Structure-Based Epitope Profiling with the Structural Profiling of Antibodies to Cluster by Epitope 2 (SPACE2) Algorithm.

Computational epitope profiling methods group antibodies that bind to the same epitope. They can be used to predict epitopes and reduce the number of antibodies that need to be characterized experimentally. Conventionally, computational epitope profiling is achieved by clustering antibodies by sequence similarity. While sequence-similar antibodies are likely to share a common function, such methods neglect that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. The SPACE2 algorithm described here is an epitope profiling method that clusters antibodies based on the structural similarity of models predicted with a state-of-the-art protein structure prediction tool. SPACE2 accurately clusters antibodies that engage the same epitope and exhibits far greater data coverage than conventional methods. Furthermore, SPACE2 detects signals of functional convergence and, unlike sequence-based methods, is able to link antibodies diverse in sequence, genetic lineage, and species origin. These results reiterate that structural data provides orthogonal information to sequence and improves our ability to study antibodies and their epitopes.