Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind the same epitope

Abstract

The function of an antibody is intrinsically linked to which epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modelling. It was also only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning based structure prediction technology combined with a novel clustering protocol and benchmark it on binding data that has epitope level resolution. On six diverse sets of antigen specific antibodies we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher data set coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improves our ability to identify antibodies that bind the same epitope, adding information to sequence-based methods, especially in data sets of antibodies from diverse sources. SPACE2 is openly available on Github ( https://github.com/oxpig/SPACE2 ).