Precise Antigen-Antibody Structure Predictions Enhance Antibody Development with HelixFold-Multimer
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
The accurate prediction of antigen-antibody structures is essential for
advancing immunology and therapeutic development, as it helps elucidate
molecular interactions that underlie immune responses. Despite recent progress
with deep learning models like AlphaFold and RoseTTAFold, accurately modeling
antigen-antibody complexes remains a challenge due to their unique evolutionary
characteristics. HelixFold-Multimer, a specialized model developed for this
purpose, builds on the framework of AlphaFold-Multimer and demonstrates
improved precision for antigen-antibody structures. HelixFold-Multimer not only
surpasses other models in accuracy but also provides essential insights into
antibody development, enabling more precise identification of binding sites,
improved interaction prediction, and enhanced design of therapeutic antibodies.
These advances underscore HelixFold-Multimer's potential in supporting antibody
research and therapeutic innovation.
