Institute for Physical Science and Technology, University of Maryland, College Park 20742, United States.
Biophysics Program, University of Maryland, College Park 20742, United States.
J Chem Inf Model. 2024 Apr 8;64(7):2637-2644. doi: 10.1021/acs.jcim.3c01698. Epub 2024 Mar 7.
Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.
识别和发现可药用的蛋白质结合位点是计算机辅助药物发现的重要早期步骤,但这仍然是一项艰巨的任务,大多数研究都依赖于实验中结合位点的知识。在这里,我们提出了一种称为图注意力位点预测(GrASP)的结合位点预测方法,并重新评估了从数据集准备到模型评估的几乎每个站点预测工作流程步骤中的假设。GrASP 能够在恢复 PDB 结构中的结合位点方面实现最先进的性能,同时保持高精度,这将最大限度地减少下游任务(如对接和自由能扰动)中的计算浪费。