Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016 (2016), Article ID 3981478, 5 pages
http://dx.doi.org/10.1155/2016/3981478
Research Article

Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition

1College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
2Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China

Received 13 July 2016; Accepted 31 July 2016

Academic Editor: Ren-Zhi Cao

Copyright © 2016 Yun Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. L. Daly and D. J. Craik, “Structural studies of conotoxins,” IUBMB Life, vol. 61, no. 2, pp. 144–150, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Liao, Y. Ju, and Q. Zou, “Prediction of G protein-coupled receptors with SVM-prot features and random forest,” Scientifica, vol. 2016, Article ID 8309253, 10 pages, 2016. View at Publisher · View at Google Scholar
  3. H. Terlau and B. M. Olivera, “Conus venoms: a rich source of novel ion channel-targeted peptides,” Physiological Reviews, vol. 84, no. 1, pp. 41–68, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. T. S. Han, R. W. Teichert, B. M. Olivera, and G. Bulaj, “Conus venoms—a rich source of peptide-based therapeutics,” Current Pharmaceutical Design, vol. 14, no. 24, pp. 2462–2479, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. M. R. Watters, “Tropical marine neurotoxins: venoms to drugs,” Seminars in Neurology, vol. 25, no. 3, pp. 278–289, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Mondal, R. Bhavna, R. M. Babu, and S. Ramakumar, “Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification,” Journal of Theoretical Biology, vol. 243, no. 2, pp. 252–260, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. H. Lin and Q.-Z. Li, “Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant,” Biochemical and Biophysical Research Communications, vol. 354, no. 2, pp. 548–551, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. Q. Zou, Q. Hu, M. Guo, and G. Wang, “HAlign: fast multiple similar DNA/RNA sequence alignment based on the centre star strategy,” Bioinformatics, vol. 31, no. 15, pp. 2475–2481, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. N. Zaki, F. Sibai, and P. Campbell, “Conotoxin protein classification using pairwise comparison and amino acid composition,” in Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO '11), pp. 323–330, ACM, Dublin, Ireland, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Zaki, S. Wolfsheimer, G. Nuel, and S. Khuri, “Conotoxin protein classification using free scores of words and support vector machines,” BMC Bioinformatics, vol. 12, article 217, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. Y.-X. Fan, J. Song, X. Kong, and H.-B. Shen, “PredCSf: an integrated feature-based approach for predicting conotoxin superfamily,” Protein and Peptide Letters, vol. 18, no. 3, pp. 261–267, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J.-B. Yin, Y.-X. Fan, and H.-B. Shen, “Conotoxin superfamily prediction using diffusion maps dimensionality reduction and subspace classifier,” Current Protein and Peptide Science, vol. 12, no. 6, pp. 580–588, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Koua, A. Brauer, S. Laht et al., “ConoDictor: a tool for prediction of conopeptide superfamilies,” Nucleic Acids Research, vol. 40, no. 1, pp. W238–W241, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Koua, S. Laht, L. Kaplinski et al., “Position-specific scoring matrix and hidden Markov model complement each other for the prediction of conopeptide superfamilies,” Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics, vol. 1834, no. 4, pp. 717–724, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Laht, D. Koua, L. Kaplinski, F. Lisacek, R. Stöcklin, and M. Remm, “Identification and classification of conopeptides using profile Hidden Markov Models,” Biochimica et Biophysica Acta (BBA)—Proteins and Proteomics, vol. 1824, no. 3, pp. 488–492, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. K. H. Gowd, K. K. Dewan, P. Iengar, K. S. Krishnan, and P. Balaram, “Probing peptide libraries from Conus achatinus using mass spectrometry and cDNA sequencing: identification of δ and ω-conotoxins,” Journal of Mass Spectrometry, vol. 43, no. 6, pp. 791–805, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Saha and G. P. S. Raghava, “Prediction of neurotoxins based on their function and source,” In Silico Biology, vol. 7, no. 4-5, pp. 369–387, 2007. View at Google Scholar · View at Scopus
  18. R. Soli, B. Kaabi, M. Barhoumi, M. El-Ayeb, and N. Srairi-Abid, “Bioinformatic characterizations and prediction of K+ and Na+ ion channels effector toxins,” BMC Pharmacology, vol. 9, article 4, 2009. View at Publisher · View at Google Scholar
  19. L.-F. Yuan, C. Ding, S.-H. Guo, H. Ding, W. Chen, and H. Lin, “Prediction of the types of ion channel-targeted conotoxins based on radial basis function network,” Toxicology in Vitro, vol. 27, no. 2, pp. 852–856, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Ding, E.-Z. Deng, L.-F. Yuan et al., “ICTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels,” BioMed Research International, vol. 2014, Article ID 286419, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Magrane and UniProt Consortium, “UniProt Knowledgebase: a hub of integrated protein data,” Database, vol. 2011, Article ID bar009, 2011. View at Publisher · View at Google Scholar
  22. K.-C. Chou, “Prediction of protein cellular attributes using pseudo-amino acid composition,” Proteins: Structure, Function and Genetics, vol. 43, no. 3, pp. 246–255, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Chen, T.-Y. Lei, D.-C. Jin, H. Lin, and K.-C. Chou, “PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition,” Analytical Biochemistry, vol. 456, no. 1, pp. 53–60, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. W. Chen, X. Zhang, J. Brooker, H. Lin, L. Zhang, and K.-C. Chou, “PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions,” Bioinformatics, vol. 31, no. 1, pp. 119–120, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, and K. Chou, “Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences,” Nucleic Acids Research, vol. 43, no. W1, pp. W65–W71, 2015. View at Publisher · View at Google Scholar
  26. B. Liu, F. Liu, L. Fang, X. Wang, and K.-C. Chou, “repRNA: a web server for generating various feature vectors of RNA sequences,” Molecular Genetics and Genomics, vol. 291, no. 1, pp. 473–481, 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Liu, F. Liu, L. Fang, X. Wang, and K.-C. Chou, “RepDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects,” Bioinformatics, vol. 31, no. 8, pp. 1307–1309, 2015. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Tang, W. Chen, and H. Lin, “Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique,” Molecular BioSystems, vol. 12, no. 4, pp. 1269–1275, 2016. View at Publisher · View at Google Scholar
  29. P.-P. Zhu, W.-C. Li, Z.-J. Zhong et al., “Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition,” Molecular BioSystems, vol. 11, no. 2, pp. 558–563, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Wang, Y. Xu, and B. Liu, “Recombination spot identification Based on gapped k-mers,” Scientific Reports, vol. 6, Article ID 23934, 2016. View at Publisher · View at Google Scholar
  31. D. Li, Y. Ju, and Q. Zou, “Protein Folds Prediction with Hierarchical Structured SVM,” Current Proteomics, vol. 13, no. 2, pp. 79–85, 2016. View at Publisher · View at Google Scholar
  32. R. Cao, Z. Wang, and J. Cheng, “Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment,” BMC Structural Biology, vol. 14, no. 1, article 13, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Cao, Z. Wang, Y. Wang, and J. Cheng, “SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines,” BMC Bioinformatics, vol. 15, no. 1, article 120, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. J. Chen, X. Wang, and B. Liu, “IMiRNA-SSF: improving the identification of MicroRNA precursors by combining negative sets with different distributions,” Scientific Reports, vol. 6, article 19062, 2016. View at Publisher · View at Google Scholar · View at Scopus