Protein-protein interactions (PPIs) play a crucial role in the life cycles of living cells. Thus, it is important to understand the underlying mechanisms of PPIs. Although many high-throughput technologies have generated large amounts of PPI data in different organisms, the experiments for detecting PPIs are still costly and time-consuming. Therefore, novel computational methods are urgently needed for predicting PPIs. For this reason, developing a new computational method for predicting PPIs is drawing more and more attention. In this study, we proposed a novel computational method based on texture feature of protein sequence for predicting PPIs. Especially, the Gabor feature is used to extract texture feature and protein evolutionary information from Position-Specific Scoring Matrix, which is generated by Position-Specific Iterated Basic Local Alignment Search Tool. Then, random forest–based classifiers are used to infer the protein interactions. When performed on PPI data sets of yeast, human, and Helicobacter pylori, we obtained good results with average accuracies of 92.10%, 97.03%, and 86.45%, respectively. To better evaluate the proposed method, we compared Gabor feature, Discrete Cosine Transform, and Local Phase Quantization. Our results show that the proposed method is both feasible and stable and the Gabor feature descriptor is reliable in extracting protein sequence information. Furthermore, additional experiments have been conducted to predict PPIs of other 4 species data sets. The promising results indicate that our proposed method is both powerful and robust.

蛋白质-蛋白质相互作用（PPI）在活细胞的生命周期中起着至关重要的作用。因此，了解PPI的基本机制很重要。尽管许多高通量技术已在不同生物体中生成了大量PPI数据，但是检测PPI的实验仍然是昂贵且费时的。因此，迫切需要新颖的计算方法来预测PPI。因此，开发一种新的预测PPI的计算方法引起了越来越多的关注。在这项研究中，我们提出了一种新的基于蛋白质序列纹理特征的预测PPI的计算方法。特别是，Gabor特征用于从特定位置评分矩阵中提取纹理特征和蛋白质进化信息，由特定于位置的迭代基本局部路线搜索工具生成。然后，使用基于森林的随机分类器来推断蛋白质相互作用。当对PPI数据集执行酵母，人类和幽门螺杆菌，我们获得了良好的结果分别为92.10％，97.03％，86.45和％，平均精度。为了更好地评估所提出的方法，我们比较了Gabor特征，离散余弦变换和局部相位量化。我们的结果表明，该方法既可行又稳定，Gabor特征描述符在提取蛋白质序列信息方面是可靠的。此外，还进行了其他实验来预测其他4种数据集的PPI。有希望的结果表明，我们提出的方法既强大又鲁棒。