Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique.

Hua Tang, Yunchun Yang, Chunmei Zhang, Rong Chen, Po Huang, Chenggang Duan, Ping Zou
Author Information
  1. Hua Tang: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China. ORCID
  2. Yunchun Yang: Department of Anesthesiology, The Affiliated Traditional Chinese Medical Hospital of Southwest Medical University, Luzhou 646000, China.
  3. Chunmei Zhang: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
  4. Rong Chen: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
  5. Po Huang: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
  6. Chenggang Duan: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China. ORCID
  7. Ping Zou: Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China. ORCID

Abstract

Presynaptic and postsynaptic neurotoxins are proteins which act at the presynaptic and postsynaptic membrane. Correctly predicting presynaptic and postsynaptic neurotoxins will provide important clues for drug-target discovery and drug design. In this study, we developed a theoretical method to discriminate presynaptic neurotoxins from postsynaptic neurotoxins. A strict and objective benchmark dataset was constructed to train and test our proposed model. The dipeptide composition was used to formulate neurotoxin samples. The analysis of variance (ANOVA) was proposed to find out the optimal feature set which can produce the maximum accuracy. In the jackknife cross-validation test, the overall accuracy of 94.9% was achieved. We believe that the proposed model will provide important information to study neurotoxins.

References

  1. J Neurosci. 2004 Nov 24;24(47):10687-92 [PMID: 15564585]
  2. Toxicon. 1998 Dec;36(12):1871-85 [PMID: 9839671]
  3. Bioinformatics. 2012 Dec 1;28(23):3150-2 [PMID: 23060610]
  4. BMC Bioinformatics. 2016 Dec 5;17 (1):495 [PMID: 27919220]
  5. Toxicol Lett. 2004 Apr 1;149(1-3):91-101 [PMID: 15093253]
  6. Nucleic Acids Res. 2014 Dec 1;42(21):12961-72 [PMID: 25361964]
  7. J Neural Eng. 2017 Feb;14 (1):016002 [PMID: 27900948]
  8. J Theor Biol. 2014 Dec 21;363:412-8 [PMID: 25123433]
  9. Amino Acids. 2015 Mar;47(3):461-8 [PMID: 25583603]
  10. Nucleic Acids Res. 2005 Jan 1;33(Database issue):D154-9 [PMID: 15608167]
  11. Curr Med Chem. 2011;18(36):5687-93 [PMID: 22172073]
  12. ScientificWorldJournal. 2014;2014:173869 [PMID: 25045727]
  13. Mol Biosyst. 2016 Apr;12 (4):1269-75 [PMID: 26883492]
  14. Int J Mol Sci. 2016 Feb 06;17 (2):218 [PMID: 26861308]
  15. BMC Bioinformatics. 2016 Nov 25;17 (1):487 [PMID: 27887571]
  16. Stat Appl Genet Mol Biol. 2012 Jan 06;11(1):Article 6 [PMID: 22499686]
  17. Toxicol In Vitro. 2009 Mar;23(2):346-8 [PMID: 19138734]
  18. Sci Rep. 2016 Jul 22;6:30441 [PMID: 27443605]
  19. Nucleic Acids Res. 2008 Jan;36(Database issue):D293-7 [PMID: 17933766]
  20. Oncotarget. 2016 Jul 12;7(28):44310-44321 [PMID: 27322424]
  21. J Immunol Methods. 2013 Jan 31;387(1-2):284-92 [PMID: 23058675]
  22. Neuroscience. 2009 Jan 12;158(1):293-300 [PMID: 19041375]
  23. Oncotarget. 2016 Oct 25;7(43):69783-69793 [PMID: 27626500]
  24. Med Biol Eng Comput. 2015 Apr;53(4):331-44 [PMID: 25564182]

MeSH Term

Amino Acids
Computational Biology
Humans
Models, Theoretical
Neurotoxins
Presynaptic Terminals

Chemicals

Amino Acids
Neurotoxins

Word Cloud

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