A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.

ABSTRACT: Deep learning has significantly enhanced protein structure prediction, and AlphaFold2 marked a particular milestone among these methods for predicting protein monomer and complex structures. The AlphaFold3 represents a pivotal further advancement in biomolecular structure prediction, extending beyond proteins to model diverse assemblies. Despite attracting a huge number of users, there is still an absence of third-party benchmarks to fairly demonstrate the performance of the AlphaFold3. In this work, we benchmark AlphaFold3's performance across nine datasets, protein monomers, orphan proteins, alternative conformations, protein multimers, peptide-protein complexes, antigen-antibody complexes, RNA, RNA multimers, and protein-nucleic acid complexes, compared to AlphaFold2, AlphaFold-Multimer

SUBMITTER: Peng C 

PROVIDER: S-EPMC12661943 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature