Qian Yuqing, Ding Yijie, Zou Quan, Guo Fei
IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar-Apr;20(2):1234-1245. doi: 10.1109/TCBB.2022.3191325. Epub 2023 Apr 3.
Membrane proteins are the main undertaker of biomembrane functions and play a vital role in many biological activities of organisms. Prediction of membrane protein types has a great help in determining the function of proteins and understanding the interactions of membrane proteins. However, the biochemical experiment is expensive and not suitable for the large-scale identification of membrane protein types. Therefore, computational methods were used to improve the efficiency of biological experiments. Most existing computational methods only use a single feature of protein, or use multiple features but do not integrate these well. In our study, the protein sequence is described via three different views (features), including amino acid composition, evolutionary information and physicochemical properties of amino acids. To exploit information among all views (features), we introduce a coupling strategy for Kernel Sparse Representation based Classification (KSRC) and construct a new model called Multi-view KSRC (MvKSRC). We implement our method on 4 benchmark data sets of membrane proteins. The comparison results indicate that our method is much superior to all existing methods.
膜蛋白是生物膜功能的主要承担者，在生物体的许多生物活动中发挥着至关重要的作用。膜蛋白类型的预测对于确定蛋白质的功能以及理解膜蛋白的相互作用有很大帮助。然而，生化实验成本高昂，并不适合大规模鉴定膜蛋白类型。因此，人们采用计算方法来提高生物实验的效率。大多数现有的计算方法只使用蛋白质的单一特征，或者使用多个特征但没有很好地整合这些特征。在我们的研究中，通过三种不同的视角（特征）来描述蛋白质序列，包括氨基酸组成、进化信息和氨基酸的物理化学性质。为了利用所有视角（特征）之间的信息，我们为基于核稀疏表示的分类（KSRC）引入了一种耦合策略，并构建了一个名为多视角KSRC（MvKSRC）的新模型。我们在4个膜蛋白基准数据集上实现了我们的方法。比较结果表明，我们的方法远优于所有现有方法。
