scholarly journals Conformational B-Cell Epitope Prediction Method Based on Antigen Preprocessing and Mimotopes Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Pingping Sun ◽  
Haixu Ju ◽  
Baowen Zhang ◽  
Yu Gu ◽  
Bo Liu ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Prediction Method ◽  
Epitope Prediction ◽  
Testing Dataset ◽  
B Cell Epitope ◽  
Degree Of Satisfaction ◽  
Humoral Responses ◽  
High Degree ◽  
Antigen Structure

Identification of epitopes which invokes strong humoral responses is an essential issue in the field of immunology. Various computational methods that have been developed based on the antigen structures and the mimotopes these years narrow the search for experimental validation. These methods can be divided into two categories: antigen structure-based methods and mimotope-based methods. Though new methods of the two kinds have been proposed in these years, they cannot maintain a high degree of satisfaction in various circumstances. In this paper, we proposed a new conformational B-cell epitope prediction method based on antigen preprocessing and mimotopes analysis. The method classifies the antigen surface residues into “epitopes” and “nonepitopes” by six epitope propensity scales, removing the “nonepitopes” and using the preprocessed antigen for epitope prediction based on mimotope sequences. The proposed method gives out the meanFscore of 0.42 on the testing dataset. When compared with other publicly available servers by using the testing dataset, the new method yields better performance. The results demonstrate the proposed method is competent for the conformational B-cell epitope prediction.

scholarly journals Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Pingping Sun ◽  
Haixu Ju ◽  
Zhenbang Liu ◽  
Qiao Ning ◽  
Jian Zhang ◽  
...  
Keyword(s):  
B Cell ◽  
High Speed ◽  
Low Cost ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Experimental Methods ◽  
Time Cost ◽  
B Cell Epitopes ◽  
B Cell Epitope ◽  
Humoral Responses

Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed.

A novel conformational B-cell epitope prediction method based on mimotope and patch analysis

2016 ◽  
Vol 394 ◽  
pp. 102-108 ◽  
Author(s):  
Pingping Sun ◽  
Jialiang Qi ◽  
Yizhu Zhao ◽  
Yanxin Huang ◽  
Guifu Yang ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Prediction Method ◽  
Epitope Prediction ◽  
B Cell Epitope

scholarly journals Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

2021 ◽  
Vol 22 (6) ◽  
pp. 3210
Author(s):  
Kosmas A. Galanis ◽  
Katerina C. Nastou ◽  
Nikos C. Papandreou ◽  
Georgios N. Petichakis ◽  
Diomidis G. Pigis ◽  
...  
Keyword(s):  
B Cell ◽  
Cell Epitope ◽  
Prediction Method ◽  
Epitope Prediction ◽  
Vaccine Design ◽  
Disease Diagnosis ◽  
Command Line ◽  
Command Line Interface ◽  
B Cell Epitope ◽  
Linear B

Linear 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 cheaper vaccine design process, a crucial necessity in the COVID-19 era. 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 of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

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