Xie Yuhao, Zhang Chengwei, Li Shimian, Du Xinyu, Lu Yanjiao, Wang Min, Hu Yingtong, Chen Zhenyu, Liu Sirui, Gao Yi Qin
Changping Laboratory, Beijing, China.
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Nat Methods. 2025 Sep 15. doi: 10.1038/s41592-025-02820-1.
Protein complex structure prediction is crucial for understanding of biological activities and advancing drug development. While various experimental methods can provide structural insights into protein complexes, the knowledge obtained is often sparse or approximate. A general tool is needed to integrate limited experimental information for high-throughput and accurate prediction. Here we introduce GRASP to efficiently and flexibly incorporate diverse forms of experimental information. GRASP outperforms existing tools in handling both simulated and real-world experimental restraints including those from crosslinking, covalent labeling, chemical shift perturbation and deep mutational scanning. For example, GRASP excels at predicting antigen-antibody complex structures, even surpassing AlphaFold3 when using experimental deep mutational scanning or covalent-labeling restraints. Beyond its accuracy and flexibility in restrained structure prediction, GRASP's ability to integrate multiple forms of restraints enables integrative modeling. We also showcase its potential in modeling protein structural interactome under near-cellular conditions using previously reported large-scale in situ crosslinking data for mitochondria.
蛋白质复合物结构预测对于理解生物活性和推进药物开发至关重要。虽然各种实验方法可以提供有关蛋白质复合物的结构见解，但所获得的知识往往是稀疏的或近似的。需要一种通用工具来整合有限的实验信息，以进行高通量和准确的预测。在这里，我们引入GRASP，以高效灵活地整合各种形式的实验信息。GRASP在处理模拟和实际实验限制（包括来自交联、共价标记、化学位移扰动和深度突变扫描的限制）方面优于现有工具。例如，GRASP擅长预测抗原-抗体复合物结构，在使用实验深度突变扫描或共价标记限制时，甚至超过了AlphaFold3。除了在受限结构预测中的准确性和灵活性之外，GRASP整合多种形式限制的能力还实现了整合建模。我们还展示了它在使用先前报道的线粒体大规模原位交联数据对近细胞条件下的蛋白质结构相互作用组进行建模方面的潜力。
