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Immunoinformatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses
Marwan Mustafa Badawi1,
, Afra AbdElhamid Fadl Alla1, Salma Sleak Alam2, Wafa Aljack Mohamed3, Duaa Adil Nasr-Eldin Osman2, Samar Ali Abd Alrazig Ali2, Entissar Mohamed Elhassan Ahmed2, Abdah AbdElmonim Adam4, Ranya Omar Abdullah5, Mohamed Ahmed Salih1
1Department of Biotechnology, Africa city of Technology- Khartoum, Sudan
2Omdurman Islamic university- Khartoum, Sudan
3Ibn Sina University - Khartoum, Sudan
4Elrazi University - Khartoum, Sudan
5ALneealin University- Khartoum, Sudan
Abstract
Newcastle disease virus (NDV) is negative sense single stranded RNA belongs to the Avulavirus genus of the Paramyxoviridae family which can be transmitted by inhalation or ingestion. Birds infected shed these viruses in feces as well as respiratory secretions. The aim of this study is to analyze fusion (F) protein of all virulent Newcastle strains reported in NCBI database using in silico approaches to select all possible epitopes that can be used as a therapeutic peptide vaccine. A total of 56 virulent NDV fusion protein variants retrieved from NCBI database. The conserved regions were introduced into IEDB analysis resource to predict B and T cell epitopes, as well as predicting the binding affinity of the conserved epitopes with BF2 21:01, from the predominantly expressed chicken MHC I molecule. Epitopes with high scores in both B and T cell epitopes predicting tools were suggested. Peptide vaccine against virulent NDV is strongly supersedes the conventional vaccines, as it designed to cover variant virulent mutated strains, which will reduce the recurrent outbreaks and their huge accompanied economical loss to a minimum.
Keywords: newcastle disease virus (NDV), peptide vaccine, Immune Epitope Database IEDB, epitopes, Vaccine
Copyright © 2016 Science and Education Publishing. All Rights Reserved.Cite this article:
- Marwan Mustafa Badawi, Afra AbdElhamid Fadl Alla, Salma Sleak Alam, Wafa Aljack Mohamed, Duaa Adil Nasr-Eldin Osman, Samar Ali Abd Alrazig Ali, Entissar Mohamed Elhassan Ahmed, Abdah AbdElmonim Adam, Ranya Omar Abdullah, Mohamed Ahmed Salih. Immunoinformatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses. American Journal of Infectious Diseases and Microbiology. Vol. 4, No. 3, 2016, pp 61-71. https://pubs.sciepub.com/ajidm/4/3/3
- Badawi, Marwan Mustafa, et al. "Immunoinformatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses." American Journal of Infectious Diseases and Microbiology 4.3 (2016): 61-71.
- Badawi, M. M. , Alla, A. A. F. , Alam, S. S. , Mohamed, W. A. , Osman, D. A. N. , Ali, S. A. A. A. , Ahmed, E. M. E. , Adam, A. A. , Abdullah, R. O. , & Salih, M. A. (2016). Immunoinformatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses. American Journal of Infectious Diseases and Microbiology, 4(3), 61-71.
- Badawi, Marwan Mustafa, Afra AbdElhamid Fadl Alla, Salma Sleak Alam, Wafa Aljack Mohamed, Duaa Adil Nasr-Eldin Osman, Samar Ali Abd Alrazig Ali, Entissar Mohamed Elhassan Ahmed, Abdah AbdElmonim Adam, Ranya Omar Abdullah, and Mohamed Ahmed Salih. "Immunoinformatics Predication and in silico Modeling of Epitope-Based Peptide Vaccine Against virulent Newcastle Disease Viruses." American Journal of Infectious Diseases and Microbiology 4, no. 3 (2016): 61-71.
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1. Introduction
Newcastle disease is viral infection affect various avian species. It was first reported from Java, Indonesia, and New- castle-upon-Tyne, England, in 1926 but there are earlier reports of similar disease outbreaks in Central Europe before this date. In early 1990 there was marked increase in reported outbreaks of ND in Western Europe, peaking with 239 outbreaks in European Union [EU] countries in 1994. In 1998 two outbreaks of virulent ND in Australia and further outbreaks were reported in 1999 and 2000. Now it is endemic in many parts of the world including countries in Asia, the Middle East, Africa, and Central and South America and considered as a great economic threat to the poultry industry [1-6][1].
Newcastle disease is caused by Newcastle disease virus which belongs to the Avulavirus genus of the Paramyxoviridae family which can be transmitted by inhalation or ingestion, birds infected shed these viruses in feces as well as respiratory secretions [4, 7].
This enveloped virus has a negative sense, non-segmented, single stranded RNA genome of almost 15.200 nucleotides in length. It contains six genes which encode the nucleocapsid protein (NP), phosphoprotein (P), matrix protein (M), large polymerase protein (L) and the two surface glycoproteins; fusion protein (F) and hemagglutinin neuraminidase (HN), [1, 8, 11].
There are 9 serotypes of avian paramyxovirus that is capable of infecting avian species. NDV belongs to serotype 1; the Avian Paramyxovirus type 1 (APMV-1) which can cause varying clinical signs, depending on the pathogenicity of the strain and the species of bird. NDVs have been categorized According to pathogenicity into three strains, highly virulent (velogenic) which caused severe, often fatal, illnesses in chickens, intermediate (mesogenic) that cause respiratory disease and non virulent (lentogenic) which do not usually cause disease in adult birds. [9]
The level of pathogenicity is attributed to amino acid sequence motif present in the protease cleavage site of the precursor F protein and the ability of cellular proteases to cleave the F protein. The fusion (F) protein is the most immunogenic protein of the virus and it plays important role in the beginning of infection by mediating fusion of the virus into the host cell membrane enabling viral entry into the cell membrane. It is also considered as the main target of immune response to NDV [1, 8, 9, 10].
Vaccines are commonly used to protect chickens, pheasants, some exotic birds and other species from Newcastle disease [3], it can protect birds from clinical signs, and may decrease virus shedding and transmission. Vaccination programs for chickens usually consist of inactivated vaccines and/or live vaccines, vaccination with inactivated vaccines is time consuming, labor intensive, expensive, and often inaccurate. however, usage of NDV live vaccine is widespread but still cause disease signs, depending upon environmental conditions and the presence of complicating infections [12]. To improve the efficacy and safety of current NDV live vaccines, Scientists work on developing genetic modification of NDV DNA which is supposed to offer the same broad immunologic advantages as live vaccine without the safety concern, still the cost is a disadvantage [3, 8].
Many studies showed the immunological efficacy of peptide-based vaccines against infectious diseases. The development of peptide-based vaccines has significantly advanced with the identification of specific epitopes derived from infectious pathogens. Understanding of the molecular basis of antigen recognition and HLA binding motifs has resulted in the development of designed vaccines based on motifs predicted to bind to host class I or class II MHC. [13]
The use of this type of vaccine offers practical advantages, such as inclusion of specific protective epitopes and their exposure to the immune system, exclusion of suppressive epitopes, relative ease of construction and production, chemical stability and an avoidance of any infection or autoimmune potential hazard. [13]
The aim of our study is to analyze fusion (F) protein of all virulent Newcastle strains reported in NCBI database using in silico approaches to select all possible epitopes that can be used as a therapeutic peptide vaccine.
2. Materials and Methods
2.1. Protein Sequence RetrievalA total of 56 virulent Newcastle Fusion proteins variants were retrieved from NCBI (https://www.ncbi.nlm.nih.gov/protein/) database in April 2016. These 56 strains retrieved were collected from different parts of the world; retrieved fusion protein strains and their accession numbers as well as date and area of collection are listed in Table 1.
2.2. Determination of Conserved RegionsThe retrieved sequences were aligned to obtain conserved regions using multiple sequence alignment (MSA). Sequences aligned with the aid of ClustalW as implemented in the BioEdit program, version 7.0.9.0 (Hall, 1999) for finding the conserved regions among international virulent fusion glycoprotein variants. Later on, the candidate epitopes were analyzed by different prediction tools from Immune Epitope Database IEDB analysis resource (https://www.iedb.org/), Figure 1 - Figure 3. [14, 15].
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B cell epitope is the portion of an immunogen, which interacts with B-lymphocytes. As a result, the B-lymphocyte is differentiated into antibody-secreting plasma cell and memory cell. B cell epitope is characterized by being hydrophilic and accessible [16]. Thus, the classical propensity scale methods and hidden Markov model programmed softwares from IEDB analysis resource were used for the following aspects:
Prediction of linear B-cell Epitopes: BepiPred from immune epitope database [17] was used for linear B-cell epitopes prediction from the conserved region with a default threshold value of 0.35.
Prediction of surface accessibility: by using Emini surface accessibility prediction tool of the immune epitope data base (IEDB) ([18] the surface accessible epitopes were predicted from the conserved region holding the default threshold value 1.000 or higher.
Prediction of Epitopes antigenicity sites: [19] the kolaskar and tongaonker antigenicity method was used to determine the antigenic sites with a default threshold value of 1.042.
2.4. MHC Class I binding predictionsAnalysis of peptide binding to MHC class I molecules was assessed by the IEDB MHC I prediction tool at https://tools.iedb.org/mhci/. Prediction for several organisms is supported by this tool as chicken is not among them. However, several studies suggest some similarities between HLA alleles and chicken MHC, [55, 56, 57, 58, 59] MHC-I peptide complex presentation to T lymphocytes undergo several steps, the attachment of cleaved peptides to MHC molecules step was predicted. Prediction methods can be achieved by Artificial neural network (ANN), Stabilized matrix method (SMM), or Scoring Matrices derived from Combinatorial Peptide Libraries (Comblib_Sidney2008). Consensus method was used which combines ANN, SMM and comblib methods. [20, 21, 22, 23, 24] Prior to prediction, all epitope lengths were set as 9mers, conserved epitopes that bind to many HLA alleles at score equal or less than 1.0 percentile rank were selected for further analysis.[25]
2.5. MHC Class II Binding PredictionsAnalysis of peptide binding to MHC class II molecules was assessed by the IEDB MHC II prediction tool at https://tools.immuneepitope.org/mhcii/ [26, 27]. For MHC-II binding predication, human allele reference set were used. MHC class II groove has the ability to bind to peptides with different lengths. This variability in binding makes prediction as difficult as less accurate [28]. There are five prediction methods for IEDB MHC II prediction tool; SMM_align, NN- align, Compinatorial Libraries, Sturniolo's method and NetMHCIIpan in addition to the consensus method. SMM-align is a matrix-based method with extensions incorporating flanking residues outside of binding grooves, NN- align uses the artificial neural networks that allows for simultaneous identification of the MHC class II binding core epitopes and binding affinity, Compinatorial Libraries apply positional scanning combinatorial libraries approach which utilizes a pool of random peptide libraries to systematically measure the contribution to MHC binding from each amino acid at each of the nine positions at the binding peptide, Sturniolo's method and NetMHCIIpan predict peptide binding to HLA-DR molecule which make them less useful. The consensus approach was used which combine the outcome of the three SMM_align, NN- align, Compinatorial Libraries methods which firstly run a random scan of Swiss-Prot proteins and achieve scores for 2,000,000 random peptides, thereafter, act as reference to rank new predictions. The consensus method uses the median rank of the three approaches as the final prediction score [29]. All conserved epitopes that bind to many alleles at score equal or less than 10-percentile rank is selected for further analysis.
2.6. Homology ModelingNewcastle fusion protein 3D structure as well as FB2 21:01 chicken allele were obtained by phyre2, (https://www.sbg.bio.ic.ac.uk/phyre2) which uses advanced remote homology detection methods to build 3D models. Both protein sequences were retrieved from the corresponding reference sequence in NCBI. UCSF Chimera (version 1.8) was used to visualize the 3D structures, Chimera currently available within the Chimera package and available from the chimera web site (https://www.cgl.ucsf.edu/cimera). Homology modeling was achieved to establish docking, and for further verification of the service accessibility and hydrophilicity of B lymphocyte epitopes predicted, as well as to visualize all predicted T cell epitopes in the structural level [30, 31].
2.6. DockingEpitopes of MHC I alleles that predicted to bind with percentile rank below 0.5 were selected as the ligands, which are modeled using PEP- FOLD online peptide modeling tool. The receptor BF2 21:01 chicken MHC I allele 3D structure was obtained from phyre2 modeling server. Removal of water and non polar hydrogen from protein was performed using PyMOL. Autodock vina program of the pyrx python virtual screening tool was used for all dockings. Conversion of pdp files into pdpqt files were performed, as well as the default grid box features of PyRx were maintained for active site determination. PyMol was used for visualization and determination of binding affinity and polar ligands contacts and to show the suitable epitopes binding with the lowest energy. [30, 32, 33, 34]
3. Result
3.1. B-cell Epitope PredictionThe reference F protein was analyzed using Bepipred Linear Epitope Prediction, Emini surface accessibility and Kolaskar and Tongaonkar antigenicity to determine the default threshold of the protein, Regarding Bepipred Linear Epitope Prediction method 7 epitopes were predicted eliciting B lymphocyte from the conserved regions (DGR, AVHE, GPQITSP, PQITSP, IISQNYG, IISQNY, ESN).
Emini surface accessibility prediction tool predict four of them passing the default threshold (DGR, GPQITSP, PQITSP, ESN).
Kolaskar and Tongaonkar antigenicity predict their antigenisity and only two epitopes gave score above threshold (AVHE, IISQNY), however, there is no single epitope successfully overlapped the three tools. The result is illustrated in Table 2 below and (Figure 1-Figure 3), and their positions in the structural level is shown in Figure 4.
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The reference F protein was analyzed using IEDB MHC-1 binding prediction tool to predict T cell epitopes suggested interacting with different types of MHC Class I alleles. Based on Consensus (ann/smm/comblib_sidney2008) with percentile rank ≤1; 6 conserved epitopes were predicted to interact with different HLA MHC-1 alleles. Epitopes and their corresponding alleles are shown in Table 3, and shown in the structural level in Figure 5.
Listed below in Table 4, is the result of 15 predicted conserved epitopes found to interact with MHC-II alleles. The peptide (core), IQALYNLAG had high affinity to interact with seven alleles (HLA-DRB1*01:01, HLA-DRB1*04:01, HLA-DRB1*04:05, HLA-DRB1*08:02, HLA-DRB1*11:01, HLA-DRB1*15:01, HLA-DRB5*01:01), LYLTELTTV interact with six alleles (HLA-DPA1*01/DPB1*04:01, HLA-DPA1*01:03/DPB1*02:01, HLA DPA1*02:01/DPB1*01:01HLA-DPA1*03:01/DPB1*04:02, HLA-DQA1*01:01/DQB1*05:01, HLA-DRB4*01:01), while LNLYLTELT was found to interact with five alleles (HLA-DRB1*04:01, HLA-DRB1*04:05, HLA-DRB1*08:02, HLA-DRB1*12:01, HLA-DRB1*15:01).
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4. Docking
All docked epitopes appear to have good binding affinity with low binding energy, Table 5 represent the binding energy score needed for all epitopes as well as the polar contacts which were found to be around 5-10 polar bonds. epitope NYGEAVSLI is showing the best polar contacts as well as good binding energy. Figure 7 - Figure 10 visualize the polar binding interactions between receptor and epitopes in the structural level.
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5. Discussion
Vaccination has proven to be the mainstay in prevention of various deadly infectious diseases. Historically, live-attenuated or inactivated forms of microbial pathogens (viruses, bacteria, etc.) including NDV have been used for induction of antigen-specific responses that protect the host against infections. Based on the pathogen being used, such vaccine formulations can contain several proteins. However, protective immunity is usually dependent on a few of them, whereas the majority of proteins are unnecessary for the induction of protective immunity. Furthermore, these additional proteins may induce allergenic responses, this led to an interest of subunit vaccine, and peptide vaccine. There are many peptide vaccines under development, such as vaccine for human immunodeficiency virus (HIV), hepatitis C virus (HCV), malaria, foot and mouth disease, swine fever, influenza, anthrax, human papilloma virus (HPV), therapeutic anti-cancer vaccines for pancreatic cancer, melanoma, non-small cell lung cancer, advanced hepatocellular carcinoma, cutaneous T-cell lymphoma and B-Cell chronic lymphocytic leukemia. [35-54][35]
Keeping NDV under control through vaccination has proven to be difficult (Huang et al. 2003), there are several conventional live attenuated vaccines available as well as recombinant DNA vaccine for Newcastle disease viral infection; (Meulemans,1988), Fowlpox virus (Boursnell et al, 1990, Karaca et al, 1998, Olabode et al., 2010), Pigeonpox virus (Letellier et al., 1991), Herpesvirus of turkeys (Heckert et al., 1996 ; Morgan et al., 1992; Reddy et al., 1996), Marek’s disease virus (Sakaguchi et al., 1998) and avian adeno-associated virus (Perozo et al., 2008), as well as other approaches include the development of subunit vaccines based on the large scale expression of NDV proteins (usually F and/or HN) using baculovirus vectors (Fukanoki et al, 2001; Lee et al, 2008; Mori et al, 1994; Nagy et al, 1991) and the use of DNA vaccines (Loke et al., 2005; Rajawat et al., 2008). The desired profile of Newcastle disease viral infection is achieved when cross-protection against variant strains, increased safety and minimal side effects are maintained, which is best addressed by designing a multi epitope conserved peptide vaccine.
Among the retrieved strains, the earliest strain (AFY26252) was collected at 1947 from Mexico, while the last strain collected among retrieved were (AJE26306) from Pakistan, as it is collected in 2013. In between are strains collected from different countries in Africa, Europe, North America and Asia as illustrated in Table 1, as a result, the conserved regions among international strains accompanied with good level of confidence.
To determine a potential and effective peptide antigen for B cell, epitopes should get above threshold scores in Bepipred linear epitope prediction, Emini surface accessibility, and Kolaskar and Tongaonkar antigenicity prediction methods. According to Epitopes illustrated in Table 2, these epitopes are the only conserved regions from all declared virulent strains of Newcastle virus fusion glycoprotein in NCBI database until 15th of April 2016, that have high probability of activating B lymphocyte, however, these epitopes predicted satisfied Bepipred linear epitope prediction as well as Emini surface accessibility scales, but none of them succeeded the Kolaskar and Tongaonkar antigenicity prediction method. There are many antigenic sites that passed the threshold of all three predilection methods, but according to the high mutation rates in Newcastle fusion protein, none of them is observed as absolutely conserved region.
Since the immune response of T cell is long lasting response comparing with B cell, where the antigen can easily escape the antibody memory response and CD8+ T and CD4+ T cell responses play a major role in antiviral immunity, designing of vaccine against T cell epitope is much more promising. T cell activation prediction was not predicted as B cell, alleles of MHC class I & II in chicken are not as human alleles, hence there is no database or software available that can calculate the binding affinity of protein with specific chicken MHC class one and two alleles. However, there are several studies that conclude some similarities between some human and chicken MHC molecules. The conserved epitopes illustrated in (Table 3, Table 4) was found to interact with some of HLA-A, HLA-B and HLA-C alleles for MHC class I, as well as some of HLA-DR, HLA-DP and HLA-DQ alleles for MHC class II. [54, 55, 56, 57]
Kokh M et, al, 2007 suggested a promiscuous peptide binding affinity of BF2 21:01, one from the most expressed MHC I alleles in chicken, and conclude its uniqueness in flexibility of binding different peptides. After docking this allele with the conserved epitopes of fusion glycoprotein, we found good conserved binders as shown in Table 5, those binder epitopes and specially NYGEAVSLI are the most recommended among MHC I human alleles binding epitopes. [58, 59, 60, 61]
6. Conclusion
Conventional peptide vaccine development methods are costly, and time consuming, in silico prediction tools is highly appreciated as it selects specific peptides in protein, which then tested in vitro and in vivo to verify and prove the effectiveness of the proposed epitopes to induce an immune response, peptide vaccine against NDV is strongly supersedes the conventional vaccines, as it designed to cover variant virulent mutated strains, which will reduce the recurrent outbreaks and their huge accompanied economical loss to a minimum.
Acknowledgments
Authors would like to thank African City of Technology members for their cooperation.
Competing Interest
The authors declare that they have no competing interests.
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