论文标题
深度多属性图表对蛋白质结构学习
Deep Multi-attribute Graph Representation Learning on Protein Structures
论文作者
论文摘要
图作为一种数据结构的类型最近引起了极大的关注。几何图表的表示学习在包括分子，社会和金融网络在内的许多领域取得了巨大的成功。自然地将蛋白质作为图表，其中节点表示残基和边缘表示残基之间的成对相互作用。但是，很少直接研究3D蛋白结构。挑战包括：1）蛋白质是由数千原子组成的复杂大分子，使其比微分子更难建模。 2）捕获蛋白质结构建模的远程成对关系仍然不足。 3）很少有研究重点是一起学习蛋白质的不同属性。为了应对上述挑战，我们提出了一个新的图神经网络结构，以将蛋白质表示为3D图，并一起预测距离几何图表示和二面的几何图表示。这给出了重要的优势，因为该网络从序列到结构开辟了新的路径。我们在四个不同的数据集上进行了广泛的实验，并证明了该方法的有效性。
Graphs as a type of data structure have recently attracted significant attention. Representation learning of geometric graphs has achieved great success in many fields including molecular, social, and financial networks. It is natural to present proteins as graphs in which nodes represent the residues and edges represent the pairwise interactions between residues. However, 3D protein structures have rarely been studied as graphs directly. The challenges include: 1) Proteins are complex macromolecules composed of thousands of atoms making them much harder to model than micro-molecules. 2) Capturing the long-range pairwise relations for protein structure modeling remains under-explored. 3) Few studies have focused on learning the different attributes of proteins together. To address the above challenges, we propose a new graph neural network architecture to represent the proteins as 3D graphs and predict both distance geometric graph representation and dihedral geometric graph representation together. This gives a significant advantage because this network opens a new path from the sequence to structure. We conducted extensive experiments on four different datasets and demonstrated the effectiveness of the proposed method.
