Prediction of Major Histocompatibility Complex Binding Peptides and Epitopes from Fatty-Acid-Binding Protein of the Human Blood Fluke Schistosoma Japonicum

Schistosoma japonicum are blood flukes of humans that cause chronic, highly debilitating diseases involving extensive liver damage. In the present study, fatty-acid-binding protein of the human blood fluke Schistosoma japonicum is being used to find out highly suitable MHC binding peptides and epitopes. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class in response to almost all antigens and it give effects on specific sites. Predicted MHC binding regions acts like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. Fragments identified through this approach tend to be high efficiency binders, in which larger percentage of their atoms are directly involved in binding as compared to larger molecules. Binding ability prediction of peptides to major histocompatibility complex (MHC) class I & II molecules is important in vaccine development from fatty-acid-binding protein of the human blood fluke Schistosoma japonicum. MHC molecules have been well characterized in terms of their role in immune reactions. They bind to some of the peptide fragments generated after proteolytic cleavage of antigen [11]. These binding sites are antigen specific and generate immune response against the parent antigen. Prediction methods to find out the small peptides fragments from a protein which may represent the whole protein and excite the immune response are available [12]. The present paper deals with the possibilities of exploiting fatty-acid-binding protein of the human blood fluke Schistosoma japonicum to find out the highly suitable MHC binding peptide and have high affinity to TAP biding peptides that can be used for inducing cross protection and as immunogen to produce antiserum for the development of sero-diagnostics for Schistosoma japonicum. Materials and Methods Protein sequence used For recognition of immunologically relevant regions, hydrophilicity, antigenecity, solvent accessible regions and MHC class peptide binding of the fatty-acid-binding protein of the human blood fluke Schistosoma japonicum (GenBank accession no. AAA64426.1) had been considered.


Introduction
Schistosomiasis, caused by trematode blood flukes of the genus Schistosoma, is recognized as the most important human helminth infection in terms of morbidity and mortality. Infection follows direct contact with freshwater harbouring free-swimming larval (cercaria) forms of the parasite. Despite the existence of the highly effective antischistosome drug praziquantel (PZQ), schistosomiasis is spreading into new areas, and although it is the cornerstone of current control programs, PZQ chemotherapy does have limitations. In particular, mass treatment does not prevent reinfection. Furthermore, there is increasing concern about the development of parasite resistance to PZQ. Consequently, vaccine strategies represent an essential component for the future control of schistosomiasis as an adjunct to chemotherapy. An improved understanding of the immune response to schistosome infection, both in animal models and in humans, suggests that development of a vaccine may be possible [1]. Approximately 200 million people in 74 countries are infected with schistosomes; 120 million are symptomatic, and 20 million suffer severe illness [2,3].
Schistosomiasis is the most important human helminth infection in terms of morbidity and mortality; a recent meta-analysis assigned 2 to 15% disability weight to the disease [4]. There is also emerging evidence that schistosome infections may impact the etiology and transmission of human immunodeficiency virus/AIDS (HIV/AIDS) [5][6][7]. In particular, the possible interaction between schistosomiasis and HIV/AIDS is receiving increasing attention, given the role of immune responses in both diseases and the geographic overlap in distribution; low CD4 + T-cell counts resulting from HIV infection may increase susceptibility to schistosome infection and influence egg excretion [8][9][10]. Thus, schistosomiasis imposes a high socioeconomic burden on many affected developing countries.

Prediction of secondary structure of protein and its antigenicity
The secondary structure diagram based on Garnier algorithm provides additional information about possible sequence accessibility [13]. The aim of secondary structure prediction is to provide the location of alpha helices, and beta strands within a protein or protein family. Residue conformational propensities, sequence edge effects, moments of hydrophobicity, position of insertions and deletions in aligned homologous sequence, moments of conservation, autocorrelation, residue ratios, secondary structure feedback effects, and filtering [12,14] are the important concepts involved in secondary structure prediction.
Antigenicity prediction tools adopted in this study predict those segments from fatty-acid-binding protein that are likely to be antigenic by eliciting an antibody response using Hopp and Woods [15], Welling [16], Parker [17], B-EpiPred Server [18] and Kolaskar and Tongaonkar [19].

Targeting the location in solvent accessible regions
Protein antigenecity is a surface property. Antigenic epitopes can be located as those segments of primary structure that are markedly hydrophilic [15]. Hydrophilicity plots provide a measure of distribution of polar and apolar amino acid residues within the protein sequence. The Kyte-Doolittle scale [20] provides a measure of hydrophobicity with each amino acid. Similarly Hopp-Woods scale was used to predict potential antigenic sites. This may be useful in predicting membranespanning domains, potential antigenic sites and regions that are likely exposed on the protein surface [21][22][23][24].

Prediction of MHC binding peptide
Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential epitopes [12]. Several methods have been used to predict MHC binding peptides, including those based on binding motifs [25,26], quantitative matrices [27], artificial neural networks (ANNs) [28][29][30] and support vector machine (SVM) [27, 31,32]. Binding motifs specify which residues at given positions within the peptide are necessary or favourable for binding to a specific MHC molecule [33]. In this study, prediction of MHC peptide binding is performed using neural networks trained on C terminals of known epitopes. An elegant machine learning technique i.e. SVM based method is used for prediction of promiscuous MHC class II binding peptides. In SVM based method, the average accuracy is reported to be high as compared to other methods since SVM can handle noise or non linearity in data very well [27, 32,34]. The predicted peptides from fatty-acid-binding protein under study and their affinity to TAP biding peptides are determined by the scoring based on the average score / affinity of an amino acid at particular position and calculated as follows: Ai,r = Average affinity of peptides having residues r in position i, where Ai,r is the matrix entry of residue r in position i, r may be any natural amino acid and i varies from 1 to 9 [27].

Results
Study refers to the Sj-FABPc fatty-acid-binding protein of the human blood fluke Schistosoma japonicum having 132 aa protein described under material and methods.

Determination of antigenic peptides
Parameters such as hydrophilicity, flexibility, accessibility, turns, exposed surface, polarity and antigenic propensity of polypeptides chains have been correlated with the location of continuous epitopes. Hydrophobicity (or hydrophilicity) plots are designed to display the distribution of polar and non-polar residues along a protein sequence. In our study, antigenic determinants have been targeted by locating the positive peaks in hydrophilicity plots, thus identifying the regions of maximum potential antigenecity. Hopp-Woods scale [15] was used for predicting potential antigenic sites of protein which is essentially a hydrophilic index, with non-polar residues assigned negative values ( Figure 1). Welling antigenecity plot [16] gives antigenecity value as the log of the quotient between percentage in a sample of known antigenic regions and percentage in average proteins ( Figure 2). Parker [17], Kolaskar and Tongaonkar antigenecity methods [19] and B-EpiPred Server [18] were also studied (Figures 3-5).

Secondary alignment
For the protein under study, secondary structure was predicted using Garnier-Osguthorpe-Robson (GOR) method [13]. It assumes that the amino acids flanking the central amino acid also influence the secondary structure. Values for alpha helix, beta sheet, turns and

Solvent accessible regions
To predict potential antigenic sites of globular proteins, which are likely to be rich in charged and polar residues, solvent accessible scales are developed which delineate hydrophobic and hydrophilic characteristics of amino acids. The protein under study was exposed to Janin, Kyte & Doolittle, Abraham & Leo and Bull & Breese methods to predict its nature and prediction flexibility (Figures 7-10).

Determination of MHC binding peptides
The binding between peptide epitopes and MHC protein(s) is an important event in the cellular immune response. SVMs are a class of learning based on non-linear modelling techniques with proven performance in a wide range of practical applications [35]. The prediction method used in our study is based on this elegant machine learning technique. The cascade support vector machine approach based on amino acid sequence and properties was used to predict MHCI and MHCII binding regions. In this assay, prediction of the binding affinity of fatty-acid-binding protein having 132 amino acids, showing 124 nonamers was performed. SVM was trained on the binary input of single amino acid sequence. The binding regions obtained are reported in Table 1 and Table 2.
Fifty Eight peptide regions were found to have high affinity to TAP binding peptides. The data presented in Table 1 showed top The SVM based method for prediction of promiscuous MHC Class II binders are reported in Table 2 Table 3 shows the predicted antigenic epitopes from fatty-acid-binding protein of the human blood fluke Schistosoma japonicum.

Discussion
Schistosoma japonicum are blood flukes of humans that cause chronic, highly debilitating diseases involving extensive liver damage and requires attention. In the present study, B-EpiPred Server, Hopp   Kolaskar and Tongaonkar antigenecity are the sites of molecules that are recognized by antibodies of the immune system for the fattyacid-binding protein. The region of maximal hydrophilicity is likely to be an antigenic site, having hydrophobic characteristics, because C-terminal regions of fatty-acid-binding protein is solvent accessible and unstructured. Antibodies against those regions are also likely to recognize the native protein. Seven antigenic determinant sites in the fatty acid binding protein sequence were predicted. The highest pick is recorded between sequence of amino acid in the regions 102-VTKDSESKITHTQKDSKNT-19, 81-EFDEKTSDGRSVK-13 and 39-RQIGNTVTPT-10 ( Table 3). The average propensity for the fattyacid-binding protein is found to be 1.014. All residues having above 1.0 propensity are always potentially antigenic.
Fragment identified through this approach tend to be high efficiency binders, in which larger percentage of their atoms are directly involved in binding as compared to larger molecules. These fragments may, therefore, be used in cross protection and to develop human blood fluke Schistosoma japonicum specific antibodies that can be exploited in serodiagnostics.