MHC: Major Histocompatibility Complex; CTL: Cytotoxic T lymphocytes; TAP: Transporter associated with Antigen Processing; SVM: Support Vector Machine; ANN: Artificial Neural Network

Lymphatic filariasis

Lymphatic filariasis also known Elephantiasis is a parasitic infection caused by filarial roundworm, Wuchereria bancrofti. The infection is usually acquired in childhood, its indication of the existence occur later in life, causing temporary or permanent disability of an infected organ, causes severe damage and painful swelling, disfiguring swelling of the legs and genital organs is a classic symptom of late disease stage. In many tropical countries, lymphatic filariasis has a major social and economic impact, though World Health Organization is trying to eliminate it completely by the year 2020 yet there is no effective drug or vaccine has been invented to treat/prevent Elephantiasis (WHO, lymphatic Filariasis).

Pathogen transmission

An Infected mosquito deposits larvae on the individual’s skin while biting and larvae enter Wound. The Larvae migrates into lymphatic system where they grow, Adult male worms are about 3-4 cm long while female worms are 8-10 cm long. When male and female worms mate they form nests; these nests cause blockages in the human lymphatic system resulting symptoms like swelling and fever. Female worm produces microscopic worms called microfilaria. When mosquito bites to an infected individual, ingests microfilaria with blood and infects to healthy individual, Microfilaria develops into adult worm over a week and the cycle continues [WHO, lymphatic Filariasis].

Strategy

This approach is based on the phenomenon of cross-protection [1] hereby an individual infected with a mild strain of pathogen possess immunity against more severe strain of the same pathogen. Body proteins are necessary for its production in or on all food commodities. An exemption from the requirement of a tolerance is established for residues of the drugs or chemicals. The lymphatic system is an important component of the body’s immune system, it include several centres of initial infection so the immune response can be generated in the lymph organ even with single antigen subunit.

MHC class binding peptides

The new paradigm in vaccine design is emerging; following essential discoveries in immunology and development of new CTL binding peptides prediction tools [2]. MHC molecules are cell surface glycoproteins, which take active part in host immune reactions. The involvement of MHC class-I in response to almost all antigens and considering its binding affinity with peptides of 9 amino acids, we have predicted the 9 amino acid long epitopes from extracted data MHCBN Comprehensive data base of MHC-I and MHC-II binding as well as non-binding epitopes. MHC molecules have been well characterized in terms of their role in immune reactions. They bind to several peptide fragments generated after proteolytic cleavage of antigen [3]. This binding act like red flags for specific antigen and generates immune response against the parent antigen. So an antigen from Wuchereria bancrofti troponin subunit can induce immune response against Wuchereria bancrofti activity. CTL epitopes are most suitable for subunit vaccine development because with single epitope, the immune response can be generated in large population. MHC peptide complexes will be translocated on the surface of antigen presenting cells (APCs). This theme is implemented in developing subunit and synthetic peptide vaccines [4-7]. One of the important problems in subunit vaccine design is to search for antigenic regions in an antigen protein [8] that can stimulate T-cells called T-cell epitopes. In literature, fortunately, a large amount of data about such peptides is available. Previously and presently, a number of databases have been developed to provide comprehensive information related to T-cell epitopes [9-13].

Protein sequence analysis

Wuchereria bancrofti troponin protein is a diagnostic antigen against lymphatic filariasis. The antigenic protein sequence of W. bancrofti troponin antigen protein was analyzed to study the antigenicity solvent accessible regions and MHC class binding peptide, which allows potential drug targets to predict active sites against Lymphatic filariasis.

A Wuchereria bancrofti troponin (gi- 373938659) protein sequence is 136 amino acids long as-MFDRGKQGYIMATQIGQIMHAMEQDF DEKQLRKLIRKFDADGSGKLEFDEFCALVYTVANTVDKETLQKEL REAFRLFDKEGNGYISRPTLKALLKEIADDLSDEQLEAAVDEIDED GSGKIEFEEFWELMAGDAD

Antigenicity prediction

Antigenicity Prediction program results those segments from Wuchereria bancrofti troponin antigen protein that are likely to be antigenic by eliciting an antibody response. Antigenic epitopes are determined using the Gomase, (2007), Hopp and Woods, Welling, Parker, B-EpiPred Server and Kolaskar and Tongaonkar antigenicity methods [14-18].

Protein secondary structure prediction

The important concepts in secondary structure prediction are identified as: residue conformational propensities, sequence edge effects, hydrophobicity moments, insertions and Deletions positions in aligned homologous sequence, moments of conservation, autocorrelation, residue ratios, secondary structure feedback effects and filtering [19,20].

Finding the location in solvent accessible regions

Finding the location in solvent accessible regions in protein, type of plot determines the hydrophobic and hydrophilic scales and it is utilized for prediction. This may be useful in predicting membranespanning domains, potential antigenic sites and regions that are likely exposed on the protein surface [21-41].

CTL epitope prediction

The CTL Epitopes of Wuchereria bancrofti troponin are obtained from MHCBN comprehensive database of MHC binding and nonbinding peptides using two different methods firstly with Support Vector Machine based method (Cut off is 0.36) and then by Artificial Neural Network based method (Cut off is 0.51). In this work predicted MHC-Peptide binding is a log-transformed value related to the IC50 values in nM units.

The average accuracy of Support Vector Machine (SVM) based epitope prediction method is ~76% at cut off 0.36. SVM has been trained on the binary input of single amino acid sequence. In Case of Artificial Neural Network ANN based epitope prediction method the average accuracy is ~74% at the cut off score 0.51 [2].

TAP binding prediction

TAP (Transporter associated with Antigen Peptide) play an important role in transportation of MHC-Peptide complexes, which elicits the immune response for clearing various intracellular infections. The prediction of TAP binding peptides is crucial in identifying the MHC class-I restricted T cell epitopes. The Prediction is based on cascade SVM, using properties of amino acid sequence at correlation coefficient of 0.88 as per Jack-Knife validation test [42].

Peptide structure prediction

Peptide Structure prediction is a challenge, which would aid our understanding of sequence–structure–function relationships towards synthetic peptide vaccine design. Peptide Structure prediction depends on the complexity and the accuracy of the models used to represent them. We have used Hidden Markov Model derived structural alphabet (SA) based tool that discretizes peptide experimental conformation as series of overlapping fragments of four residues length. The predicted structure can be considered to have an average accuracy of ~1.1A rootmean- square deviation (RMSd) [43-46].

Antigenic peptides prediction

In this assay we predicted the antigenic determinants by finding the area of highest local hydrophilicity. The Hopp & Woods scale was designed to predict the locations of antigenic determinants in linear antigen protein sequence, assuming that the antigenic determinants would be exposed on the protein surface and thus would be located in hydrophilic regions (Figure 1). Its values are derived from the transferfree energies for amino acid side chains between ethanol and water. Welling antigenicity plot gives value as the log of the quotient between percentage in a sample of known antigenic regions and percentage in average proteins (Figure 2). We also studied B-EpiPred Server, Parker, Kolaskar and Tongaonkar antigenicity methods and the predicted antigenic fragments can bind to MHC molecule is the first bottlenecks in vaccine design (Figures 3-6).

Figure 1: Hydrophobicity plot of Hopp and Woods (1981) of Wuchereria bancrofti troponin.

Figure 2: Hydrophobicity plot of Welling et al. (1985) of Wuchereria bancrofti troponin.

Figure 3: B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system of the Wuchereria bancrofti troponin.

Figure 4: Kolaskar and Tongaonkar antigenicity are the sites of molecules that are recognized by antibodies of the immune system for the Wuchereria bancrofti troponin.

Figure 5: Hydrophobicity plot of HPLC / Parker et al. (1986) of Wuchereria bancrofti troponin.

Figure 6: Hydrophobicity plot of Emini Surface Accessibility Prediction (1990) of Wuchereria bancrofti troponin.

Secondary alignment

The Robson and Garnier method applied for secondary structure prediction of the Wuchereria bancrofti troponin protein. Each residue have specific assigned value for alpha helix (Shown in Red), beta sheet (Shown in Blue) and coil (Shown in Pink) using a window of 7 residues (Figure 7). Using these information parameters, the likelihood of a given residue assuming each of the four possible conformations alpha, beta, reverse turn or coils calculated and the conformation with the largest likelihood is assigned to the residue.

Figure 7: Secondary structure plot of the Wuchereria bancrofti troponin.

Solvent accessible regions

Solvent accessible scales for delineating hydrophobic and hydrophilic characteristics of amino acids and scales are developed for predicting potential antigenic sites of globular proteins, which are likely to be rich in charged and polar residues. It was shown that Wuchereria bancrofti troponin protein is hydrophobic in nature and contains segments of low complexity and high-predicted flexibility (Figures 8-28).

Figure 8: Hydrophobicity plot of Rao and Argos (1986) for the Wuchereria bancrofti troponin.

Figure 9: Hydrophobicity plot of Rose et al. (1985) for the Wuchereria bancrofti troponin.

Figure 10: Hydrophobicity Sweet plot of OMH for the Wuchereria bancrofti troponin.

Figure 11: Hydrophobicity plot of Kyte and Doolittle (1982) for the Wuchereria bancrofti troponin.

Figure 12: Hydrophobicity plot of Abraham and Leo (1987) for the Wuchereria bancrofti troponin.

Figure 13: Hydrophobicity plot of Bull and Breese (1974) for the Wuchereria bancrofti troponin.

Figure 14: Hydrophobicity plot of Guy (1985) for the Wuchereria bancrofti troponin.

Figure 15: Hydrophobicity plot of Miyazawa and Jernigen (1985) for the Wuchereria bancrofti troponin.

Figure 16: Hydrophobicity plot of Roseman (1988) for the Wuchereria bancrofti troponin.

Figure 17: Hydrophobicity plot of Wolfenden et al. (1981) for the Wuchereria bancrofti troponin.

Figure 18: Hydrophobicity Wilson et al. (1981) plot of HPLC for the Wuchereria bancrofti troponin.

Figure 19: Hydrophobicity Cowan and Whittaker (1990) plot of HPLC pH 3.4 for the Wuchereria bancrofti troponin.

Figure 20: Hydrophobicity plot of RF mobility for the Wuchereria bancrofti troponin.

Figure 21: Hydrophobicity plot of Chothia (1976) for the Wuchereria bancrofti troponin.

Figure 22: Hydrophobicity plot of Eisenberg et al. (1984) for the Wuchereria bancrofti troponin.

Figure 23: Hydrophobicity plot of Manavalan and Ponnuswamy (1978) for the Wuchereria bancrofti troponin.

Figure 24: Hydrophobicity plot of Black and Mould (1991) for the Wuchereria bancrofti troponin.

Figure 25: Hydrophobicity plot of Fauchere and Pliska (1983) for the Wuchereria bancrofti troponin.

Figure 26: Hydrophobicity plot of Janin (1979) for the Wuchereria bancrofti troponin.

Figure 27: Hydrophobicity plot of Tanford (1962) for the Wuchereria bancrofti troponin.

Figure 28: Hydrophobicity Cowan and Whittaker (1990) plot of HPLC pH 7.5 for the Wuchereria bancrofti troponin.

Prediction of MHC binding peptides

MHC molecules are cell surface glycoproteins, which take active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens so these MHC binding peptides are sufficient for eliciting the desired immune response. In analysis we determined the CTL binding regions, TAP binding regions and several potential antigenic epitopes. In this study we predicted the binding affinity of Wuchereria bancrofti troponin protein having 136 amino acids, which shows number of peptides. The CTL epitope prediction is based on an elegant machine learning technique Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM has been trained on the binary input of single amino acid sequence. In this assay we have predicted the binding affinity of Wuchereria bancrofti troponin protein sequence having 136 amino acids, which shows 128 nonamers.The SVM based CTL epitopes, 86-ISRPTLKAL, 46-LEFDEFCAL, 84-GYISRPTLK, 122-IEFEEFWEL, 47-EFDEFCALV, 29-KQLRKLIRK, 80-KEGNGYISR, 87-SRPTLKALL, 69-KELREAFRL, 50-EFCALVYTV, 72-REAFRLFDK, 2-FDRGKQGYI, 11-MATQIGQIM, 4-RGKQGYIMA, 90-TLKALLKEI, 123-EFEEFWELM (optimal score is 1.129); the ANN based CTL epitopes recorded are, 60-NTVDKETLQ, 12-ATQIGQIMH, 36-RKFDADGSG, 85-YISRPTLKA, 109-AAVDEIDED, 76-RLFDKEGNG, 20-HAMEQDFDE, 69-KELREAFRL, 94-LLKEIADDL, 32-RKLIRKF-DA, 50-EFCALVYTV, 67-LQKELREAF, 112-DEIDEDGSG, 54-LVYTVANTV, 98-IADDLSDEQ, 41-DGSGKLEFD, 89-PTLKALLKE, 79-DKEGNGYIS, 82-GNGYISRPT, 104-DEQLEAAVD, 100-DDLSDEQLE, 40-ADGSGKLEF, 62-VDKETLQKE, 27-DEKQLRKLI, 90-TLKALLKEI, 108-EAAVDEIDE, 42-GSGKLEFDE, 52-CALVYTVAN, 43-SGKLEFDEF, 61-TVDKETLQK, 53-ALVYTVANT, which represented predicted binders from Wuchereria bancrofti troponin protein (Tables 1 and 2). In addition to the CTL epitopes we also have predicted several high Affinity TAP binders as 102-LSDEQLEAA, 23-EQDFDEKQL, 26-FDEKQLRKL, 12-ATQIGQIMH, 92-KALLKEIAD, 73-EAFRLFDKE, 98-IADDLSDEQ, 57-TVANTVDKE, 82-GNGYISRPT,125-EEFWELMAG, 35-IRKFDADGS, 9-YIMATQIGQ, 21-AMEQDFDEK, 74-AFRLFDKEG, 95-LKEIADDLS, 44-GKLEFDEFC, 11-MATQIGQIM, 36-RKFDADGSG, 94-LLKEIADDL, 66-TLQKELREA, 51-FCALVYTVA, 48-FDEFCALVY, 124-FEEFWELMA, 33-KLIRKFDAD, 37-KFDADGSGK, 45-KLEFDEFCA, 22-MEQDFDEKQ, 99-ADDLSDEQL, 53-ALVYTVANT, 96-KEIADDLSD, 63-DKETLQKEL, 70-ELREAFRLF, 90-TLKALLKEI, 10-IMATQIGQI, 88-RPTLKALLK, 24-QDFDEKQLR, 123-EFEEFWELM, 52-CALVYTVAN, 120-GKIEFEEFW, 14-QIGQ-IMHAM, 116-EDGSGKIEF, these TAP binders are important in generating immune response (Table 3). The optimal propensity for the Wuchereria bancrofti troponin protein found is 1.223 (Figure 4). All residues having above 1.0 propensity are always potentially antigenic (Table 4). The predicted segments in Wuchereria bancrofti troponin protein are 31-LRKLIRK-37, 49-DEFCALVYTVANT-61, 87-SRPTLKALLKE- 97, 108-EAAVDE-113 Fragments identified through this approach tend to be high-efficiency binders, which is a larger percentage of their atoms are directly involved in binding as compared to larger molecules. These MHC binding peptides are sufficient for inducing the desired immune response. Predicted MHC binding regions in Wuch-ereria bancrofti troponin sequence and these are actively taking part in immune reactions.

Table 1: SVM based Predicted CTL-Epitopes of Wuchereria bancrofti troponin protein.

Table 2: ANN based Predicted CTL-Epitopes of Wuchereria bancrofti troponin protein.

Table 3: Cascade SVM based High affinity TAP epitopes of Wuchereria bancrofti troponin protein.

Table 4: Potential Antigenic epitopes of Wuchereria bancrofti troponin protein.

Tertiary structure prediction of the predicted epitopes

We have predicted the experimentally confirmed structures of the two potential epitopes 49-DEFCALVYTVANT-61, 87-SRPTLKALLKE-97, these are the longest epitopes predicted from the Wuchereria bancrofti troponin protein showing the highest pick. The peptide structures are validated by applying A Hidden Markov Model based approach which predicts the tertiary confirmation of the peptide sequence at accuracy ~1.1 RMSd. The validated structures are shown in Balls & Sticks model over solvent-accessible surface (VDW+1.4 angstrom) (Figures 29 and 30) [47,48].

Figure 29: Experimentally Confirmed Structure of 49-DEFCALVYTVANT-61 epitope shown over Solvent-Accessible Surface.

Figure 30: Experimentally Confirmed Structure of 87-SRPTLKALLKE-97 epitope shown over Solvent-Accessible Surface.

Gomase method (2007), B-EpiPred Server, Hopp and Woods, Welling, Parker, Kolaskar and Tongaonkar antigenicity scales were designed to predict the locations of antigenic determinants in Wuchereria bancrofti troponin protein sequence. It shows beta sheets regions, which have higher antigenic response than helical region of this peptide and shows high antigenicity (Figures 1-5) [49-52]. We also found the Sweet hydrophobicity, Kyte & Doolittle hydrophobicity, Abraham & Leo, Bull & Breese hydrophobicity, Guy, Miyazawa hydrophobicity, Roseman hydrophobicity, Cowan HPLC pH 7.5 hydrophobicity, Rose hydrophobicity, Eisenberg hydrophobicity, Manavalan hydrophobicity, Black hydrophobicity, Fauchere hydrophobicity, Janin hydrophobicity, Rao & Argos hydrophobicity, Wolfenden hydrophobicity, Wilson HPLC hydrophobicity, Cowan HPLC pH 3.4, Tanford hydrophobicity, RF mobility hydrophobicity and Chothia hydrophobicity scales, Theses scales are essentially a hydrophilic index, with a polar residues assigned negative values (Figures 8-28). In this assay we predicted the binding affinity of Wuchereria bancrofti troponin protein having 136 amino acids, which shows 128 nonamers. We predicted SVM and ANN based CTL epitopes (Tables 1 and 2) [53,54].

We have determined sixteen CTL predicted epitopes (Optimal score is 1.129) at cut off 0.36 using SVM based method (Table 1) and thirty one CTL predicted epitopes using ANN based method at cut off 0.51 (Optimal score is 1.000) (Table 2) which represents predicted peptide binders from Wuchereria bancrofti troponin protein. We also have predicted Cascade SVM based forty one High affinity TAP binding epitopes (Optimal score is 8.641) (Table 3) in addition to four potentially antigenic peptides recognized by antibodies of the immune system for troponin protein in the region of maximum local hydrophilicity (Table 4) [55]. Kolaskar and Tongaonkar antigenicity are the sites of molecules that are recognized by antibodies of the immune system for troponin protein, analysis shows epitopes present in the troponin protein eliciting desired immune response [18]. The region of maximal hydrophilicity is likely to be an antigenic site, having hydrophobic characteristics, because C- terminal regions of Wuchereria bancrofti troponin protein is solvent accessible and unstructured, antibodies against those regions are also likely to recognize the native protein. For the prediction of antigenic determinant site of Wuchereria bancrofti troponin protein, we predicted four antibody recognized antigenic determinant sites in the Wuchereria bancrofti troponin sequence. The highest pick is recorded between sequence of amino acid in the region are 49- DEFCALVYTVANT-61 and 87-SRPTLKALLKE-97 (Table 4) [56]. We also predicted the surface accessible tertiary structure for the peptide recorded with the highest pick; these are the longest peptides predicted in the antigenic sequence of the Wuchereria bancrofti troponin protein, the experimentally confirmed structures of the predicted peptides are considered to have an average accuracy of ~1.1A RMSd, the predicted peptide structures can be applied in synthetic peptide vaccine design approach to understand the sequence–structure–function relationship of the Wuchereria bancrofti troponin protein [57-59].

This method will be useful in cellular immunology, Vaccine design, immunodiagnostics, immunotherapeutics and molecular understanding of autoimmune susceptibility. Wuchereria bancrofti troponin protein sequence contains multiple antigenic components to direct and empower the immune system to protect an individual from lymphatic filariasis disease. MHC molecules are cell surface proteins, which actively participates in host immune reactions and involvement of MHC class in response to almost all antigens and it give effects on target sites. Predicted MHC binding regions acts like red flags for specific antigen and generate immune response against the whole antigen. So a small antigen fragment can generate immune response against entire antigen. The method integrates prediction of Peptide-MHC class binding; proteosomal C terminal cleavage and TAP transport efficiency. This approach is implemented in designing subunit and synthetic peptide vaccines.