Antigenicity prediction was performed through VaxiJen serv- er. VaxiJen is based on the physicochemical properties of pro- teins without recourse to sequence alignment [10].
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Epitope designing on avian influenza disease
Suchitra kumari
Department of Bioinformatics, NTHRYS Biotech Labs, Hyderabad, India
Abstract— Avian influenza is a disease which caused by influenza virus. Avian influenza is a very variable and highly contagious virus that is widespread in birds, particularly wild waterfowl and shorebird. In this present work, we predicted “FKRTNGSSV” epitope for avian influenza disease and designed 3D structure epitope by using Pepitope tool. Through Bioinformatics study, we predicted that this epitope is able to evoke immunity against avian influenza virus means produce the B cell along T cell. There are total 17 alleles of MHC, which binds with designing epitope. This epitope can be used for new vaccines production, discovery and development of diagnostic and therapeutic antibodies after wet lab validation.
Index Terms— Avian influenza, Bioinformatics, Epitope design, BCpred, TMHMM, ProPred, MHCpred, Pepitope.
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Avian influenza is a highly contagious viral disease .Influenza A viruses infect many different animals, including ducks, chickens, pigs, whales, horses, and seals. Typically infection and transmission among one animal species sometimes can cross over and cause illness in another species [1]. Avian influ- enza is also known as bird flu. The Avian influenza A subtype H5N1 is a highly pathogenic (HPAI) strain of the virus that has been confirmed in poultry populations across Asia, Russia and some southern European countries [1]. Avian influenza results from infection by viruses in the influenza virus A ge- nus and influenza A species of the family Orthomyxoviridae.
In this study, we designed epitope for avian influenza disease by epitope mapping. Epitope mapping is
the process of identification and characterization of the mini- mum molecular structures that are recognized by the Immune System, mainly T and B cells. Most epitopes recognized by antibodies or B cells are three-dimensional surface features of a protein antigen; these features fit precisely and thus bind to antibodies [2]. The new vaccine production, discovery and development of diagnostic are the main objective of epitope mapping.
First, we have selected protein target sequence for our study. We kept some criteria for target protein selection- target pro- tein should be essential and non- homologous with human. There are total 60 protein sequences present in all strains of influenza A virus genome. After scanning of 60 protein se- quences, we got only one target protein sequence.The selected target was showing essential protein or not, is predicted by
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Database of Essential Genes (DEG), database. The functions encoded by essential genes are considered as the foundation of life and these are required for their growth, reproduction and their survival [3]. Non-homology criteria were checked by using human blast (BLASTp). We found Polymerase PB2 is essential for the pathogens and non-homolog with human proteins. So, we have selected polymerase PB2 as a target pro- tein.
After target selection, epitope checked either the target protein lies on transmembrane or not. Prediction of transmembrane was checked by the TMHMM tool [4]. In TMHMM, Protein sequence which lies in the transmembrane region shows anti- genicity. For allergenicity prediction, EVALLER tool is used. EVALLER [5] is a web-tool, used for prediction of allergenicity of amino acid sequence.
Prediction of a B cell epitope of the target sequence is predict- ed by BCPred server. The BCPreds server [6] is used for B-cell epitope prediction.
The ProPred I is an on-line service for identifying the MHC Class I binding regions in antigens. It implements matrices for
47 MHC Class-I alleles, proteasomal and immunoproteasomal models [7]. The aim of ProPred server is to predict MHC Class-II binding regions in an antigen sequence. There are to- tal 51 MHC Class-II alleles present in ProPred server [8] MHCPred is used to predict the binding affinity of major his- tocompatibility complex (MHC) class I and II molecules and also to the transporter associated with Processing [9]
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Antigenicity prediction was performed through VaxiJen serv- er. VaxiJen is based on the physicochemical properties of pro- teins without recourse to sequence alignment [10].
Pepitope server is used to computationally predict epitopes. Pepitope is mainly based on two algorithm namely pepsurf and mapitope. PepSurf aligns each peptide to a graph which represents the surface of the input 3D structure. Mapitope first identifies pairs of residues that are significantly over repre- sented in the panel of peptides, compared to their expected frequencies [11].
The exomembrane sequence was checked by using TMHMM. The whole target protein i.e. Polymerase PB2 (759 amino ac- ids) was outside the membrane [Fig.1] which favors for epitope designing. A good epitope should be cell exposed and this target full- fills this criteria. Allergenicity was predicted by EVALLER tool. The target protein shows no allergenicity. After exomembrane confirmation, we have predicted B cell epitope of target sequence. There are total 13 B cell epitopes were predicted by using BCpred. All 13 B cell epitopes shows the good score value. In the next step, the selection of epitope for T-cell epitope was predicted using ProPred and ProPred I. Considered those T cell epitopes as a part of B cell epitopes and that lies in the transmembrane region. There are 55 epitopes nanomer sequence comes after performing ProPred analysis. After propred II, we predicted that there are total 16 epitopes. Out of 16 epitopes, only one epitope “FKRTNGSSV” is showing antigenicity by vaxiJen [Table.2].
“FKRTNGSSV” is the promising vaccine peptide as it binds to most MHC alleles with good confidence value and showing antigenicity from vaxiJen result. “FKRTNGSSV” epitope binds with 3 alleles DRB1-0101, DRB1-0701 and DRB1- 0703 from ProPred analysis. From ProPred I, our de- sign epitope binds with 14 allele- Hla –A1, HLA-A3, HLA- A*3302, HLA-B40, HLA-B*5301, HLA-B*5401, HLA-B*51, HLA-B*5801, HLA-B61, HLA-B8, MHC-Db. Since total, 17 alleles of MHC-I and MHC-II, binds to our proposed epitope. From MHCpred, the binding affinity of DRB0101 is less than
500nm i.e. 69.82 nm to the epitope. It shows the high binding affinity with FKRTNGSSV.
Fig1:TMHMM result
TABLE .1. BCpred result
EPITOPE | SCORE |
TSESQLTITKEKKEELQDCK | 0.996 |
TFKRTNGSSVKKEEEVLTGN | 0.995 |
TQGTCWEQMYTPGGEVRNDD | 0.989 |
PERNEQGQTLWSKTNDAGSD | 0.979 |
VDHMAIIKKYTSGRQEKNPA | 0.975 |
HGTFGPVHFRNQVKIRRRVD | 0.958 |
AKEAQDVIMEVVFPNEVGAR | 0.957 |
WWNRNGPTTSTVHYPKVYKT | 0.95 |
NGPESVLVNTYQWIIRNWET | 0.942 |
MRILVRGNSPVFNYNKATKR | 0.941 |
PFAAAPPEPSRMQFSSLTVN | 0.927 |
LSPEEVSETQGTEKLTITYS | 0.893 |
GKDAGALTEDPDEGTAGVES | 0.816 |
TABLE.2. VaxiJen result
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Fig 2: Surfaced exposed epitope “FKRTNGSSV” epitope (red color). The target protein chain is represented as a space-filling model colored in grey.
Fig 3: List of residues that the cluster contains and the pep- tides that are aligned to cluster 1.
The structure of polymerase PB2 was retrieved from PDB (PDB ID: 2VY6) for epitope mapping study. Our design epitope is exposed on the outer surface (red colour) [Fig.2]. By using combined algorithm i.e. pepsurf and mapitope, we found four center residues [Fig.3]. Fom Pepsurf algorithm, we got score 7.85 with eight residues: LEU648; ASN652; SER653; ASN657; TYR658; LYS663; ARG664; THR666 from best cluster result. This 7.85 score is very less which indicates that the pre- dicted epitopes have less frequency with respect to the discov- ered epitopes. So, “FKRTNGSSV” may be very novel epitope.
We can say that this epitope can evoke immunity against avian influenza virus and produce the B cell along T cell. This research will provide new insight for accelerating immuno- technology for development of vaccines.
In summary, in this study, we have predicted epitope “FKRTNGSSV”. This epitope can be checked for their predict- ed activity so that avian influenza treatment must meet its importance. This epitope can be used for new vaccine produc- tion, discovery and development of diagnostic and therapeutic antibodies after wet lab validation.
Express my sincere gratitude to Mr. Balaji S Rao, Director, Nthrys Biotech Labs, Hyderabad. Special thanks to Krishna Kant Gupta, Ankit Gupta, Narayana Murthy Lalam for providing their valuable suggestion.
[1] HSE information sheet: www.hse.gov.uk/biosafety/diseases/avianflu.htm
[2] http://www.creative-biostructure.com/others3.htm
[3] Zhang R, Ou HY, Zhang CT. (2004), “DEG: a database of essential genes”, Nucleic Acids Res., 1; 32(Database issue):D271-2.
[4] Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting trans- membrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3): 567–580. doi: 10.1006/jmbi.2000.4315.
[5] Alvaro Martinez Barrio, Daniel Soeria-Atmadja, Anders Nister, Mats G. Gus- tafsson, Ulf Hammerling, and Erik Bongcam-Rudloff ; EVALLER: a web server for in silico assessment of potential protein allergenicity; Nucleic Acids Res. 2007 July; 35(Web Server issue): W694–W700; Published online 2007 May
30. doi: 10.1093/nar/gkm370
[6] El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting linear B-cell epitopes using string kernels. J Mol Recognit. 21(4): 243–255.
[7] Singh H, Raghava GP (2003) ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 19(8): 1009–1014. doi:10.1093/bioinformatics/btg108.
[8] Singh H, Raghava GP (2001) ProPred: prediction of HLA-DR binding sites.
Bioinformatics 17(12): 1236–7. doi: 10.1093/bioinformatics/17.12.1236.
[9] Guan P, Doytchinova IA, Zygouri C, Flower DR (2003) MHCPred: A server for quantitative prediction of peptide-MHC binding, Nucleic Acids Res.
31(13): 3621–3624.
[10] Irini A Doytchinova and Darren R Flower. Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties. Vaccine. 2007 25:856-866.
[11] Mayrose I, Penn O, Erez E, Rubinstein ND, Shlomi T, et al. (2007) Pepitope:
epitope mapping from affinity-selected peptides. 3244–3246. doi:
10.1093/bioinformatics/btm493.
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