Login Register Cart 0
Generic placeholder image

Current Drug Metabolism

Editor-in-Chief

ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Using Reduced Amino Acid Alphabet and Biological Properties to Analyze and Predict Animal Neurotoxin Protein

Author(s): Yao Yu, Shiyuan Wang, Yakun Wang, Yiyin Cao, Chunlu Yu, Yi Pan, Dongqing Su, Qianzi Lu, Yongchun Zuo* and Lei Yang*

Volume 21, Issue 10, 2020

Page: [810 - 817] Pages: 8

DOI: 10.2174/1389200221666200520090555

Price: $65

Abstract

Aims: Because of the high affinity of these animal neurotoxin proteins for some special target site, they were usually used as pharmacological tools and therapeutic agents in medicine to gain deep insights into the function of the nervous system.

Background and Objective: The animal neurotoxin proteins are one of the most common functional groups among the animal toxin proteins. Thus, it was very important to characterize and predict the animal neurotoxin proteins.

Methods: In this study, the differences between the animal neurotoxin proteins and non-toxin proteins were analyzed.

Result: Significant differences were found between them. In addition, the support vector machine was proposed to predict the animal neurotoxin proteins. The predictive results of our classifier achieved the overall accuracy of 96.46%. Furthermore, the random forest and k-nearest neighbors were applied to predict the animal neurotoxin proteins.

Conclusion: The compared results indicated that the predictive performances of our classifier were better than other two algorithms.

Keywords: Neurotoxin protein, reduced amino acid alphabet, biological property, support vector machine, non-toxin protein, pharmacological tools.

« Previous Next »
Graphical Abstract

[1]
Calvete, J.J.; Sanz, L.; Angulo, Y.; Lomonte, B.; Gutiérrez, J.M. Venoms, venomics, antivenomics. FEBS Lett., 2009, 583(11), 1736-1743.
[http://dx.doi.org/10.1016/j.febslet.2009.03.029] [PMID: 19303875]
[2]
Saha, S.; Raghava, G.P. Prediction of neurotoxins based on their function and source. In Silico Biol. (Gedrukt), , 2007, 7, pp. (4-5)369-387.
[PMID: 18391230]
[3]
Rossetto, O.; Montecucco, C. Presynaptic neurotoxins with enzymatic activities. Handb. Exp. Pharmacol., 2008, 184, 129-170.
[http://dx.doi.org/10.1007/978-3-540-74805-2_6] [PMID: 18064414]
[4]
Halpert, J.; Fohlman, J.; Eaker, D. Amino acid sequence of a postsynaptic neurotoxin from the venom of the Australian tiger snake Notechis scutatus. Biochimie, 1979, 61(5-6), 719-723.
[http://dx.doi.org/10.1016/S0300-9084(79)80172-8] [PMID: 497256]
[5]
Harris, J.B. Polypeptides from snake venoms which act on nerve and muscle. Prog. Med. Chem., 1984, 21, 63-110.
[http://dx.doi.org/10.1016/S0079-6468(08)70407-7] [PMID: 6100622]
[6]
Harris, J.B. Snake venoms in science and clinical medicine. 3. Neuropharmacological aspects of the activity of snake venoms. Trans. R. Soc. Trop. Med. Hyg., 1989, 83(6), 745-747.
[http://dx.doi.org/10.1016/0035-9203(89)90313-1] [PMID: 2617644]
[7]
Liu, D.Y.; Li, G.P.; Zuo, Y.C. Function determinants of TET proteins: the arrangements of sequence motifs with specific codes. Brief. Bioinform., 2018, 20, 1825-1835.
[http://dx.doi.org/10.1093/bib/bby053] [PMID: 29947743]
[8]
Chen, W.; Feng, P.; Song, X.; Lv, H.; Lin, H. iRNA-m7G: identifying N7-methylguanosine sites by fusing multiple features. Mol. Ther. Nucleic Acids, 2019, 18, 269-274.
[http://dx.doi.org/10.1016/j.omtn.2019.08.022] [PMID: 31581051]
[9]
Lin, H.; Liang, Z.; Tang, H.; Chen, W. Identifying sigma70 promoters with novel pseudo nucleotide composition. IEEE/ACM Trans. Comput. Biol. Bioinform., 2019, 16, 1316-1321.
[http://dx.doi.org/10.1109/TCBB.2017.2666141]
[10]
Chen, Y.L.; Li, Q.Z. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J. Theor. Biol., 2007, 248(2), 377-381.
[http://dx.doi.org/10.1016/j.jtbi.2007.05.019] [PMID: 17572445]
[11]
Chen, Y.L.; Li, Q.Z. Prediction of the subcellular location of apoptosis proteins. J. Theor. Biol., 2007, 245(4), 775-783.
[http://dx.doi.org/10.1016/j.jtbi.2006.11.010] [PMID: 17189644]
[12]
Chou, K.C.; Zhang, C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30(4), 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID: 7587280]
[13]
Pan, Y.; Wang, S.; Zhang, Q.; Lu, Q.; Su, D.; Zuo, Y.; Yang, L. Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions. J. Theor. Biol., 2019, 462, 221-229.
[http://dx.doi.org/10.1016/j.jtbi.2018.11.010] [PMID: 30452961]
[14]
Lin, H.; Li, Q.Z. Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochem. Biophys. Res. Commun., 2007, 354(2), 548-551.
[http://dx.doi.org/10.1016/j.bbrc.2007.01.011] [PMID: 17239817]
[15]
Fan, Y.X.; Song, J.; Shen, H.B.; Kong, X. PredCSF: an integrated feature-based approach for predicting conotoxin superfamily. Protein Pept. Lett., 2011, 18(3), 261-267.
[http://dx.doi.org/10.2174/092986611794578341] [PMID: 20955172]
[16]
Yin, J.B.; Fan, Y.X.; Shen, H.B. Conotoxin superfamily prediction using diffusion maps dimensionality reduction and subspace classifier. Curr. Protein Pept. Sci., 2011, 12(6), 580-588.
[http://dx.doi.org/10.2174/138920311796957702] [PMID: 21787305]
[17]
Chou, K.C. Prediction of signal peptides using scaled window. Peptides, 2001, 22(12), 1973-1979.
[http://dx.doi.org/10.1016/S0196-9781(01)00540-X] [PMID: 11786179]
[18]
Ehsan, A.; Mahmood, K.; Khan, Y.D.; Khan, S.A.; Chou, K-C. A novel modeling in mathematical biology for classification of signal peptides. Sci. Rep., 2018, 8(1), 1039.
[http://dx.doi.org/10.1038/s41598-018-19491-y] [PMID: 29348418]
[19]
Chang, C.C.; Lin, C.J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011, 2, 1-27.
[http://dx.doi.org/10.1145/1961189.1961199]
[20]
Liao, Z.J.; Li, D.P.; Wang, X.R.; Li, L.S.; Zou, Q. Cancer diagnosis through isomiR expression with machine learning method. Curr. Bioinform., 2018, 13, 57-63.
[http://dx.doi.org/10.2174/1574893611666160609081155]
[21]
Wang, S.P.; Zhang, Q.; Lu, J.; Cai, Y.D. Analysis and prediction of nitrated tyrosine sites with the mRMR method and support vector machine algorithm. Curr. Bioinform., 2018, 13, 3-13.
[http://dx.doi.org/10.2174/1574893611666160608075753]
[22]
Zou, Q.; Xing, P.; Wei, L.; Liu, B. Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA. RNA, 2019, 25(2), 205-218.
[http://dx.doi.org/10.1261/rna.069112.118] [PMID: 30425123]
[23]
Zeng, X.; Wang, W.; Deng, G.; Bing, J.; Zou, Q. Prediction of potential disease-associated microRNAs by using neural networks. Mol. Ther. Nucleic Acids, 2019, 16, 566-575.
[http://dx.doi.org/10.1016/j.omtn.2019.04.010] [PMID: 31077936]
[24]
Ru, X.; Li, L.; Zou, Q. Incorporating distance-based top-n-gram and random forest to identify electron transport proteins. J. Proteome Res., 2019, 18(7), 2931-2939.
[http://dx.doi.org/10.1021/acs.jproteome.9b00250] [PMID: 31136183]
[25]
Lv, Z.; Jin, S.; Ding, H.; Zou, Q. A random forest sub-Golgi protein classifier optimized via dipeptide and amino acid composition features. Front. Bioeng. Biotechnol., 2019, 7, 215.
[http://dx.doi.org/10.3389/fbioe.2019.00215] [PMID: 31552241]
[26]
Laxton, R.R. The measure of diversity. J. Theor. Biol., 1978, 70(1), 51-67.
[http://dx.doi.org/10.1016/0022-5193(78)90302-8] [PMID: 625122]
[27]
Naamati, G.; Askenazi, M.; Linial, M. ClanTox: a classifier of short animal toxins. Nucleic Acids Res., 2009, 37W363-8
[http://dx.doi.org/10.1093/nar/gkp299] [PMID: 19429697]
[28]
Saha, S.; Raghava, G.P. BTXpred: prediction of bacterial toxins. In Silico Biol. (Gedrukt), 2007, 7, pp. (4-5)405-412.
[PMID: 18391233]
[29]
Buczek, O.; Bulaj, G.; Olivera, B.M. Conotoxins and the posttranslational modification of secreted gene products. Cell. Mol. Life Sci., 2005, 62(24), 3067-3079.
[http://dx.doi.org/10.1007/s00018-005-5283-0] [PMID: 16314929]
[30]
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), 252-260.
[http://dx.doi.org/10.1016/j.jtbi.2006.06.014] [PMID: 16890961]
[31]
Yang, L.; Li, Q. Prediction of presynaptic and postsynaptic neurotoxins by the increment of diversity. Toxicol. In Vitro, 2009, 23(2), 346-348.
[http://dx.doi.org/10.1016/j.tiv.2008.12.015] [PMID: 19138734]
[32]
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]
[33]
Zuo, Y.C.; Li, Q.Z. Using K-minimum increment of diversity to predict secretory proteins of malaria parasite based on groupings of amino acids. Amino Acids, 2010, 38(3), 859-867.
[http://dx.doi.org/10.1007/s00726-009-0292-1] [PMID: 19387791]
[34]
Zuo, Y.C.; Li, Q.Z. Using reduced amino acid composition to predict defensin family and subfamily: integrating similarity measure and structural alphabet. Peptides, 2009, 30(10), 1788-1793.
[http://dx.doi.org/10.1016/j.peptides.2009.06.032] [PMID: 19591890]
[35]
Boeckmann, B.; Bairoch, A.; Apweiler, R.; Blatter, M.C.; Estreicher, A.; Gasteiger, E.; Martin, M.J.; Michoud, K.; O’Donovan, C.; Phan, I.; Pilbout, S.; Schneider, M. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res., 2003, 31(1), 365-370.
[http://dx.doi.org/10.1093/nar/gkg095] [PMID: 12520024]
[36]
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), 122-124.
[http://dx.doi.org/10.1093/bioinformatics/btw564] [PMID: 27565583]
[37]
Zheng, L.; Huang, S.; Mu, N.; Zhang, H.; Zhang, J.; Chang, Y.; Yang, L.; Zuo, Y. 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]
[38]
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]
[39]
Zuo, Y.C.; Su, W.X.; Zhang, S.H.; Wang, S.S.; Wu, C.Y.; Yang, L.; Li, G.P. Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure. Mol. Biosyst., 2015, 11(3), 950-957.
[http://dx.doi.org/10.1039/C4MB00681J] [PMID: 25607774]
[40]
Zuo, Y.C.; Peng, Y.; Liu, L.; Chen, W.; Yang, L.; Fan, G.L. Predicting peroxidase subcellular location by hybridizing different descriptors of Chou’ pseudo amino acid patterns. Anal. Biochem., 2014, 458, 14-19.
[http://dx.doi.org/10.1016/j.ab.2014.04.032] [PMID: 24802134]
[41]
Wei, L.; Wan, S.; Guo, J.; Wong, K.K.L. A novel hierarchical selective ensemble classifier with bioinformatics application. Artif. Intell. Med., 2017, 83, 82-90.
[http://dx.doi.org/10.1016/j.artmed.2017.02.005] [PMID: 28245947]
[42]
Wei, L.; Xing, P.; Shi, G.; Ji, Z.; Zou, Q. Fast prediction of protein methylation sites using a sequence-based feature selection technique. IEEE ACM T Comput. Biol. Bioinform., 2019, 16, 1264-1273.
[http://dx.doi.org/10.1109/TCBB.2017.2670558]
[43]
Wang, J.; Du, P.F.; Xue, X.Y.; Li, G.P.; Zhou, Y.K.; Zhao, W.; Lin, H.; Chen, W. VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences. Bioinformatics, 2020, 36(4), 1277-1278.
[PMID: 31504195]
[44]
Guruprasad, K.; Reddy, B.V.; Pandit, M.W. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng., 1990, 4(2), 155-161.
[http://dx.doi.org/10.1093/protein/4.2.155] [PMID: 2075190]
[45]
Ikai, A. Thermostability and aliphatic index of globular proteins. J. Biochem., 1980, 88(6), 1895-1898.
[PMID: 7462208]
[46]
Aboderin, A.A. An empirical hydrophobicity scale for α-amino-acids and some of its applications. Int. J. Biochem, 1971, 2, 537-544.
[http://dx.doi.org/10.1016/0020-711X(71)90023-1]

Rights & Permissions Print Cite
Article Metrics
21
© 2024 Bentham Science Publishers | Privacy Policy