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.

翻译：尽管单体蛋白质结构预测工具已展现出令人瞩目的准确性，但蛋白质复合物结构的预测仍是该领域一项艰巨挑战。这一挑战在涉及跨物种蛋白质链复合物（如抗原-抗体相互作用）的场景中尤为突出，其预测精度往往不足。受限于复合物预测的准确性，基于精确蛋白质-蛋白质相互作用分析的任务也面临障碍。本报告重点阐述了我们的蛋白质复合物结构预测模型HelixFold-Multimer的持续进展，强调了其性能的提升。HelixFold-Multimer能够为多种蛋白质复合物结构（尤其是治疗性蛋白质相互作用）提供精准预测。值得注意的是，HelixFold-Multimer在抗原-抗体及多肽-蛋白质结构预测中取得了显著成功，大幅超越了AlphaFold 3。目前，HelixFold-Multimer已在PaddleHelix平台上面向公众开放使用，提供通用版本与抗原-抗体版本。研究人员可便捷地访问并使用该服务，满足其开发需求。