Zheng Jie, Xiao Xuan, Qiu Wang-Ren
Department of Computer Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
Front Genet. 2021 Sep 9;12:738274. doi: 10.3389/fgene.2021.738274. eCollection 2021.
Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer's disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel-drug, computational approaches are effective and efficient compared with the costly, labor-intensive, and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channels of knowing 3D structures; however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge, while most of their results need to be improved, or predicting web servers are missing. In this paper, a sequence-based classifier, called "iCDI-W2vCom," was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILES-word2vec, FP2-word2vec, SMILES-node2vec, and ECFPs a 1184D vector, ion channel was represented by the word2vec a 64D vector, and the prediction engine was operated by the LightGBM classifier. The accuracy and AUC achieved by iCDI-W2vCom the fivefold cross validation were 91.95% and 0.9703, which outperformed other existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be a potential method for predicting target-drug interaction.
离子通道是第二大药物靶点家族。离子通道功能障碍可能导致多种疾病，如阿尔茨海默病、癫痫、头痛和II型糖尿病。在预测离子通道-药物的研究工作中，与成本高、劳动强度大且耗时的实验方法相比，计算方法有效且高效。现有的大多数方法只能用于处理已知三维结构的离子通道；然而，大多数离子通道的三维结构仍然未知。为应对这一挑战，开发了许多基于蛋白质序列的预测器，但其大多数结果仍需改进，或者预测网络服务器缺失。本文开发了一种基于序列的分类器，称为“iCDI-W2vCom”，用于识别离子通道与药物之间的相互作用。在该预测器中，药物化合物由SMILES-word2vec、FP2-word2vec、SMILES-node2vec和ECFPs（一个1184维向量）表示，离子通道由word2vec（一个64维向量）表示，预测引擎由LightGBM分类器运行。iCDI-W2vCom在五折交叉验证中获得的准确率和AUC分别为91.95%和0.9703，优于该领域其他现有的预测器。在http://www.jci-bioinfo.cn/icdiw2v上建立了一个用户友好的iCDI-W2vCom网络服务器。所提出的方法也可能是预测靶点-药物相互作用的一种潜在方法。
