Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor

2015 ◽  
Vol 19 (10) ◽  
pp. 648-658 ◽  
Author(s):  
Vijayakumar Saravanan ◽  
Namasivayam Gautham
PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e62216 ◽  
Author(s):  
Harinder Singh ◽  
Hifzur Rahman Ansari ◽  
Gajendra P. S. Raghava

2013 ◽  
Vol 63 (12) ◽  
pp. 28-32 ◽  
Author(s):  
Kavitha KV ◽  
Saritha R ◽  
Vinod Chandra S S

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kanokporn Polyiam ◽  
Waranyoo Phoolcharoen ◽  
Namphueng Butkhot ◽  
Chanya Srisaowakarn ◽  
Arunee Thitithanyanont ◽  
...  

AbstractSARS-CoV-2 continues to infect an ever-expanding number of people, resulting in an increase in the number of deaths globally. With the emergence of new variants, there is a corresponding decrease in the currently available vaccine efficacy, highlighting the need for greater insights into the viral epitope profile for both vaccine design and assessment. In this study, three immunodominant linear B cell epitopes in the SARS-CoV-2 spike receptor-binding domain (RBD) were identified by immunoinformatics prediction, and confirmed by ELISA with sera from Macaca fascicularis vaccinated with a SARS-CoV-2 RBD subunit vaccine. Further immunoinformatics analyses of these three epitopes gave rise to a method of linear B cell epitope prediction and selection. B cell epitopes in the spike (S), membrane (M), and envelope (E) proteins were subsequently predicted and confirmed using convalescent sera from COVID-19 infected patients. Immunodominant epitopes were identified in three regions of the S2 domain, one region at the S1/S2 cleavage site and one region at the C-terminus of the M protein. Epitope mapping revealed that most of the amino acid changes found in variants of concern are located within B cell epitopes in the NTD, RBD, and S1/S2 cleavage site. This work provides insights into B cell epitopes of SARS-CoV-2 as well as immunoinformatics methods for B cell epitope prediction, which will improve and enhance SARS-CoV-2 vaccine development against emergent variants.


Author(s):  
Sasan Nasirahmadi ◽  
Jamil Zargan

Background: There are many diseases around the world that threaten human health and its related hygienic issues. Cancer is among the conditions mentioned above that cause many problems for health sectors worldwide.Methods: The present research analyzed the linear B-cell epitope of viscumin from European mistletoe using bioinformatics tools. We also provided references for the fast detection of biological agents. Several important tools, such as Protparam, NCBI, PDB, T-coffee, BCpred, Bptope, Ellipro, and Cn3D were used to predict the viscumin linear epitope and its physical and chemical properties.Results: The 9-mer epitope found as QQTTGEEYF embedded in the A-chain of protein by the least sequence homology with other homologous rivals. Its molecular weight, theoretical isoelectric point, and the total number of negatively charged residues were 1102.1, 3.79, and 2, respectively.Conclusion: Using different databases and establishing the accuracy level of ˃50% for linear B-cell epitope prediction, the selected epitope passed the related criteria and was introduced as a new linear epitope as a potential biological element in biosensors for cancer (viscumin) fast therapeutic detection.


Author(s):  
Maximilian Collatz ◽  
Florian Mock ◽  
Emanuel Barth ◽  
Martin Hölzer ◽  
Konrad Sachse ◽  
...  

2019 ◽  
Author(s):  
Kosmas A. Galanis ◽  
Katerina C. Nastou ◽  
Nikos C. Papandreou ◽  
Georgios N. Petichakis ◽  
Vassiliki A. Iconomidou

ABSTRACTLinear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and significantly cheaper vaccine design process. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology, of some the most widely used linear B-cell epitope predictors: BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope and LBEEP. Additionally, we attempt to remedy performance issues of the individual methods by developing a consensus classifier, that combines the separate predictions of these methods into a single output. The performance of these methods was evaluated using a large unbiased data set. All methods performed worse than documented in the original manuscripts, since all predictors performed marginally better than random classification against the test data set. While the method comparison was performed with some necessary caveats, we hope that this update in performance can aid researchers towards the choice of a predictor, whilst conducting their research. The necessary files for the execution of the consensus method that we developed can be found at http://thalis.biol.uoa.gr/BCEconsensus/.KEY POINTSReview of the performance and methodology of currently available BCE predictorsDesign and development of consensus predictorComparison of consensus with state-of-the-art BCE prediction methodsConsensus method available at http://thalis.biol.uoa.gr/BCEconsensus/Kosmas A. Galanis has a BSc in Biology and has performed his undergrad thesis in Bioinformatics. He is interested in the development of computational methods for protein function prediction.Katerina C. Nastou is a Biologist with a PhD in Bioinformatics. Her research focuses on the study of biological networks, the computational prediction of protein function and biological database development.Nikos C. Papandreou has a PhD in Biophysics and works as Special Laboratory Teaching Staff in “Bioinformatics-Biophysics” at the Department of Biology, National & Kapodistrian University of Athens.Georgios N. Petichakis is a Computer Scientist with an MSc in Bioinformatics. His research focuses on the development of computational methods for the functional annotation of proteomes.Vassiliki A. Iconomidou is an Assistant Professor of Molecular Biophysics and the group leader of the Biophysics and Bioinformatics Lab at the Department of Biology, National and Kapodistrian University of Athens.


2018 ◽  
Vol 13 (2) ◽  
pp. 149-156
Author(s):  
Xiangyu Wang ◽  
Zhonglu Ren ◽  
Qi Sun ◽  
Xuan Wan ◽  
Yaqing Sun ◽  
...  

2019 ◽  
Vol 14 (3) ◽  
pp. 226-233 ◽  
Author(s):  
Cangzhi Jia ◽  
Hongyan Gong ◽  
Yan Zhu ◽  
Yixia Shi

Background: B-cell epitope prediction is an essential tool for a variety of immunological studies. For identifying such epitopes, several computational predictors have been proposed in the past 10 years. Objective: In this review, we summarized the representative computational approaches developed for the identification of linear B-cell epitopes. </P><P> Methods: We mainly discuss the datasets, feature extraction methods and classification methods used in the previous work. Results: The performance of the existing methods was not very satisfying, and so more effective approaches should be proposed by considering the structural information of proteins. Conclusion: We consider existing challenges and future perspectives for developing reliable methods for predicting linear B-cell epitopes.


PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e30617 ◽  
Author(s):  
Chun-Hung Su ◽  
Nikhil R. Pal ◽  
Ken-Li Lin ◽  
I-Fang Chung

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