Computational prediction of immunodominant antigenic regions & potential protective epitopes for dengue vaccination

Similar documents
Selection of epitope-based vaccine targets of HCV genotype 1 of Asian origin: a systematic in silico approach

CS229 Final Project Report. Predicting Epitopes for MHC Molecules

DOI: /ICJ (Dengue fever) [2] (Aedes aegypti) (Aedes albopictus) (Dengue. (Flavivirus) 50 nm. ssrna) (capsid, C) ( ) (membrane, prm/m)

Bioinformation by Biomedical Informatics Publishing Group

A HLA-DRB supertype chart with potential overlapping peptide binding function

COMPUTATIONAL ANALYSIS OF CONSERVED AND MUTATED AMINO ACIDS IN GP160 PROTEIN OF HIV TYPE-1

Gene Vaccine Dr. Sina Soleimani

Bjoern Peters La Jolla Institute for Allergy and Immunology Buenos Aires, Oct 31, 2012

Epitope discovery and Rational vaccine design Morten Nielsen

Rajesh Kannangai Phone: ; Fax: ; *Corresponding author

Foot and Mouth Disease Vaccine Research and Development in India

Viral vaccines. Lec. 3 أ.د.فائزة عبد هللا مخلص

Immune Epitope Database NEWSLETTER Volume 6, Issue 2 July 2009

Vaccine Design: A Statisticans Overview

Cover Page. The handle holds various files of this Leiden University dissertation

D-LAB HEALTH SP 725. Jose Gomez-Marquez

Section D. Identification of serotype-specific amino acid positions in DENV NS1. Objective

Comparison between serological & molecular methods for diagnosis of dengue fever and its correlation with duration of illness

The Immune Epitope Database Analysis Resource: MHC class I peptide binding predictions. Edita Karosiene, Ph.D.

The early pathogenesis of FMD and the implications for control measures

Short peptide epitope design from hantaviruses causing HFRS

Lecture 11. Immunology and disease: parasite antigenic diversity

WORLD HEALTH ORGANIZATION. Smallpox eradication: destruction of Variola virus stocks

HIV Anti-HIV Neutralizing Antibodies

MHC class I MHC class II Structure of MHC antigens:

A Novel Recombinant Virus Reagent Products for Efficient Preparation Of Hepatitis B Animal Models

Multiple sequence alignment

Vaccines and other immunological antimicrobial therapy 1

Third line of Defense

Updated Questions and Answers related to the dengue vaccine Dengvaxia and its use

Min Levine, Ph. D. Influenza Division US Centers for Disease Control and Prevention. June 18, 2015 NIBSC

I. Critical Vocabulary

Making Dengue a Vaccine Preventable Disease

A. Study Purpose and Rationale

T cell Vaccine Strategies for HIV, the Virus. With a Thousand Faces

Fayth K. Yoshimura, Ph.D. September 7, of 7 HIV - BASIC PROPERTIES

Biotechnology-Based Vaccines. Dr. Aws Alshamsan Department of Pharmaceutics Office: AA87 Tel:

The challenge of an HIV vaccine from the antibody perspective. Dennis Burton The Scripps Research Institute

Dengue Symptoms Significance in Anti-Dengue Drug Development: Road Less Travelled

VACCINE ENGINEERING Dr.T.V.Rao MD

Chapter 14 Part One Biotechnology and Industry: Microbes at Work

Immune protection against dengue infection. Vaccine performance

Palindromes drive the re-assortment in Influenza A

08/02/59. Tumor Immunotherapy. Development of Tumor Vaccines. Types of Tumor Vaccines. Immunotherapy w/ Cytokine Gene-Transfected Tumor Cells

SEROLOGICAL DIAGNOSIS OF DENGUE INFECTIONS

Introduction. In the past 15 years, several technological advancements have open new perspectives and applications in the field of vaccinology.

Combinatorial Vaccines for AIDS and other Infectious Diseases

The Influence of Climate Change on Insect. Director Australian Animal Health Laboratory, Geelong

BARDA INFLUENZA PROGRAM OVERVIEW

For questions 1-5, match the following with their correct descriptions. (24-39) A. Class I B. Class II C. Class III D. TH1 E. TH2

SEQUENCE FEATURE VARIANT TYPES

The humoral immune responses to IBV proteins.

Boosts Following Priming with gp120 DNA

DOI: /ICJ poliovirus. [14] (Hand-Foot-Mouth Disease, HFMD) (Herpangina) 71 [26] [17,18,27] 71 picornaviridae

Pre-clinical Development of a Dengue Vaccine. Jeremy Brett Sanofi Pasteur, Singapore

Identification of Mutation(s) in. Associated with Neutralization Resistance. Miah Blomquist

Identification and characterization of merozoite surface protein 1 epitope

Section Lectures: Immunology/Virology Time: 9:00 am 10:00 am LRC 105 A & B

Fondation Merieux J Craig Venter Institute Bioinformatics Workshop. December 5 8, 2017

Current Strategies in HIV-1 Vaccine Development Using Replication-Defective Adenovirus as a Case Study

Antigen Presentation to T lymphocytes

Use of BONSAI decision trees for the identification of potential MHC Class I peptide epitope motifs.

GOVX-B11: A Clade B HIV Vaccine for the Developed World

IMMUNOINFORMATICS: Bioinformatics Challenges in Immunology

An Exploration to Determine if Fab Molecules are Efficacious in Neutralizing Influenza H1 and H3 Subtypes. Nick Poulton June September 2012

Lines of Defense. Immunology, Immune Response, and Immunological Testing. Immunology Terminology

By:Reham Alahmadi NOV The production of antibodies and vaccination technology

Our T cell epitope test service offers you:

24 26 January 2013, Hong Kong SAR, CHINA. TITLE from VIEW and SLIDE MASTER February 27, 2013

Vaccines. Vaccines ( continued 1) February 21, 2017 Department of Public Health Sciences

Supplementary Figure 1. ALVAC-protein vaccines and macaque immunization. (A) Maximum likelihood

Flaviviruses New Challenges, New Vaccines

NIH Public Access Author Manuscript Immunogenetics. Author manuscript; available in PMC 2014 September 01.

Haemogram profile of dengue fever in adults during 19 September 12 November 2008: A study of 40 cases from Delhi

! " ## $%& &'( ) Page 1 of 7

Exploring HIV Evolution: An Opportunity for Research Sam Donovan and Anton E. Weisstein

Profiling HLA motifs by large scale peptide sequencing Agilent Innovators Tour David K. Crockett ARUP Laboratories February 10, 2009

Synthetic Genomics and Its Application to Viral Infectious Diseases. Timothy Stockwell (JCVI) David Wentworth (JCVI)

RAISON D ETRE OF THE IMMUNE SYSTEM:

CHAPTER 18: Immune System

Under the Radar Screen: How Bugs Trick Our Immune Defenses

Development of Recombinant MERS-CoV Spike (S) Nanoparticle Vaccine

Dengue Vaccine Butantan Institute. DCVMN, Bangkok, 2015

Mapping population & genotype/serotype diversity: game changers in designing viral vaccines

Modeling the Antigenic Evolution of Influenza Viruses from Sequences

Adenovirus-mediated gene therapy against viral biothreat agents

Topics in Parasitology BLY Vertebrate Immune System

DETECTION OF DENGUE INFECTION BY COMBINING THE USE OF AN NS1 ANTIGEN BASED ASSAY WITH ANTIBODY DETECTION

A Multicistronic DNA Vaccine Induces Significant Protection against Tuberculosis in Mice and Offers Flexibility in the Expressed Antigen Repertoire.

PART A. True/False. Indicate in the space whether each of the following statements are true or false.

BBS 2711 Virology. Virus Vaccines

Definition of MHC supertypes through clustering of MHC peptide binding repertoires

DIAGNOSTICS ALGORITHMS IN DENGUE INFECTIONS

SURVEILLANCE TECHNICAL

Clinical Trials of Pandemic Vaccines: Key Issues. John Treanor University of Rochester Rochester, NY

Foot and Mouth Disease Control, A vaccine perspective. John Barlow, DVM PhD

5 Cell recognition and the immune system Support. AQA Biology. Cell recognition and the immune system. Specification reference. Learning objectives

VIRUSES. Biology Applications Control. David R. Harper. Garland Science Taylor & Francis Group NEW YORK AND LONDON

Mutants and HBV vaccination. Dr. Ulus Salih Akarca Ege University, Izmir, Turkey

Memory NK cells during mousepox infection. Min Fang, Ph.D, Professor Institute of Microbiology, Chinese Academy of Science

Transcription:

Indian J Med Res 144, October 2016, pp 587-591 DOI: 10.4103/0971-5916.200894 Quick Response Code: Computational prediction of immunodominant antigenic regions & potential protective epitopes for dengue vaccination Karthikeyan Muthusamy, Krishnasamy Gopinath & Dharmalingam Nandhini Department of Bioinformatics, Science Campus, Alagappa University, Karaikudi, India Received March 2, 2014 Background & objectives: Epitope-based vaccines (EVs) are specific, safe and easy to produce. However, vaccine failure has been frequently reported due to variation within epitopic regions. Therefore, development of vaccines based on conserved epitopes may prevent such vaccine failure. This study was undertaken to identify highly conserved antigenic regions in the four dengue serotypes to produce an epitope-based dengue vaccine. Methods: Polyprotein sequences of all four dengue serotypes were collected and aligned using MAFFT multiple sequence alignment plugin with Geneious Pro v6.1. Consensus sequences of the polyproteins for all four dengue serotypes were designed and screened against experimentally proven epitopes to predict potential antigenic regions that are conserved among all four dengue serotypes. Results: The antigenic region VDRGWGNGCGLFGKG was 100 per cent conserved in the consensus polyprotein sequences of all four dengue serotypes. Fifteen experimentally proven epitopes were identical to the immunodominant antigenic region. Interpretation & conclusions: Computationally predicted antigenic regions may be considered for use in the development of EVs for protection against dengue virus. Such vaccines would be expected to provide protection against dengue infections caused by all dengue serotypes because these would contain antigenic regions highly conserved across those serotypes. Therefore, the immunodominant antigenic region (VDRGWGNGCGLFGKG) and 15 potential epitopes may be considered for use in dengue vaccines. Key words Antigenic region - dengue infection - epitope-based vaccine - sequence analysis Dengue viruses (DENVs) are the aetiologic agent for dengue fever and can sometimes cause fatal haemorrhagic fever/shock syndrome. It is estimated that 50-100 million dengue infections occur annually in over 100 endemic countries 1. In India, 201771 infected cases and 920 deaths were reported from 2007 to 2013 and in 2015 alone, 99913 infected cases were reported 2. Vaccination may be the most viable option to prevent dengue infection, but unfortunately, it has been largely unsuccessful in preventing outbreaks. The ideal dengue vaccine must induce long-term immunity against all the four dengue serotypes. Even after more than 60 yr of research, a licensed vaccine against dengue is still elusive. At present, only candidate dengue vaccines 587

588 INDIAN J MED RES, OCTOBER 2016 are available. Candidate dengue vaccine development has been based on a variety of technologies including live attenuated virus, recombinant proteins, chimeric vaccines, recombinant viral vectors and DNA vaccines 3. Epitopes are the regions of an antigen that are bound by antigen-specific adaptive immune membrane receptors on lymphocytes or by secreted antibodies. Epitopes provide valuable information for disease prevention, diagnosis, treatment 4, and also represent a new strategy for prophylactic and therapeutic induction of pathogenspecific immunity 5. Epitope-based vaccines (EVs) can induce both cellular (T-cell epitopes) and humoral (B-cell epitopes) immune responses. T-cell epitopes bind to major histocompatibility complex (MHC) and interact with the T-cell receptor, stimulating a T-cell response. B-cell epitopes interact with antibodies. Recognition of the appropriate peptide- MHC complexes by the antigen-specific receptor of T-lymphocytes leads to cell proliferation and a cascade of cellular immune responses 6. MHC-II molecules bind longer peptides (15-20 amino acids), whereas MHC-I molecules bind shorter peptides (9 amino acids or less) 7. Prediction of such epitope binding is critical. Hence, various computational algorithms and methods have been used for the prediction of epitopes 8 and mostly conserved epitopes should be considered for vaccine development 9. The identification of immunodominant antigenic regions has significantly advanced the development of peptide-based vaccines. Potential advantages of the epitope-based approach include increased safety and the opportunity to rationally engineer epitopes for increased potency and breadth 10. Antigenic regions induce a more specific immune response to a disease compared to other vaccination methods 11. Computational approaches in predicting epitopes are an alternative to the expensive experimental discovery. However, computational methods may not predict functional protective epitopes due to variation among epitopic regions across strains or serotypes. Advantages of computational prediction of highly conserved epitopes for use in vaccines are the time saved and the safety of such vaccines 10. This study was aimed to identify highly conserved antigenic regions in the four dengue serotypes (DENV1, DENV2, DENV3, DENV4) with the goal to develop an epitope-based dengue vaccine. Experimentally proven epitopes were used to identify potential immunodominant antigenic regions. These antigenic regions were identified by screening experimentally proven epitopes against consensus sequences from all four dengue polyproteins. Material & Methods Computational methods were carried out in the Pharmacogenomics and Computer Aided Drug Design (CADD) Laboratory, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India, during March-November 2013. Sequence collection: To identify the conserved antigenic regions, all available DENV whole genomes for the four dengue serotypes were retrieved from the NCBI Nucleotide database 12. Genomic regions, encoding for the polyproteins, were identified from GenBank (www.ncbi.nlm.nih.gov/genbank/). The respective protein sequences were retrieved from the NCBI protein database (www.ncbi.nlm.nih.gov/ protein) and saved in FASTA format (www.ncbi.nlm. nih.gov/blast/fasta.shtml) These four sets of protein sequences were used for the generation of consensus sequences from multiple sequences. Multiple sequence alignment: Geneious Pro v6.1 (Biomatters Ltd, New Zealand) 13 was used for multiple sequence alignment (MSA) and consensus sequence generation from all four sets of polyprotein sequences. MSA was generated for polyproteins of all four serotypes using MAFFT Plugin 14. FFT-NS1 algorithm 15, BLOSUM 62 was selected and Gap open penalty - 1.53 and Offset value 0.123 was set to generate MSA. MSA of four dengue serotype with 100 per cent identical sites (amino acid residue matching in all the sequences) was used for the generation of consensus sequence. Collection of experimentally proved positive epitopes: The positive epitopes were collected from Immune Epitopes Database (IEDB) 16 which contains both positive and negative experimental results, to predict potential immunodominant antigenic regions in all serotypes. Experimentally proven positive epitope sequences were collected as these were previously shown to be recognized in the context of an immune response against the pathogen. The IEDB includes epitopes from all four serotypes (DENV1, DENV2, DENV3 and DENV4). The information associated with positive epitopes was exported as an excel file from the results page. This information included Epitope ID, peptide sequence, source antigen and source organism. Epitope sequences were collected from the exported excel file and saved in FASTA format. Dengue type, Epitope ID and position in polyprotein were added to the FASTA description line.

MUTHUSAMY et al: EPITOPES FOR DENGUE VACCINATION 589 Identification of potential immunodominant antigenic region: The IEDB analysis resource was used to predict the antigenic region 17 conserved in all dengue serotypes. The epitope conservancy analysis tool was used to calculate the degree of conservancy of an epitope within a given protein sequence set at different degrees of sequence identity 18. The positive epitopes of each serotype were screened against its respective consensus sequence. Epitopes having 100 per cent identity with its target consensus sequence were collected and saved in a single FASTA file. These collected sequences were again screened against all consensus sequences to predict antigenic regions conserved in all dengue serotypes. Epitopes having 100 per cent identity with four consensus sequences were considered highly conserved, and thus ideal candidates for EVs useful against all serotypes during dengue viral infection. Results Sequence collection and generation of consensus sequence: All available whole genomes were retrieved from the NCBI nucleotide database. Whole genome information for the four types was collected in GenBank format. All four serotypes were differentiated, and protein sequences were collected from coding DNA sequences row with the sequences saved in FASTA format. Collected sequences were saved as separate files with FASTA extension. MSAs were generated for each serotype, and these MSAs were used to generate consensus sequences for further prediction of a conserved antigenic region among all serotypes. Collection of experimentally proved positive epitopes: The IEDB includes both positive and negative epitopes; however; only positive epitopes (DENV1: 168, DENV2: 645, DENV3: 119 and DENV4: 79) were used to screen for potential antigenic regions. Identification of immunodominant antigenic regions: To predict a conserved antigenic region among all four serotypes, epitopes from each dengue serotype (DENV1-289, DENV2-1290, DENV3-315 and DENV 4-95) were screened against their respective consensus sequence to find the degree of similarity between these sequences. Epitope sequences having 100 per cent identity with the target sequences were collected. A sum of 87 epitopes (DENV1-2, DENV2-55, DENV3-6 and DENV4-24) with 100 per cent identity to the target sequence were identified. These 87 sequences were combined together in a single FASTA file. These sequences were screened against all four serotypes to find the conserved immunodominant antigenic region. The antigenic region VDRGWGNGCGLFGKG was found to have no genetic variation and was conserved in all of the dengue polyproteins. This region was considered immunodominant as it was conserved in all four dengue serotypes. It is expected that this region will not allow antigenic resistance. Among these 87 sequences, only 15 epitopes had 100 per cent identity with this immunodominant antigenic region. These 15 epitopes were from the envelope glycoprotein and were experimentally tested against DENV with positive responses. Since this region was conserved among all four serotypes, it was expected to be potentially protective against all four dengue types. The Table provides information about predicted 15 potential immunodominant antigenic regions along with the experimental details obtained from the IEDB for the predicted epitopes 19-22. Discussion Traditional vaccines are based on the intact pathogen, either inactivated or live attenuated, or proteins 23. EVs are able to immunize using the minimal structure, consisting of a well-defined antigen, with respect to its antigenicity and immunogenicity. However, in clinical usage, some EVs have failed to protect against pathogens. Therefore, development of a vaccine based on conserved epitopes may avoid such antigenic resistance 24. Computational approaches in predicting T-cell epitopes and analysis of sequence conservation have been widely used for the epitope-based vaccination against various pathogens such as HIV-1, Vaccinia virus, DENV, West Nile virus, bacteria and protozoan parasites 25. It is a powerful alternative strategy for the experimental discovery of candidate epitopes which is less expensive 26. The major concern with the experimental approach for screening of positive epitopes is that these may be too narrow in scope and may not be effective against other pathogens or in different hosts. This challenge for EVs may be overcome by identifying epitopes conserved across multiple strains/serotype of an organism to protect against a genetically diverse community 27. The in silico prediction of potential immunodominant antigenic regions is another approach that can greatly reduce the time and effort involved in screening potential epitopes 28. Thus, this approach can be used as a template for the analysis of other pathogens, providing a novel and generalized approach to the formulation of EVs that are effective against a broad diversity of pathogens 27.

590 INDIAN J MED RES, OCTOBER 2016 Table. Details concerning predicted 15 potential immunodominant antigenic regions that have 100 per cent identity with all four serotypes Epitope number IEDB epitope ID Source organism 1 23250 YFV and dengue fever virus Epitope sequence Position in polyprotein Model system Detection method GWGNGCGLF 381-389 Transgenic mice Peptide HLA I binding assay 19 2 6309 DENV2 virus env gp CGLFGK 385-390 Human Solid phase binding assay & anti peptide immunoassay 20 3 9978 DENV2 virus env gp DRGWGN 378-383 Human Solid phase binding assay & 4 18866 DENV2 virus env gp GCGLFG 384-389 Human Solid phase binding assay & 5 20850 DENV2 virus env gp GLFGKG 386-391 Human Solid phase binding assay & 6 21431 DENV2 virus env gp GNGCGL 382-387 Human Solid phase binding assay & 7 23249 DENV2 virus env gp GWGNGC 380-385 Human Solid phase binding assay & anti peptide immunoassay 20 8 43963 DENV2 virus env gp NGCGLF 383-388 Human Solid phase binding assay & 9 54016 DENV2 virus env gp RGWGNG 379-384 Human Solid phase binding assay & 10 68057 DENV2 virus env gp VDRGWG 377-382 Human Solid phase binding assay & 11 72483 DENV2 virus env gp WGNGCG 381-386 Human Solid phase binding assay & 12 72484 DENV2 non structural 1 and env gp WGNGCGLFG 381-389 BALB/c mice and mice 13 9979 DENV2 virus env gp DRGWGNGC 378-385 Human, mouse and rabbit 14 43964 DENV2 virus env gp NGCGLFGK 383-390 Human, mouse and rabbit 15 78288 DENV2 virus non structural 1 and env gp ELISA and immunoblot assays 21 ELISA 22 ELISA 22 VDRGWGNGC 377-385 BALB/c mice ELISA and immunoblot assays 21 YEV, yellow fever virus; IEDB, Immune Epitopes Database; HLB, human leucocyte antigen; DENV, dengue viruses; env gp: Envelope glycoprotein In this study, an immunodominant antigenic region VDRGWGNGCGLFGKG was found to be conserved (100%) in all dengue polyprotein sequences. Conserved regions in the viral protein sequences are the prime target for EV formulation 29. The use of this antigenic region in EVs would confer broader protection across multiple strains 30 because these antigenic regions are conserved throughout the evolution. In this study 15 experimentally proven epitopes with 100 per cent identity to this immunodominant antigenic region were identified. Since the immunodominant antigenic region is conserved in all dengue serotypes, it is anticipated that identified peptides from DENV1 and DENV2 would also elicit immune responses against all other serotypes. The antigenicity of the identified polypeptide was previously verified using the ELISA method 19-22. Hence, these 15 polypeptides may be considered as potentially protective epitopes for use in dengue vaccination and are expected to overcome the problem of the shift of antigenic sites due to drug resistance and may also have the potential to trigger an immune response against all four dengue serotypes. Acknowledgment Authors acknowledge the infrastructure facilities provided by the Department of Bioinformatics, Alagappa University, Postgraduate Diploma in Structural Pharmacogenomics (funded by UGC, Government of India).

MUTHUSAMY et al: EPITOPES FOR DENGUE VACCINATION 591 Conflicts of Interest: None. References 1. Dengue and severe dengue. Geneva: World Health Organization; 2014. Available from: http://www.who.int/ mediacentre/factsheets/fs117/en/, accessed on January 8, 2014. 2. Dengue. Delhi: Directorate of National Vector Borne Disease Control Programme (NVBDCP); Available from: http://www. nvbdcp.gov.in/den-cd.html, accessed on January 13, 2016. 3. Whitehead SS, Blaney JE, Durbin AP, Murphy BR. Prospects for a dengue virus vaccine. Nat Rev Microbiol 2007; 5 : 518-28. 4. Goldsby RA, Kindt TJ, Kuby J, Osborne BA. Immunology. 5 th ed. New York: W. H. Freeman; 2002. 5. Khan AM, Miotto O, Heiny AT, Salmon J, Srinivasan KN, Nascimento EJ, et al. A systematic bioinformatics approach for selection of epitope-based vaccine targets. Cell Immunol 2006; 244 : 141-7. 6. Rothbard JB, Gefter ML. Interactions between immunogenic peptides and MHC proteins. Annu Rev Immunol 1991; 9 : 527-65. 7. McFarland BJ, Beeson C. Binding interactions between peptides and proteins of the class II major histocompatibility complex. Med Res Rev 2002; 22 : 168-203. 8. Bremel RD, Homan EJ. An integrated approach to epitope analysis I: dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches. Immunome Res 2010; 6 : 7. 9. Good MF, Berzofsky JA, Miller LH. The T cell response to the malaria circumsporozoite protein: an immunological approach to vaccine development. Annu Rev Immunol 1988; 6 : 663-88. 10. Sette A, Keogh E, Ishioka G, Sidney J, Tangri S, Livingston B, et al. Epitope identification and vaccine design for cancer immunotherapy. Curr Opin Investig Drugs 2002; 3 : 132-9. 11. Toussaint NC, Kohlbacher O. Towards in silico design of epitope-based vaccines. Expert Opin Drug Discov 2009; 4 : 1047-60. 12. Nucleotide. Bethesda, MD: National Library of Medicine (US); 1999. Available from: https://www.ncbi.nlm.nih.gov/ nuccore, accessed on July 15, 2013. 13. Drummond AJ, Ashton B, Buxton S, Cheung M, Cooper A, Duran C, et al. Geneious 5.4 [Software Created by Biomatters Ltd., Auckland, New Zealand], 2011. Available from: http:// www.geneious.com, accessed on October 9, 2014. 14. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 2013; 30 : 772-80. 15. Feng DF, Doolittle RF. Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 1987; 25 : 351-60. 16. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, et al. The immune epitope database 2.0. Nucleic Acids Res 2010; 38 : D854-62. 17. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, et al. Immune epitope database analysis resource. Nucleic Acids Res 2012; 40 : W525-30. 18. Bui HH, Sidney J, Li W, Fusseder N, Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics 2007; 8 : 361. 19. Lund O, Nascimento EJ, Maciel M Jr., Nielsen M, Larsen MV, Lundegaard C, et al. Human leukocyte antigen (HLA) class I restricted epitope discovery in yellow fewer and dengue viruses: importance of HLA binding strength. PLoS One 2011; 6 : e26494. 20. Innis BL, Thirawuth V, Hemachudha C. Identification of continuous epitopes of the envelope glycoprotein of dengue type 2 virus. Am J Trop Med Hyg 1989; 40 : 676-87. 21. Falconar AK. Monoclonal antibodies that bind to common epitopes on the dengue virus type 2 nonstructural-1 and envelope glycoproteins display weak neutralizing activity and differentiated responses to virulent strains: implications for pathogenesis and vaccines. Clin Vaccine Immunol 2008; 15 : 549-61. 22. Aaskov JG, Geysen HM, Mason TJ. Serologically defined linear epitopes in the envelope protein of dengue 2 (Jamaica strain 1409). Arch Virol 1989; 105 : 209-21 23. Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol 2013; 3 : 120139. 24. Claesson MH. Why current peptide-based cancer vaccines fail: lessons from the three Es. Immunotherapy 2009; 1 : 513-6. 25. Desai DV, Kulkarni-Kale U. T-cell epitope prediction methods: an overview. Methods Mol Biol 2014; 1184 : 333-64. 26. Sánchez-Burgos G, Ramos-Castañeda J, Cedillo-Rivera R, Dumonteil E. Immunogenicity of novel dengue virus epitopes identified by bioinformatic analysis. Virus Res 2010; 153 : 113-20. 27. Bremel RD, Homan EJ. An integrated approach to epitope analysis II: a system for proteomic-scale prediction of immunological characteristics. Immunome Res 2010; 6 : 8. 28. De Groot AS, Berzofsky JA. From genome to vaccine New immunoinformatics tools for vaccine design. Methods 2004; 34 : 425-8. 29. Tong JC, Ren EC. Immunoinformatics: current trends and future directions. Drug Discov Today 2009; 14 : 684-9. 30. Staneková Z, Varecková E. Conserved epitopes of influenza A virus inducing protective immunity and their prospects for universal vaccine development. Virol J 2010; 7 : 351. Reprint requests: Dr Karthikeyan Muthusamy, Department of Bioinformatics, Science Campus, Alagappa University, Karaikudi 630 004, Tamil Nadu, India e-mail: mkbioinformatics@gmail.com