginalis.  
            In silico immunity simulation against protein antigen            designed as a vaccine candidate using C-ImmSim web            server. Simulations after three injections at steps 1,            336, and 672 are presented. a B cell population.            b TH cell population. c Antigen and            immunoglobulin. d Cytokine production          
    Codon optimization was performed using the JCat tool. After    codon optimization, the sequence length of the designed    structure was 2352 nucleotides. The codon compatibility index    and the GC content of the nucleotide sequence before the    optimization were 0.311% and 66.24%, respectively. After codon    optimization, the parameters were 1% and 50.73%, respectively    (Fig.11a, b). The    simulation of the optimized sequence of the vaccine candidate    in pET-28a(+) using the SnapGene software showed    that the vaccine candidate sequence is clonable in    pET-28a(+) (Fig.12a). In the    middle of the designed construct, there is a cleavage site for    HindIII and BsrGI enzymes, so we set the first and last    sequence of the construct with NcoI and XhoI enzymes,    respectively. Double digestion with NcoI and XhoI enzymes    showed presence of vaccine candidate (2346bp) together    with pET-28a(+) vector (5231bp)    (Fig.12b).  
            Codon optimization using the JCat web server. a            Before optimization, b after optimization          
            a Cloning of the designed protein construct into            the pET-28a vector (shown in blue). b            Informatics evaluation of the cloning of the designed            protein by double digest          
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Introduction of protein vaccine candidate based on AP65, AP33, and -actinin proteins against Trichomonas vaginalis ... - Parasites & Vectors