Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Genomics. 2013 Oct;102(4):237-42. doi: 10.1016/j.ygeno.2013.05.006. Epub 2013 Jun 6.
Protein-protein interactions regulate a variety of cellular processes. There is a great need for computational methods as a complement to experimental methods with which to predict protein interactions due to the existence of many limitations involved in experimental techniques. Here, we introduce a novel evolutionary based feature extraction algorithm for protein-protein interaction (PPI) prediction. The algorithm is called PPIevo and extracts the evolutionary feature from Position-Specific Scoring Matrix (PSSM) of protein with known sequence. The algorithm does not depend on the protein annotations, and the features are based on the evolutionary history of the proteins. This enables the algorithm to have more power for predicting protein-protein interaction than many sequence based algorithms. Results on the HPRD database show better performance and robustness of the proposed method. They also reveal that the negative dataset selection could lead to an acute performance overestimation which is the principal drawback of the available methods.
蛋白质-蛋白质相互作用调节着多种细胞过程。由于实验技术存在许多限制，因此非常需要计算方法作为实验方法的补充，以预测蛋白质相互作用。在这里，我们引入了一种新的基于进化的蛋白质-蛋白质相互作用（PPI）预测特征提取算法。该算法称为 PPIevo，它从具有已知序列的蛋白质的位置特异性评分矩阵（PSSM）中提取进化特征。该算法不依赖于蛋白质注释，并且特征基于蛋白质的进化历史。这使得该算法在预测蛋白质-蛋白质相互作用方面比许多基于序列的算法更具优势。在 HPRD 数据库上的结果表明，该方法具有更好的性能和鲁棒性。它们还表明，负数据集选择可能导致性能的急剧高估，这是现有方法的主要缺点。
