Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures

By Pernille Haste Andersen, Morten Nielsen, Ole Lund. Protein Science, 2006

604 citations recorded by the source corpus.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c6d5dc8f3ec1c2f55ccabef8ee76500012d7c2a3

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A generic method for assignment of reliability scores applied to solvent accessibility predictions (2009, positive): “Knowledge of the degree of surface exposure of an amino acid is valuable and it has been used to enhance the understanding of a variety of biological problems including protein-protein interactions [6,7], structural epitopes [8], active sites [9], and prediction of disease-related single nucleotide ”
Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System (2010, methodology): “For the last several years, we have developed computational methods that can predict T cell epitopes [3,4,6] or B cell epitopes [66,67] in protein sequences.”
Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking (2012, methodology): “However, performance of the two methods, and other methods utilizing a vast number of features, still only achieve predictive performance values comparable to much simpler models employing two or three attributes [4][12][18][19][20].”
Structural analysis of B-cell epitopes in antibody:protein complexes (2012, methodology): “Previously, non-epitope residues have been defined as either all non-interacting residues in antigens (Andersen et al., 2006), all residues in the PDB database (Ofran et al., 2008) or antigen residues with a relative surface accessibility above 5% (Rubinstein et al., 2008).”
High-throughput prediction of protein antigenicity using protein microarray data (2010, background): “The goal of this article is to develop and test a predictor of protein antigenicity that can be used on a high-throughput scale on existing or new proteomes to identify key antigenic proteins that may have protective qualities and may be used in vaccine design applications.”
Machine Learning Methods for Protein Structure Prediction (2008, methodology): “For example, machine learning methods are being used to predict protein solubility [115], protein stability [116], protein signal peptides [117], [118], protein cellular localization [117], protein post-translation modification sites, such as phosphorilation sites [119], and protein epitopes [120]–[”
ElliPro: a new structure-based tool for the prediction of antibody epitopes (2008, methodology): “For DiscoTope, only one epitope is predicted, while for CEP no ranking is available to identify the top 10 predictions.”
Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “It was followed by DiscoTope [119], which, in addition to solvent accessibility, considered amino acid statistics and spatial information to predict conformational B-cell epitopes.”