iCDI-PseFpt

iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. 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 extbf{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 extbf{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, extbf{iCDI-PseFpt} is freely accessible to the public at the website url{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.


References in zbMATH (referenced in 15 articles , 1 standard article )

Showing results 1 to 15 of 15.
Sorted by year (citations)

  1. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  2. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  3. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  4. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  5. Nasiri, Jaber; Naghavi, Mohammad Reza; Kayvanjoo, Amir Hossein; Nasiri, Mojtaba; Ebrahimi, Mansour: Precision assessment of some supervised and unsupervised algorithms for genotype discrimination in the genus \textitpisumusing SSR molecular data (2015)
  6. Chen, Xiao; Peng, Qinke; Han, Libin; Zhong, Tao; Xu, Tao: An effective haplotype assembly algorithm based on hypergraph partitioning (2014)
  7. Ding, Shuyan; Yan, Shoujiang; Qi, Shuhua; Li, Yan; Yao, Yuhua: A protein structural classes prediction method based on PSI-BLAST profile (2014)
  8. Lin, Thy-Hou; Tsai, Tsung-Lin: Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM (2014)
  9. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  10. Mondal, Sukanta; Pai, Priyadarshini P.: Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction (2014)
  11. Nanni, Loris; Lumini, Alessandra; Brahnam, Sheryl: A set of descriptors for identifying the protein-drug interaction in cellular networking (2014)
  12. Pavesi, Angelo: Prediction of the determinants of thermal stability by linear discriminant analysis: the case of the glutamate dehydrogenase protein family (2014)
  13. Podder, Avijit; Jatana, Nidhi; Latha, N.: Human dopamine receptors interaction network (DRIN): a systems biology perspective on topology, stability and functionality of the network (2014)
  14. Yang, Lei; Lv, Yingli; Li, Tao; Zuo, Yongchun; Jiang, Wei: Human proteins characterization with subcellular localizations (2014)
  15. Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen: iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints (2013)