HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
This work addresses a critical bottleneck in structural biology for researchers studying protein-protein interactions, especially in therapeutic contexts, though it appears incremental as an advancement over existing models.
The paper tackles the challenge of predicting protein complex structures, particularly for cross-species interactions like antigen-antibody complexes, and reports that HelixFold-Multimer greatly surpasses AlphaFold 3 in accuracy for these cases.
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions, where accuracy often falls short. Limited by the accuracy of complex prediction, tasks based on precise protein-protein interaction analysis also face obstacles. In this report, we highlight the ongoing advancements of our protein complex structure prediction model, HelixFold-Multimer, underscoring its enhanced performance. HelixFold-Multimer provides precise predictions for diverse protein complex structures, especially in therapeutic protein interactions. Notably, HelixFold-Multimer achieves remarkable success in antigen-antibody and peptide-protein structure prediction, greatly surpassing AlphaFold 3. HelixFold-Multimer is now available for public use on the PaddleHelix platform, offering both a general version and an antigen-antibody version. Researchers can conveniently access and utilize this service for their development needs.