论文标题
基于粗粒距离矩阵的图形卷积神经网络基于柔性对接
Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix
论文作者
论文摘要
在计算结构生物学和药物设计中，对柔性蛋白质蛋白质配合物的预测仍然是一个具有挑战性的问题。在这里，我们提供了两种新型的深层神经网络方法，与标准对接相比，在大型多样的蛋白质系统集合中，结合模式预测的效率和准确性显着提高。尽管第一个图形卷积网络用于重新排列，但第二种方法旨在生成和排名与标准对接方法无关。这种新颖的方法依赖于配体原子和蛋白质C_alpha原子之间距离矩阵的预测，从而隐含了侧链柔韧性。
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matrices between ligand atoms and protein C_alpha atoms thus incorporating side-chain flexibility implicitly.
