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.

MOTIVATION: Accurate protein function prediction is crucial for understanding biological processes and advancing biomedical research. However, the rapid growth of protein sequences far outpaces the experimental characterization of their functions, necessitating the development of automated computational methods. RESULTS: We present InterLabelGO+, a hybrid approach that integrates a deep learning-based method with an alignment-based method for improved protein function prediction. InterLabelGO+ incorporates a novel loss function that addresses label dependency and imbalance and further enhances performance through dynamic weighting of the alignment-based component. A preliminary version of InterLabelGO+ achieved a strong performance in the CAFA5 challenge, ranking sixth out of 1625 participating teams. Comprehensive evaluations on large-scale protein function prediction tasks demonstrate InterLabelGO+'s ability to accurately predict Gene Ontology terms across various functional categories and evaluation metrics. AVAILABILITY AND IMPLEMENTATION: The source code and datasets for InterLabelGO+ are freely available on GitHub at https://github.com/QuanEvans/InterLabelGO. A web-server is available at https://seq2fun.dcmb.med.umich.edu/InterLabelGO/. The software is implemented in Python and PyTorch, and is supported on Linux and macOS.