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.

翻译：抗原-抗体结构的准确预测对于推动免疫学研究和治疗开发至关重要，因为它有助于阐明免疫反应背后的分子相互作用机制。尽管AlphaFold和RoseTTAFold等深度学习模型近期取得了进展，但由于抗原-抗体复合物独特的进化特征，对其进行精确建模仍然存在挑战。为此专门开发的HelixFold-Multimer模型基于AlphaFold-Multimer框架构建，在抗原-抗体结构预测精度上展现出显著提升。该模型不仅在准确性上超越其他现有模型，更为抗体开发提供了关键洞见：能够更精确地识别结合位点、改进相互作用预测，并优化治疗性抗体的设计。这些进展凸显了HelixFold-Multimer在支持抗体研究和治疗创新方面的巨大潜力。