iCTX-Type: A Sequence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels. Conotoxins are small disulfide-rich neurotoxic peptides, which can bind to ion channels with very high specificity and modulate their activities. Over the last few decades, conotoxins have been the drug candidates for treating chronic pain, epilepsy, spasticity, and cardiovascular diseases. According to their functions and targets, conotoxins are generally categorized into three types: potassium-channel type, sodium-channel type, and calcium-channel types. With the avalanche of peptide sequences generated in the postgenomic age, it is urgent and challenging to develop an automated method for rapidly and accurately identifying the types of conotoxins based on their sequence information alone. To address this challenge, a new predictor, called iCTX-Type, was developed by incorporating the dipeptide occurrence frequencies of a conotoxin sequence into a 400-D (dimensional) general pseudoamino acid composition, followed by the feature optimization procedure to reduce the sample representation from 400-D to 50-D vector. The overall success rate achieved by iCTX-Type via a rigorous cross-validation was over 91%, outperforming its counterpart (RBF network). Besides, iCTX-Type is so far the only predictor in this area with its web-server available, and hence is particularly useful for most experimental scientists to get their desired results without the need to follow the complicated mathematics involved.

References in zbMATH (referenced in 21 articles )

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  1. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  2. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  3. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  4. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  5. 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)
  6. Dehzangi, Abdollah; López, Yosvany; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok: PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction (2017)
  7. Khan, Muslim; Hayat, Maqsood; Khan, Sher Afzal; Ahmad, Saeed; Iqbal, Nadeem: Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (2017)
  8. Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)
  9. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  10. Mishra, Avdesh; Iqbal, Sumaiya; Hoque, Md Tamjidul: Discriminate protein decoys from native by using a scoring function based on ubiquitous phi and psi angles computed for all atom (2016)
  11. Yang, Lianping; Zhang, Xiangde; Fu, Haoyue; Yang, Chenhui: An estimator for local analysis of genome based on the minimal absent word (2016)
  12. Aram, Reza Zohouri; Charkari, Nasrollah Moghadam: A two-layer classification framework for protein fold recognition (2015)
  13. Ding, Yanrui; Wang, Xueqin; Mou, Zhaolin: Communities in the iron superoxide dismutase amino acid network (2015)
  14. Ganjtabesh, Mohammad; Montaseri, Soheila; Zare-Mirakabad, Fatemeh: Using temperature effects to predict the interactions between two RNAs (2015)
  15. Ju, Zhe; Cao, Jun-Zhe; Gu, Hong: iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou’s general PseAAC (2015)
  16. Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali: Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model (2015)
  17. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (2015)
  18. Kumar, Ravindra; Srivastava, Abhishikha; Kumari, Bandana; Kumar, Manish: Prediction of (\beta)-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine (2015)
  19. Ruiz-Blanco, Yasser B.; Marrero-Ponce, Yovani; Prieto, Pablo J.; Salgado, Jesús; García, Yamila; Sotomayor-Torres, Clivia M.: A Hooke’s law-based approach to protein folding rate (2015)
  20. Zhang, Qiang; Li, Hong; Zhao, Xiaoqing; Zheng, Yan; Zhou, Deliang: Distribution bias of the sequence matching between exons and introns in exon joint and EJC binding region in \textitC. elegans (2015)

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