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

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, and RoseTTAFoldNA, RhoFold+, NuFold and trRosettaRNA. For protein monomers, AlphaFold3 demonstrates improved local structural accuracy over AlphaFold2, though global accuracy gains are limited. In modeling general protein complexes, AlphaFold3 surpasses AlphaFold-Multimer in local structural prediction. For peptide-protein complexes, their performances are nearly indistinguishable, whereas on antigen-antibody complexes, AlphaFold3 is significantly superior. AlphaFold3 shows substantial superiority over RoseTTAFoldNA in protein-nucleic acid predictions, with significant gains in TM-score, local distance difference test scores, and interaction network fidelity scores, whereas for RNA multimers its advantage is limited to significant gains in local distance difference test scores. For RNA monomers, trRosettaRNA achieves higher global prediction accuracy. These results highlight AlphaFold3's ability to predict both structural detail and interactions, positioning it as a versatile tool for diverse biomolecular systems and suggesting promising applications in structural biology and molecular interaction research, while at the same time highlighting areas ripe for continuing improvements in performance.