Recent Progress in Antibody Epitope Prediction

Abstract

Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.

Keywords: antibody; antibody design; artificial intelligence; epitope.

Figure 1

Illustration of a surface spiral vector. The table lists the shortest distance for a group of neighboring surface residues. From reference [61].

Figure 2

(A) Representation of the method and definition of Conjoint Triad amino acid classes on a sequence level. (B) Conjoint Triad classes mapped to an epitope patch structure. (C) Illustration of the three principal components on an epitope patch. (D) The 4th order of Zernike Moments’ descriptive shape excluding orders 0 and 1. (E) Correlation matrix of structural and physicochemical features of the true paired paratope and epitope patches. From reference [73].

Figure 3

A structural interaction motif notation that accounts simultaneously for gaps and interacting residues in both paratopes and epitopes. Reproduced from reference [74].