In silico epitope prediction and 3D model analysis of peste des petits ruminants virus nucleoprotein

2016 ◽  
Vol 07 (06) ◽  
Author(s):  
Bakang Baloi
Keyword(s):  
In Silico ◽  
3D Model ◽  
Epitope Prediction ◽  
Model Analysis ◽  
Peste Des Petits Ruminants

scholarly journals A Highly Conserved Epitope (RNNQIPQDF) of Porcine teschovirus Induced a Group-Specific Antiserum: A Bioinformatics-Predicted Model with Pan-PTV Potential

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1225
Author(s):  
Tung-Hsuan Tsai ◽  
Chia-Yi Chang ◽  
Fun-In Wang
Keyword(s):  
In Silico ◽  
Neutralizing Antibodies ◽  
Age Groups ◽  
Epitope Prediction ◽  
Toxin B ◽  
Swine Pathogen ◽  
Porcine Teschovirus ◽  
A Subunit ◽  
Predicted Model ◽  
Conserved Epitope

Porcine teschovirus (PTV) is an OIE-listed pathogen with 13 known PTV serotypes. Heterologous PTV serotypes frequently co-circulate and co-infect with another swine pathogen, causing various symptoms in all age groups, thus highlighting the need for a pan-PTV diagnostic tool. Here, a recombinant protein composed of a highly conserved “RNNQIPQDF” epitope on the GH loop of VP1, predicted in silico, and a tandem repeat of this epitope carrying the pan DR (PADRE) and Toxin B epitopes was constructed to serve as a PTV detection tool. This recombinant GST-PADRE-(RNNQIPQDF)n-Toxin B protein was used as an immunogen, which effectively raised non-neutralizing or undetectable neutralizing antibodies against PTV in mice. The raised antiserum was reactive against all the PTV serotypes (PTV–1–7) tested, but not against members of the closely related genera Sapelovirus and Cardiovirus, and the unrelated virus controls. This potential pan-PTV diagnostic reagent may be used to differentiate naturally infected animals from vaccinated animals that have antibodies against a subunit vaccine that does not contain this epitope or to screen for PTV before further subtyping. To our knowledge, this is the first report that utilized in silico PTV epitope prediction to find a reagent broadly reactive to various PTV serotypes.

scholarly journals An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lenka Potocnakova ◽  
Mangesh Bhide ◽  
Lucia Borszekova Pulzova
Keyword(s):  
B Cell ◽  
In Silico ◽  
Epitope Mapping ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Diagnostic Tools ◽  
Accurate Identification ◽  
B Cell Epitopes ◽  
Epitope Identification ◽  
B Cell Epitope

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

scholarly journals Chikungunya virus: genomic microevolution in Eastern India and its in-silico epitope prediction

3 Biotech ◽  
2018 ◽  
Vol 8 (7) ◽  
Author(s):  
Sudip Kumar Dutta ◽  
Tamanash Bhattacharya ◽  
Anusri Tripathi
Keyword(s):  
In Silico ◽  
Chikungunya Virus ◽  
Epitope Prediction ◽  
Eastern India

Building an accurate 3D model of a circular feature for robot vision

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
L. Li
Keyword(s):  
Efficient Method ◽  
Error Compensation ◽  
3D Model ◽  
Robot Vision ◽  
Model Analysis ◽  
Perspective Projection ◽  
Ellipse Fitting ◽  
Projection Scheme ◽  
Fitting Method ◽  
Data Points

AbstractIn this paper, an accurate 3D model analysis of a circular feature is built with error compensation for robot vision. We propose an efficient method of fitting ellipses to data points by minimizing the algebraic distance subject to the constraint that a conic should be an ellipse and solving the ellipse parameters through a direct ellipse fitting method by analysing the 3D geometrical representation in a perspective projection scheme, the 3D position of a circular feature with known radius can be obtained. A set of identical circles, machined on a calibration board whose centres were known, was calibrated with a camera and did the model analysis that our method developed. Experimental results show that our method is more accurate than other methods.

scholarly journals Linear and Conformational B Cell Epitope Prediction of the HER 2 ECD-Subdomain III by in silico Methods

2012 ◽  
Vol 13 (7) ◽  
pp. 3053-3059 ◽  
Author(s):  
Manijeh Mahdavi ◽  
Hassan Mohabatkar ◽  
Mehrnaz Keyhanfar ◽  
Abbas Jafarian Dehkordi ◽  
Mohammad Rabbani
Keyword(s):  
B Cell ◽  
In Silico ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
B Cell Epitope ◽  
Her 2 ◽  
In Silico Methods

scholarly journals A multi-method and structure-based in silico vaccine designing against Echinococcus granulosus through investigating enolase protein

Bioimpacts ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 131-144 ◽  
Author(s):  
Mohammad Mostafa Pourseif ◽  
Mitra Yousefpour ◽  
Mohammad Aminianfar ◽  
Gholamali Moghaddam ◽  
Ahmad Nematollahi
Keyword(s):  
Echinococcus Granulosus ◽  
In Silico ◽  
3D Structure ◽  
Three Dimensional ◽  
Epitope Prediction ◽  
Intermediate Hosts ◽  
Epitope Vaccine ◽  
Causal Organism ◽  
Multi Stage ◽  
Helper Epitope

Introduction: Hydatid disease is a ubiquitous parasitic zoonotic disease, which causes different medical, economic and serious public health problems in some parts of the world. The causal organism is a multi-stage parasite named Echinococcus granulosus whose life cycle is dependent on two types of mammalian hosts viz definitive and intermediate hosts. Methods: In this study, enolase, as a key functional enzyme in the metabolism of E. granulosus (EgEnolase), was targeted through a comprehensive in silico modeling analysis and designing a host-specific multi-epitope vaccine. Three-dimensional (3D) structure of enolase was modeled using MODELLER v9.18 software. The B-cell epitopes (BEs) were predicted based on the multi-method approach and via some authentic online predictors. ClusPro v2.0 server was used for docking-based T-helper epitope prediction. The 3D structure of the vaccine was modeled using the RaptorX server. The designed vaccine was evaluated for its immunogenicity, physicochemical properties, and allergenicity. The codon optimization of the vaccine sequence was performed based on the codon usage table of E. coli K12. Finally, the energy minimization and molecular docking were implemented for simulating the vaccine binding affinity to the TLR-2 and TLR-4 and the complex stability. Results: The designed multi-epitope vaccine was found to induce anti-EgEnolase immunity which may have the potential to prevent the survival and proliferation of E. granulosus into the definitive host. Conclusion: Based on the results, this step-by-step immunoinformatics approach could be considered as a rational platform for designing vaccines against such multi-stage parasites. Furthermore, it is proposed that this multi-epitope vaccine is served as a promising preventive anti-echinococcosis agent.

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