Cross-reactivity occurs when an antibody binds to the epitope of a protein that is not the targeted antigen. This is problematic in the analysis of immunoassay diagnostics. Detecting a protein incorrectly might cause issues such as incorrect mapping of metabolic conditions for research or diagnosis. In this study, articles have been collected within two main fields. The first of which is focused on bioinformatic tools to predict cross-reactivity risk and the second field investigates how single substitutions affect the antibody-antigen binding. The results from the collected articles were analyzed with the aim of providing as much information surrounding the topic as possible, to gain a further understanding of how protein similarities impact cross-reactivity. FASTA alignments proved to be efficient in classifying cross-reactive proteins based on sequence similarity. Moreover, epitope analysis, using PD tool or Cross-React, can provide an even more precise subset of proteins with risk of causing cross-reactivity. Individual residues of the epitopes of the subset can then be analyzed. Specific residue’s physicochemical properties such as hydrophobicity, polarity, size and charge have proven to be relevant for the binding affinity, with charge having the largest impact. The position of an amino acid has also shown great importance. More centrally located amino acids within the epitope contribute more to paratope affinity than those on the outer positions. However, a conclusive classifier based on specific residues within epitopes is difficult to implement in cross-reactivity analysis. A workflow of the different prediction steps has been constructed into a workflow that may be implemented as an automated pipeline in the future.

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.