In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants


Research Paper (postgraduate), 2016
29 Pages

Free online reading

Contents

INTRODUCTION

Material and Methods
Protein Sequence Retrieval
Retrieved Strains Phylogeny
Identification of Conserved Regions
Epitope Prediction
B cell Epitope Prediction
T cell Epitope Prediction
Visualization of 3D Structures modeling for selected Predicted Epitopes

RESULTS
Phylogenetic Analysis of Retrieved Strains
Prediction of B-cell epitope and Modeling
Prediction of Cytotoxic T-lymphocyte epitope and interaction with Mouse MHC Class I and Modeling
Prediction of T helper cell epitope and interaction with Mouse MHC Class II and modeling

DISCUSSION

CONCLUSION

Acknowledgments

Competing Interests

References

ABSTRACT

Schmallenberg (SBV) is a new virus of the Bunyaviridae family within the genus Orthobunyavirus. Viral infection causes mild clinical signs: fever, reduced milk production diarrhea and considerable economic loss. Unfortunately currently there is no treatment or vaccine for infected animals. We aimed to design peptide vaccine using Immunoinformatics approach to stimulate the immune system and reducing the potential negative effects of using live vaccines. In this study total of 47 strains of complete M polyprotein sequence (Gn/NSm/GC) and 61 strains of nonstructural protein in S segment (NSs) of Schmallenberg virus which chosen for this study were taken from NCBI. Potentially continuous B and T cell epitopes were predicted using tools from immune epitope data base analysis resource (IEDB-AR). We found that Gn and Gc regions of M polyprotein in SBV was clearly suitable and could be used for the preparation of immunological constructs. Our studies suggested that; B cell epitope [764]QQQACSS[770] and CTL epitopes [251]YMYNKYFKL[259], [46]SECCVKDDI[54] and [234]IVYVFIPIF[242] could be used as a potential vaccine candidate against SBV. We considered this study distinctive because no research ever dealt with peptide based vaccine on virulent strains of SBV using in silico approach.

Key words: Schmallenberg (SBV), ruminants, epitopes, overlapping & vaccine

INTRODUCTION

In 2011, in the German town of Schmallenberg a new virus of the Bunyaviridae family, a member of the Simbu serogroup within the genus Orthobunyavirus has been identified for first time, which was called later Schmallenberg virus (SBV)[[1],[2],[3]]. SBV genome consists of three segments of negative sense, single-stranded RNA the L (large), M (medium) and S (small) segments [[4],[5]]. The L segment encodes the RNA dependent RNA polymerase; M segment encodes surface glycoproteins Gn and Gc which they form spikes on the virus particle and are essential for viral attachment and cell fusion, and nonstructural protein NSm. The S segment encodes nucleocapsid protein N and nonstructural protein NSs. NSs is considered a major virulence factor for orthobunyaviruses and has able to counteract host antiviral responses [[6]-[8]].

SBV affect mainly domestic and wild ruminants, such as cattle, sheep, goats, mouflon, moose, alpacas, buffalos, bison, and deer [[9],[10]]. Moreover antibodies of virus were also found in Swedish dogs [11]. SBV is transmitted by an arthropod vector, principally Culicoides biting midges, while virus has also been identified in the semen of bulls but venereal transmission has not yet been demonstrated [[12]-[14]] Viral infection causes mild clinical signs in adult cattle fever, reduced milk production and diarrhea, but infection of susceptible pregnant animals can associated with abortions, stillbirths and malformations of the skeletal and central nervous system (CNS) in newborn ruminants [[15]-[18]].

After the initial detection the virus has been spread to at least 20 countries in Europe such as Netherlands, Belgium, United Kingdom, France, Italy, Spain, Poland and Ireland, 8,730 herds and flocks reported infected by May 2013 just in Western Europe. Furthermore, a high percentage of antibody-positive animals in some ruminants of the Zambezia Province in Mozambique were recorded [[19]-[27]].

Since the emergence of SBV in Europe, efforts was exerted to reduce the mortality and morbidity rats, one of the experimental vaccine, the trivalent vaccine against the Simbu serogroup viruses Akabane virus and Aino virus and the reovirus Chuzan, but this vaccine failed to provide prevention from SBV infection. However two commercial inactivated vaccines have already been granted a provisional marketing authorization in the United Kingdom and France, but highly efficacious and safe live vaccines are still not available[[3], [28]-[31]]. Antibodies specific for Simbu serogroup viruses frequently cross-react with more than one other member of the two surface glycoproteins Gn and Gc and the viral polymerase complex composed of the polymerase L protein and the nucleoprotein N. This complex is responsible for the transcription and replication of the viruses that occur exclusively in the cytoplasm. Inside the virus particle, the viral genome is present as a ribonucleoprotein (RNP) associated with many copies of the nucleoprotein N and a few copies of the polymerase L. is serogroup [[32], [33]]. Since Akabane virus (AKAV) or Aino virus (AINOV) causes similar clinical signs to Schmallenberg virus [3]. Scientists found that vaccines against AKAV and AINOV, could potentially offer a tool for disease control until an SBV-specific vaccine is ready for use [3].

This study took a different direction by design peptide vaccine work to stimulate the immune system and reduce the potential negative effects of using live vaccines generally, based on in silico approach and computational methods.

Material and Methods

Protein Sequence Retrieval

The 47 strains of complete M polyprotein sequence (Gn/NSm/GC) and 61 strains of nonstructural protein in S segment (NSs) of Schmallenberg virus which chosen for this study were taken from NCBI database (http://www.ncbi.nlm.nih.gov) in FASTA format in May 2016. The Nonstructural protein (NSm) in M polyprotein was excluded from this study. The length of M polyprotein was found between 1391-1403 amino acids, Table (1) while the length of NSs protein was 91 amino acids. These strains were isolated from different geographical regions from Bos taurus (cattle), Ovis aries (sheep) and Capra hircus (goats) from 2011-2014, Table (2).

Retrieved Strains Phylogeny

The relationships of all retrieved strains of M polyprotein sequence were studied using phylogeny.fr online software (http://phylogeny.lirmm.fr/phylo_cgi/index.cgi) [34].

Table (1): M polyprotein of Schmallenburgvirus and its length

illustration not visible in this excerpt

Table (2): retrieved sequences with their hosts and area of collection

illustration not visible in this excerpt

Identification of Conserved Regions

The retrieved sequences were subjected to multiple sequence alignment (MSA) using BioEdit software [35] to obtain conserved regions with conservancy percentage (100%). These conserved regions were used for B and T Cells predictions. The conservancy across antigen tool (http://tools.iedb.org/tools/conservancy/iedb_input) [36] was applied for more confirmation.

Epitope Prediction

IEDB-AR (http://www.iedb.org/ ) [37] was used for searching and exporting immune epitopes that could activate B and T cells.

B cell Epitope Prediction

For B cell epitopes prediction, four algorithms (http://tools.iedb.org/bcell/) were used: Bepipred linear epitope prediction method was used using hidden Markov model with default threshold 0.35 [38]. Scales with default threshold values for Emini surface accessibility prediction (1.000) [39], Kolaskar and Tongaonkar Antigenicity (1.039/1.029 in M polyprotein /NSs protein) [40] and Parker Hydrophilicity prediction (0.999/0.353 in M polyprotein /NSs protein) [41] tools were interpreted to choose between epitopes. Chou and Fasman Beta turn prediction method with default thresholds (0.981/0.986. 353 in M polyprotein/ NSs protein) [42] was used for more confirmation. Epitopes which passed these tests were predicted as B cell epitope.

T cell Epitope Prediction

For T cell epitopes, MHC I binding prediction tools (http://tools.iedb.org/mhci/) were applied to predict Cytotoxic T cell (CTL) epitopes using mouse MHC class-I alleles (H-2-Db, H-2-Dd, H-2-Kk, H-2-Kb, H-2-Kd and H-2-Ld) based on Stabilized Matrix Method (SMM) [43] and percentile rank ≤1 and half-maximal inhibitory concentration of a biological substance IC50<500 nm. Likewise, MHC II binding prediction tools (http://tools.iedb.org/mhcii/) [44] were used to predict helper T-cell (HTL) epitopes. The percentile rank for strong binding peptides was set at ≤10 and IC50<5000 nm to determine the interaction potentials of helper T-cell epitope peptide using mouse MHC class II alleles (H2-IAb, H2-IAd and H2-IEd) and based on Stabilized Matrix Method (SMM). The predicted T cell epitopes were classified into high affinity (IC50<50) intermediate affinity (IC50<500) and low affinity (IC50<5000) in binding with mouse MHC alleles.

Visualization of 3D Structures modeling for selected Predicted Epitopes

For protein structure; the secondary structures of predicted amino acids were obtained from Protein Homology/analogY Recognition Engine V 2.0 (phyre2) server (http://www.sbg.bio.ic.ac.uk/phyre2) [45]. The tertiary predicted model of protein was done using UCSF Chimera visualization tool 1.8[46] to visualize and confirm the predicted B and T cell epitopes.

RESULTS

Phylogenetic Analysis of Retrieved Strains

The relationships of all retrieved strains of M polyprotien sequence are illustrated in Figure (1) below.

illustration not visible in this excerpt

Figure (1): Phylogenetic tree of thr retrived sequences M polyprotien of SBV. (The branch length is proportional to the number of substitutions per site).

Prediction of B-cell epitope and Modeling

M polyprotein (glycoprotein Gn/Gc) sequence was subjected to Bepipred linear epitope prediction, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity, Parker hydrophobicity and Chou and Fasman beta turn prediction methods in IEDB with default thresholds setting. Only one epitope was found to have cutoff prediction scores above threshold scores, namely QQQACSS from 764 to 770 in Glycoprotein Gn region, Figure (2). The other epitopes had not satisfied the threshold values of other scales especially Kolaskar and Tongaonkar antigenicity value. The result of all conserved predicted B cell epitopes are listed in Table (3).

Table (3): list of B- cell epitopes predicted by different scales for M poly- and NSs protein

illustration not visible in this excerpt

G1: Glycoprotein Gn G2: Glycoprotein Gc * proposed Epitope. M polyprotein Threshold: Emini Surface: 1.000, Antigenicity: 1.039, Hydrophilicity: 0.999 and Beta Turn: 0.981. NSs protein Threshold: Emini Surface: 1.000 Antigenicity: 1.029, Hydrophilicity: 0.353 and Beta Turn: 0.986

illustration not visible in this excerpt

Figure (2): proposed B-Cell Epitope of M polyprotein

Prediction of Cytotoxic T-lymphocyte epitope and interaction with Mouse MHC Class I and Modeling

M polyprotein (glycoprotein Gn/Gc) was analyzed using IEDB MHC-1 binding prediction tool to predict T cell epitope interaction with different types of mouse MHC Class I alleles. 16 peptides had interacted with different mouse MHC-1 alleles. The peptide YMYNKYFKL from 251 to 259 in Glycoprotein Gc region had high affinity to interact with one mouse allele (H-2-Kb) IC50=19.02 nm and intermediate affinity to bind with H-2-Ld (IC50=157.34 nm), followed by SECCVKDDI from 46 to 54 and IVYVFIPIF from 234 Gc to 242 54 in Glycoprotein Gn which they high affinity to interact with H-2-Kk (IC50=40.03) and H-2-Kb (IC50=40.84) respectively, (Figure 3). The epitopes and their corresponding mouse MHC-1 alleles are shown in Table (4)

Table (4): list of the CTL epitopes which had high and intermediate binding affinity with the mouse MHC Class I alleles in M polyprotein

illustration not visible in this excerpt

IC50<50: High affinity, Ic50<500: Intermediate affinity, G1: Glycoprotein Gn, G2: Glycoprotein Gc * proposed Epitope

illustration not visible in this excerpt

Figure (3): proposed CTL Epitopes of M polyprotein

Prediction of T helper cell epitope and interaction with Mouse MHC Class II and modeling

T-cell epitopes from M polyprotein (glycoprotein Gn/Gc) were predicted using MHC-II binding prediction method. 29 predicted conserved HTL epitopes found to interact with mouse MHC-II alleles (H2-IAb and H2-IAd). The 9-mer peptide (core) SAYTKPSIS and YIESHIPAI in Glycoprotein Gn region had intermediate affinity to interact with H2-IAb allele (IC50= 323 and 464, respectively). While YRISGTMHV and NHYRISGTM in Glycoprotein Gc region had intermediate affinity to interact with H2-IAb allele (IC50= 490and 497, respectively). The other predicted HTL Epitope had low affinity with these two mice alleles. The result is listed in Table (5) below.

There were several overlaps between MHC Class I epitopes and MHC Class II epitopes. These overlaps are illustrated in Table (6).

Table (5): list of the HTL epitopes which had intermediate and low binding affinity with the mouse MHC Class II alleles in M poly- and NSs protein

illustration not visible in this excerpt

IC50<500: Intermediate affinity, Ic50<5000: Low affinity, G1: Glycoprotein Gn, G2: Glycoprotein Gc *proposed Epitope

Table (6): Overlapping between MHC class I and II T cell epitopes in M polyprotein

illustration not visible in this excerpt

The underlined and highlighted residues are the 9-mer MHC class I T cell epitopes overlapping the 15-mer MHC class II T cell epitopes. G1: Glycoprotein Gn, G2: Glycoprotein Gc

DISCUSSION

SBV can cause considerable economic loss [[47],[48]]. Unfortunately there is no treatment or vaccine for animals infected with the Schmallenberg virus [49]. Vaccination against SBV could play an important role in disease control. So; in this study we aimed to determine the highly potential immunogenic epitopes for B and T cells as vaccine candidate for M polyprotein (glycoprotein Gn/Gc) and NSs protein in Schmallenberg virus.

In this study, we excluded the NSm in M polyprotein segment because this part is not important for viral replication, in contrast with NSs protein in S segment contributes to viral pathogenesis by blocking the production of interferon (IFN) leading to inhibition the innate responses of the host [50]. We focused on the envelope glycoproteins Gn and Gc regions on M polyprotein also, because these regions have essential role for viral attachment and cell fusion [[6]-[8]] and can be recognized by neutralizing antibod­ies [51].

As we all know; B cell epitopes may be linear (continuous) or conformational (discontinuous). The protective linear B-cell epitopes may lead to the synthesis of the efficient peptide vaccine against viral disease [52]. Based on this fact; we chose our predicted B cell epitopes to be linear (continuous). To determine a potential and effective epitopes for B cell, predicted epitopes should get above threshold scores in Bepipred linear epitope prediction, Emini surface accessibility, Parker hydrophilicity, Kolaskar and Tongaonkar antigenicity and Chou and Fasman beta turn prediction methods in IEDB. As the results illustrated in Table (3); we found [764] QQQACSS [770] epitope in Glycoprotein Gn region of M polyprotein was the only epitope that had cutoff prediction scores above threshold scores. We predicted only one conserved epitope In NSs region namely [35] SSSTRRRP [42] and we found that this epitope had satisfied all scales except antigenicity test.

Designing vaccine against T cell epitope is much more promising due to long lasting immune response and antigenic drift where antigen can easily escape the antibody memory response [53]. We used mouse MHC alleles to identify epitopes in a cattle, goat and sheep study, based on Gurung R.B. et.al. (2012) study where they found more than 80% identity between the MHC alleles of mice, sheep, and cow. In addition, bovine MHC profile in particular is more complex than the mouse MHC profile [54]. In our study; we found three CTL Epitopes in Glycoprotein GC region namely [251] YMYNKYFKL [259], [46] SECCVKDDI [54] and [234] IVYVFIPIF [242] had high affinity to interact with one mouse allele. We did not predict any promising CTL epitopes in NSs region against SBV.

According to Table (5); we found four 9-mer HTL epitopes (core) interacted with mouse MHC-II alleles (H2-IAb and H2-IAd) namely [1299] SAYTKPSIS [1307] and [943] YIESHIPAI [951] in Glycoprotein Gn region and [140] YRISGTMHV [149] and [139] NHYRISGTM [147] in Glycoprotein Gc region but we found that their bindings were intermediate affinity. While three epitopes were predicted in NSs region STRRRPRWS, SSSTRRRPR and RRPRWSYIR but their affinities were low. Also we found [74] IKYYRLYQV [82] epitope was common in both MHC class I and II.

Our results represented in Table (6). We observed several Overlaps between MHC class I and II T cell epitopes in M polyprotein. We expected these overlaps suggest the possibility of antigen presentation to immune cells via both MHC class I and II pathways.

In summary; after screening the epitopes; it was clear that Glycoprotein regions (Gn and Gc) of M polyprotein in SBV can be used for the preparation of immunological constructs. Our studies suggested that; B cell epitope [764] QQQACSS [770] and CTL epitopes [251] YMYNKYFKL [259], [46] SECCVKDDI [54] and [234] IVYVFIPIF [242] can be used as a potential vaccine candidate against SBV.

CONCLUSION

Our study involved the usage of peptide vaccine strategy based on the predictive and analytic tool (IEDB-AR). This strategy is the up-to-date approach to develop vaccines. Also it depends on the usage of short peptide fragments (epitopes) contained within single protein of the microbes to induce positive, desirable T- and B-cell mediated immune responses. In addition, peptide vaccines have the advantage of the exclusion of unnecessary antigenic load and does not induce immune response. We can confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted vaccine for ruminants against SBV.

Acknowledgments

Authors would like to thanks African City of Technology members for their assistance and help.

Competing Interests

The authors declare that they have no competing interests.

References

1. Lazutka J, Zvirbliene A, Dalgediene I, Petraityte-Burneikiene R, Spakova A, Sereika V .ZX et. al . Generation of Recombinant Schmallenberg Virus Nucleocapsid Protein in Yeast and Development of Virus-Specific Monoclonal Antibodies. Journal of Immunology Research 2014; 2014. Available at http://dx.doi.org/10.1155/2014/160316
2. Doceul V, Lara E, Sailleau C, Belbis G, Richardson J, Bréard E. et. al. Epidemiology, molecular virology and diagnostics of Schmallenberg virus, an emerging orthobunyavirus in Europe. Veterinary Research. 2013; 44:31. DOI: 10.1186/1297-9716-44-31
3. Hechinger S, Wernike K, Beer M. Evaluating the protective efficacy of a trivalent vaccine containing Akabane virus, Aino virus and Chuzan virus against Schmallenberg virus infection . Vet Res.2013; 44(1):114. DOI : 10.1186/1297-9716-44-1141.
4. Hover S, King B, Hall B, Loundras EA, Taqi H, Daly J, et. al. Modulation of Potassium Channels Inhibits Bunyavirus Infection. Biological chemistry 2016;291(7):3411–3422. DOI: 10.1074/jbc.M115.692673jbc.M115.692673.
5. Poskin A, Verite S, Comtet L, Stede Y, Cay B, Regge N. Persistence of the protective immunityand kinetics of the isotype specific antibody response against the viral nucleocapsid protein after experimental Schmallenberg virus infection of sheep. Veterinary research. 2015;46:119. DOI:10.1186/s13567-015-0260-6
6. Varela M, Pinto RM, Caporale M, Piras IM, Taggart A, Seehusen F. et. al. Mutations in the Schmallenberg Virus Gc Glycoprotein Facilitate Cellular Protein Synthesis Shutoff and Restore Pathogenicity of NSs Deletion Mutants in Mice. J Virol 2016; 90 (11):5440-50 . DOI : 10.1128/JVI.00424-16.
7. Blomström AL, Gu Q, Barry G, Wilkie G, Skelton JK, Baird M. e.t al. Transcriptome analysis reveals the host response to Schmallenberg virus in bovine cells and antagonistic effects of the NSs protein. BMC Genomics 2015; 16:324. DOI : 10.1186/s12864-015-1538-9
8. Wernike K, Brocchi E, Cordioli P, Sénéchal Y, Schelp C, Wegelt A. et. al. A novel panel of monoclonal antibodies against Schmallenberg virus nucleoprotein and glycoprotein Gc allows specific orthobunyavirus detection and reveals antigenic differences. Veterinary Research 2015; 46:27. DOI: 10.1186/s13567-015-0165-4
9. Fieke M. Molenaar, S. Anna La Rocca, Meenakshi Khatri, Javier Lopez, Falko Steinbach, Akbar Dastjerdi. Exposure of Asian Elephants and Other Exotic Ungulates to Schmallenberg Virus. PLoS ONE 2015; 10(8): e0135532. DOI: 10.1371/journal.pone.0135532
10. Mouchantat S, Wernike K, Lutz W, Hoffmann B, Ulrich RG, Börner K, Wittstatt U, Beer M. A broad spectrum screening of Schmallenberg virus antibodies in wildlife animals in Germany. Veterinary Research. 2015; 46:99. DOI: 10.1186/s13567-015-0232-x
11. Jon Wensman JJ, Blomqvist G, Hjort M, Holst BS. Presence of Antibodies to Schmallenberg Virus in a Dog in Sweden. Journ al of Clinical Microbiology 2013, 51 (8):2802–2803. DOI:10.1128/JCM.00877-13
12. Manley R, Harrup LE, Veronesi E, Stubbins F, Stoner J, Gubbins S, et. al. Testing of UK Populations of Culex pipiens L. for Schmallenberg Virus Vector Competence and Their Colonization. PLoS ONE 2015; 10(8):e0134453. DOI:10.1371/journal. pone.0134453
13. Kluiters G, Pagès N, Carpenter S, Gardès L, Guis H, Baylis M, Garros C. Morphometric discrimination of two sympatric sibling species in the Palaearctic region, Culicoides obsoletus Meigen and C. scoticus Downes & Kettle (Diptera: Ceratopogonidae), vectors of bluetongue and Schmallenberg viruses. Parasites & Vectors 2016; 9(1):262. DOI 10.1186/s13071-016-1520-7.
14. Ponsart C, Pozzi N, Bréard E, Catinot V, Viard G, Sailleau C. et. al . Evidence of excretion of Schmallenberg virus in bull semen Veterinary Research 2014; 45:37. DOI: 10.1186/1297-9716-45-37.
15. Varela M, Schnettler E, Caporale M, Murgia C, Barry G, McFarlane M, . e.t al. Schmallenberg Virus Pathogenesis, Tropism and Interaction with the Innate Immune System of the Host . PLoS Pathog. 2013; 9(1):e1003133. DOI:10.1371/journal.ppat.1003133
16. Martinelle L, Poskin A, Dal Pozzo F, De Regge N, Cay B, Saegerman. Experimental Infection of Sheep at 45 and 60 Days of Gestation with Schmallenberg Virus Readily Led to Placental Colonization without Causing Congenital Malformations. PLoS ONE 2015; 10(9): e0139375. DOI:10.1371/journal.pone.0139375
17. Wernike K, Holsteg M, Schirrmeier H, Hoffmann B, Beer M. Natural Infection of Pregnant Cows with Schmallenberg Virus – A Follow-Up Study. PLoS ONE 2014; 9(5): e98223. DOI:10.1371/journal.pone.0098223
18. Herder V, Hansmann F, Wohlsein P, Peters M, Varela M, Palmarini M, et al. Immunophenotyping of Inflammatory Cells Associated with Schmallenberg Virus Infection of the Central Nervous System of Ruminants . PLoS ONE 2013; 8(5): e62939. DOI:10.1371/journal.pone.0062939
19. Barrett DJ, More SJ, O’Neill RG, Collins DM, O'Keefe C, Regazzoli V, Sammin D. Short communication:Exposure to Schmallenberg virus in Irish sheep in 2013. Veterinary Record 2015; 177(19):494. DOI: 10.1136/vr.103318
20. Harris KA, Eglin RD, Hayward S, Milnes A, Davies I, Cook AJ, Downs SH. Paper:Impact of Schmallenberg virus on British sheep farms during the 2011/2012 lambing season. Veterinary Record 2014;176246. DOI: 10.1136/vr.102295
21. Monaco F, Goffredo M, Federici V, Carvelli A, Capobianco Dondona A, Polci A, Pinoni C, Danzetta ML, Selli L, Bonci M, Quaglia M, Calistri P. First cases of Schmallenberg virus in Italy: surveillance strategies. Vet Ital. 2013; 49(3): 269-275. DOI:10.12834/VetIt.1101.11
22. Blomström AL, Stenberg H, Scharin I, Figueiredo J, Nhambirre O, Abilio AP, Fafetine J, Berg M. Serological Screening Suggests Presence of Schmallenberg Virus in Cattle, Sheep and Goat in the Zambezia Province, Mozambique. Transbound Emerg Dis. 2014; 61(4):289–292. DOI: 10.1111/tbed.12234.
23 . Paul R. Bessell, Harriet K. Auty, Kate R. Searle, Ian G. Handel, Bethan V. Purse & B. Mark de C. Bronsvoort. Scientific Reports. Impact of temperature, feeding preference and vaccination on Schmallenberg virus transmission in Scotland. Scientific Reports. 2014; 5746(4). DOI: 10.1038/srep05746
24. Luttikholt S, Veldhuis A, van den Brom R, Moll L, Lievaart-Peterson K, Peperkamp K, et al. Risk Factors for Malformations and Impact on Reproductive Performance and Mortality Rates of Schmallenberg Virus in Sheep Flocks in the Netherlands. PLoS ONE 2014; 9(6): e100135. DOI:10.1371/journal.pone.0100135
25. Barrett D, More SJ, O’Neill R, Bradshaw B, Casey M, Keane M, McGrath G, Sammin D. Prevalence and distribution of exposure to Schmallenberg virus in Irish cattle during October 2012 to November 2013. BMC Vet Res. 2015; 11: 267. DOI:10.1186/s12917-015-0564-9
26. Larska M, Krzysiak MK, Kęsik-Maliszewska J, Rola J. Cross-sectional study of Schmallenberg virus seroprevalence in wild ruminants in Poland at the end of the vector season of 2013. BMC Vet Res. 2014; 10:967. DOI 10.1186/s12917-014-0307-3
27. Dominguez M., Gache K., Touratier A,. Perrin J.B., Fediaevsky A., Collin E., Bréard E. et. al. Spread and impact of the Schmallenberg virus epidemic in France in 2012-2013. BMC Veterinary Research 2014; 10:248.
28. Kraatz F, Wernike K, Hechinger S, König P, Granzow H, Reimann I, Beer M. Deletion mutants of Schmallenberg virus are avirulent and protect from virus challenge. J Virol 2015; 89:1825–1837. DOI:10.1128/JVI.02729-14.
29. Wernike K, Nikolin VM, Hechinger S, Hoffmann B, Beer M. Inactivated Schmallenberg virus prototype vaccines. Vaccine 2013; 31 (35):3558-63. DOI:10.1016/j.vaccine.2013.05.062 ·
30. Hechinger S., Wernike K., Beer M. Single immunization with an inactivated vaccine protects sheep from Schmallenberg virus Infection. Veterinary Research 2014;45:79. DOI: 10.1186/s13567-014-0079-6.
31. Johnson A, Bradshaw B, Boland C, Ross P. A bulk milk tank study to detect evidence of spread of Schmallenberg virus infection in the south west of Ireland in 2013. Irish Veterinary Journal 2014; 67:11. DOI: 10.1186/2046-0481-67-11
32. Mellor PS, Boorman J, Baylis M. Culicoides biting midges: their role as arbovirus vectors. Annu Rev Entomol 2000;45 :307–40. [PMID: 10761580] DOI: 10.1146/annurev.ento.45.1.307
33. Zientara S, MacLachlan NJ, Calistri P, Sanchez-Vizcaino JM, Savini G. Bluetongue vaccination in Europe. Expert Rev Vaccines 2010; 9(9): 989–91. DOI: 10.1586/erv.10.97.
34. Dereeper A., Guignon V., Blanc G., Audic S., Buffet S., Chevenet F., Dufayard J.F., Guindon S., Lefort V., Lescot M., Claverie J.M., Gascuel O. Phylogeny.fr: robust phylogenetic analysis for the non-specialist . Nucleic Acids Res. 2008; (Web Server issue):W465-9. [PMID: 18424797]
35. Tom H. BioEdit: An important software for molecular biology. GERF Bulletin of Biosciences. 2011; 2(1): 60-61
36. Bui H. H,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(1):361. PMID: 17897458
37. Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, Wheeler DK, Gabbard JL, Hix D, Sette A, Peters B. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2014 Oct 9. pii: gku938. [Epub ahead of print] PubMed PMID: 25300482.
38. Larsen JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. ImmunomeRes. 2006;2:2 PMID: 16635264
39. Emini EA, Hughes JV, Perlow DS, Boger J. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol. 1985; 55(3):836-9. PMID: 2991600
40. Kolaskar AS, Tongaonkar PC. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 1990; 276(1-2):172-4. PMID: 1702393
41. Parker JM, Guo D, Hodges RS. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry. 1986; 25(19):5425-32. PMID: 2430611
42. Chou PY, Fasman GD. Prediction of the secondary structure of proteins from their amino acid sequence. Adv Enzymol Relat Areas Mol Biol.1978; 47:45-148. PMID: 364941
43. Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 2005 ; 6:132. PMID: 15927070
44. Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, Peters B. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics. 2010 ; 11:568.
PMID: 21092157
45. Kelley LA, Mezulis S., Yates CM., Wass MN. , Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nature Protocols 2015; (10): 845-858. DOI:10.1038/nprot.2015.05
46. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605-12. PMID:15264254
47. Martinelle L, Dal Pozzo F, Gauthier B, Kirschvink N, Saegerman C. Field veterinary survey on clinical and economic impact of Schmallenberg virus in Belgium . Transbound Emerg Dis. 2014 ;61(3):285-8. DOI:10.1111/tbed.12030
48. Dominguez M, Hendrikx P, Zientara S, Calavas D, Jay M, Touratier A, et al. Preliminary estimate of Schmallenberg virus infection impact in sheep flocks – France. Vet Rec. 2012;171 (17):426. DOI: 10.1136/vr.100883.
49. Wernike K, Nikolin VM, Hechinger S, Hoffmann B, Beer M. Inactivated Schmallenberg virus prototype vaccines. Vaccine. 2013; 31(35): 3558–63. DOI: 10.1016/j.vaccine.2013.05.062.
50. Varela M, Schnettler E, Caporale M, Murgia C, Barry G, McFarlane M, et al. Schmallenberg Virus Pathogenesis, Tropism and Interaction with the Innate Immune System of the Host. PLoS Pathog 2013; 9(1): e1003133. DOI:10.1371/journal.ppat.1003133
51. Pawaiya RV, Gupta VK. A review on Schmallenberg virus infection: a newly emerging disease of cattle, sheep and goats. Veterinarni Medicina. 2013; 58(10):516-26.
52. Saha S., Raghava G.P.S. Prediction of Continuous B-Cell Epitopes in an Antigen Using Recurrent Neural Network . PROTEINS: Structure, Function, and Bioinformatics 2006; 65:40–48. DOI: 10.1002/prot.21078
53. Shawan MM, Mahmud HA, Hasan M, Parvin A, Rahman M, Rahman SM.. In Silico Modeling and Immunoinformatics Probing Disclose the Epitope Based PeptideVaccine Against. Zika Virus Envelope Glycoprotein . Indian J. Pharm. Biol. Res. 2014; 2(4):44-57.
54. Ratna B. Gurung, Auriol C. Purdie, Douglas J. Begg and Richard J. Whittington. In Silico Identification of Epitopes in Mycobacterium avium subsp.paratuberculosis Proteins That Were Upregulated under Stress Condition. Clin. Vaccine Immunol. 2012, 19(6):855. DOI:10.1128/CVI.00114-12.s

[...]


29 of 29 pages

Details

Title
In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants
Authors
Year
2016
Pages
29
Catalog Number
V341580
ISBN (Book)
9783668335295
File size
1292 KB
Language
English
Tags
silico, modeling, identification, novel, epitopes-based, vaccine, gn/gc, schmallenberg, virus, ruminants
Quote paper
Marwa Osman (Author)et al. (Author), 2016, In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants, Munich, GRIN Verlag, https://www.grin.com/document/341580

Comments

  • No comments yet.
Read the ebook
Title: In Silico Modeling and Identification of Novel Epitopes-based Vaccine of M polyprotein (Gn/Gc) against Schmallenberg Virus for Ruminants


Upload papers

Your term paper / thesis:

- Publication as eBook and book
- High royalties for the sales
- Completely free - with ISBN
- It only takes five minutes
- Every paper finds readers

Publish now - it's free