- Research article
- Open Access
Prediction of CTL epitope, in silico modeling and functional analysis of cytolethal distending toxin (CDT) protein of Campylobacter jejuni
© Ingale and Goto; licensee BioMed Central Ltd. 2014
- Received: 19 September 2013
- Accepted: 11 February 2014
- Published: 19 February 2014
Campylobacter jejuni is a potent bacterial pathogen culpable for diarrheal disease called campylobacteriosis. It is realized as a major health issue attributable to unavailability of appropriate vaccines and clinical treatment options. As other pathogens, C. jejuni entails host cellular components of an infected individual to disseminate this disease. These host–pathogen interfaces during C. jejuni infection are complex, vibrant and involved in the nicking of host cell environment, enzymes and pathways. Existing therapies are trusted only on a much smaller number of drugs, most of them are insufficient because of their severe host toxicity or drug-resistance phenomena. To find out remedial alternatives, the identification of new biotargets is highly anticipated. Understanding the molecules involved in pathogenesis has the potential to yield new and exciting strategies for therapeutic intervention. In this direction, advances in bioinformatics have opened up new possibilities for the rapid measurement of global changes during infection and this could be exploited to understand the molecular interactions involved in campylobacteriosis.
In this study, homology modeling, epitope prediction and identification of ligand binding sites has been explored. Further attempt to generate strapping 3D model of cytolethal distending toxin protein from C. jejuni have been described for the first time.
CDT protein isolated from C. jejuni was analyzed using various bioinformatics and immuno-informatics tools including sequence and structure tools. A total of fifty five antigenic determinants were predicted and prediction results of CTL epitopes revealed that five MHC ligand are found in CDT. The three potential pocket binding site are found in the sequence that can be useful for drug designing.
This model, we hope, will be of help in designing and predicting novel CDT inhibitors and vaccine candidates.
- Cytolethal distending toxin (CDT)
- CTL epitope prediction
- Campylobacter jejuni
- Homology modeling
Campylobacter jejuni is a prominent bacterial cause of enteric campylobacteriosis in the entire world . Campylobacter is extensively distributed in poultry; nevertheless, cattle, pigs, sheep, and pet animals may also be a source of these microorganisms. This infection may be due to either eating of semi cooked meat or cross-contamination of ready-to-eat food at the time of preparation or storage. C. jejuni-linked enterocolitis is characteristically coupled with a local acute inflammatory response that involves intestinal tissue damage . The genome of C. jejuni has been sequenced, yet only a few prospective virulence factors produced by C. jejuni are well considered .
Cytolethal distending toxins (CDT) are a class of heterotrimeric toxins produced by C. jejuni and also by closely related spp., such as C. fetus, C. coli [4, 5], Shigella  and Escherichia coli . This toxin is rearward transported across the golgi complex and the endoplasmic reticulum, and afterward translocated into the nuclear compartment, where it applies the toxic activity . The CDT comprises of three protein subunits namely CdtA, CdtB, and CdtC causes progressive cellular distention with ultimate cell death and have been proposed as virulence factors in the pathogenesis of C. jejuni . These results suggest that the CDTs are involved invasion, survival and internalization into the host cell [10–13]. Although CDT from C. jejuni has been studied and characterized in laboratory [14, 15], but research on immune responses and pathogenesis of C. jejuni remains unexploited.
The progress in computational methods competent of predicting immune epitopes for B lymphocytes and T lymphocytes will facilitate the viewing of pathogens for immunogenic antigens. The epitope based vaccines encourage an immune response by presenting immunogenic peptides unite to major histocompatibility complex to TCR . Considering the unavailability of 3D structure of CDT, it is challenging to select proper target that would lead to predict epitope and ligand binding sites in protein. Hence, this study aims to investigate the CDT of C. jejuni with special focus on the structural and functional aspects through bioinformatics approach. This study has important implications on the selection of CTL epitope, a critical step in the development of vaccines.
Sequence acquisition and analysis
We have received the sequence of CDT of C. jejuni from the NCBI database by inserting query as “CDT C. jejuni”. The sequence was saved in FASTA format and used for further analysis. The primary structure analysis was done by using expasy ProtParam (http://www.expasy.org). The secondary structure of the protein was computed using different servers like Jpred3, GOR-IV and SOPMA  to check the presence of alpha helix and beta plated sheets in the structure. To determine the possible function of C. jejuni, the sequence was subjected to comparative protein structure modeling in the different servers.
3D-Model building and validation
Cytolethal distending toxin sequence of C. jejuni (CDTCJ) [EDZ32284.1] was used to develop 3D structure through homology modeling because crystal or NMR structure of the CTD protein was not available in the Protein Data Bank (PDB). The 3D structure of the CDT protein was done using a restrained-based approach in Modeller. The 3D model was generated using the ModWeb server that generates 3D models along with their confidence score (C-Score). The template selection for the homology modeling of the CDT protein was performed by submitting amino acid sequence of the target protein to ModWeb server . The crystal structure of CDT from Haemophillus ducreyi (PDB ID:1SR4) was used as a template. After generating the 3D model, structure analysis and stereochemical analysis were performed using different evaluation and validation tools. The final model was validated by using SAVES online tool (http://nihserver.mbi.ucla.edu/SAVES/). The Ramachandran plot was obtained using PROCHECK  and RAMPAGE  which helped in evaluating backbone conformation. Ramachandran plot was also used to check non-GLY residues at the disallowed regions. The verify 3D and PROSA web tool  was used to determine Z-scores. The ERRAT was used to predict overall quality for model and quality of the model was assured using Z- scores.
Epitope prediction of protein antigens
SEPPA (Spatial Epitope Prediction of Protein Antigens) server at the Life Science and Technology School, Tongji University, Shanghai China, (http://lifecenter.sgst.cn/seppa/) was used to predict conformational B-cell epitope.
The 3D protein structure predicted by Modeller was used as an input, each residue in the query protein will be given a score according to its neighborhood residues information. Higher score corresponds to higher probability of the residue to be involved in an epitope . The default values of THRESHOLD was set at 1.80, this help to specify the epitope residues . Transmembrane topology of the CDTCJ protein was checked using TMHMM online tool  and antigenicity of protein was checked using SVMTriP online antigen prediction server . The several algorithms are available that can predict the location and binding specificity of CTL epitopes in the protein sequences. In this study, the cytotoxic T-lymphocyte epitope prediction was done using NetCTL-1.2 server .
Sub cellular localization prediction
The sub cellular localization of CDT was predicted using CELLO, an approach based on multi-class SVM classification system . CELLO uses four types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. TargetP1.1 server was also used to predict cleavage site prediction of CDT .
Protein interaction network mapping
Protein-protein interactions were achieved from the STRING database  comprising known and predicted physical and functional protein-protein interactions. STRING in protein mode was used, and only interactions with high confidence levels (>0.7) were kept. STRING quantitatively integrates interaction data from these sources for many organisms, and transfers information among these organisms where applicable. Network visualization was done with the Cytoscape software .
Ligand binding sites prediction
We used MetaPocket 2.0 server (http://metapocket.eml.org) to identify ligand-binding sites on the protein surface. The MetaPocket is a consensus method  developed at Technical University of Dresden and Zhejiang University jointly, in which the predicted binding sites from eight methods i.e., PASS11 (PAS), LigsiteCS (LCS), Q_SiteFinder (QSF), GHECOM (GHE), POCASA (PCS), Fpocket (FPK), SURFNET (SFN), ConCavity (CON) are combined to improve the prediction success rate.
The structure comparison was executed by using DaliLite server .
The sub cellular localization of CDT was predicted using CELLO, an approach based on a two-level support vector machine (SVM) system. This server predicts sub cellular localization of protein for Gram negative bacteria by supporting vector machines based on n-peptide compositions. The CELLO output gave significant reliability for outer membrane (0.198), periplasmic (1.76) extracellular (0.803) and cytoplasmic (2.493), it indicates that the protein is cytoplasmic.
Model function and validation
Structure comparison analysis
Epitope prediction of protein antigens
Cytotoxic T-Lymphocytes (CTL) epitopes
Epitope predictors are routinely tested on large sets of epitopes derived from various pathogens. Schellens et al. identified eighteen new CTL epitopes out of a set of twenty two predicted CTL epitopes in HIV-1 using NetCTL. We screened all possible peptide fragments of 9aa within a particular protein, and eliminated those fragments that cannot be correctly processed by either the proteasome, TAP or the MHC class I molecules. Prediction results of CTL epitopes revealed that five MHC ligands were found in CDT sequence having high e-value score are positioned at 10CCFMTFFLY18, 39DTDPLKLGL47, 132AQGNWIWGY140, 170KTNTCLNAY178 and 217IQAPITNLY225. These are the immunodominant epitopes restricted by MHC class I located arbitrarily in the protein sequence. This data indicate that CTL epitopes in CDT are randomly distributed, and this distribution is similar to those of CTL epitopes in proteins from other proteomes.
Protein interaction network mapping
List of predicted interactive proteins with CDTCJ of C. jejuni
Amino acid residue
Cytolethal distending toxin, subunit B
Cytolethal distending toxin, subunit C
Cytochrome d ubiquinol oxidase, subunit II
Cytochrome d ubiquinol oxidase, subunit I
L lactate permease
Fibronectin binding protein
DNA directed RNA polymerase, Subunit omega
Ligand binding sites
Predicted ligand binding site in residues
Header binding site 1
Header binding site 2
Header binding site 3
The purpose of the present study was to perform a global screening for new immunogenic HLA class I (HLA-I) restricted cytotoxic T cell (CTL) epitopes of potential utility as a vaccine candidate against campylobacteroisis. The five epitopes of CDTCJ were identified. It is anticipated that, the peptide 170KTNTCLNAY178 can serve as novel potential vaccine candidate against diarrhea. These results have important implications for the rational design of CTL epitope-based CDT campylobacteriosis diagnostics and vaccines applicable to all ethnic groups. The presented research offered a backbone for understanding structural and functional insights of CDT protein. The additional experimental work is required to validate this epitope. The identification of ligand-binding sites is often the starting point for protein function annotation and structure-based drug design. In this study, we identify three predicted potential binding sites in CDT protein of C. jejuni. These are active sites on protein surface that performs protein functions.
Dr. Arun Ingale (Associate Professor and Head)
Department of Biotechnology, School of Life Sciences, North Maharashtra University, Jalgaon 425001, India.
Dr. Susumu Goto (Associate Professor)
Bioinformatics Centre, Institute of Chemical Research, Kyoto University, Kyoto, Japan.
We are thankful to the Matsumae International Foundation, Japan for providing MIF fellowship to Arun G. Ingale to pursue the above research. We also extend our gratitude towards Prof. A.B. Chaudhari who critically evaluated the manuscript.
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