Department of Information Engineering, Xijing University, Xi'an 710123, China.
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
Int J Mol Sci. 2018 Mar 29;19(4):1029. doi: 10.3390/ijms19041029.
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular biology. As researchers have become aware of the importance of computational methods in predicting PPIs, many techniques have been developed for performing this task computationally. However, there are few technologies that really meet the needs of their users. In this paper, we develop a novel and efficient sequence-based method for predicting PPIs. The evolutionary features are extracted from the position-specific scoring matrix (PSSM) of protein. The features are then fed into a robust relevance vector machine (RVM) classifier to distinguish between the interacting and non-interacting protein pairs. In order to verify the performance of our method, five-fold cross-validation tests are performed on the dataset. A high accuracy of 94.56%, with 94.79% sensitivity at 94.36% precision, was obtained. The experimental results illustrated that the proposed approach can extract the most significant features from each protein sequence and can be a bright and meaningful tool for the research of proteomics.
蛋白质-蛋白质相互作用 (PPI) 是细胞周期、DNA 复制和细胞信号转导中蛋白质功能和调节的关键。因此，检测一对蛋白质是否相互作用对于分子生物学的研究非常重要。由于研究人员已经意识到计算方法在预测 PPI 中的重要性，因此已经开发了许多用于通过计算执行此任务的技术。然而，很少有技术真正满足用户的需求。在本文中，我们开发了一种新颖而有效的基于序列的方法来预测 PPI。从蛋白质的位置特异性评分矩阵 (PSSM) 中提取进化特征。然后，将这些特征输入到强大的相关向量机 (RVM) 分类器中，以区分相互作用和非相互作用的蛋白质对。为了验证我们方法的性能，在数据集上进行了五重交叉验证测试。获得了 94.56%的高精度，94.79%的灵敏度和 94.36%的精度。实验结果表明，所提出的方法可以从每个蛋白质序列中提取最显著的特征，并且可以成为蛋白质组学研究的一个有希望和有意义的工具。
