alexa Prediction of Antigenic MHC Peptide Binders and TAP Binder of COX1 Protein through In Silico Approach | Open Access Journals
ISSN: 2157-7609
Journal of Drug Metabolism & Toxicology
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Prediction of Antigenic MHC Peptide Binders and TAP Binder of COX1 Protein through In Silico Approach

Mishra S* and Gomase VS

Department of Biotechnology, Mewar University, Chittorgarh, India

*Corresponding Author:
Sonu Mishra
Department of Biotechnology
MewarUniversity, Chittorgarh, India
Tel: 9560808369
[email protected]; [email protected]

Received date: Mar 14, 2016; Accepted date: Mar 30, 2016; Published date: April 4, 2016

Citation: Mishra S, Gomase VS (2016) Prediction of Antigenic MHC Peptide Binders and TAP Binder of COX1 Protein through in silico Approach. J Drug Metab Toxicol 7:201. doi:10.4172/2157-7609.1000201

Copyright: © 2016 Mishra S, et al. 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.

Visit for more related articles at Journal of Drug Metabolism & Toxicology


In the current analysis Cytochrome c oxidase subunit I (CO1 or MT-CO1) protein sequence from GWD has been used to study the MHC binding antigenic peptide, antigenic peptide prediction through different B cell prediction method, protein solvent accessibility, polar and nonpolar residue to analyze the regions which are probably exposed on the protein surface. From the protein the peptide fragment can be used to analyze and specific nonamer can be selected for the rational vaccine designing. In this investigation, PSSM and SVM algorithms are applied for finding of MHC I and MHC II binding peptides. We also predicted the high affinity TAP binding peptides of CO1 protein from GWD, having 205 amino acids which show 197 nonamers. From the opted outcomes we predict that, the possibilities that, the antigenic peptide of cytochrome c oxidase subunit I (mitochondrion) protein might play a major role and could be the most suitable candidate for subunit vaccine development on the bases of the finding that, with single epitope, the immune response can be generated in large population.


Antigenic peptides; GWD; MHC-binders; TapPred; PSSM; SVM; Nonamers; COX1


CCOI: Cytochrome C Oxidase Subunit I; MT-COI: Mitochondrial Cytochrome C Oxidase Subunit I; ETC: Electron Transport Chain; mtDNAormDNA: Mitochondrial DNA (Deoxyribonucleic Acid); MTCO3: Mitochondrial Cytochrome C Oxidase Subunit I; DCCD: Di- Cyclohexylcarbodiimide; MHC I: Major Histocompatibility Complex- Class I; MHC II: Major Histocompatibility Complex-Class II; PSSM: Position Specific Scoring Matrices; SVM: Support Vector Machine; GWD: Guinea Worm Disease; UniProt: The Universal Protein Resource; NCBI: National Center for Biotechnology Information; TAP: Transporter Associated with Antigen Processing; HPLC: High Performance Liquid Chromatography; GRAVY: Grand Average of Hydropathicity; TapPred: TAPPred is an on-line service for predicting binding affinity of peptides toward the TAP transporter. The prediction of TAP binding peptides is important in order to identify the MHC class-1 restricted T cell epitopes. The Prediction is based on cascade SVM, using sequence and properties of the amino acids. The correlation coefficient of 0.88 was obtained by using jack-knife validation test

Rankpep: This server predicts peptide binders to MHCI and MHCII molecules from protein sequence/s or sequence alignments using Position Specific Scoring Matrices (PSSMs). In addition, it predicts those MHCI ligands whose C-terminal end is likely to be the result of proteasomal cleavage


COX1 (Cytochrome c Oxidase I) are commonly known as Mitochondrially encoded Cytochrome c Oxidase I (MT-CO1). This protein encodes of MT-CO1 gene in humans, whereas in other eukaryotes, this gene is addressed as COX1, CO1 or COI [1]. COX 1 is a primary subunit of cytochrome c oxidase complex. The subunit I of Cytochrome c Oxidase (CO1 or MT-CO1) is one out of three mitochondrial DNA (mtDNA) encoded subunits (MT-CO1, MT-CO2, MT-CO3) of respiratory complex IV. Complex IV is considered to be the third and final enzyme of mitochondrial oxidative phosphorylation of the ETC. In aerobic metabolism, Cytochrome c oxidase (EC is a primal enzyme. Study suggest that in prokaryotes, this enzyme complex is consist of three to four subunits and up to thirteen polypeptides in mammals, but out of which only the catalytic subunit is found in all heme-copper respiratory oxidases. The enzyme complexes modify in heme and copper composition, substrate type and substrate affinity. The different respiratory oxidases allow the cells to tailor-make their respiratory systems followed by a diverse environmental growth circumstances [2]. Catalytic activity of the COX1 protein in oxidative phosphorylation is:

4 ferrocytochrome c + O2 + 4 H+ = 4 ferricytochrome c + 2 H2O.

COX1 involved in oxidative phosphorylation pathway, which is division of Energy metabolism. In order to identify the animal species or closely related species analysis, this COX1 gene is used as the barcode, due to its fastest rate of mutation and sequence conserved specificity among con-specifics. This COX1 gene is frequently practiced as a DNA barcode to identify animal species. In contradiction, raised by the skeptics suggest that, the MT-CO1 sequence differences are available in too minute to be detected among nearly related species, more than 2% sequence divergency is generally detected between such organisms [3]. MTCO1 is encoded by the guanine-rich heavy (H) strand of the mtDNA and situated between nucleotide pairs (nps) 5904 and 7444 [4,5]. It is maternally inherited along with the mtDNA [6,7]. The predicted molecular weight (MW) of MTCO1 is 57 kD [4,5]. However, its apparent MW on SDS polyacrylamide gels (PAGE) is somewhat less. Using Tris-glycine buffer it runs at 39.5 kD [8-10], whereas results in urea-phosphate test, it gives an apparent MW of 45 kD [11,12]. Investigations suggest that, this protein is extremely expressed in the cytoplasm of colonic crypts (intestinal glands) of the human colon (large intestine). However, with age in the human it is frequently gets lost in colonic crypts and also it is often absent in field defects that give rise to colon cancers or in portions of colon cancers [13]. CCOI is coded for by the mitochondrial chromosome. The occurrence of the chromosome in the mitochondria in multiple copy forms, which varies between two and six / per mitochondrion [14-16]. The generation of new type of mitochondria occurs due to random segregation of chromosome during the mitochondrial fission, if a mutation takes place in CCOI in one chromosome of a mitochondrion. The occurrence of mitochondria per cell is 100 - 700, depending upon the cell type [15,16]. In rats, the average half-life of mitochondria depends on cell type, and found between 9 and 24 days [17], in mice about 2 days [18], whereas, in human it varies from days to week depending on cell type. The inadequacy of CCOI in a mitochondrion heads to lower reactive oxygen production (and less oxidative damage) and this provides an exclusive vantage in competition with other mitochondria within the same cell to bring forth homoplasmy for CCOI-deficiency [13]. The phylogenetic analysis study conducted by Ngui et al. [19] on the cytochrome c oxidase subunit 1 (cox 1) sequence of A. ceylanicum from positive human and animal fecal samples suggest that, considerable level of genetic variation within the cox 1 sequence of A. ceylanicum might be a potential haplotype-linked divergences in zoonotic, epidemiological and pathobiological characteristics, a hypothesis which still needs a further future in depth investigation [19]. In another study, the analyses of multiple sequence alignments of mitochondrial 16S rDNA (ribosomal DNA) and cox 1 of Trichurisskrjabini revealed high homology with those of Trichinella species and this was the first time when the mitochondrial DNA gene sequences of one species of trichurid nematode have been cited [20]. MT-CO1 may act as agent in the pathogenesis of acquired idiopathic sideroblastic anemia, this sickness is characterized by inadequate formation of heme and excessive aggregation of iron in mitochondria. The overloaded iron in mitochondrial may be attributable to mutations of mitochondrial DNA, because impairing the reduction of ferric iron to ferrous iron can cause respiratory chain dysfunction. Whereas, study suggest that the reduced form of iron is substantive in the last step of mitochondrial heme biosynthesis. Insufficiency of COX drives a clinically heterogeneous variety of neuromuscular and nonneuromuscular disorders in childhood and adulthood. The mutation in the COX1 results in the several other variety of the disorders such as Leber hereditary optic neuropathy (LHON) (primary mitochondrial DNA mutations affecting the respiratory chain complexes), mitochondrial complex IV (MTC4D) deficiency, recurrent myoglobinuria mitochondrial (RM-MT), Deafness, sensorineural, mitochondrial disorder, colorectal cancer. Considering the COX1 importance, we have taken this protein for investigation of its antigenicity role, its solvent accessibility property, polar and nonpolar residue analysis. By this time we all are aware that, the regions that are likely exposed on the surface of proteins could be the potentially antigenic that allows potential drug targets to identify active sites against infection as well as for designing and development of effective drug to treat infections. Cytochrome c Oxidase subunit I (mitochondrion) comprised of 205 amino acid residues obtained from Dracunculus medinensis for the study of MHC class I and II binding peptide, antigenicity, Solvent accessibility, polar and nonpolar residue to analyze the regions that are likely exposed on the surface of proteins. A little dragon from Medina (D. medinesis ) is the only species of Dracunculus genus [21-24] which causes dracunculiasis in humans, commonly well known as “Guinea Worm Disease (GWD)”. The other Dracunculus species generally resides in the internal tissues and body cavities of non-human mammals and reptiles (snake and turtles) [25]. This parasite follows a very unique life wheels, comprised of six developmental steps with the longest incubation period of about one an half years approximately. This is also the one of the most neglected tropical parasites which has got clinical grandness and needs to be eradicated completely [26]. Once this parasite approaches the maturation stage, these worms copulate and a millions of eggs is formed in uterus of adult female, whereas male worm dies after copulation. Once the incubation period is over the larvae emerge out from the blister once the blister burst out (predominantly localized in the lower extremities (80% - 90%) in most of the reported cases) when an infected individual comes in contact with water. The symptoms developed in the infected individual are slight fever, local skin redness, swelling and severe pruritus around the blister. Other symptoms includes are diarrhea, nausea, vomiting and dizziness [27]. Immersing or pouring water over the blister provides pain relief but this is the point when the adult female is exposed to the external environment [28]. During emergence of the limbs in open water sources it recognizes the temperature difference and releases the milky white liquid in the water which contains millions of immature larvae, when larvae released in water are ingested by copepods where they mount twice and become infective larvae within two weeks [29]. The antigen peptides of D.medinensis could be most desirable segment for the development of subunit vaccine on the bases of fact that, the immune response can be generated in large population with the single epitope. This approach is generally based on the phenomenon of crossprotection, whereby the individual can be infected with the mild strain and is protected against a more severe strain of pathogen of the same. The resistant transgenic host’s phenotype includes of fewer centers of initial infection, following a delay development in symptom with low accumulation. There is the possibilities that the predicted antigenic peptides from D. medinensis could contribute a major role in drug formulation (or peptide vaccine) and disease eradication [30] because a single protein subunit can generate sufficient immune response. In this current investigation work, we have applied the in silico approach for MHC class I and class II binding antigenic peptide identification. MHC molecules are cell surface protein which binds to peptides derived from host or antigenic proteins and present them to cell surface for realization by T-cells. T cell recognition is a significant mechanism of the adaptive immune system by which the host distinguishes and responds to foreign antigens [30,31]. The two forms of MHC molecule 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. MHC molecules take active part in host immune reactions and their contribution in immune response to almost all antigens and it give impacts on specific sites. MHCI binds to some of the peptide fragments generated after proteolytic cleavage of antigen [32]. Identification of MHC-binding peptides and T-cell epitopes helps to improve our understanding of specificity of immune responses [33-36]. Antigenic peptides are most suitable for peptide or synthetic vaccine development.


Database searching

The cytochrome c oxidase subunit I (mitochondrion) protein sequence of Dracunculus medinensis was recovered from, UniProt databases which is the essential important step for further investigation [37,38].

Prediction of the physico-chemical properties of the protein

The physico-chemical properties like molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient [39-41], estimated half-life [42,43], instability index [44], aliphatic index [45] and Grand Average of Hydropath city (GRAVY) [46] were analyzed by the ProtParam ( Effect of temperature on protein solubility and denaturation and this physiochemical property prediction will helpful to understand the effect of pH on protein solubility and protein isoelectric point, interaction between protein and water molecules and hydrogen bonds, protein-ligand binding affinity, function of the protein. These property analysis plays an important role and taken into the consideration in the drug development or designing.

Prediction of antigenicity

The prediction antigenicity program, predicts the segments from cytochrome c oxidase subunit I (mitochondrion) protein that are likely to be antigenic by reducing an antibody reaction. In this research work antigenic epitopes of Dracunculus medinensis - cytochrome c oxidase subunit I (mitochondrion) are determined by using the various antigenic prediction methods such as Hopp and Woods, Welling, Parker, Bepipred, Kolaskar and Tongaonkar antigenicity methods [47-59].

Prediction of MHC binding peptide

The Major Histocompatibility Complex (MHC) peptides binding of protein from Dracunculus medinensisis were predicted using neural networks trained on C terminals of known epitopes. Rank pep tool 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). The sequence similarities are shared by the peptides that bind to a given MHC molecule. Traditionally, the sequence patterns used for the prediction of peptides binding to MHC molecules, such sequence patterns are however, have proven to be too simple, as the complexity of the binding motif cannot be precisely represented by the few residues present in the pattern [60]. RANKPEP uses “Position Specific Scoring Matrices (PSSMs) or profiles” from set of aligned peptides known to bind to a given MHC molecule as the forecaster of MHC-peptide binding and overpower the complexity of the binding motif limitation. Whereas, in the other hand the 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 [61-64].

Prediction of antigenic peptides by cascade SVM based TAPPred method

We predicted the cascade SVM based several TAP binders which was based on the sequence and the features of amino acids [65]. We found the MHCI binding regions, the binding affinity of cytochrome c oxidase subunit I (mitochondrion).

Solvent accessible regions

We also investigated the solvent accessible regions of proteins holding highest probability that a given protein region lies on the surface of a protein Surface Accessibility, backbone or chain flexibility via Emini et al. [66] and Karplus and Schulz [67]. By using different scale we predict the hydrophobic and hydrophilic characteristics of amino acids that are rich in charged and polar residues [68-70] (Figure 1).


Figure 1: Methodology flow diagram.

Results and Discussion

Dracunculus medinensisis protein Cytochrome c oxidase subunit I (mitochondrion), is consist of 205 amino acids long residue with 197 nonamers.


Prediction of the physicochemical properties of the protein

The physico-chemical properties of Cytochrome c oxidase subunit I like molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life instability index, aliphatic index and grand average of hydropathicity (GRAVY) were analyzed by the ProtParam ( and found the instability index is 32.56 which infers that the protein is the stable protein and its grand average hydropathicity predicted is 0.876 (Table 1).

Protein Gene ID No. of amino acids Molecular weight Theoretical PI Total no. of –vely charged Residue(Arg+Lys) Total no. of +vely charged Residue(Arg+Lys) Total no. of atoms Extinction coefficient Instability index Aliphatic Index GRAVY
COX1 GI563582560 205 22230.4 7.06 7 7 3144 Extinction coefficients are in units of M-1 cm-1, at 280 nm measured in water.Ext. coefficient 33710 Abs 0.1% (=1 g/l) 1.516, assuming all pairs of Cys residues form cysteines. Ext. coefficient 33460 Abs 0.1% (=1 g/l) 1.505, assuming all Cys residues are reduced 32.56 (This classifies the protein as stable). 109.8 0.876

Table 1: The physicochemical properties of the Cytochrome c oxidase subunit I (mitochondrion).

Prediction of antigenic peptides

In antigenic peptide study, we detected the antigenic determinants by finding the area of greatest local hydrophilicity. In the Hopp-Woods scale Hydrophilicity prediction analysis of the protein found high in position: 29, Score: 0.433 (max), i.e., 23-RAELCKP-32 in a protein sequence, assuming that the antigenic determinants would be displayed on the surface of the protein and thus would be located in hydrophilic regions (Figure 2).


Figure 2: Hydrophobicity plot of Hopp and Woods.

In the Welling et al. [48] antigenicity plot gives value as the log of the quotient between percentage in average proteins and percentage in a sample of known antigenic regions, the prediction result data found high in position: 168, Score: 0.289 (max) (177-VTVFLLI-183) (Figure 3).


Figure 3: Hydrophobicity plot of Welling et al. [48].

We also study the hydrophobicity plot of HPLC / Parker [49] and the highest peak is obtained in position: 110 (Residue: C) with highest score: 5.971 (107-DSSCGTS-113) (Figure 4).


Figure 4: Hydrophobicity plot of HPLC / Parker et al. [49].

There are 10 antigenic a determinant sequence is found by Kolaskar and Tongaonkar [50] antigenicity scales (Figures 5a and 5b).


Figure 5a: Kolaskar and Tongaonkar antigenicity plot (propensity: 1.0531) [50].


Figure 5b: The 9 antigenic determinants.

Kolaskar and Tongaonkar antigenicity methods (Figure 6).


Figure 6: Kolaskar and Tongaonkar antigenicity plot.

Predicted peptides result found, i.e., 4-GSMYLIF-10;17- VGAGLSILIRAELCKPGF-34;40-QLYNAVITS-48;54-




ASISLEHLSLFVWTVFVTVFLLILTLPVLAGAITM-195 and the predicted antigenic fragments can bind to MHC molecule is the first step in the process of the drug designing or vaccine development.

Solvent accessible regions

The solvent accessible regions in proteins are also predicted. The different measurement was performed for the prediction of antigenic activity, surface region of peptides. Emini et al. [66] (Figure 7).


Figure 7: Emini surface accessibility.

Predicts the highest probability i.e. found In position: 201(Resisdue: S), the sequence is 199-DRSFNT-204 with highest score: 4.77, 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 [67] (Figure 8).


Figure 8: Karplus and Schulz flexibility prediction.

The highest score (score: 1.079) is found in the position: 127(Residue: 124) and the sequence is 124-GHPGNSV-130. The other second highest peak has been found in the position: 126(Residue: P) with the sequence 123-SGHPGNS-129 (Score: 1.078). 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., Bull and Breese [68] result high in Position: 125 Score: 0.509 (max) 122-TSGHPGN-128 (Figure 9).


Figure 9: Bull and Breese use surface tension to measure hydrophobicity.

Roseman [69] result found high in Position: 180 Score: 1.491 (max) 177-TVFLLI-183 (Figure 10).


Figure 10: Hydrophobicity plot of Roseman [69].

Wilson et al. [70] in Position: 180, Score: 5.411 (max) 177- TVFLLI-183 (Figure 11).


Figure 11: Hydrophobicity / HPLC plot of Wilson et al. [70].

Prediction of MHC binding peptide

The binding of peptides to a number of different alleles using PSSM were identified from cytochrome c oxidase subunit I (mitochondrion) protein of Dracunculus medinensis having 247 nonamers. We have predicted MHC-I peptide binders of cytochrome c oxidase subunit I (mitochondrion) from Dracunculus medinensis .

We found predicted MHC-I peptide binders of protein for 8mer_H2_Db alleles with the consensus sequence QNWNCCTI that yields the maximum score i.e. 52.494, 9mer_H2_Db with, the consensus sequence FCIHNCDYM that yields the maximum score i.e. 50.365, 10mer_H2_Db with, the consensus sequence SGYYNFFWCL that yields the maximum score, i.e., 58.858, 11mer_H2_Db with, the consensus sequence CGVYNFYYCCY that yields the maximum score, i.e., 79.495 (Table 2).

8mer_H2_Db 1 147 SIL GGINFMTT VKN 821.93 13.596 25.90%
8mer_H2_Db 2 109 VDS SCGTSWTI YPP 812.92 10.984 20.92%
8mer_H2_Db 3 67 GGF GNWMVPLM LGA 906.15 10.568 20.13%
8mer_H2_Db 4 29 RAE LCKPGFFF GSG 940.18 10.043 19.13%
8mer_H2_Db 5 16 WSG MVGAGLSI LIR 728.9 8.059 15.35%
9mer_H2_Db 1 147 SIL GGINFMTTV KNM 921.06 16.377 32.52%
9mer_H2_Db 2 146 SSI LGGINFMTT VKN 935.09 14.44 28.67%
9mer_H2_Db 3 66 IGG FGNWMVPLM LGA 1053.33 14.031 27.86%
9mer_H2_Db 4 124 STS GHPGNSVDL AIF 876.92 12.987 25.79%
9mer_H2_Db 5 39 FGS GQLYNAVIT SHA 960.09 11.476 22.79%
10mer_H2_Db 1 76 LML GAPDMSFPRL NNV 1072.26 10.794 18.34%
10mer_H2_Db 2 146 SSI LGGINFMTTV KNM 1034.22 9.981 16.96%
10mer_H2_Db 3 153 NFM TTVKNMRSAS ISL 1076.22 9.867 16.76%
10mer_H2_Db 4 39 FGS GQLYNAVITS HAI 1047.17 7.555 12.84%
10mer_H2_Db 5 166 ISL EHLSLFVWTV FVT 1189.41 7.286 12.38%
11mer_H2_Db 1 146 SSI LGGINFMTTVK NMR 1162.39 20.946 26.35%
11mer_H2_Db 2 43 QLY NAVITSHAIMM IFF 1169.41 12.764 16.06%
11mer_H2_Db 3 139 SLH CSGVSSILGGI NFM 974.14 12.41 15.61%
11mer_H2_Db 4 160 NMR SASISLEHLSL FVW 1138.3 9.483 11.93%
11mer_H2_Db 5 31 ELC KPGFFFGSGQL YNA 1166.35 7.18 9.03%

Table 2: Promiscuous MHC ligands, having C-terminal ends are proteosomal cleavage sites of Dracunculus medinensis . (All rows highlighted in red represent predicted binders and A peptide highlighted in violet has a C-teminus predicted by the cleavage model used).

MHC-II peptide binders for I Ab with the consensus sequence YYAPWCNNA that yields the maximum score, i.e., 35.632,I_Ad with the consensus sequence QMVHAAHAE that yields the maximum score, i.e., 53.145, MHC-II I_Ag7 with the consensus sequence WYAHAFKYV that yields the maximum score, i.e., 40.873 for MHC II allele was tested. The prediction of high affinity binders are performed using a cascade SVM based TAPPred method, where more than 63 High affinity TAP Transporter peptide regions were obtained This high affinity binders represents predicted TAP binders residues which occur at N and C termini from Dracunculus medinensis antigen cytochrome c oxidase subunit I (mitochondrion). 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. 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 Dracunculus medinensis antigen cytochrome c oxidase subunit I (mitochondrion). 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 MHCIrestricted 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 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 as I_Ab.p, I_Ad.p,I_Ag7.p,I_Ak.p alleles, which is highlighted in red represent predicted binders (Table 3).

MHC-II I_Ab 1 42 GQL YNAVITSHA IMM 957.05 10.099 28.34%
MHC-II I_Ab 2 80 APD MSFPRLNNV SYW 1059.25 10.012 28.10%
MHC-II I_Ab 3 187 LTL PVLAGAITM LLM 854.07 9.53 26.75%
MHC-II I_Ab 4 90 NVS YWLMPVSLM LIL 1098.42 8.674 24.34%
MHC-II I_Ab 5 16 WSG MVGAGLSIL IRA 842.06 8.556 24.01%
MHC-II I_Ad 1 43 QLY NAVITSHAI MMI 907.03 18.11 34.08%
MHC-II I_Ad 2 166 ISL EHLSLFVWT VFV 1090.28 8.935 16.81%
MHC-II I_Ad 3 157 TVK NMRSASISL EHL 960.12 7.307 13.75%
MHC-II I_Ad 4 140 LHC SGVSSILGG INF 757.84 5.106 9.61%
MHC-II I_Ad 5 135 LAI FSLHCSGVS SIL 918.04 4.79 9.01%
MHC-II I_Ag7 1 80 APD MSFPRLNNV SYW 1059.25 8.379 20.50%
MHC-II I_Ag7 2 57 IFF MVMPSLIGG FGN 886.13 7.943 19.43%
MHC-II I_Ag7 3 16 WSG MVGAGLSIL IRA 842.06 7.45 18.23%
MHC-II I_Ag7 4 46 NAV ITSHAIMMI FFM 998.26 7.024 17.18%
MHC-II I_Ag7 5 25 SIL IRAELCKPG FFF 968.19 6.141 15.02%
MHC-II I_Ak 1 107 CLV DSSCGTSWT IYP 901.93 17.05 42.73%
MHC-II I_Ak 2 65 LIG GFGNWMVPL MLG 979.19 16.596 41.59%
MHC-II I_Ak 3 11 LIF GFWSGMVGA GLS 870.02 11.251 28.20%
MHC-II I_Ak 4 37 FFF GSGQLYNAV ITS 889.96 9.513 23.84%
MHC-II I_Ak 5 79 GAP DMSFPRLNN VSY 1075.21 8.672 21.73%

Table 3: Prediction of MHC II ligands all rows highlighted in red represent predicted binders to the MHC-II Allele i.e. MHC-II I_Ab, MHC-II I_Ad, MHC-II I_Ag7. (All rows highlighted in red represent predicted binders).

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 63 High affinity TAP Transporter peptide regions (Table 4).

Peptide Rank Start Position Sequence Score Predicted Affinity
1 187 PVLAGAITM 8.644 High
2 17 VGAGLSILI 8.644 High
3 9 IFGFWSGMV 8.642 High
4 39 GQLYNAVIT 8.635 High
5 148 GINFMTTVK 8.631 High
6 16 MVGAGLSIL 8.627 High
7 6 MYLIFGFWS 8.612 High
8 147 GGINFMTTV 8.61 High
9 103 ACLVDSSCG 8.599 High
10 156 KNMRSASIS 8.596 High

Table 4: Cascade SVM based high affinity TAP binders of COX1 from Dracunculus medinensis .

This represents predicted TAP binders residues which occur at N and C termini from Dracunculus medinensis (cytochrome c oxidase subunit I (mitochondrion)).


MHC molecules are the cell surface proteins, which actively take part in the host immune responses against pathogens and reason of its involvement in the response to almost all variety of antigens and it gives effects on specific sites. By considering the above result we can concluded that the antigenic peptide that binds 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 Dracunculus medinensis antigen cytochrome c oxidase subunit I (mitochondrion) and could be helpful in the designing of synthetic peptide vaccine. This approach can help reduce the time and cost of experimentation for determining functional properties of Dracunculus medinensis antigen cytochrome c oxidase subunit I (mitochondrion). Overall, the results are encouraging, both the ‘sites of action’ and ‘physiological functions’ can be predicted with very high accuracies which ultimately facilitating the minimization of number of validation experiments.


Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

  • European Biopharma Congress
    November 16-17, Vienna, Austria

Article Usage

  • Total views: 8148
  • [From(publication date):
    June-2016 - Nov 17, 2017]
  • Breakdown by view type
  • HTML page views : 8078
  • PDF downloads : 70

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals


[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

© 2008- 2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version