2.1 Selection and retrieval of leishmania antigenic protein sequences:
The complete amino acid sequences of twelve leishmania-specific antigenic proteins were retrieved from Uniprot (Universal Protein Resource) database (https://www.uniprot.org/uniprot/) in FASTA format (accessed on 21.03.2022). All these proteins were reported as immunogenic, highly conserved and potential vaccine candidates as found in previous immunological studies involving either animal models or computational in silico prediction methods (Coler et al. 2005; Kedzierski, 2010; Nagill et al. 2011; Brito et al. 2018). The selected protein sequences belonged to any of the five pathogenic species of Leishmania, namely Leishmania major, Leishmania maxicana, Leishmania donovani, Leishmania chagasi and Leishmania amazoensis among which L. donovani is the primary parasite causing visceral leishmaniasis (VL) while the others are mostly responsible for causing cutaneous (CL) or muco-cutaneous leishmaniasis (MCL). Among the proteins under investigation, the LACK (Leishmania-activated C-kinase Antigen) protein is a highly conserved protein across Leishmania species and is considered a viable vaccine candidate against human leishmaniasis (Fernandez et al. 2018). It has been proved to be a key target of immune response in sensitive BAL b/c mice (Kelly et al. 2003). KMP-11 (Kinetoplastid Membrane Protein-11) is present in all kinetoplastid protozoa and is also considered a potential vaccine candidate that is shown to increase IL-10 levels in murine models indicating its immunogenic potential (Mendonka et al. 2015 & Atapour et al. 2021). Leishmanolysin or GP63 is a surface proteinase that has been postulated as a virulence factor involved in direct interaction of the parasite with the host macrophage receptor (Joshi et al. 2001). The membrane proteins LCR1 and GP46 are also found to increase IFN-gamma production and hence, are regarded useful for developing a general vaccine against Leishmania infections (McMahon-Pratt et al. 1993; Joshi et al. 2014). Heat shock proteins (HSPs) are highly conserved molecules which are highly immunogenic too inducing both MHC-I and MHC-II pathways of adaptive immunity and are thought to have a pertinent role in vaccine development against infectious leishmaniasis (Holakuyee et al. 2012). Leishmania Hydrophilic Acylated Surface Protein-B (HASP-B) is expressed only in infective parasites suggesting a role in parasite virulence and therefore, a potential vaccine candidate (MacLean et al. 2016). Kinetoplastid paraflagellar rod (PFR) proteins are also of therapeutic and prophylactic importance due to their restricted evolutionary distribution, high order organization and high immunogenicity (Maharana et al. 2015). Likewise, cysteine proteases and proteophosphoglycans also act as essential virulence factors for the parasites in mammalian hosts and are attractive drug targets for leishmaniasis (Mahmoudzadeh and McKerrow 2004; Mundodi et al. 2005; Rogers, 2012). Moreover, beta-tubulin protein had also previously been characterized as T-cell stimulating antigen from Leishmania although its efficacy as vaccine candidate has not been tried as yet (Bhowmick et al. 2009). Beta-tubulin of L.donovani is as well considered a potential drug target against visceral leishmaniasis (Assis et al. 2014). Lastly, the translation factor protein eIF5A from L. braziliensis had been shown to induce heterologous protection against leishmaniasis in animals when administered as vaccine in combination with another recombinant protein and increases the secretion of parasite-specific cytokines in vaccinated animals. eIF5A protein is also found to be conserved in all eukaryotes (Duarte et al. 2016).
The rationale behind the choice of such diverse types of conserved and immunogenic proteins across different species of Leishmania was to design a general peptide vaccine that would confer broad spectrum cross-immunity against both the forms of leishmaniases (VL and CL) that commonly affect humans. The antigenicity of the selected proteins were computed by employing the ANTIGENpro server (http://scratch.proteomics.ics.uci.edu/) that computes antigenicity of an input protein sequence by exploiting a SVM-based two stage classifier validated by 10-fold cross validation approach (Magnan et al. 2010). The proteins were then subjected to signal peptide analyses using SignalP- 4.1 server (SignalP − 4.1 - Services - DTU Health Tech) in order to discriminate classical secretory and non-secretory (transmembrane) proteins. This method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks (Petersen et al. 2011 & Nielsen 2017). Localization predictions for all the proteins were performed next using DeepLoc (http://www.cbs.dtu.dk/services/DeepLoc/), a template-free algorithm that uses deep neural networks to predict protein subcellular localization exploiting only sequence information, achieving good accuracy (Armenteros et al. 2017). All functional protein sequences as obtained from SignalP 4.1 server were next subjected to predict the presence of antigenic determinants (epitopes) that will be recognized by B and T cell receptors. This was done to ensure that the designed vaccine construct contains only immuno-dominant T-cell and B-cell epitopes and so, would be able to drive an efficient immune response involving both humoral and cell-mediated pathways of immune mechanisms.
2.2 Cytotoxic T lymphocyte (CTL) epitope prediction:
Prediction of Cytotoxic T lymphocyte (CTL) epitope is an essential aspect for designing an ideal peptide vaccine (Ali et al. 2017). A freely accessible web-server namely NetCTL1.2 (https://www.cbs.dtu.dk/services/NetCTL) was utilized to predict the CTL epitopes for all the selected proteins. The method integrates prediction of peptide MHC class I binding, proteasomal C terminal cleavage and TAP transport efficiency (Larsen MV et al. 2007) all of which are essential features that a sequence of CTL binding epitope should possess. The server allows for predictions of CTL epitopes restricted to 12 MHC class I super type among which only A1 super type was used for the present study. MHC class I binding and proteasomal cleavage is performed using artificial neural networks. TAP transport efficiency is predicted using weight matrix (Larsen MV et al. 2007). The threshold value for epitope prediction was set at 0.75 (default) that corresponds to higher sensitivity (0.70) and specificity (0.970) for more accurate predictions. The server predicts motifs of nine amino acid residues from each input protein sequences and represents them with a particular score that reflects their MHC-I binding potency. Sequences with higher binding scores are considered potent CTL epitopes.
In addition to NetCTL 1.2, another publicly available web-server based approach namely SYFPEITHI (http://www.syfpeithi.de/bin/MHCServer.dll/EpitopePrediction.htm) was also availed for prediction of CTL epitopes from all the selected leishmania proteins. SYFPEITHI is a database comprising of more than 7000 peptide sequences known to bind class I and class II MHC molecules. The entries are compiled from published reports only. The prediction is based on published motifs (pool sequencing, natural ligands) and takes into consideration the amino acids in the anchor and auxiliary anchor positions, as well as other frequent amino acids (Rammensee et al. 1999 & Schuler et al. 2017). From the list of MHC types provided in this server, HLA-A*01, HLA-A*02:01 and HLA-A*03 were chosen for the prediction. The epitope search was specified for nonamer sequences (9 amino acid sequence motifs) for uniform comparison with the results given by the NetCTL 1.2 server. Only the common epitopes from each protein (if any) which were predicted by both of the NetCTL 1.2 and SYFPEITHI servers with compatible higher scores were selected for the final vaccine construct. This strategy was devised to improve the accuracy and sensitivity of the prediction.
2.3 Helper T lymphocyte (HTL) epitope prediction:
Helper T lymphocytes are probably the most important immune cells as they are required in all types of adaptive immune responses. They function essentially to stimulate antibody production by B cells, to activate macrophages for phagocytosis of the invading pathogens and also for the activation of the cytotoxic T cells to kill the infected target cells. Prediction of HTL epitopes (mostly of the Th-1 type), therefore, becomes an essential element for designing an effective peptide vaccine. In the current study, MHC-II binding analysis tool in the IEDB ( Immuno Epitope Data Base and Analysis) server (http://tools.iedb.org/mhcii/) was employed for prediction of 15-mer HTL epitopes for a set of three human alleles namely HLA-DRB1*01:01, HLA- DRB1*01:02 and HLA-DRB1*01:03. Human HLA alleles (as opposed to mouse alleles) were chosen for more realistic estimation of the MHC-II binding affinity of the epitopes as the vaccine was being designed against human leishmaniases. The predicted output is given in units of IC50nM for combinatorial library and SMM align. Therefore, a lower number indicates higher affinity. As a rough guideline, peptides with IC50values < 50 nM are considered high affinity, < 500 nM intermediate affinity and < 5000 nM low affinity (Ali et al. 2017). Most known epitopes have high or intermediate affinity. IEDB recommended prediction method was used in this approach in which low adjusted rank is indicative of good MHC-II binders and hence, can be defined as potent HTL epitopes.
2.4 Identification and Selection of cytokine inducing HTL epitopes:
Cytokines are of immense importance for proper functioning of the immune system and a battery of cytokines like IL-2, IL-4, IL-6 and interferron-γ have the ability to induce both CTL-mediated and humoral immune response (Pandey et al. 2018). Hence, the HTL epitopes that can trigger cytokine production are preferable for designing an immune-prophylactic vaccine. With this view, IFNepitope Server ((http://crdd.osdd.net/raghava/ifnepitope/)) was used for identification of HTL epitopes having cytokine inducing abilities. IFNepitope is an online prediction server that aims to predict the peptides from protein sequences having the capacity to induce IFN-gamma released from CD4+ T cells. The web server has been developed on the basis of a dataset which comprises of IFN-gamma inducing and non-inducing MHC class II binders (Dhanda et al. 2013). The HTL epitopes predicted by IEDB server with higher MHC-II binding affinities (lower adjusted ranks) were provided as input to check for their cytokine producing ability by the IFNepitope server. The epitopes thus identified were then subjected to next level of analyses by IL4pred server (IFNepitope (iiitd.edu.in) and also by IL10Pred server (https://webs.iiitd.edu.in/raghava/il10pred/predict3.php) in order to ensure that the selected HTL epitopes would as well evoke the secretion of IL-4 and IL-10, respectively. IL4pred and IL10Pred servers exploit motif-based as well as SVM based methods trained on experimentally validated positive and negative datasets. They also consider positional conservation of amino acid residues and amino acid composition of the peptides for more accurate prediction (Dhanda et al. 2013; Nagpal et al. 2016). The MHC-II binding HTL epitopes that were predicted to be positive by all the three servers were selected for incorporation in the final vaccine construct. This strategy was adopted so as to improve the efficacy of the vaccine since it would be able to instigate an intense response involving a wide variety of cytokines (IFN-gamma, IL-4 and IL-10) leading to an accelerated and long-lasting immunity.
2.5 Epitope conservation analysis:
Epitope conservation analysis becomes important to check whether the chosen epitope(s) is conserved across different species of Leishmania proteins. Designing a subunit vaccine consisting of conserved epitopes offers a promising scope for broad spectrum protective immunity against leishmaniases. Conservation analysis for each of the selected CTL and HTL epitopes was done by using IEDB “Epitope Conservation Analysis” tool available within the IEDB portal (http://tools.iedb.org/conservancy/). This tool calculates the degree of conservation of the target epitope (e) within a set of homologous protein sequences (P) as the fraction of {p} that matched the aligned e above the chosen identity level while considering that the target epitope is sequential in nature (Bui et al. 2007). Firstly, the corresponding source proteins from which the CTL and HTL epitopes were finally selected were subjected to a BLAST search for which we used the Uniprot BLAST tool (https://www.uniprot.org/blast/). From the BLAST result, the aligned protein sequences (belonging to the genus Leishmania) that showed significantly high similarity with the query sequence (> 40%) were selected. Such sequences were compiled to constitute an epitope-specific dataset and the same was provided as the input set of proteins (P) to be used for conservation analysis by the tool. Next, the sequences for selected CTL and HTL epitopes were screened individually against their respective datasets that revealed the relative conservation of the epitopes across a set of related leishmania proteins. The threshold identity level was set at > 100% (default).
2.6 Designing of multi epitope vaccine sequence:
On the basis of the immunoinformatic analyses, a primary sequence of the subunit vaccine was constructed by incorporating the selected CTL and HTL epitope sequences. These epitopes were linked together by AAY and GPGPG linkers, respectively (Ali et al. 2017; Khatoon et al. 2017; Pandey et al. 2018). Linkers are inserted for proper separation of the individual epitopes that is required for efficient functioning of the vaccine construct (Nezafat et al. 2014; Ali et al. 2017). Secondly, addition of an appropriate adjuvant is crucial in a subunit vaccine in order to boost up the immune response (Pandey et al. 2018). The choice of a suitable adjuvant is cardinal in designing vaccines for human trials. IL-12 produced by various immune cells is important in developing cell-mediated immunity against leishmaniasis and the potential of IL-12 as an adjuvant in vaccines against leishmaniasis has been reported in murine models (Afonso et al. 1994; Handman, 2001; Mutiso et al. 2010). In reference to this, IL-12 was selected as adjuvant for designing the vaccine and the sequence of the same was included in the N terminal of the vaccine construct by using EAAK linker (Ali et al. 2017; Khatoon et al. 2017; Pandey et al. 2018). The sequence of human IL-12 alpha chain was retrieved from Uniprot database (www.uniprot.org; Accession No- P29459). Finally, the peptide vaccine construct was obtained having adjuvant, linker, CTL epitopes and HTL epitopes (with intra-epitopic AAY and GPGPG linkers) in a sequence moving from N terminal to C terminal.
2.7 B cell epitope prediction:
Humoral immunity involves synthesis and secretion of specific antibodies by activated B cells which requires the presence of antigenic determinants that are recognized by B cell receptors present on the surface of B lymphocytes. An ideal immune-protective peptide vaccine should have such components in order to interact with and thereby, stimulate the B cells. Therefore, two servers, namely ABCpred and BepiPred 2.0 were applied for authentic prediction of B cell epitopes in the primary sequence of the designed vaccine. ABCpred server (https://webs.iiitd.edu.in/raghava/abcpred/ABC_submission.html ) is an artificial neural network (ANN) based approach which predicts B cell epitopes in a given antigen sequence using fixed length parameters (Saha et al. 2006). The training dataset used in this method contains B cell epitopes from B cell epitope database (BCIPEP). The server is able to predict epitopes with 65.93% accuracy using recurrent neural network (Saha et al. 2006). The primary sequence of the final vaccine construct was provided as the input in a plain format and the ABCpred server default parameters including a threshold value of 0.51 with a window length of 16 amino acid residues were used for the prediction.
BepiPred 2.0 server (BepiPred − 2.0 - Services - DTU Health Tech), on the other hand predicts sequential B cell epitopes from an input sequence by random forest algorithm trained on epitopes and non-epitope amino acids determined from crystal structures followed by a sequential prediction smoothing (Jespersen MC et al. 2017). The primary sequence of the vaccine was provided in a FASTA format as the input. The application of two different servers that predict B cell epitopes on the basis of two different algorithms makes the prediction more realistic.
2.8 Profiling of antigenicity, allergenicity and toxicity of the vaccine construct:
Antigenicity is an important criterion for an immune-prophylactic vaccine and to ensure that the designed vaccine construct would be able to elicit long-lasting immune response by interacting with the B and T cell receptors, antigenicity of the same was evaluated by using ANTIGENpro and VaxiJen 2.0 servers. ANTIGENpro (http://scratch.proteomics.ics.uci.edu/) server correctly classifies 82% of the known protective antigens when trained using only the protein microarray datasets. The accuracy on the combined dataset is estimated at 76% by 10-fold cross-validation experiments that allows a significantly better recognition of antigenic peptides. It runs on a two stage architecture including a SVM-based second stage classifier. For a new input protein sequence, the probability computed by the second stage SVM predictor is the final ANTIGENpro prediction score (Magnan et al. 2010). VaxiJen2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) is another publicly accessible web-server that evaluates antigenicity of a protein sequence by an alignment-free approach which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties and allows antigen classification solely based on various physicochemical properties of the protein without referring to sequence alignment (Doytchinova et al. 2007).
Secondly, in order to rule out any possibility that the vaccine construct could trigger an allergic response in an individual, AlgPred (https://webs.iiitd.edu.in/raghava/algpred/submission.html) server was used to determine its allergenicty. The server tool provides a variety of approaches including mapping of IgE epitopes, MEME/MAST motif, SVM method based on dipeptide composition and BLAST ARPs all of which are exploited for a meticulous prediction. The threshold was set at -0.4 (default) for higher precision of the prediction. In addition, AllerTop v.2.0 (https://www.ddg-pharmfac.net/AllerTOP/index.html), a freely accessible web-server based tool was also applied to confirm the non-allergic nature of the vaccine construct. This method is based on auto cross covariance (ACC) transformation of protein sequences into uniform equal-length vectors. It has been applied to quantitative structure-activity relationships (QSAR) studies of peptides with different length and the proteins are classified by k-nearest neighbor algorithm (Dimitrov et al. 2014).
Toxicity, if any, was predicted by utilizing the ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred/protein.php) server. This prediction tool works on the basis of both machine learning technique and quantitative method that is trained on a dataset of various toxic and non-toxic peptides obtained from SwissProt and TrEMBL (Gupta et al. 2013). Toxicity profiling is crucial to assess the safety of the vaccine and other such immunotherapeutic peptides that are intended for human trial. The analysis was performed by choosing SVM (TrEMBL) + Motif based approach at an E-value threshold of 10.0 for motif based method and at a SVM threshold of 0.1.
2.9 Evaluation of physicochemical properties:
Describing the physical and chemical features of a chimeric peptide becomes important since when administered as a vaccine, it should be able to induce a proper immune response. The ProtParam server available with the ExPASY (Expert Protein Analysis System) portal (https://web.expasy.org/protparam/) was used to compute various physicochemical parameters of the primary vaccine construct that included features like molecular weight, theoretical pI, instability index, aliphatic index, in-vivo and in-vitro half-life, Grand Average of Hydrophobicity (GRAVY) and others.
2.10 Secondary structure prediction of the vaccine peptide:
Secondary structural elements of the designed vaccine construct were predicted by applying PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred/) and RaptorX Property server (http://raptorx.uchicago.edu/StructurePropertyPred/predict/) both of which are freely accessible on line protein structure analysis servers. The primary sequence of the vaccine peptide was provided as the input. PSIPRED predicts the secondary structure of the input protein sequence by exploiting two feed-forward neural networks that function on the basis of position-specific scoring matrices (PSSM) generated by PSI-BLAST (Jones et al. 1999). It incorporates identification of sequences that are homologous to our vaccine construct by PSI-BLAST followed by generation of PSSM. PSIPRED 3.2 and higher versions were found to achieve an average Q3 score of 81.6% as evaluated by a stringent three-fold cross validation method which makes this approach significantly precise and accurate. RaptorX Property web server engages a very recent machine learning technique called DeepCNF (Deep Convolutional Neural fields) for predicting various features like secondary structure, solvent accessibility and disordered regions, simultaneously. DeepCNF is an integrated approach combining both Conditional Random Fields (CRFs) and shallow neural networks that models complex sequence-structure relationship by a deep hierarchical architecture and can obtain approximately 84% Q3 accuracy for 3-class secondary structure (Wang et al. 2016).
2.11 Tertiary structure prediction:
The tertiary structure of the final vaccine peptide was predicted by using the on line web server I-TASSER (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). I-TASSER is a hierarchical protein structure modeling approach based on Profile-Profile Threading Alignment (PPA) and iterative structure assembly simulations followed by atomic level structure refinement (Zhang, 2008; Xu & Zhang, 2011; Yang & Zhang, 2015).It works on three stages: structural template identification, iterative structure assembly and structure based function annotation (Yang & Zhang, 2015). The top ranked structure models with global and local accuracy estimations are returned with their representative C-scores and TM-scores which are correlated with the quality of the predicted model. The I-TASSER program represents one of the most successful methods demonstrated in CASP for automated predictions of protein structure and function (Yang & Zhang, 2015).
2.12 Refinement of vaccine tertiary structure:
Conventional computational modeling of a protein structure alone does not guarantee the authenticity and accuracy of the predicted model since such modeling strategies largely depend on the degree of likeliness of the input (target) with the available template structures. Enhancement of the quality of the template based model beyond the accuracy was, therefore, thought to be necessary and this could be achieved by refining the whole protein structure. With this perspective, the tridimensional model output from I-TASSER server with the best C-score was subjected to further refinement by GalaxyRefine server (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE). It is a CASP10 tested refinement approach in which protein side chains are rebuilt first and then side chain repacking and overall structure relaxation are performed by molecular dynamics simulation. This leads to precise improvement of both local and global tertiary structures of target proteins. (Heo et al. 2013).
2.13 Validation of vaccine tertiary structure:
The refined tertiary structure of the vaccine needed to be validated to spot potential errors that might occur in the predicted 3D model. ProSA-web server, (https://prosa.services.came.sbg.ac.at/prosa.php) an interactive web-based platform based on the standard ProSa programme was used for this purpose. It uses knowledge based potentials of mean force to evaluate model quality and subsequently generates z-scores that indicate model quality as well as overall deviation of total energy of the predicted model from energy obtained from random conformations (Wiederstein et al. 2007). The refined model obtained from GalaxyRefine server was provided as the input structure in a .pdb format. Besides, Ramachandran Plot is another very useful approach for visualizing energetically allowed and disallowed regions of phi (ϕ) and psi (ψ) dihedral angles of the amino acid side chains that are considered crucial components for validation of a protein structure. Hence, Molprobity server (Main page - MolProbity (duke.edu))was employed that generated Ramachandran plot for the given model of the vaccine based on python based molprobity style phenix validation (Williams et al. 2018). We further used ERRAT server (SAVESv6.0 - Structure Validation Server (ucla.edu)) that analyzes the statistics of non-bonded interactions between different atoms in the model and plots the value of error function versus a 9-residue sliding (Colovos et al. 1993). This step was performed to get more sophisticated and precise validation of the modeled structure.
2.14 Prediction of continuous and discontinuous B cell epitopes in the vaccine peptide:
Most of the antigenic determinants recognized by the B cell receptor and antibodies are discontinuous meaning they contain amino acid residues that are located distantly in the primary structure of the immunogen but are brought adjacent to each other during the folding of the protein (Barlow et al. 1986; Van-Regenmortel, 1996). Prediction of such discontinuous B cell epitopes in the validated tertiary structure of the vaccine was performed by utilizing ElliPro server (http://tools.iedb.org/ellipro/) incorporated within the IEDB portal. ElliPro is a freely accessible web tool that implements modified Thronton’s method and takes into account each residue’s center of mass rather than the Cα atoms. By doing so, it relates each of the epitopes to an assigned score defined as PI (Protrusion Index) averaged over epitope residues. Discontinuous epitopes are defined based on their respective PI values and clustered based on the distance between residue’s centers of mass(R). Larger values of R correlate to larger discontinuous epitopes being predicted (Ponomarenko et al. 2008). At the same time, linear B cell epitopes in the input protein model can also be predicted and visualized by using ElliPro. Detection of both linear and discontinuous B cell epitopes in the refined 3D model of the vaccine ensures a successful vaccine designing strategy.
2.15 Disulfide engineering of the vaccine peptide:
Disulfide bonds contribute to protein stability by lowering the conformational entropy and also by increasing the free energy of the denatured state. Introduction of novel disulfide bonds has been considered as a key biotechnological tool to improve the thermostability of native, folded proteins. Disulfide by Design 2 (DbD 2) v 2.12 server was used to enhance the overall structural stability of the designed vaccine by introducing disulfide linkages. This method utilizes native geometry and calculates an energy value for each potential disulfide, thereby providing a means to rank the candidate disulfide bonds. The disulfide bonds that confer maximum stability were the candidates with high B factors (Craig et al. 2013). The flexible regions of the peptide were selected based on high B factor values and corresponding stabilizing mutations were created by forming disulfide linkages between the selected residue pairs (Khatoon et al. 2017).
2.16 Molecular docking of vaccine construct with immune receptors (Human TLRs-2, 4, 5, 8 and mouse TLR-9):
Toll like receptors (TLR) are major attributes of cellular immune responses which recognize pathogen-associated molecular patterns (PAMPs) and evoke innate immune responses against infections (Faria et al. 2011). The direct activation of TLR-2 with leishmania components was subsequently reported (Faria et al, 2011). In other related experimental studies, lack of TLR-4 was resulted in increased parasitic growth and delayed healing of cutaneous lesions caused by L. major infections indicating the plausible role of TLR-4 in inducing immunity against leishmania (Kropf et al. 2014). On the other hand, TLR-2, 4 and 9 were found to be involved in immuno-pathologic spectrum of CL caused by L. braziliensis and L. amazonensis (Campos et al, 2018). TLR-9 was also evaluated as a key player in activation of dendritic cells in pathogenesis of VL in humans (Tuon et al. 2008). Some of the leishmania-derived components have been shown to activate TLR-2, 4 and 9 in majority of studies conducted in this field (Faria et al. 2011). These observations emphasize the fact that various TLRs are involved in triggering protective immunity against Leishmania. Therefore, an interaction between the TLRs and the designed vaccine construct was considered to be necessary for eliciting effective immunity against leishmania infections. This was checked by performing molecular docking of the multi epitopic vaccine peptide with human TLRs- 2, 4, 5, 8 and mouse TLR- 9 using PatchDock server (PatchDock Server (tau.ac.il)). The PatchDock algorithm has three main stages namely, molecular shape representation; surface patch matching and filtering and scoring. It is a geometry-based molecular docking algorithm that principally operates on molecular shape complementarity between the receptor and the ligand. The complementary patches are matched and candidate transformations are generated. Each of these transformations is further ranked by assigning a scoring function that considers both atomic desolvation energy and geometric fit (Schneidman et al, 2003 & Schneidman et al 2005). In our study, human TLR-2 (PDB id: 4G8A), TLR-4(PDB id: 6NIG), TLR-5 (PDB id: 3J0A) and TLR-8 (PDB id: 4QC0) and mouse TLR-9 (PDB id: 3WPF) were selected as receptors and their respective PDB files were retrieved from Protein DataBank (www.rcsb.org). The refined 3D model of the vaccine peptide was used as the ligand for all the docking simulations. The PatchDock server was set at default parameters (clustering RMSD: 4.0).
We further analyzed best docking model using Cluspro server which is based on possible bond rotation algorithm & possible interactions between amino acids were analyzed using Pymol software (Kozakov et al. 2017; Vajda et al. 2017; Desta et al.2020).
2.17 Codon optimization, mRNA structure prediction and in silico cloning for expression of vaccine protein:
Optimization of codon is required to achieve maximum expression of foreign genes in the host organism when the usage of codon by the host differs significantly from that of the native host from which the original sequences for the final vaccine construct had been culled. JCat (Java Codon Adaptation Tool) available publicly at (http://www.jcat.de/) was used for reverse translation and codon optimization by providing primary sequence of the final vaccine construct as the input. The codon usage was optimized to the most sequenced prokaryotic organism, E.coli K12 (Grote et al. 2005 & Khatoon et al. 2017). Three additional options provided by the tool were selected to avoid rho-independent transcription terminators, prokaryotic ribosome binding sites and unwanted restriction cleavage sites. The codon adaptation index (CAI) and GC content as calculated by the JCat tool are indicative of how good the optimization was. Better optimization ensures higher expression of the vaccine protein. Further, restriction sites for the enzymes XhoI and NdeI were incorporated into the optimized cDNA sequence provided by the JCat tool to facilitate cloning. The modified sequence (with restriction sites) was then inserted into the E.coli pET 28-a(+) plasmid vector by using the restriction cloning module of SnapGene tool (Khatoon et al. 2017; Aathmanathan et al. 2018; Pandey et al. 2018; Khan et al. 2019). SnapGene software is available on the web at (https://www.snapgene.com/).
Besides, we had also used the mfold web server (http://unafold.rna.albany.edu/?q=mfold/RNA-Folding-Form) for prediction of secondary structure of the mRNA encoded by the genes of the chimeric peptide. The reverse translated optimized sequence obtained from JCat tool was provided as the input. The mfold program is based on a core algorithm that predicts a minimum free energy, ∆ G as well as minimum free energies for folding that must contain any particular base pair. Various folding constraints are also applied for accurate prediction of folding energies. The folding energy for RNA folding is fixed at 37⁰C while the ionic conditions are fixed at [Na+] = 1M and [Mg++] = 0 (Zuker, 2003).
2.18 C-immsim based immune simulation:
C-immsim ( https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php?page=1 ) is server based immune response prediction tool which utilized agent based class & it’s prediction rely on neural networks. It can predict both humoral & cell-mediated immune responses. C-immsim utilizes Celda-Seiden model, bit-string polyclonal lattice model & Simpson index etc. to characterize prediction of active, resting & memory cells and their longevity. It helps to predict cytokine profile of given reaction to evaluate inflammatory responses (Rapin et al. 2010 & Castiglione et al. 2021).
2.19 Molecular Dynamic simulation:
Molecular dynamic simulation was performed to evaluate stability of our best complex i.e TLR with proposed vaccine using iMOD (iMod Server home page (csic.es) server. This web based server uses improved normal mode (NMA) analysis for characterization of various dynamic properties of biomolecules by measuring internal coordinates, protein flexibility & stability using intra-atomic force field. In this current study, we analyzed the main chain deformities, B-factor, egienvalues, covariance factor & elastic network model as standard NMA parameters for evaluation of protein-protein complexes obtained by in silico approach (Alexandrov et al. 2005; Lopéz-Blanco et al. 2011; López-Blanco et al. 2014 ; Bauer & Bauerová-Hlinková 2020).