MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions

作者：Dejun Jiang, Zhaofeng Ye, Chang‐Yu Hsieh, Ziyi Yang, Xujun Zhang, Yu Kang, Hongyan Du, Zhenhua Wu, Jike Wang, Yundian Zeng, Haotian Zhang, Xiaorui Wang, Mingyang Wang, Xiaojun Yao, Shengyu Zhang, Jian Wu, Tingjun Hou · 发表于：Chemical Science · 年份：2023 · DOI：10.1039/d2sc06576b · 被引用次数：25 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science、Protein Structure and Dynamics

, PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.