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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties

Author(s): Yiyin Cao, Chunlu Yu, Shenghui Huang, Shiyuan Wang, Yongchun Zuo* and Lei Yang*

Volume 16, Issue 3, 2021

Published on: 07 July, 2020

Page: [364 - 370] Pages: 7

DOI: 10.2174/1574893615999200707150512

Price: $65

Abstract

Background: Presynaptic and postsynaptic neurotoxins are two important categories of neurotoxins. Due to the important role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, their identification has become very important biologically.

Methods: In this study, statistical tests and F-scores were used to calculate differences between amino acids and biological properties. The support vector machine was used to predict presynaptic and postsynaptic neurotoxins using reduced amino acid alphabet types.

Results: Using the reduced amino acid alphabet as input parameters of the support vector machine, the overall accuracy of our classifier increased to 91.07%, which was the highest overall accuracy observed in this study. When compared with the other published methods, better predictive results were obtained by our classifier.

Conclusion: In summary, we analyzed the differences between two neurotoxins with respect to amino acids and biological properties, constructing a classifier that predicts these two neurotoxins using the reduced amino acid alphabet.

Keywords: Neurotoxin, reduced amino acid alphabet, biological property, support vector machine, machine learning, jackknife test.

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[1]
Montecucco C, Rossetto O. How do presynaptic PLA2 neurotoxins block nerve terminals? Trends Biochem Sci 2000; 25(6): 266-70.
[http://dx.doi.org/10.1016/S0968-0004(00)01556-5] [PMID: 10838563]
[2]
Jeyaseelan K, Poh SL, Nair R, Armugam A. Structurally conserved alpha-neurotoxin genes encode functionally diverse proteins in the venom of Naja sputatrix. FEBS Lett 2003; 553(3): 333-41.
[http://dx.doi.org/10.1016/S0014-5793(03)01039-1] [PMID: 14572646]
[3]
Phui Yee JS, Nanling G, Afifiyan F, et al. Snake postsynaptic neurotoxins: gene structure, phylogeny and applications in research and therapy. Biochimie 2004; 86(2): 137-49.
[http://dx.doi.org/10.1016/j.biochi.2003.11.012] [PMID: 15016453]
[4]
Rossetto O, Montecucco C. Presynaptic neurotoxins with enzymatic activities. Handb Exp Pharmacol 2008; (184): 129-70.
[http://dx.doi.org/10.1007/978-3-540-74805-2_6] [PMID: 18064414]
[5]
Montecucco C, Rossetto O, Caccin P, et al. Different mechanisms of inhibition of nerve terminals by botulinum and snake presynaptic neurotoxins. Toxicon 2009; 54(5): 561-4.
[http://dx.doi.org/10.1016/j.toxicon.2008.12.012] [PMID: 19111566]
[6]
Tang L, Zhou YC, Lin ZJ. Crystal structure of agkistrodotoxin, a phospholipase A2-type presynaptic neurotoxin from agkistrodon halys pallas. J Mol Biol 1998; 282(1): 1-11.
[http://dx.doi.org/10.1006/jmbi.1998.1987] [PMID: 9733637]
[7]
Rossetto O, Rigoni M, Montecucco C. Different mechanism of blockade of neuroexocytosis by presynaptic neurotoxins. Toxicol Lett 2004; 149(1-3): 91-101.
[http://dx.doi.org/10.1016/j.toxlet.2003.12.023] [PMID: 15093253]
[8]
Hodgson WC, Dal Belo CA, Rowan EG. The neuromuscular activity of paradoxin: a presynaptic neurotoxin from the venom of the inland taipan (Oxyuranus microlepidotus). Neuropharmacology 2007; 52(5): 1229-36.
[http://dx.doi.org/10.1016/j.neuropharm.2007.01.002] [PMID: 17313963]
[9]
Halpert J, Fohlman J, Eaker D. Amino acid sequence of a postsynaptic neurotoxin from the venom of the Australian tiger snake Notechis scutatus scutatus. Biochimie 1979; 61(5-6): 719-23.
[http://dx.doi.org/10.1016/S0300-9084(79)80172-8] [PMID: 497256]
[10]
Afifiyan F, Armugam A, Gopalakrishnakone P, Tan NH, Tan CH, Jeyaseelan K. Four new postsynaptic neurotoxins from Naja naja sputatrix venom: cDNA cloning, protein expression, and phylogenetic analysis. Toxicon 1998; 36(12): 1871-85.
[http://dx.doi.org/10.1016/S0041-0101(98)00108-1] [PMID: 9839671]
[11]
Gong N, Armugam A, Jeyaseelan K. Postsynaptic short-chain neurotoxins from Pseudonaja textilis. cDNA cloning, expression and protein characterization. Eur J Biochem 1999; 265(3): 982-9.
[http://dx.doi.org/10.1046/j.1432-1327.1999.00800.x] [PMID: 10518793]
[12]
Zuo YC, Li QZ. Using reduced amino acid composition to predict defensin family and subfamily: integrating similarity measure and structural alphabet. Peptides 2009; 30(10): 1788-93.
[http://dx.doi.org/10.1016/j.peptides.2009.06.032] [PMID: 19591890]
[13]
Chen YL, Li QZ, Zhang LQ. Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet. Amino Acids 2012; 42(4): 1309-16.
[http://dx.doi.org/10.1007/s00726-010-0825-7] [PMID: 21191803]
[14]
Zuo Y, Lv Y, Wei Z, Yang L, Li G, Fan G. iDPF-PseRAAAC: a web-server for identifying the defensin peptide family and subfamily using Pseudo reduced amino acid alphabet composition. PLoS One 2015; 10(12)e0145541
[http://dx.doi.org/10.1371/journal.pone.0145541] [PMID: 26713618]
[15]
Chen W, Feng P, Lin H. Prediction of ketoacyl synthase family using reduced amino acid alphabets. J Ind Microbiol Biotechnol 2012; 39(4): 579-84.
[http://dx.doi.org/10.1007/s10295-011-1047-z] [PMID: 22042516]
[16]
Boeckmann B, Bairoch A, Apweiler R, et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 2003; 31(1): 365-70.
[http://dx.doi.org/10.1093/nar/gkg095] [PMID: 12520024]
[17]
Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 2010; 26(5): 680-2.
[http://dx.doi.org/10.1093/bioinformatics/btq003] [PMID: 20053844]
[18]
Zheng L, Huang SH, Mu NJ, et al. RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule. Database 2019; 2019baz131
[http://dx.doi.org/10.1093/database/baz131]
[19]
Aboderin AA. An empirical hydrophobicity scale for α-amino-acids and some of its applications. Int J Biochem 1971; 2(11): 537-44.
[http://dx.doi.org/10.1016/0020-711X(71)90023-1]
[20]
Cruciani G, Baroni M, Carosati E, Clementi M, Valigi R, Clementi S. Peptide studies by means of principal properties of amino acids derived from MIF descriptors. J Chemometr 2004; 18(3‐4): 146-55.
[http://dx.doi.org/10.1002/cem.856]
[21]
Liang G, Li Z. Factor analysis scale of generalized amino acid information as the source of a new set of descriptors for elucidating the structure and activity relationships of cationic antimicrobial peptides. QSAR Comb Sci 2007; 26(6): 754-63.
[http://dx.doi.org/10.1002/qsar.200630145]
[22]
Eisenberg D, Weiss RM, Terwilliger TC. The hydrophobic moment detects periodicity in protein hydrophobicity. Proc Natl Acad Sci USA 1984; 81(1): 140-4.
[http://dx.doi.org/10.1073/pnas.81.1.140] [PMID: 6582470]
[23]
Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011; 2(27): 1-27.
[http://dx.doi.org/10.1145/1961189.1961199]
[24]
Huo H, Li T, Wang S, Lv Y, Zuo Y, Yang L. Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components. Sci Rep 2017; 7(1): 5827.
[http://dx.doi.org/10.1038/s41598-017-06195-y] [PMID: 28724993]
[25]
Zuo Y, Li Y, Chen Y, Li G, Yan Z, Yang L. PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition. Bioinformatics 2017; 33(1): 122-4.
[http://dx.doi.org/10.1093/bioinformatics/btw564] [PMID: 27565583]
[26]
Chou KC. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins 2001; 43(3): 246-55.
[http://dx.doi.org/10.1002/prot.1035] [PMID: 11288174]
[27]
Chen YL, Li QZ. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J Theor Biol 2007; 248(2): 377-81.
[http://dx.doi.org/10.1016/j.jtbi.2007.05.019] [PMID: 17572445]
[28]
Chen YL, Li QZ. Prediction of the subcellular location of apoptosis proteins. J Theor Biol 2007; 245(4): 775-83.
[http://dx.doi.org/10.1016/j.jtbi.2006.11.010] [PMID: 17189644]
[29]
Chou KC, Cai YD. Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 2002; 277(48): 45765-9.
[http://dx.doi.org/10.1074/jbc.M204161200] [PMID: 12186861]
[30]
Chou KC, Elrod DW. Bioinformatical analysis of G-protein-coupled receptors. J Proteome Res 2002; 1(5): 429-33.
[http://dx.doi.org/10.1021/pr025527k] [PMID: 12645914]
[31]
Cai YD, Ricardo PW, Jen CH, Chou KC. Application of SVM to predict membrane protein types. J Theor Biol 2004; 226(4): 373-6.
[http://dx.doi.org/10.1016/j.jtbi.2003.08.015] [PMID: 14759643]
[32]
Mondal S, Bhavna R, Mohan Babu R, Ramakumar S. Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. J Theor Biol 2006; 243(2): 252-60.
[http://dx.doi.org/10.1016/j.jtbi.2006.06.014] [PMID: 16890961]
[33]
Lin H, Li QZ. Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J Comput Chem 2007; 28(9): 1463-6.
[http://dx.doi.org/10.1002/jcc.20554] [PMID: 17330882]
[34]
Lin H, Li QZ. Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochem Biophys Res Commun 2007; 354(2): 548-51.
[http://dx.doi.org/10.1016/j.bbrc.2007.01.011] [PMID: 17239817]
[35]
Li FM, Li QZ. Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids 2008; 34(1): 119-25.
[http://dx.doi.org/10.1007/s00726-007-0545-9] [PMID: 17514493]
[36]
Chou KC. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology Curr Proteomics. 2009; 6: 262-74.
[http://dx.doi.org/10.2174/157016409789973707]
[37]
Chou KC, Shen HB. Reveiw: Recent advances in developing web-servers for predicting protein attributes. Nat Sci 2009; 1(2): 63-92.
[38]
Chen YW, Lin CJ. Combining SVMs with various feature selection strategies feature extraction. Springer 2006; pp. 315-24.

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