浙江大学郑明月团队的最新工作表明，SurfDock是一种基于表面信息的扩散生成模型，可用于可靠且精确的蛋白质-配体复合物预测。相关论文于2024年11月27日在线发表于国际学术期刊《自然—方法学》。

研究人员表示，准确预测蛋白质-配体相互作用对理解细胞过程至关重要。

研究人员介绍了 SurfDock，一种深度学习方法，通过将蛋白质序列、三维结构图和表面特征整合到一个等变架构中，解决了这一挑战。SurfDock采用了一种生成性扩散模型，作用于非欧几里得流形，优化分子平移、旋转和扭转，以生成可靠的结合姿势。

研究人员在多个基准测试中的广泛评估表明，SurfDock在对接成功率和遵循物理约束方面优于现有方法。它还在未见过的蛋白质和预测的apo结构上表现出显著的泛化能力，同时在虚拟筛选任务中实现了最先进的性能。

在一个真实世界的应用中，SurfDock在针对醛脱氢酶1B1（细胞代谢中的关键酶）的虚拟筛选项目中发现了七种新型命中分子。这展示了SurfDock揭示细胞过程分子机制的能力。这些结果突显了SurfDock作为结构生物学领域一项变革性工具的潜力，提供了更高的准确性、物理合理性和实际应用性，并帮助理解蛋白质-配体相互作用。

附：英文原文

Title: SurfDock is a surface-informed diffusion generative model for reliable and accurate protein–ligand complex prediction

Author: Cao, Duanhua, Chen, Mingan, Zhang, Runze, Wang, Zhaokun, Huang, Manlin, Yu, Jie, Jiang, Xinyu, Fan, Zhehuan, Zhang, Wei, Zhou, Hao, Li, Xutong, Fu, Zunyun, Zhang, Sulin, Zheng, Mingyue

Issue&Volume: 2024-11-27

Abstract: Accurately predicting protein–ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, optimizing molecular translations, rotations and torsions to generate reliable binding poses. Our extensive evaluations across various benchmarks demonstrate SurfDock’s superiority over existing methods in docking success rates and adherence to physical constraints. It also exhibits remarkable generalizability to unseen proteins and predicted apo structures, while achieving state-of-the-art performance in virtual screening tasks. In a real-world application, SurfDock identified seven novel hit molecules in a virtual screening project targeting aldehyde dehydrogenase 1B1, a key enzyme in cellular metabolism. This showcases SurfDock’s ability to elucidate molecular mechanisms underlying cellular processes. These results highlight SurfDock’s potential as a transformative tool in structural biology, offering enhanced accuracy, physical plausibility and practical applicability in understanding protein–ligand interactions.

DOI: 10.1038/s41592-024-02516-y

Source:https://www.nature.com/articles/s41592-024-02516-y