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The prediction of B-cell epitope via biostatistical and bioinformatic methodology and applications

Bo Yao, University of Nebraska - Lincoln

Abstract

By creating antibodies against antigens, B-cells, also named B-lymphocytes, play an important role in the immune system to fight against foreign invasion to the host body. Within the antigen specific to a certain B-cell antibody, the sections recognized and bound by antibody are called B-cell epitopes. As antigenic determinants, B-cell epitope identification is of vital importance in many immunological processes, such as vaccine design, immunodiagnostic tests, and antibody production. Towards this goal, biologists and immunologists have applied a variety of methods to identify B-cell epitopes through both experiments and bioinformatic predictions. Since the experiments for searching B-cell epitopes are time-consuming and expensive, bioinformatic methodologies have become important for the high-throughput study of B-cell epitopes. ^ There are two kinds of B-cell epitopes: linear (continuous) epitopes and conformational (discontinuous) epitopes. The methodologies and difficulties of bioinformatic predictions for the two categories are quite different. Due to more challenges of conformational B-cell epitope prediction, currently most of prediction tools aim to linear B-cell epitope. ^ The importance of B-cell epitopes has driven the development of faster and more precise tools in the past thirty years. Unfortunately, the limited success of these existing methods cannot match expectation because the achieved specificity and sensitivity leave room to be desired. In this dissertation, we developed new linear B-cell epitope tool SVMTriP with a sensitivity of 80.1% and a precision of 55.2%, which is higher than other tools such as BCPred and AAP (Chapter Two). We also developed new conformational B-cell prediction tool EPSVR and a meta server EPmeta based on Support Vector Regression (Chapter Three). Comparing to other conformational B-cell prediction tools such as DiscoTope, EPSVR shows a better prediction with AUC (Area Under receiver operating characteristic Curve) of 0.597. In addition, we are working on the tool SVMKER to predict epitopic residues on antigen (Chapter Four). To our knowledge, SVMKER is the first epitopic residue prediction tool just using protein sequence as input. These online tools will provide more choices for the identification of protein epitope by bioinformatic methodology.^

Subject Area

Biology, Bioinformatics

Recommended Citation

Yao, Bo, "The prediction of B-cell epitope via biostatistical and bioinformatic methodology and applications" (2014). ETD collection for University of Nebraska - Lincoln. AAI3631021.
http://digitalcommons.unl.edu/dissertations/AAI3631021

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