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BioMed Research International
Volume 2013 (2013), Article ID 701317, 13 pages
http://dx.doi.org/10.1155/2013/701317
Research Article

iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking

1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333046, China
2Information School, ZheJiang Textile & Fashion College, NingBo 315211, China
3Gordon Life Science Institute, Belmont, MA 02478, USA
4Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia

Received 7 August 2013; Accepted 17 September 2013

Academic Editor: Tatsuya Akutsu

Copyright © 2013 Jian-Liang Min 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. A. Bairoch, “The ENZYME database in 2000,” Nucleic Acids Research, vol. 28, no. 1, pp. 304–305, 2000. View at Scopus
  2. S. H. Koenig and R. D. Brown, “H2CO3 as substrate for carbonic anhydrase in the dehydration of H2CO3(−),” Proceedings of the National Academy of Sciences of the United States of America, vol. 69, no. 9, pp. 2422–2425, 1972. View at Scopus
  3. S. X. Lin and J. Lapointe, “Theoretical and experimental biology in one,” Journal of Biomedical Science and Engineering, vol. 6, pp. 435–442, 2013. View at Publisher · View at Google Scholar
  4. K. C. Chou and S. P. Jiang, “Studies on the rate of diffusion-controlled reactions of enzymes. Spatial factor and force field factor,” Scientia Sinica, vol. 17, no. 5, pp. 664–680, 1974. View at Scopus
  5. K. C. Chou, “The kinetics of the combination reaction between enzyme and substrate,” Scientia Sinica, vol. 19, no. 4, pp. 505–528, 1976. View at Scopus
  6. K. C. Chou and G. P. Zhou, “Role of the protein outside active site on the diffusion-controlled reaction of enzyme,” Journal of the American Chemical Society, vol. 104, no. 5, pp. 1409–1413, 1982. View at Scopus
  7. R. A. Poorman, A. G. Tomasselli, R. L. Heinrikson, and F. J. Kezdy, “A cumulative specificity model for proteases from human immunodeficiency virus types 1 and 2, inferred from statistical analysis of an extended substrate data base,” The Journal of Biological Chemistry, vol. 266, no. 22, pp. 14554–14561, 1991. View at Scopus
  8. K.-C. Chou, “A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins,” The Journal of Biological Chemistry, vol. 268, no. 23, pp. 16938–16948, 1993. View at Scopus
  9. G. Z. Liang and S. Z. Li, “A new sequence representation as applied in better specificity elucidation for human immunodeficiency virus type 1 protease,” Biopolymers, vol. 88, no. 3, pp. 401–412, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. J. J. Chou, “Predicting cleavability of peptide sequences by HIV protease via correlation-angle approach,” Journal of Protein Chemistry, vol. 12, no. 3, pp. 291–302, 1993. View at Scopus
  11. Q. S. Du, S. Wang, D. Q. Wei, S. Sirois, and K. C. Chou, “Molecular modeling and chemical modification for finding peptide inhibitor against severe acute respiratory syndrome coronavirus main proteinase,” Analytical Biochemistry, vol. 337, no. 2, pp. 262–270, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Gan, H. Huang, Y. Huang et al., “Synthesis and activity of an octapeptide inhibitor designed for SARS coronavirus main proteinase,” Peptides, vol. 27, no. 4, pp. 622–625, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. S. Du, H. Sun, and K. C. Chou, “Inhibitor design for SARS coronavirus main protease based on ‘distorted key theory’,” Medicinal Chemistry, vol. 3, no. 1, pp. 1–6, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. K. C. Chou, “Prediction of human immunodeficiency virus protease cleavage sites in proteins,” Analytical Biochemistry, vol. 233, no. 1, pp. 1–14, 1996. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Knowles and G. Gromo, “Target selection in drug discovery,” Nature Reviews Drug Discovery, vol. 2, no. 1, pp. 63–69, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. A. C. Cheng, R. G. Coleman, K. T. Smyth et al., “Structure-based maximal affinity model predicts small-molecule druggability,” Nature Biotechnology, vol. 25, no. 1, pp. 71–75, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Rarey, B. Kramer, T. Lengauer, and G. Klebe, “A fast flexible docking method using an incremental construction algorithm,” Journal of Molecular Biology, vol. 261, no. 3, pp. 470–489, 1996. View at Publisher · View at Google Scholar · View at Scopus
  18. K. C. Chou, “Review: structural bioinformatics and its impact to biomedical science,” Current Medicinal Chemistry, vol. 11, no. 16, pp. 2105–2134, 2004. View at Scopus
  19. K. C. Chou, D. Q. Wei, and W. Z. Zhong, “Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS,” Biochemical and Biophysical Research Communications, vol. 308, pp. 148–151, 2003, (Erratum in: Biochemical and Biophysical Research Communications, vol. 310, p. 675, 2003).
  20. K. C. Chou, D. Q. Wei, Q. S. Du, S. Sirois, and W. Z. Zhong, “Progress in computational approach to drug development againt SARS,” Current Medicinal Chemistry, vol. 13, no. 27, pp. 3263–3270, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. G. P. Zhou and F. A. Troy, “NMR studies on how the binding complex of polyisoprenol recognition sequence peptides and polyisoprenols can modulate membrane structure,” Current Protein and Peptide Science, vol. 6, no. 5, pp. 399–411, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. R. B. Huang, Q. S. Du, C. H. Wang, and K. C. Chou, “An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus,” Biochemical and Biophysical Research Communications, vol. 377, no. 4, pp. 1243–1247, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. Q. S. Du, R. B. Huang, C. H. Wang, X. M. Li, and K. C. Chou, “Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus,” Journal of Theoretical Biology, vol. 259, no. 1, pp. 159–164, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. M. J. Berardi, W. M. Shih, S. C. Harrison, and J. J. Chou, “Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching,” Nature, vol. 476, no. 7358, pp. 109–114, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. J. R. Schnell and J. J. Chou, “Structure and mechanism of the M2 proton channel of influenza A virus,” Nature, vol. 451, no. 7178, pp. 591–595, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. B. OuYang, S. Xie, M. J. Berardi et al., “Unusual architecture of the p7 channel from hepatitis C virus,” Nature, vol. 498, pp. 521–525, 2013.
  27. K. Oxenoid and J. J. Chou, “The structure of phospholamban pentamer reveals a channel-like architecture in membranes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 31, pp. 10870–10875, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. M. E. Call, K. W. Wucherpfennig, and J. J. Chou, “The structural basis for intramembrane assembly of an activating immunoreceptor complex,” Nature Immunology, vol. 11, no. 11, pp. 1023–1029, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. R. M. Pielak, J. R. Schnell, and J. J. Chou, “Mechanism of drug inhibition and drug resistance of influenza A M2 channel,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 18, pp. 7379–7384, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Wang, R. M. Pielak, M. A. McClintock, and J. J. Chou, “Solution structure and functional analysis of the influenza B proton channel,” Nature Structural and Molecular Biology, vol. 16, no. 12, pp. 1267–1271, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. K. C. Chou, “Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein,” Journal of Proteome Research, vol. 4, no. 5, pp. 1681–1686, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. K. C. Chou, “Insights from modeling three-dimensional structures of the human potassium and sodium channels,” Journal of Proteome Research, vol. 3, no. 4, pp. 856–861, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. K. C. Chou, “Some remarks on protein attribute prediction and pseudo amino acid composition,” Journal of Theoretical Biology, vol. 273, no. 1, pp. 236–247, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. W. Z. Lin, J. A. Fang, X. Xiao, and K. C. Chou, “iLoc-animal: a multi-label learning classifier for predicting subcellular localization of animal proteins,” Molecular BioSystems, vol. 9, pp. 634–644, 2013.
  35. X. Xiao, P. Wang, W. Z. Lin, J. H. Jia, and K. C. Chou, “iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types,” Analytical Biochemistry, vol. 436, pp. 168–177, 2013.
  36. W. Chen, P. M. Feng, H. Lin, and K. C. Chou, “iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition,” Nucleic Acids Research, vol. 41, article e69, 2013. View at Publisher · View at Google Scholar
  37. K. C. Chou, Z. C. Wu, and X. Xiao, “ILoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites,” Molecular BioSystems, vol. 8, no. 2, pp. 629–641, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Kotera, M. Hirakawa, T. Tokimatsu, S. Goto, and M. Kanehisa, “The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals,” Methods in Molecular Biology, vol. 802, pp. 19–39, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, “Prediction of drug-target interaction networks from the integration of chemical and genomic spaces,” Bioinformatics, vol. 24, no. 13, pp. i232–i240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. P. Finn, S. Muggleton, D. Page, and A. Srinivasan, “Pharmacophore discovery using the Inductive Logic Programming system PROGOL,” Machine Learning, vol. 30, no. 2-3, pp. 241–270, 1998. View at Scopus
  41. I. Vogt, D. Stumpfe, H. E. Ahmed, and J. Bajorath, “Methods for computer-aided chemical biology. Part 2: evaluation of compound selectivity using 2D molecular fingerprints,” Chemical Biology and Drug Design, vol. 70, pp. 195–205, 2007.
  42. H. Eckert and J. Bajorath, “Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches,” Drug Discovery Today, vol. 12, no. 5-6, pp. 225–233, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Laurent, L. V. Elst, and R. N. Muller, “Comparative study of the physicochemical properties of six clinical low molecular weight gadolinium contrast agents,” Contrast Media & Molecular Imaging, vol. 1, no. 3, pp. 128–137, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. E. Gregori-Puigjané, R. Garriga-Sust, and J. Mestres, “Indexing molecules with chemical graph identifiers,” Journal of Computational Chemistry, vol. 32, no. 12, pp. 2638–2646, 2011. View at Publisher · View at Google Scholar · View at Scopus
  45. B. Ren, “Application of novel atom-type AI topological indices to QSPR studies of alkanes,” Computers and Chemistry, vol. 26, no. 4, pp. 357–369, 2002. View at Publisher · View at Google Scholar · View at Scopus
  46. N. M. O'Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, and G. R. Hutchison, “Open Babel: an open chemical toolbox,” Journal of Cheminformatics, vol. 3, p. 33, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. V. J. Gillet, P. Willett, and J. Bradshaw, “Similarity searching using reduced graphs,” Journal of Chemical Information and Computer Sciences, vol. 43, no. 2, pp. 338–345, 2003. View at Publisher · View at Google Scholar · View at Scopus
  48. D. Butina, “Unsupervised data base clustering based on daylight's fingerprint and Tanimoto similarity: a fast and automated way to cluster small and large data sets,” Journal of Chemical Information and Computer Sciences, vol. 39, no. 4, pp. 747–750, 1999. View at Publisher · View at Google Scholar · View at Scopus
  49. C. E. Shannon, “A mathematical theory of communication,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 5, pp. 3–55, 2001.
  50. V. D. Gusev, L. A. Nemytikova, and N. A. Chuzhanova, “On the complexity measures of genetic sequences,” Bioinformatics, vol. 15, no. 12, pp. 994–999, 1999. View at Scopus
  51. K. C. Chou and H. B. Shen, “Review: recent progress in protein subcellular location prediction,” Analytical Biochemistry, vol. 370, no. 1, pp. 1–16, 2007. View at Publisher · View at Google Scholar · View at Scopus
  52. S. F. Altschul, “Evaluating the statistical significance of multiple distinct local alignments,” in Theoretical and Computational Methods in Genome Research, S. Suhai, Ed., pp. 1–14, Plenum, New York, NY, USA, 1997.
  53. J. C. Wootton and S. Federhen, “Statistics of local complexity in amino acid sequences and sequence databases,” Computers and Chemistry, vol. 17, no. 2, pp. 149–163, 1993. View at Scopus
  54. H. Nakashima, K. Nishikawa, and T. Ooi, “The folding type of a protein is relevant to the amino acid composition,” Journal of Biochemistry, vol. 99, no. 1, pp. 153–162, 1986. View at Scopus
  55. K. C. Chou and C. T. Zhang, “Predicting protein folding types by distance functions that make allowances for amino acid interactions,” The Journal of Biological Chemistry, vol. 269, no. 35, pp. 22014–22020, 1994. View at Scopus
  56. K.-C. Chou, “A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space,” Proteins, vol. 21, no. 4, pp. 319–344, 1995. View at Publisher · View at Google Scholar · View at Scopus
  57. H. Nakashima and K. Nishikawa, “Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies,” Journal of Molecular Biology, vol. 238, no. 1, pp. 54–61, 1994. View at Publisher · View at Google Scholar · View at Scopus
  58. G. P. Zhou, “An intriguing controversy over protein structural class prediction,” Journal of Protein Chemistry, vol. 17, no. 8, pp. 729–738, 1998. View at Publisher · View at Google Scholar · View at Scopus
  59. I. Bahar, A. R. Atilgan, R. L. Jernigan, and B. Erman, “Understanding the recognition of protein structural classes by amino acid composition,” Proteins, vol. 29, pp. 172–185, 1997.
  60. J. Cedano, P. Aloy, J. A. Perez-Pons, and E. Querol, “Relation between amino acid composition and cellular location of proteins,” Journal of Molecular Biology, vol. 266, no. 3, pp. 594–600, 1997. View at Publisher · View at Google Scholar · View at Scopus
  61. G. P. Zhou and K. Doctor, “Subcellular location prediction of apoptosis proteins,” Proteins, vol. 50, no. 1, pp. 44–48, 2003. View at Publisher · View at Google Scholar · View at Scopus
  62. K. Chou, “Prediction of protein cellular attributes using pseudo-amino acid composition,” Proteins, vol. 43, pp. 246–255, 2001, (Erratum in: Proteins, vol. 44, p. 60, 2001).
  63. K. C. Chou, “Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes,” Bioinformatics, vol. 21, no. 1, pp. 10–19, 2005. View at Publisher · View at Google Scholar · View at Scopus
  64. D. Zou, Z. He, J. He, and Y. Xia, “Supersecondary structure prediction using Chou's pseudo amino acid composition,” Journal of Computational Chemistry, vol. 32, no. 2, pp. 271–278, 2011. View at Publisher · View at Google Scholar · View at Scopus
  65. M. M. Beigi, M. Behjati, and H. Mohabatkar, “Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach,” Journal of Structural and Functional Genomics, vol. 12, no. 4, pp. 191–197, 2011. View at Publisher · View at Google Scholar · View at Scopus
  66. Y. K. Chen and K. B. Li, “Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition,” Journal of Theoretical Biology, vol. 318, pp. 1–12, 2013.
  67. C. Huang and J. Q. Yuan, “A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types,” The Journal of Membrane Biology, vol. 246, pp. 327–334, 2013.
  68. S. S. Sahu and G. Panda, “A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction,” Computational Biology and Chemistry, vol. 34, no. 5-6, pp. 320–327, 2010. View at Publisher · View at Google Scholar · View at Scopus
  69. M. Hayat and A. Khan, “Discriminating outer membrane proteins with fuzzy K-nearest neighbor algorithms based on the general form of Chou's PseAAC,” Protein and Peptide Letters, vol. 19, no. 4, pp. 411–421, 2012. View at Scopus
  70. M. Khosravian, F. K. Faramarzi, M. M. Beigi, M. Behbahani, and H. Mohabatkar, “Predicting antibacterial peptides by the concept of Chou's pseudo-amino acid composition and machine learning methods,” Protein & Peptide Letters, vol. 20, pp. 180–186, 2013.
  71. H. Mohabatkar, M. M. Beigi, K. Abdolahi, and S. Mohsenzadeh, “Prediction of allergenic proteins by means of the concept of Chou's pseudo amino acid composition and a machine learning approach,” Medicinal Chemistry, vol. 9, pp. 133–137, 2013.
  72. L. Nanni, A. Lumini, D. Gupta, and A. Garg, “Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou's pseudo amino acid composition and on evolutionary information,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 467–475, 2012. View at Publisher · View at Google Scholar · View at Scopus
  73. T. H. Chang, L. C. Wu, T. Y. Lee, S. P. Chen, H. D. Huang, and J. T. Horng, “EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC,” Journal of Computer-Aided Molecular Design, vol. 27, pp. 91–103, 2013.
  74. S. Zhang, Y. Zhang, H. Yang, C. Zhao, and Q. Pan, “Using the concept of Chou's pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies,” Amino Acids, vol. 34, no. 4, pp. 565–572, 2008. View at Publisher · View at Google Scholar · View at Scopus
  75. R. Zia Ur and A. Khan, “Identifying GPCRs and their types with Chou's pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix,” Protein & Peptide Letters, vol. 19, pp. 890–903, 2012.
  76. X. Y. Sun, S. P. Shi, J. D. Qiu, S. B. Suo, S. Y. Huang, and R. P. Liang, “Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform,” Molecular BioSystems, vol. 8, pp. 3178–3184, 2012.
  77. L. Nanni and A. Lumini, “Genetic programming for creating Chou's pseudo amino acid based features for submitochondria localization,” Amino Acids, vol. 34, no. 4, pp. 653–660, 2008. View at Publisher · View at Google Scholar · View at Scopus
  78. M. Esmaeili, H. Mohabatkar, and S. Mohsenzadeh, “Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses,” Journal of Theoretical Biology, vol. 263, no. 2, pp. 203–209, 2010. View at Publisher · View at Google Scholar · View at Scopus
  79. H. Mohabatkar, “Prediction of cyclin proteins using Chou's pseudo amino acid composition,” Protein and Peptide Letters, vol. 17, no. 10, pp. 1207–1214, 2010. View at Publisher · View at Google Scholar · View at Scopus
  80. H. Mohabatkar, M. M. Beigi, and A. Esmaeili, “Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine,” Journal of Theoretical Biology, vol. 281, no. 1, pp. 18–23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  81. Y. Xu, J. Ding, L. Y. Wu, and K. C. Chou, “iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition,” PLoS ONE, vol. 8, Article ID e55844, 2013.
  82. W. Chen, H. Lin, P. M. Feng, C. Ding, Y. C. Zuo, and K. C. Chou, “iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties,” PLoS ONE, vol. 7, Article ID e47843, 2012.
  83. B. Li, T. Huang, L. Liu, Y. Cai, and K. C. Chou, “Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network,” PLoS ONE, vol. 7, no. 4, Article ID e33393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  84. Y. Jiang, T. Huang, C. Lei, Y. F. Gao, Y. D. Cai, and K. C. Chou, “Signal propagation in protein interaction network during colorectal cancer progression,” BioMed Research International, vol. 2013, Article ID 287019, 9 pages, 2013. View at Publisher · View at Google Scholar
  85. P. Du, X. Wang, C. Xu, and Y. Gao, “PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions,” Analytical Biochemistry, vol. 425, no. 2, pp. 117–119, 2012. View at Publisher · View at Google Scholar · View at Scopus
  86. D. S. Cao, Q. S. Xu, and Y. Z. Liang, “Propy: a tool to generate various modes of Chou's PseAAC,” Bioinformatics, vol. 29, pp. 960–962, 2013.
  87. H. Shen and K. C. Chou, “PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition,” Analytical Biochemistry, vol. 373, no. 2, pp. 386–388, 2008. View at Publisher · View at Google Scholar · View at Scopus
  88. K. C. Chou, “Using pair-coupled amino acid composition to predict protein secondary structure content,” Journal of Protein Chemistry, vol. 18, no. 4, pp. 473–480, 1999. View at Publisher · View at Google Scholar · View at Scopus
  89. W. Liu and K. C. Chou, “Prediction of protein secondary structure content,” Protein Engineering, vol. 12, no. 12, pp. 1041–1050, 1999. View at Scopus
  90. M. Bhasin and G. P. S. Raghava, “Classification of nuclear receptors based on amino acid composition and dipeptide composition,” The Journal of Biological Chemistry, vol. 279, no. 22, pp. 23262–23266, 2004. View at Publisher · View at Google Scholar · View at Scopus
  91. H. Lin and H. Ding, “Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition,” Journal of Theoretical Biology, vol. 269, no. 1, pp. 64–69, 2011. View at Publisher · View at Google Scholar · View at Scopus
  92. H. Lin and Q. Li, “Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components,” Journal of Computational Chemistry, vol. 28, no. 9, pp. 1463–1466, 2007. View at Publisher · View at Google Scholar · View at Scopus
  93. L. Nanni and A. Lumini, “Combing ontologies and dipeptide composition for predicting DNA-binding proteins,” Amino Acids, vol. 34, no. 4, pp. 635–641, 2008. View at Publisher · View at Google Scholar · View at Scopus
  94. K.-C. Chou, “The convergence-divergence duality in lectin domains of selectin family and its implications,” FEBS Letters, vol. 363, no. 1-2, pp. 123–126, 1995. View at Publisher · View at Google Scholar · View at Scopus
  95. A. A. Schäffer, L. Aravind, T. L. Madden et al., “Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements,” Nucleic Acids Research, vol. 29, no. 14, pp. 2994–3005, 2001. View at Scopus
  96. S. F. Altschul and E. V. Koonin, “Iterated profile searches with PSI-BLAST: a tool for discovery in protein databases,” Trends in Biochemical Sciences, vol. 23, no. 11, pp. 444–447, 1998. View at Publisher · View at Google Scholar · View at Scopus
  97. J. Deng, “Grey entropy and grey target decision making,” Journal of Grey System, vol. 22, no. 1, pp. 1–24, 2010. View at Scopus
  98. B. M. Bolstad, R. A. Irizarry, M. Åstrand, and T. P. Speed, “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias,” Bioinformatics, vol. 19, no. 2, pp. 185–193, 2003. View at Publisher · View at Google Scholar · View at Scopus
  99. J.-Y. Shi, S.-W. Zhang, Q. Pan, Y.-M. Cheng, and J. Xie, “Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition,” Amino Acids, vol. 33, no. 1, pp. 69–74, 2007. View at Publisher · View at Google Scholar · View at Scopus
  100. T. Denoeux, “A κ-nearest neighbor classification rule based on Dempster-Shafer theory,” IEEE Transactions on Systems, Man and Cybernetics, vol. 25, no. 5, pp. 804–813, 1995. View at Publisher · View at Google Scholar · View at Scopus
  101. J. M. Keller, M. R. Gray, and J. A. Givens, “A fuzzy k-nearest neighbours algorithm,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 4, pp. 580–585, 1985. View at Scopus
  102. X. Xiao, P. Wang, and K. Chou, “iNR-physchem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix,” PLoS ONE, vol. 7, no. 2, Article ID e30869, 2012. View at Publisher · View at Google Scholar · View at Scopus
  103. I. Roterman, L. Konieczny, W. Jurkowski, K. Prymula, and M. Banach, “Two-intermediate model to characterize the structure of fast-folding proteins,” Journal of Theoretical Biology, vol. 283, no. 1, pp. 60–70, 2011. View at Publisher · View at Google Scholar · View at Scopus
  104. X. Xiao, P. Wang, and K. C. Chou, “GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions,” Molecular BioSystems, vol. 7, no. 3, pp. 911–919, 2011. View at Publisher · View at Google Scholar · View at Scopus
  105. X. Xiao, P. Wang, and K. C. Chou, “Quat-2L: a web-server for predicting protein quaternary structural attributes,” Molecular Diversity, vol. 15, no. 1, pp. 149–155, 2011. View at Publisher · View at Google Scholar · View at Scopus
  106. X. Zheng, C. Li, and J. Wang, “An information-theoretic approach to the prediction of protein structural class,” Journal of Computational Chemistry, vol. 31, no. 6, pp. 1201–1206, 2010. View at Scopus
  107. H. Shen, J. Yang, X. Liu, and K. C. Chou, “Using supervised fuzzy clustering to predict protein structural classes,” Biochemical and Biophysical Research Communications, vol. 334, no. 2, pp. 577–581, 2005. View at Publisher · View at Google Scholar · View at Scopus
  108. K.-C. Chou and C.-T. Zhang, “Review: prediction of protein structural classes,” Critical Reviews in Biochemistry and Molecular Biology, vol. 30, no. 4, pp. 275–349, 1995. View at Scopus
  109. P. C. Mahalanobis, “On the generalized distance in statistics,” Proceedings of the National Institute of Sciences of India, vol. 2, pp. 49–55, 1936.
  110. R. M. Centor, “Signal detectability: the use of ROC curves and their analyses,” Medical Decision Making, vol. 11, no. 2, pp. 102–106, 1991. View at Scopus
  111. K.-C. Chou, “Using subsite coupling to predict signal peptides,” Protein Engineering, vol. 14, no. 2, pp. 75–79, 2001. View at Scopus
  112. K. C. Chou, “Prediction of protein signal sequences and their cleavage sites,” Proteins, vol. 42, pp. 136–139, 2001.
  113. K. C. Chou, “Prediction of signal peptides using scaled window,” Peptides, vol. 22, no. 12, pp. 1973–1979, 2001. View at Scopus
  114. K. C. Chou, “Some remarks on predicting multi-label attributes in molecular biosystems,” Molecular Biosystems, vol. 9, pp. 1092–1100, 2013.
  115. K. C. Chou and H. B. Shen, “Cell-PLoc 2. 0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms,” Natural Science, vol. 2, pp. 1090–1103, 2010. View at Publisher · View at Google Scholar
  116. M. Gribskov and N. L. Robinson, “Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching,” Computers and Chemistry, vol. 20, no. 1, pp. 25–33, 1996. View at Scopus
  117. Y. Yamanishi, M. Kotera, M. Kanehisa, and S. Goto, “Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework,” Bioinformatics, vol. 26, no. 12, pp. i246–i254, 2010. View at Publisher · View at Google Scholar · View at Scopus
  118. Z. He, J. Zhang, X. Shi, et al., “Predicting drug-target interaction networks based on functional groups and biological features,” PLoS ONE, vol. 5, no. 3, Article ID e9603, 2010.