A Computational Approach for Designing a Universal Epitope-Based Peptide Vaccine Against Nipah Virus
09 Jul 2015-Interdisciplinary Sciences: Computational Life Sciences (International Association of Scientists in the Interdisciplinary Areas)-Vol. 7, Iss: 2, pp 177-185
TL;DR: The attachment (G) and fusion (F) glycoproteins of NiV, responsible for the viral attachment and entry to the host cell, were selected to develop epitope-based vaccine against Nipah virus and showed an acceptable percentage in population coverage and efficient binding with HLA molecule by molecular docking study.
Abstract: Nipah virus (NiV) is highly pathogenic single-stranded negative sense RNA virus. It can cause severe encephalitis and respiratory disease in humans. In addition, NiV infects a large range of host including mammals. As a result of its higher zoonotic potential and pathogenicity for human, it has been rated as an alert in recent days. A therapeutic treatment or vaccines has become elusive to fight against this virus. In this study, the attachment (G) and fusion (F) glycoproteins of NiV, responsible for the viral attachment and entry to the host cell, were selected to develop epitope-based vaccine against Nipah virus. Epitopes were identified from the conserved region of G and F protein of NiV. Both B-cell and T-cell immunity were checked to affirm it that these epitopes will be able to induce humoral and cellular immunity. A total of 6 T-cell epitopes and 19 significant HLA–epitope interactions were identified. Eventually it has shown an acceptable percentage in population coverage (46.45 %) and efficient binding with HLA molecule by molecular docking study.
Citations
More filters
TL;DR: The combined population coverage analysis revealed that the allele frequencies of these epitopes are high in endemic and non-endemic regions, and may be used for the development of epitope-based peptide vaccine against emerging RVFV.
39 citations
Cites methods from "A Computational Approach for Design..."
...…al., 2015), Zika virus glycoprotein (Alam et al., 2016; Dikhit et al., 2016), Chikungunya virus proteins (Islam et al., 2012; Kori et al., 2015), Nipah virus fusion and glycoprotein (Ali et al., 2015; Sakib et al., 2014), but yet no immunoinformatics approach has been applied to the RVFV protein....
[...]
..., 2015), Nipah virus fusion and glycoprotein (Ali et al., 2015; Sakib et al., 2014), but yet no immunoinformatics approach has been applied to the RVFV protein....
[...]
TL;DR: SNebula is a network-based algorithm that can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data and thus improve the understanding of the immune system.
Abstract: Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.
19 citations
TL;DR: Two potent T cell epitopes LLFVFGPNL and KYKIKSNPL could be potent vaccine candidates to counter Nipah virus by testing in wet lab and covered maximum number of populations in India as well as worldwide.
Abstract: Nipah virus was first appeared in Malaysian 1998, which further found be outburst in neighbor countries i.e. Bangladesh, Singapore, and India. Currently there is no effective drug or vaccine available only supportive care and prevention are way to manage it. Epitope based vaccine could be the best way to cure Nipah virus infection. In this study, the proteome of Nipah virus was retrieve from UniProt database and were subjected to check for allergenicity using Allergen FP v.1.0 tool. NetMHCII 2.3 server screened epitopes from non-allergen proteins and Vaxijen tool was used to identify the most antigenic epitopes binds with MHC class II molecules. Two potent T cell epitopes LLFVFGPNL and KYKIKSNPL were found, which binds with HLA-DRB1*01:01 and HLA-DRB1*07:01 MHC class II alleles. PepstrMod and Swiss-Model server were used to build 3D structure model of epitopes and alleles, respectively. Further identified epitopes were docked with HLA alleles using AutoDock vina tool to confirm binding ability. The epitopes LLFVFGPNL and KYKIKSNPL had binding affinity of − 8.1 kcal/mol and − 5.7 kcal/mol with HLA-DRB1*01:01 and HLA-DRB1*07:01 alleles, respectively. Solidity of predicted epitope—allele docked complex were evaluating by molecular dynamics simulation.HLA distribution analysis was performed for predicted epitopes using the population coverage tool of Immune Epitope Database (IEDB). These predicted epitopes cover maximum number of populations in India as well as worldwide. Therefore, these epitopes could be potent vaccine candidates to counter Nipah virus by testing in wet lab.
17 citations
TL;DR: This paper presents a meta-modelling scheme that allows us to assess the efforts towards in-situ applicability of EMMARM, as to provide real-time information about the response of the immune system to EMTs.
Abstract: 1 Department of Microbiology, University of Chittagong, Chittagong 4331, Bangladesh. 2 Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, Sylhet-3114, Bangladesh. 3 Department of Biotechnology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh. 4 Department of Environment and Agriculture, School of Molecular and Life Sciences, Curtin University, WA6845, Australia.
14 citations
TL;DR: Predicting an effective epitope-based vaccine against glycoprotein G of Nipah henipavirus using immunoinformatics approaches shows that the two peptides TVYHCSAVY and FLIDRINWI have showed a very strong binding affinity to MHC class I and MHCclass II alleles.
Abstract: Background Nipah belongs to the genus Henipavirus and the Paramyxoviridae family. It is an endemic most commonly found at South Asia and has first emerged in Malaysia in 1998. Bats are found to be the main reservoir for this virus, causing disease in both humans and animals. The last outbreak has occurred in May 2018 in Kerala. It is characterized by high pathogenicity and fatality rates which varies from 40% to 70% depending on the severity of the disease and on the availability of adequate healthcare facilities. Currently, there are no antiviral drugs available for NiV disease and the treatment is just supportive. Clinical presentations for this virus range from asymptomatic infection to fatal encephalitis. Objective This study is aimed at predicting an effective epitope-based vaccine against glycoprotein G of Nipah henipavirus, using immunoinformatics approaches. Methods and materials Glycoprotein G of the Nipah virus sequence was retrieved from NCBI. Different prediction tools were used to analyze the epitopes, namely, BepiPred-2.0: Sequential B Cell Epitope Predictor for B cell and T cell MHC classes II and I. Then, the proposed peptides were docked using Autodock 4.0 software program. Results and Conclusions. The two peptides TVYHCSAVY and FLIDRINWI have showed a very strong binding affinity to MHC class I and MHC class II alleles. Furthermore, considering the conservancy, the affinity, and the population coverage, the peptide FLIDRINWIT is highly suitable to be utilized to formulate a new vaccine against glycoprotein G of Nipah henipavirus. An in vivo study for the proposed peptides is also highly recommended.
10 citations
References
More filters
TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.
Abstract: AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.
20,059 citations
TL;DR: AutoDock4 incorporates limited flexibility in the receptor and its utility in analysis of covalently bound ligands is reported, using both a grid‐based docking method and a modification of the flexible sidechain technique.
Abstract: We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.
15,616 citations
TL;DR: A new program called Clustal Omega is described, which can align virtually any number of protein sequences quickly and that delivers accurate alignments, and which outperforms other packages in terms of execution time and quality.
Abstract: Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.
12,489 citations
"A Computational Approach for Design..." refers methods in this paper
...uk/Tools/msa/ clustalo/) was used to perform multiple sequence alignment for the retrieved sequences [27]....
[...]
TL;DR: This paper attempts to summarize current knowledge about immune responses to vaccines that correlate with protection, finding some vaccines have no true correlates, but only useful surrogates, for an unknown protective response.
Abstract: This paper attempts to summarize current knowledge about immune responses to vaccines that correlate with protection. Although the immune system is redundant, almost all current vaccines work through antibodies in serum or on mucosa that block infection or bacteremia/viremia and thus provide a correlate of protection. The functional characteristics of antibodies, as well as quantity, are important. Antibody may be highly correlated with protection or synergistic with other functions. Immune memory is a critical correlate: effector memory for short-incubation diseases and central memory for long-incubation diseases. Cellular immunity acts to kill or suppress intracellular pathogens and may also synergize with antibody. For some vaccines, we have no true correlates, but only useful surrogates, for an unknown protective response.
1,350 citations
"A Computational Approach for Design..." refers background in this paper
...Both G and F proteins are the major targets of the neutralizing antibodies and vaccine-induced protection of this virus [20, 21]....
[...]
TL;DR: Application of this method to a large number of proteins has shown that this method can predict antigenic determinants with about 75% accuracy which is better than most of the known methods.
1,279 citations
"A Computational Approach for Design..." refers methods in this paper
...org/bcell/) was utilized to predict the B-cell antigenicity of conserved peptide on the basis of the Kolaskar and Tongaonkar [38] method which has the ability to predict antigenic determinants with approximately 75 % accuracy....
[...]
...B-cell epitope prediction tool (http://tools.immuneepitope. org/bcell/) was utilized to predict the B-cell antigenicity of conserved peptide on the basis of the Kolaskar and Tongaonkar [38] method which has the ability to predict antigenic determinants with approximately 75 % accuracy....
[...]