alexa Immunoinformatics: Screening of Potential T Cell Antigenic Determinants in Proteome of H1N1 Swine Influenza Virus for Virus Epitope Vaccine Design | OMICS International
ISSN: 0974-276X
Journal of Proteomics & Bioinformatics
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

Immunoinformatics: Screening of Potential T Cell Antigenic Determinants in Proteome of H1N1 Swine Influenza Virus for Virus Epitope Vaccine Design

Pawan Sharma and Ajay Kumar*

Department of Biotechnology, Institute of Biomedical Education and Research, Mangalayatan University, Aligarh (U.P), India

*Corresponding Author:
Dr. Ajay Kumar
Department of Biotechnology
Institute of Biomedical Education and Research
Mangalayatan University, Aligarh (U.P)
Tel: 91-09412883081
E-mail: [email protected]

Received Date: July 22, 2010; Accepted Date: September 22, 2010; Published Date: September 24, 2010

Citation: Sharma P, Kumar A (2010) Immunoinformatics: Screening of Potential T Cell Antigenic Determinants in Proteome of H1N1 Swine Influenza Virus for Virus Epitope Vaccine Design. J Proteomics Bioinform 3: 275-278. doi: 10.4172/jpb.1000151

Copyright: © 2010 Sharma P, 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 Proteomics & Bioinformatics


Influenza (H1N1) is a highly contagious respiratory pathogen that continues to evolve and threaten both veterinary and human public health. Presently existing vaccines against in fl uenza (H1N1) are based on the generation of secondary response in form of neutralizing antibody primarily directed against surface proteins– hemagglutinin and neuraminidase. In this work, Propred and Propred I immunoinformatics tools have been used to predict the T cell epitopes from seven putative protein viz. Hemagglutinin ,neuraminidase, polymerase PA ,nucleocapsid protein, matrix protein, polymerase PB1, polymerase PB2 of in fl uenza virus A/Minnesota/2009 (H1N1). Total of 15 epitopes were predicted for HLA class I and 14 epitopes for HLA class II molecules. These epitopes are found top scoring peptides and showed binding with maximum number of HLA alleles. The threshold percent taken in this analysis was 4% to select high af fi nity peptides by Propred and Propred I. The predicted epitopes may be served as a useful diagnostic reagent for evaluating T-cell responses in the context of natural infection and also might be helpful for designing of either a DNA vaccine or a subunit vaccine against H1N1in fl uenza.


Neuraminidase; Hemagglutinin; H1N1; Influenza A virus; T cell epitopes


The H1N1 viral strain implicated in the 2009 flupandemic among humans often is called “swine flu” (New York Times, 2009). The 2009 H1N1 virus is not zoonotic swine flu, as it is not transmitted from pigs to humans, but from person to person (Trifonov et al., 2009). Clinical features of H1N1 swine flu are body aches, especially joints and throat, extreme coldness and fever, fatigue, headache, irritated watering and reddened eyes. In children, gastrointestinal symptoms such as diarrhea and abdominal pain may occur (CDC, 2009).

Swine influenza A viruses are RNA viruses with a segmented genome that is comprised of eight negative-sense, single-stranded RNA segments, belong to the viral family of Orthomyxoviridae. These eight segments encode eleven proteins (Brockwell-Staats et al., 2009). The polymerase complex includes the PB2, PB1 and PA proteins as well as the nucleoprotein (NP). There are two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA) (Brockwell-Staats et al., 2009). Swine flu viruses are causing a huge amount of death to both human and swine. The World Health Organization (WHO) figures show that worldwide more than 209 countries and overseas territories or communities have reported laboratory confirmed cases of pandemic influenza H1N1 2009, including at least 15174 deaths (WHO, 2010). Protection against influenza infection is conferred by neutralizing antibody for the two surface proteins, namely the hemagglutinin (HA) and the neuraminidase(NA) (Luke and Subbarao, 2006). It has been difficult to develop a vaccine for H1N1 influenza A virus that provides long lasting immunity. This is due to the antigenic drift of the virus where the circulating strain in an infectious cycle is different from the previously circulating strain (Thomas et al., 2006; Boni et al., 2006). Current inactivated vaccines provide essential protection when the vaccine antigen and the circulating viruses share high degree of similarity in the structural protein. Since new influenza virus antigenic variants emerge frequently from accumulation of point mutations in the structural protein, influenza vaccine antigens need to be updated frequently.

The preparation of a vaccine against H1N1 influenza needs some basic considerations about the working of the H1N1 influenza vaccine. Hemagglutinin protein is responsible for attachment of the virus to the sialic acid α-2,3 or α-2,6 galactose sugar receptor on the human host cell surface (Wan and Perez, 2006). It is reasonable to presume that influenza vaccines do not generate antibodies against the receptor binding region of the protein as this region is not subjected to much antigenic drift which would seriously compromise the infectivity of the virus. In fact, mutation in this region has resulted in change in sugar specificity leading to change of host specificity, and loosing infectivity for the original host species (Wan and Perez, 2006). Antibodies directed against this region therefore are likely to provide protection against the influenza strains.

T cell immunity has been implicated in rapid clearance of influenza virus (Thomas et al., 2006). This means that the individual with good T cell response would suffer from milder form of the disease, get cured sooner, which is reflected in decreased fatality and less spread of the virus in population. Therefore, a vaccine generating robust T cell immunity against influenza needs serious attention. Good T cell immunity along with antibody response focused on receptor binding region of the hemagglutinin protein and enzymatic active site of neuraminidase, would meet the needs of a vaccine. Especially the internal proteins contain many conserved peptides which are potential T cell antigens and hence need serious consideration as T cell focused vaccine candidates.

In this communication, we have computationally analyzed the proteome of H1N1 influenza (A/Minnesota/2009) [NCBI] to identify putative epitopes for the formulation of a vaccine for T cell immunity. This vaccine should cover the HLA haplotype of the target population, be effective against influenza A strains and generate good immune memory response. A variety of computational tools are now available for prediction of T cell epitopes (Korber et al., 2006). We have analyzed overlapping nonameric peptides of all the proteins of H1N1 influenza virus for binding to human HLA class I molecules by PROPRED I algorithm, and for binding to class II alleles by PROPRED algorithm to select peptides for develop a robust T cell vaccine. Synthethic peptides can use as vaccines to induce either humoral or cell mediated immunity requires an understanding of the nature of T cell and B cell epitopes has been reported (Singh and Raghava, 2001; Bhasin et al., 2003; Singh and Raghava, 2003).

Materials and Methods

Hemagglutinin (ADD21431), neuraminidase (ACQ76351), polymerase PA (ACU13105), Polymerase PB1(ACQ76349), Polymerase PB2(ACQ76350), Nucleocapsid protein (ACR38842), matrix protein (ACU44211) are proteins of H1N1 2009 Minnesota strain have been used for this imminoinformatic analysis. The complete sequences of H1N1 2009 Minnesota strain are available in the NCBI protein database( Physicochemical analysis such as molecular weight and isoelectric point (pI) were also analyzed using ExPasy (

All the seven structural proteins were analyzed for potential T cell epitope using immunoinformatic tools. Propred and propred I were used to analyse binding of all over lapping peptides to all HLA class I and class II alleles. This tools helped to identify those antigenic determinants peptides in all seven proteins which binds to several HLA molecules with good binding affinity.

All these peptides were predicted on the 4% threshold value with highest binding score with HLA class I and HLA class II molecule. The propred algorithms which predict binding of nonameric peptides to HLA alleles. Those nonameric peptides have highest binding affinity and maximum coverage of HLA alleles were selected.

Result and Discussion

In order to produce library of H1N1 peptides to determine the antigenic determinants for vaccine design. We analyzed the whole proteome of H1N1 2009 Minnesota strain by propred and propred I immunoinformatic tools. In this study all seven proteins of H1N1 were also analyzed for physicochemical analysis such as molecular weight and isoelectric point. The polymerase PB1 protein and Matrix protein has highest molecular weight (86.35 kDa) and lowest molecular weight (27.82 kDa) respectively. Isoelectric points (pI) of these proteins were ranged between 5.42-9.49 (Table 1). pI value of any protein indicates the stability of proteins in that particular isoelectric points. For predictions of potential T cell antigenic determinants in proteome of H1N1, we took seven putative proteins sequences of H1N1 2009 strain was divided into all possible nonamers. Each peptide undergone for binding analysis with all HLA alleles. Those peptide shown highest binding score and also coverage of maximum number of HLA alleles were selected as potent immunodominant epitopes for vaccine design. Those peptides which have higher affinity for HLA molecules are more likely to be recognized by TCR of specific T cells. Binding specificity of peptides to HLA Class I and Class II molecule by propred I and propred were analyzed at 4% threshold value respectively (Table 2 and Table 3). A total of 15 and 14 peptides were predicted as potent antigenic determinants, presented by HLA class I and II supertypes respectively. Out of these 29 peptides ‘FTTANADTL’ (Hemagglutinin), ‘GQSVVSVKL’ (Neuraminidase) peptides showed maximum binding with HLA class I molecules and for HLA class II molecules maximum binding peptides are ‘VVLLYTFTT’ (Hemagglutinin), ‘MRAIGTHPS’ (Matrix protein) and ‘LRILVRGNS’ (Polymerase PB2).

S.No Putative Proteins Accession No. Expected M.W(kDa) pI Value
1 Hemagglutinin ADD21431 31.78 8.42
2 Neuraminidase ACQ76351 51.60 6.11
3 Polymerase PA ACU13105 82.65 5.42
4 Polymerase PB1 ACQ76349 86.35 9.30
5 Polymerase PB2 ACQ76350 85.86 9.49
6 Matrix Protein ACU44211 27.82 9.40
7 Nucleocapsid Protein ACR38842 55.99 9.38

Table 1: Physicochemical properties of different putative proteins of influenza H1N1.

Hemagglutinin FTTANADTL 11-19 HLA-A1, A2, A*0201, A*0205, A*1101, A3, A*3101, A*3302, A20, B*2702, B*2705, B*5101, B*5102, B*5301, B*5401, B*51, B60, B61,B7, B8
STDTVDTVL 28-36 HLA-A1, A*1101,  A3, A*3101,  A68.1,  A2.1, B*2702 B*2705,  B*3701, B*3801,  B*3901 B*3902,B*4403,B*5103, B*5201,   B*5801, B60
Neuraminidase TIGMANLIL 15-23 A1, A*0201, A*0205 A*1101, A24, A3, A*3101, A*3302, A68.1, B*3801, B*3901, B*3902, B62, B7, B8
AIYSKDNSV 97-105  A*0205, A*1101, A3, A*3101, A*3302, A68.1, A2.1,  B*2705, B*5101, B*5102, B*5103, B*5201, B*5401, B62,  B*0702, B8
GQSVVSVKL 76- 84  A2, A*0201, A*0205, A*1101, A24, A3, A*3101,   A20 Cattle, A2.1, B*2702, B*2705, B*3701, B*3801,  B*3902, B40, B*5102, B*5201, B*5401, B60, B62, B7
Polymerase PA KLLLIVQAL 663-671 A2, A*0201, A*0205, A*1101, A24, A3, A*3101,  A2.1, B14, B*2702, B*2705, B*3501, B*5102, B*3901,B62, B*0702
FLLMDALKL 281-289 A2, A*0201, A*0205 A*1101, A3, A*3101, A2.1,B*5102, B*5301, B*5401, B*51, B62
Polymerase PB1 FVANFSMEL 500-509  A2, A*0201, A*0205, A*1101,A3,A68.1, A2.1, B*5301, B*5401, B*51, B7
STVLGVSIL 415-423  A2, A*3101, A68.1, B14, B*3901, B40, B*4403, B*5201, B*5801,B60, B62
Polymerase PB2 VLTGNLQTL 343-351  A2, A*0201, A*0205, A3, A2.1,B*3801, B*3901 B62
VVFPNEVGA 165-173 A1, A2, A*0201, A*0205, A*1101, A*3101, A3, A68.1, B*5301, B*5401, B*51
Matrix protein GILGFVFTL 57-65 A2, A*0201, A*0205, A*1101, A24, A3, A*3101,  A2.1, B14, B*2705, B*5101, B*5102, B*5103,  B*5401, B62, B7
NNMDKAVKL 90-98 A*0201, A*0205, A24,B*2705, B*3501, B*3801, B*3901, B*4403, B*5102,B60,B7,B8
Nucleocapsid protein CLPACVYGL 274-282 A2, A*0201, A*0205,A24, A3, A*3101, A2.1, B14,B*3901, B*3902,B7
FQGRGVFEL 457-465 A*0201, A*0205,A*3101, A2.1,B*2702, B*2705,B*3902, B*5301, B*5401, B*51,B60, B62

Table 2: Selected peptides of H1N1 proteome and their HLA class allele coverage (The given peptides are predicted to bind to the depicted HLA class I molecules, PROPRED I~4%).

Protein T cell epitopes Amino acid position Predicted binding to HLA Class II alleles
Hemagglutinin LKGVAPLHL 60-68 DRB1_0101, 0102,0701,0703,1104,1106,1128,1305,1311,1321,1501,1506, DRB5_0101, DRB5_0105
VVLLYTFTT 5-13 DRB1_0101, 0102,0301, 0305, 0306, 0307, 0308,0309, 0311 0401,0402,0404, 0405,0408,0410,0421,0423,0426,0801,0804,0806, 0813, 0817, 1101,1102, 1104,1106,1107,1114, 1120,1121, 1128, 1301,1302, 1304, 1305,1307, 1311,1321, 1322,1327,1328,1501,1502,1506
Neuraminidase FVIREPFIS 114-122 DRB1_0305,0309,0801,0802,0804,0813, 0817, 1101,1102, 1104,1106, 1107, 1114, 1120,1121, 1128,1302,1304, 1305,1311,1321,1322,1323,1327,1328,1502,DRB5_0101, DRB5_0105
VNISNTNFA 66-74 DRB1_0306, 0307, 0308,0311, 0401,0402, 0404,0405,0408,0410,0421,0423,0426,1102,1107,1121,1301, 1322,1327,1328
Polymerase PA FLLMDALKL 281-289 DRB1_0101,0102,0405,0408,0421,0701,0703, 0817,1101,1114, 1128,1305,1307,1321,1323,1501,1502,1506, DRB5_0101, DRB5_0105
VVNFVSMEF 516-524 DRB1_0102, 0301,0402,0404,0405,0408,0410, 0423,0701, 0703,1102, 1104,1106,1121,1128,1301,1305,1311, 1321, 1327, 1328,1501,1502,1506
Polymerase PB1 VRKMMTNSQ 280-288 DRB1_0306, 0307, 0308, 0311, 0401,0402, 0404,0405, 0408, 0410,0421, 0423,0426,0804, 0801, 0802,0806, 0813,1102,1104, 1106,1114, 1121,1301,1304,1307,1311, 1322,1323,1327,1328
MITYITRNQ 315-323 DRB1_ 0402, 0801, 0802, 0804, 0806, 1102, 1114, 1121, 1301,1304, 1322,1323,1327,1328
Polymerase PB2 LRILVRGNS 639-647 DRB1_0301,0305,0306,0307,0308,0309,0311,0801,0802,0804,0806, 0813,0817, 1101,1102,1104,1106,1107, 1114,1121, 1128,1301,1305,1307,1311,1321, 1322,1323,1327,1328,1501,1502,1506
LRISSSFSF 316-324 DRB1_0101,0102,0301,0309,0421,0701,0703,1501,1502,1506, DRB5_0101, DRB5_0105
Matrix protein FVFTLTVPS 61-69 DRB1_0101,0301,0308,0401,0402, 0404,0405,0408,0410,0421,0423,0426,0701,0703, 0802,0813,1101,1114,1120,1128,1302, 1305,1307,1321,1323
MRAIGTHPS 215-223 DRB1_0102,0301,0305,0306, 0307, 0308, 0309,0311,0401,0402,0404,0405,0408,0410,0421, 0423,0426,0802,0804,0806, 0813,1101,1102,1104,1106,1107,1114,1121, 1128,1301,1305,1307,1311,1321, 1322,1323,1327,1328,DRB5_0101, DRB5_0105
Nucleocapsid protein LVWMACHSA 327-335 DRB1_0101, 0102,0402, 0404,0423,1102, 1104,1106, 1114,1121,1311, 1322,1323, DRB5_0101, DRB5_0105
IRMMESAKP 444-452 DRB1_ 0102,0401,0402, 0404,0405,0408,0410,0421,0423,0426,0802,0804, 1101,1102, 1104,1106,1114,1121,1301, 1307,1311,1322,1323,1327,1328

Table 3: Selected peptides of H1N1 proteome and their allele coverage (The given peptides are predicted to bind to the depicted HLA class II molecules, PROPRED~4%).

Highly immunogenic, cross-conserved epitopes can be designed by carefully overlapping conserved and immunogenic 9-mer sequences found in the influenza strains of interest (De Groot et al., 2009). Computational analysis of proteome of H5N1 avian influenza virus to define T cell epitopes with vaccine potential was evaluated for vaccine potential (Parida et al., 2007). Comparative Sequence Analysis on Different Strains of Swine Influenza Virus Sub-type H1N1 for Neuraminidase and Hemagglutinin was also done (Sharma et al., 2010). Inactivated influenza vaccines elicit neutralizing antibody responses that provide reasonable protection against the homologous H1N1, H3N2, and B viruses (Palese, 2006). However, antibody-mediated selection drives changes (known as antigenic drift) in the viral hemagglutinin and neuraminidase surface glycoproteins, which in turn dictate the frequent production of a new vaccine (Peter, 2008).


Immunoinformatics approaches are currently used for prediction of antigenic determinants in the proteins sequence of influenza virus (H1N1) without using their cultures. The prediction of influenza virus nanomer epitope for T cells is recognized against HLA class I and HLA class II. The predicted epitopes may be served as a useful diagnostic reagent for evaluating T-cell responses in the context of natural infection and also might be helpful for designing of either a DNA vaccine or a subunit vaccine against H1N1influenza.


The authors are grateful to Prof. Ashok Kumar (Dean,I.B.M.E.R, Mangalayatan University Aligarh, U.P, India) for providing necessary facilites and encouragement. The authors are also thankful to all faculty members of the Institute of Biomedical Education and Research, Mangalayatan University Aligarh, U.P, India for their generous help and suggestions during the course of experimental work and manuscript preparation.


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

Share This Article

Relevant Topics

Recommended Conferences

  • Proteomics, Genomics and Bioinformatics
    May 16-17, 2018 Singapore City, Singapore
  • Glycobiology, Lipids & Proteomics
    August 27-28, 2018 Toronto, Canada
  • Computational Biology and Bioinformatics
    Sep 05-06 2018 Tokyo, Japan
  • Advancements in Bioinformatics and Drug Discovery
    November 26-27, 2018 Dublin, Ireland

Article Usage

  • Total views: 11920
  • [From(publication date):
    September-2010 - Jan 17, 2018]
  • Breakdown by view type
  • HTML page views : 8127
  • PDF downloads : 3793

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 2018-19
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- 2018 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version