Ibex可精准预测抗体等免疫蛋白的结合与未结合构象，助力药物设计。
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
- 通过有标注的结合/未结合结构对训练，区分蛋白不同构象。
- 在抗体等蛋白上实现当前最佳预测精度，且计算成本更低。
- 适合需要高精度构象预测的生物制药研发人员使用。
我们提出Ibex，一种泛免疫球蛋白结构预测模型，能在抗体、纳米抗体和T细胞受体的可变域建模中达到顶尖精度。与以往方法不同，Ibex通过在有标签的无配体（apo）和有配体（holo）结构对上训练，显式区分结合态与未结合态，从而在推理时准确预测两种构象。基于一个大规模私有高分辨率抗体结构数据集，Ibex在分布外性能上优于现有专用及通用蛋白结构预测工具。Ibex融合了前沿模型的高精度与显著降低的计算开销，为大分子药物设计与治疗开发提供可靠基础。
原文摘要 · Abstract (English)
We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Using a comprehensive private dataset of high-resolution antibody structures, we demonstrate superior out-of-distribution performance compared to existing specialized and general protein structure prediction tools. Ibex combines the accuracy of cutting-edge models with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。