PLA-SGCN：通过整合相似对与半监督图卷积网络进行蛋白质-配体结合亲和力预测
PLA-SGCN: Protein-Ligand Binding Affinity Prediction by Integrating Similar Pairs and Semi-supervised Graph Convolutional Network
AI总结：
本研究针对蛋白质-配体结合亲和力预测问题，提出PLA-SGCN端到端框架，通过流形平滑约束检索困难相似对、自动构建图并结合半监督GCN，在四个基准数据集上性能显著优于现有方法。
摘要：
蛋白质-配体结合亲和力（PLA）预测的目标是预测配体是否能与蛋白质序列结合。近年来，深度学习在PLA预测领域受到了广泛关注。基于深度学习的方法包含两个步骤：特征提取步骤和任务预测步骤。许多基于深度学习的方法专注于引入新的特征提取网络，或是整合蛋白质-蛋白质相互作用网络、基因本体知识等辅助知识，随后仅使用若干全连接层来设计任务预测网络。本文旨在利用半监督图卷积网络（GCN），将检索到的相似困难蛋白质-配体对整合到PLA预测的任务预测步骤中。研究基于流形平滑性约束，为每个输入查询样本检索困难蛋白质-配体对，随后自动学习一个图：图中每个节点代表一个蛋白质-配体对，每条边代表对与对之间的相似性。换言之，本文提出了一个端到端框架，该框架可同时完成困难相似样本检索、蛋白质-配体描述符学习、输入样本与检索到的困难相似样本的图拓扑学习（即邻接矩阵学习），以及学习半监督GCN作为任务预测器来预测结合亲和力。训练阶段会调整参数值，推理阶段则针对每个输入样本对已学习的模型进行微调。为评估所提方法的性能，研究将其应用于PDBbind、Davis、KIBA和BindingDB这四个知名数据集。结果表明，所提方法的性能显著优于同类对比方法。
英文摘要：
The protein-ligand binding affinity (PLA) prediction goal is to predict whether or not the ligand could bind to a protein sequence. Recently, in PLA prediction, deep learning has received much attention. Two steps are involved in deep learning-based approaches: feature extraction and task prediction step. Many deep learning-based approaches concentrate on introducing new feature extraction networks or integrating auxiliary knowledge like protein-protein interaction networks or gene ontology knowledge. Then, a task prediction network is designed simply using some fully connected layers. This paper aims to integrate retrieved similar hard protein-ligand pairs in PLA prediction (i.e., task prediction step) using a semi-supervised graph convolutional network (GCN). Hard protein-ligand pairs are retrieved for each input query sample based on the manifold smoothness constraint. Then, a graph is learned automatically in which each node is a protein-ligand pair, and each edge represents the similarity between pairs. In other words, an end-to-end framework is proposed that simultaneously retrieves hard similar samples, learns protein-ligand descriptor, learns the graph topology of the input sample with retrieved similar hard samples (learn adjacency matrix), and learns a semi-supervised GCN to predict the binding affinity (as task predictor). The training step adjusts the parameter values, and in the inference step, the learned model is fine-tuned for each input sample. To evaluate the proposed approach, it is applied to the four well-known PDBbind, Davis, KIBA, and BindingDB datasets. The results show that the proposed method significantly performs better than the comparable approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
