The Glutathione S-Transferases (GSTs) are detoxification enzymes which exist in variety of living organisms such as bacteria, fungi, plants and animals. These multifunctional enzymes play important roles in the biosynthesis of steroids, prostaglandins, apoptosis regulation, and stress signaling. In this study, we designed a method to independently predict the structures of animal, fungal and plant GSTs using Chou’s pseudo-amino acid composition concept. Support vector machine (SVM), Random Forests (RF), Covariance Discrimination (CD) and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) were used as powerful machine learnings algorithms. Based on our results, Random Forests demonstrated the best prediction for animal GSTs with 0.9339 accuracy and SVM showed the best results for fungal and plant GSTs with 0.8982 and 0.9655 accuracy, respectively. Our study provided an effective prediction for GSTs based on the concept of PseAAC and four different machine learning algorithms.

谷胱甘肽S-转移酶（GST）是排毒酶，存在于多种生物中，例如细菌，真菌，植物和动物。这些多功能酶在类固醇，前列腺素，细胞凋亡调节和应激信号转导的生物合成中起重要作用。在这项研究中，我们设计了一种方法，该方法使用Chou的假氨基酸组成概念独立预测动物，真菌和植物GST的结构。支持向量机（SVM），随机森林（RF），协方差判别（CD）和优化的证据理论K最近邻（OET-KNN）被用作强大的机器学习算法。根据我们的结果，Random Forests对动物GST的预测最佳，准确度为0.9339，SVM对真菌和植物GST的预测最佳，准确度为0.8982和0.9655，分别。我们的研究基于PseAAC的概念和四种不同的机器学习算法为GST提供了有效的预测。