Advances in In-silico B-cell Epitope Prediction

Abstract

Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

Current Challenges in Vaccinology

“…These novel approaches have been driven by public health urgency, demand for vaccine safety, cost considerations, and the inability of past vaccine-development paradigms to lead to viable vaccine candidates against complex and hyper-variable pathogens quickly enough to meet public health needs at an affordable cost. As a result, vaccine development is being accelerated by genetic and bioinformatics approaches (186). In the last decade, new vaccines against influenza have been developed and licensed, as have vaccines against meningococcus group B, hepatitis B, and herpes zoster using genomics-based approaches.…”

“…These and other similar initiatives demonstrate the power of sustained collaborative partnerships between academia, industry, and governmental agencies. Increasingly, sophisticated computational modeling and machine learning approaches will be leveraged to understand immune function ( 185 ), identify optimal epitopes ( 186 ), as well as design and test new vaccines ( 187 – 189 ).…”

Computational tools for modern vaccine development

“…The 3D-Epitope-Explorer (3DEX) software allow mapping of conformational epitopes using 3D structures protein based on algorithm. 124 The Artificial Neural Network (ANN) and Quantitative Matrices (QM) are the basis of nHLAPred, which is used for the prediction of MHC-I binding peptides. Whether 9-mer peptides would bind an MHC-I molecule or not will be predicted by the Kernel-based Inter-allele peptide binding prediction SyStem (KISS) in SVM.…”

Identification and Analysis of Unstructured, Linear B-Cell Epitopes in SARS-CoV-2 Virion Proteins for Vaccine Development

“…In addition, characterization of such epitopes can contribute to our understanding of mutational changes that affect the ability of the immune response to provide cross protection against related viruses. Multiple immunoinformatic approaches have been developed for the prediction of B-cell epitopes based on different criteria that aim to capture the intrinsic complexity of the binding between the antigenic epitope and the antibody paratope [ 26 ]. However, given that the sensitivity for detection of linear epitopes using computational approaches has been estimated to be around 60% [ 27 ], the integration of several methods may identify physiologically relevant B-cell epitopes more accurately.…”