Review of Immunoinformatic approaches to in-silico B-cell epitope prediction

Review of immunoinformatic approaches to in-silico B-cell epitope prediction

In this paper, the current state of in-silico, B-cell epitope prediction is discussed. Recommendations for improving some of the approaches encountered are outlined, along with the presentation of an entirely novel technique, which uses molecular mechanics for epitope classification, evaluation and prediction. INTRODUCTION The relatively new field of immunomics, which focuses on the interaction...

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

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 predict...

B-cell Epitope Prediction

When a living organism encounters a pathogenic virus ormicrobe, the B cells of the immune system recognize the pathogen’s antigens by their membrane-bound immunoglobulin receptors and, in response, produce antibodies specific to these antigens. The term antigen refers to any entity—a cell, a macromolecular assembly, or a molecule—that may be bound by either a B-cell receptor or an antibody mole...

prediction of linear and conformational b cell epitope of her2 ecd for monoclonal antibody development using immunoinformatic methods

Applying Stacked and Cascade Generalizations to B-cell Epitope Prediction

One of the major challenges in the field of vaccine design is to identify the B-cell epitopes in ever-evolving viruses. Various prediction servers have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose meta learning approaches to epitope prediction based on stacked generalization ...