School of Information Engineering, Xijing University, Xi'an 710123, China.
Molecules. 2021 Sep 3;26(17):5359. doi: 10.3390/molecules26175359.
Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.
鉴定药物-靶点相互作用（DTIs）对于药物发现至关重要。然而，传统的生物学方法存在一些不可避免的缺点，例如耗时且昂贵。因此，迫切需要开发新颖有效的计算方法来预测 DTI，以缩短新药的开发周期。在这项研究中，我们提出了一种新的计算方法来识别 DTI，该方法使用蛋白质序列信息和双树复小波变换（DTCWT）。更具体地说，对靶蛋白序列进行位置特异性评分矩阵（PSSM）以获得其进化信息。然后，使用 DTCWT 从 PSSM 中提取代表性特征，然后将这些特征与药物指纹特征相结合形成特征描述符。最后，将这些描述符发送到旋转森林（RoF）模型进行分类。我们在四个数据集（酶、离子通道、G 蛋白偶联受体（GPCRs）和核受体（NRs））上采用 5 折交叉验证（CV）来验证所提出的模型；我们的方法分别产生了 89.21%、85.49%、81.02%和 74.44%的高平均准确率。为了进一步验证我们模型的性能，我们将 RoF 分类器与两种最先进的算法：支持向量机（SVM）和 K 最近邻（KNN）分类器进行了比较。我们还将其与其他一些已发表的方法进行了比较。此外，对独立数据集的预测结果进一步表明，我们的方法对于预测潜在的 DTI 是有效的。因此，我们相信我们的方法适用于促进药物发现和开发。
