Prediction of Antigenic Peptide and MHC Binder from ITX-3 Tegenaria agrestis: Current Approach for Synthetic Vaccine Development

Sherkhane AS; Gomase VS

doi:10.4172/2168-9652.1000136

ISSN: 2168-9652

Biochemistry & Physiology: Open Access

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Prediction of Antigenic Peptide and MHC Binder from ITX-3 Tegenaria agrestis: Current Approach for Synthetic Vaccine Development

Sherkhane AS and Gomase VS^*
The Global Open University, Nagaland, India
*Corresponding Author :	Gomase VS The Global Open University, Dimapur-797 112, Nagaland, India Tel: 91-9987770696 E-mail: gomase.viren@gmail.com
Received May 23, 2014; Accepted June 05, 2014; Published June 12, 2014
Citation: Sherkhane AS, Gomase VS (2014) Prediction of Antigenic Peptide and MHC Binder from ITX-3 Tegenaria agrestis: Current Approach for Synthetic Vaccine Development. Biochem Physiol 3:136. doi:10.4172/2168-9652.1000136
Copyright: © 2014 Gomase VS. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Venom of Tegenaria agrestis species causes necrosis in humans. ITX-3 is a toxin with 68 amino acids. Antigenic peptides of Tegenaria agrestis toxic protein are most suitable for synthetic peptide vaccine development because with single epitope, the immune response can be generated in large population. In this research, we used PSSM and SVM algorithms for the prediction of MHC class I & II binding peptide, antigenicity, Solvent accessibility, polar and nonpolar residue to analyze the regions that are likely exposed on the surface of proteins which are potentially antigenic that allows potential drug targets to identify active sites as well as to design synthetic peptide vaccine.

Abstract

Venom of Tegenaria agrestis species causes necrosis in humans. ITX-3 is a toxin with 68 amino acids. Antigenic peptides of Tegenaria agrestis toxic protein are most suitable for synthetic peptide vaccine development because with single epitope, the immune response can be generated in large population. In this research, we used PSSM and SVM algorithms for the prediction of MHC class I & II binding peptide, antigenicity, Solvent accessibility, polar and nonpolar residue to analyze the regions that are likely exposed on the surface of proteins which are potentially antigenic that allows potential drug targets to identify active sites as well as to design synthetic peptide vaccine.

Keywords

ITX-3; Tegenaria agrestis; Antigenic peptides; MHC-Binders; TapPred; PSSM; SVM; Nonamers

Introduction

Tegenaria agrestis is a member of the genus of Tegenaria known scientifically known as aggressive house spider [1,2]. Venom of Tegenaria agrestis species causes necrosis, loss of limbs, fatal to healthy humans and necrotic skin lesions [3,4]. ITX-3 toxins act directly on central nervous system neurons and paralyze insects [5]. These toxins have great potential for synthetic peptide vaccine. Antigenic peptides from Tegenaria agrestis are most suitable for the development of synthetic peptide vaccine because a single toxin subunit can generate sufficient immune response. Major histocompatibility complex (MHC) molecules are cell surface proteins that binds to the peptides derived from host or antigenic proteins, and present them at the cell surface for recognition by T-cells. T cell recognition is a fundamental mechanism of the adaptive immune system by which the host identifies and responds to foreign antigens [6,7]. There are two types of MHC molecule and are extremely polymorphic. MHC class I molecules present peptides from proteins synthesized within the cell, whereas, MHC class II molecule present peptides derived from endocytosed extracellular proteins. Identification of MHC-binding peptides and T-cell epitopes helps improve our understanding of specificity of immune responses [8-11].

Methodology

Database searching

There are many different types of databases available; the antigenic protein sequence of Tegenaria agrestis was retrieved from GenBank, UniProtKB/Swiss-prot [12-14].

Prediction of antigenicity

Prediction of antigenicity program predicts those segments from neurotoxin protein that are likely to be antigenic by eliciting an antibody response. In this research work antigenic epitopes of ITX-3 Tegenaria agrestis are determined by using the Hopp and Woods, Welling, Parker, Bepipred, Kolaskar and Tongaonkar antigenicity methods [15-19].

Prediction of MHC Binding Peptide

MHC peptide binding of ITX-3 Tegenaria agrestis is predicted using neural networks trained on C terminals of known epitopes. Rankpep predicts peptide binders to MHC-I ligands whose C-terminal end is likely to be the result of proteosomal cleavage using Position Specific Scoring Matrices (PSSMs). Support Vector Machine (SVM) based method for prediction of promiscuous MHC class II binding peptides from protein sequence; SVM has been trained on the binary input of single amino acid sequence [20-24].

Prediction of Antigenic Peptides by Cascade SVM based TAPPred method

In the present study, we predict cascade SVM based several TAP binders which was based on the sequence and the features of amino acids [25]. We found the MHCI binding regions (Table 3), the binding affinity of ITX-3 Tegenaria agrestis.

Solvent Accessible Regions

We also predict solvent accessible regions of proteins having highest probability that a given protein region lies on the surface of a protein Surface Accessibility, backbone or chain flexibility by Emani et al. [26] and Karplus and Schulz [27]. By using different scale, the hydrophobic and hydrophilic characteristics of amino acids that are rich in charged and polar residues were predicted [28-37].

Results and Interpretations

ITX-3 Tegenaria agrestis contain a long residue with 68 amino acids.

MKLQLMICLVLLPCFFCEPDEICRARMTNKEFTYKSNVCNGCGDQVAACEAECFRNDVYTACHEAQKG

Prediction of antigenic peptides

In this study, we found the antigenic determinants by finding the area of greatest local hydrophilicity. The Hopp-Woods scale Hydrophilicity Prediction Result Data found high pick in position 21-23,27-28 in a protein, assuming that the antigenic determinants would be exposed on the surface of the protein and thus would be located in hydrophilic regions (Figure 1). 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 and Prediction Result Data found high in position 62-64 (Figure 2). We also study Hydrophobicity plot of HPLC/Parker Hydrophilicity Prediction Result Data found 39-CNGCGDQ-45 (5.314), 61-TDDCNPH-67 (5.400), 60-STDDCNP-66 6.029 (maximum) (Figure 3), BepiPred predicts the location of linear B-cell epitopes Result found that, 32-FT-33, 39-CNGCGDQVA-47, 64-EAQKG-68 (Figure 4), Kolaskar and Tongaonkar [19] antigenicity methods (Figure 5) Predicted peptides result found i.e. 4-QLMICLVLLPCFFCEPDEICRA-25, 35-KSNVCNGCGDQVAACEAE-52 and the predicted antigenic fragments can bind to MHC molecule is the first bottlenecks in vaccine design.

Solvent accessible regions

We also predict solvent accessible regions in proteins; different measurement was performed for the prediction of antigenic activity, surface region of peptides. Emani et al. [26], (Figure 6) predicts the highest probability i.e. found Maximum in 26-RMTNKEFTYK-35, that a given protein region lies on the surface of a protein and are used to identify antigenic determinants on the surface of proteins. Karplus and Schulz [27] (Figure 7) High score is found i.e. found 1.042 maximum in 26-RMTNKEF-32. Predict backbone or chain flexibility on the basis of the known temperature B factors of the a-carbons. The hydrophobicity and hydrophilic characteristics of amino acids is determined by using different scales that are rich in charged and polar residues i.e. Sweet et al. [28] hydrophobicity prediction Result Data found high in position 8-9, 12-14, Kyte and Doolittle [29] result high in position 8-9, Abraham and Leo [30] result high in position 8-10, 12-13, Bull and Breese [31] result high in position 41-43,63-65, Miyazawa [32] result high in position 8-9, Roseman [33] result high in position 8-10,12-14, Wolfenden [34] result high in position 8-10,11-12, Wilson et al. [35] 8-12,13-15, Cowan [36] 6-7,8-10, Chothia [37] 6-9,10-14 (Figure 7).

Prediction of MHC Binding Peptide

We found binding of peptides to a number of different alleles using Position Specific Scoring Matrix. ITX-3 Tegenaria agrestis sequence is 68 residues long, having 60 nonamers. MHC molecules are cell surface proteins, which actively participate in host immune reactions and involvement of MHC-I and MHC-II in response to almost all antigens. We have predicted MHC-I peptide binders of ITX-3 Tegenaria agrestis was tested with on a set of 4 different alleles i.e. H2-Db (mouse) 8mer, H2-Db (mouse) 9mer, H2-Db (mouse) 10mer, H2-Db (mouse) 11mer (Table 1) and MHC-II peptide binders for I_Ab, I_Ad, I_Ag7 alleles highlighted in red represent predicted binders (Table 2). Here RANKPEP report PSSM-specific binding threshold and is obtained by scoring all the antigenic peptide sequences included in the alignment from which a profile is derived, and is defined as the score value that includes 85% of the peptides within the set. Peptides whose score is above the binding threshold will appear highlighted in red and peptides produced by the cleavage prediction model are highlighted in violet. We also use a cascade SVM based TAPPred method which found 17 High affinity TAP Transporter peptide regions (Table 3) which represents predicted TAP binders residues which occur at N and C termini from ITX-3 Tegenaria agrestis [38-43].

Discussion

In this study, we found the antigenic determinants by finding the area of greatest local hydrophilicity. Hopp and Woods hydrophobicity scale is used to identify of potentially antigenic sites in proteins. Hydrophilicity Prediction result data found high in sequence position at 21-23, 27-28 in a protein this scale is basically a hydrophilic index where a polar residues have been assigned negative values. The Window size of 5-7 is good for finding hydrophilic regions, greater than 0 values are consider as hydrophilic which is consider as antigenic.

Welling used information on the relative occurrence of amino acids in antigenic regions to make a scale which is useful for prediction of antigenic regions and the predicted result data found high in sequence position 62-64. 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.

We also study Hydrophobicity plot of HPLC / Parker Hydrophilicity Prediction Result Data found 39-CNGCGDQ-45 (5.314), 61-TDDCNPH-67 (5.400), 60-STDDCNP-66 6.029 (maximum). BepiPred predicts the location of linear B-cell epitopes Result found that 32-FT-33, 39-CNGCGDQVA-47, and 64-EAQKG-68. There are 3 antigenic determinant sequences is found by Kolaskar and Tongaonkar [19] antigenicity scales the results show highest pick at position 4-QLMICLVLLPCFFCEPDEICRA-25, 35-KSNVCNGCGDQVAACEAE-52 (Figure 1-5).

Result of determined antigenic sites on proteins has revealed that the hydrophobic residues if they occur on the surface of a protein are more likely to be a part of antigenic sites. This method can predict antigenic determinants with about 75% accuracy and also gives the information of surface accessibility and flexibility. Further this region form beta sheet which show high antigenic response than helical region of this peptide and shows highly antigenicity.

We predict Solvent accessibility by using Emani et al., the result found the highest probability i.e. found 26-RMTNKEFTYK-35, that a given protein region lies on the surface of a protein and are used to identify antigenic determinants on the surface of proteins [26]. This algorithm also used to identify the antigenic determinants on the surface of proteins and Karplus and Schulz [27] predict backbone or chain flexibility on the basis of the known temperature B factors of the a-carbons here we found the result with High score is i.e. found 1.042 maximum in 26-RMTNKEF-32 (Figures 6 and 7). We predict Solvent accessibility of alpha-neurotoxins of ITX-3 Tegenaria agrestis for delineating hydrophobic and hydrophilic characteristics of amino acids. Solvent accessibility used to identify active site of functionally important residues in membrane proteins.

We also found the i.e. Sweet and Eisenberg [1983] hydrophobicity prediction result data found high in position 8-9, 12-14, Kyte and Doolittle [29] result high in position 8-9, Abraham and Leo [30] result high in position 8-10, 12-13, Bull and Breese [31] result high in position 41-43,63-65, Miyazawa and Jernigen [32] result high in position 8-9, Roseman [33] result high in position 8-10,12-14, Wolfenden et al. [34] result high in position 8-10,11-12, Wilson et al. [35] 8-12,13-15, Cowan and Whittaker [36] 6-7,8-10, Chothia [37] 6-9,10-14 (Figures 8-18). These scales are a hydrophilic with a polar residues assigned negative value. Because the N- and C- terminal regions of proteins are usually solvent accessible and unstructured, antibodies against those regions recognize the antigenic protein.

Solvent-accessible surface areas and backbone angles are continuously varying because proteins can move freely in a three-dimensional space. The mobility of protein segments which are located on the surface of a protein due to an entropic energy potential and which seem to correlate well with known antigenic determinants.

In this study, we found predicted MHC-I peptide binders of toxin protein for 4 different alleles i.e. H2-Db (mouse) 8mer, H2-Db (mouse) 9mer, H2-Db (mouse) 10mer, H2-Db (mouse) 11mer (Table 1) and MHC-II peptide binders for I_Ab, I_Ad, I_Ag7 alleles highlighted in red represent predicted binders (Table 2). We also use a cascade SVM based TAPPred method which found 17 High affinity TAP Transporter peptide regions which represents predicted TAP binders residues which occur at N and C termini from ITX-3 Tegenaria agrestis. TAP is an important transporter that transports antigenic peptides from cytosol to ER. TAP binds and translocate selective antigenic peptides for binding to specific MHC molecules. The efficiency of TAP-mediated translocation of antigenic peptides is directly proportional to its TAP binding affinity. Thus, by understanding the nature of peptides, that bind to TAP with high affinity, is important steps in endogenous antigen processing. The correlation coefficient of 0.88 was obtained by using jackknife validation test.

In this test, we found the MHCI and MHCII binding regions. T cell immune responses are derived by antigenic epitopes hence their identification is important for design synthetic peptide vaccine. T cell epitopes are recognized by MHCI molecules producing a strong defensive immune response against alpha-neurotoxins of ITX-3 Tegenaria agrestis. Therefore, the prediction of peptide binding to MHCI molecules by appropriate processing of antigen peptides occurs by their binding to the relevant MHC molecules. Because, the C-terminus of MHCI-restricted epitopes results from cleavage by the proteasome and thus, proteasome specificity is important for determining T-cell epitopes. Consequently, RANKPEP also focus on the prediction of conserved epitopes. C-terminus of MHCI-restricted peptides is generated by the proteasome, and thus RANKPEP also determines whether the C-terminus of the predicted MHCI-peptide binders is the result of proteasomal cleavage. Moreover, these sequences are highlighted in purple in the output results. Proteasomal cleavage predictions are carried out using three optional models obtained applying statistical language models to a set of known epitopes restricted by human MHCI molecules as indicated here.

Conclusion

From the above result and discussion it is concluded that the ability of RANKPEP to predict MHC binding peptides, and potential T-cell epitopes. Antigenic peptide that bind to MHC molecule are antigenic that means hydrophilic in nature. This means the increase in affinity of MHC binding peptides may result in enhancement of immunogenicity of ITX-3 Tegenaria agrestis hence helpful in the designing of synthetic peptide vaccine. This approach can help reduce the time and cost of experimentation for determining functional properties of ITX-3 Tegenaria agrestis. Overall, the results are encouraging; both the sites of action and physiological functions can be predicted with very high accuracies helping minimize the number of validation experiments.