Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China; Information School, Zhe-Jiang Textile & Fashion College, Ning-Bo 315211, China; Gordon Life Science Institute, 53 South Cottage Road, Belmont, MA 02478, United States.
Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China.
J Theor Biol. 2013 Nov 21;337:71-9. doi: 10.1016/j.jtbi.2013.08.013. Epub 2013 Aug 26.
Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug-target interaction networks.
许多生命中的关键功能，如心跳、感觉转导和中枢神经系统反应，都是通过各种离子通道的细胞信号来控制的。因此，离子通道已成为一个极好的药物靶点，研究离子通道-药物相互作用网络是药物开发的一个重要课题。然而，通过实验技术确定药物与蛋白质离子通道在细胞网络中是否相互作用既耗时又昂贵。尽管在这方面已经开发了一些基于蛋白质 3D（三维）结构知识的计算方法，但不幸的是，由于大多数蛋白质离子通道的 3D 结构仍然未知，它们的使用相当有限。随着后基因组时代产生的蛋白质序列的大量涌现，非常需要开发基于序列的计算方法来解决这个问题。为了迎接这一挑战，我们开发了一种新的预测器，称为 iCDI-PseFpt，其中蛋白质离子通道样本由灰色模型理论生成的 PseAAC（伪氨基酸组成）、药物化合物由 2D 分子指纹表示，而操作引擎是模糊 K-最近邻算法。通过 Jackknife 交叉验证，iCDI-PseFpt 的总体成功率达到 87.27%，明显高于该领域任何现有预测器的成功率。作为一个用户友好的网络服务器，iCDI-PseFpt 可在网站 http://www.jci-bioinfo.cn/iCDI-PseFpt/ 上免费供公众使用。此外，为了大多数实验科学家的方便，我们提供了一个分步指南，说明如何使用网络服务器获得所需的结果，而无需为了完整性而遵循论文中呈现的复杂数学方程。我们注意到，当前的方法也可用于研究其他药物-靶标相互作用网络。
