Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces

Abstract

<jats:title>Abstract</jats:title><jats:p>Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the “ground truth” of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. In order to increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. The framework leverages three key aspects of antibody-antigen interactions in order to learn predictive structural representations: (1) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (2) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (3) since more data is available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction.</jats:p>

A Structure-based B-cell Epitope Prediction Model Through Combing Local and Global Features

“…In order to make fair comparison, we use the same antibodyantigen complexes as PECAN [15]. Those complexes are from two separate datasets: EpiPred [13] and Docking Benchmarking Dataset (DBD) v5 [22].…”

“…As the name implies, the sequence-based approaches predict BCEs only based on the antigen sequence, while the structure-based approaches also consider its structural features. Currently, various structure-based predictors have been developed to predict and analyze BCEs including BeTop [8], Bpredictor [9], DiscoTope-2.0 [10], CE-KEG [11], CeePre [12], EpiPred [13], ASE Pred [14] and PECAN [15]. Some of those methods improve model performance by introducing novel features such as statistical features in Be-Top, thick surface patch in Bpredictor, new spatial neighborhood definition and half-sphere exposure in DiscoTope-2.0, knowledge-based energy and geometrical neighboring residue contents in CE-KEG, B factor in CeePre and surface patches in ASE Pred.…”

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition

“…Docking scores/ranks could be taken into account in order to determine the most important regions. In addition to physically-based docking methods, additional data-driven epitope prediction methods could be used to identify putative antigenic regions [ 65 , 66 , 67 ]. Dock binning.…”

Methods for sequence and structural analysis of B and T cell receptor repertoires

“…MAbTope [97] predicts epitope residues based on consensus epitopes shared by top-ranked poses; the success of this approach depends on the quality of the docking. PECAN [132] predicts binding interfaces on both antibodies and antigens by learning context-aware structural representations; it applies a unified deep learning framework that consists of a combination of graph convolutional networks, attention and transfer learning. Although there is a clear awareness of the importance of antibody information in epitope prediction, the traditional antigen-centric methods cannot easily be extended to include such information.…”