论文标题
E3-targetPred：使用深层空间编码对E3-target蛋白的预测
E3-targetPred: Prediction of E3-Target Proteins Using Deep Latent Space Encoding
论文作者
论文摘要
了解E3连接酶和靶标底物相互作用对于细胞生物学和治疗发育至关重要。但是，由于实验的劳动密集型性质，E3目标关系的实验鉴定并不是一件容易的事。在本文中，首次提出了基于序列的E3目标预测模型。所提出的框架利用K间距氨基酸对（CKSAAP）的组成来学习E3连接酶与其靶蛋白之间的关系。还设计了类可分开的潜在空间编码方案，该方案提供了特征空间的压缩表示。进行了彻底的消融研究，以确定CKSAAP的最佳差距大小以及可以成功代表E3目标关系的潜在变量的数量。在独立数据集上评估了所提出的方案，以进行多种标准定量测量。特别是，它的平均准确性在独立数据集中达到了70.63美元\％$。研究中使用的源代码和数据集可在作者的GitHub页面（https://github.com/psychemistz/e3targetPred）上获得。
Understanding E3 ligase and target substrate interactions are important for cell biology and therapeutic development. However, experimental identification of E3 target relationships is not an easy task due to the labor-intensive nature of the experiments. In this article, a sequence-based E3-target prediction model is proposed for the first time. The proposed framework utilizes composition of k-spaced amino acid pairs (CKSAAP) to learn the relationship between E3 ligases and their target protein. A class separable latent space encoding scheme is also devised that provides a compressed representation of feature space. A thorough ablation study is performed to identify an optimal gap size for CKSAAP and the number of latent variables that can represent the E3-target relationship successfully. The proposed scheme is evaluated on an independent dataset for a variety of standard quantitative measures. In particular, it achieves an average accuracy of $70.63\%$ on an independent dataset. The source code and datasets used in the study are available at the author's GitHub page (https://github.com/psychemistz/E3targetPred).
