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

ABSTRACT: The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to 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 modeling. In addition, it was 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

SUBMITTER: Spoendlin FC 

PROVIDER: S-EPMC10544996 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature