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Medicinal Chemistry

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ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

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Research Article

iATP: A Sequence Based Method for Identifying Anti-tubercular Peptides

Author(s): Wei Chen*, Pengmian Feng and Fulei Nie

Volume 16, Issue 5, 2020

Page: [620 - 625] Pages: 6

DOI: 10.2174/1573406415666191002152441

Price: $65

Abstract

Background: Tuberculosis is one of the biggest threats to human health. Recent studies have demonstrated that anti-tubercular peptides are promising candidates for the discovery of new anti-tubercular drugs. Since experimental methods are still labor intensive, it is highly desirable to develop automatic computational methods to identify anti-tubercular peptides from the huge amount of natural and synthetic peptides. Hence, accurate and fast computational methods are highly needed.

Methods and Results: In this study, a support vector machine based method was proposed to identify anti-tubercular peptides, in which the peptides were encoded by using the optimal g-gap dipeptide compositions. Comparative results demonstrated that our method outperforms existing methods on the same benchmark dataset. For the convenience of scientific community, a freely accessible web-server was built, which is available at http://lin-group.cn/server/iATP.

Conclusion: It is anticipated that the proposed method will become a useful tool for identifying anti-tubercular peptides.

Keywords: Tuberculosis, anti-tubercular peptides, g-gap dipeptide, support vector, machine, feature selection, web-server.

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[1]
Padhi, A.; Sengupta, M.; Sengupta, S.; Roehm, K.H.; Sonawane, A. Antimicrobial peptides and proteins in mycobacterial therapy: current status and future prospects. Tuberculosis (Edinb.), 2014, 94,(4), 363-373.
[http://dx.doi.org/10.1016/j.tube.2014.03.011] [PMID: 24813349]
[2]
Khusro, A.; Aarti, C.; Agastian, P. Anti-tubercular peptides: A quest of future therapeutic weapon to combat tuberculosis. Asian Pac. J. Trop. Med., 2016, 9,(11), 1023-1034.
[http://dx.doi.org/10.1016/j.apjtm.2016.09.005] [PMID: 27890360]
[3]
Teng, T.; Liu, J.; Wei, H. Anti-mycobacterial peptides: from human to phage. Cell. Physiol. Biochem., 2015, 35,(2), 452-466.
[http://dx.doi.org/10.1159/000369711] [PMID: 25613372]
[4]
De Leon Rodriguez, L.M.; Kaur, H.; Brimble, M.A. Synthesis and bioactivity of antitubercular peptides and peptidomimetics: an update. Org. Biomol. Chem., 2016, 14,(4), 1177-1187.
[http://dx.doi.org/10.1039/C5OB02298C] [PMID: 26645944]
[5]
Silva, J.P.; Appelberg, R.; Gama, F.M. Antimicrobial peptides as novel anti-tuberculosis therapeutics. Biotechnol. Adv., 2016, 34,(5), 924-940.
[http://dx.doi.org/10.1016/j.biotechadv.2016.05.007] [PMID: 27235189]
[6]
Eldholm, V.; Balloux, F. Antimicrobial resistance in Mycobacterium tuberculosis: the odd one out. Trends Microbiol., 2016, 24,(8), 637-648.
[http://dx.doi.org/10.1016/j.tim.2016.03.007] [PMID: 27068531]
[7]
Gandhi, N.R.; Nunn, P.; Dheda, K.; Schaaf, H.S.; Zignol, M.; van Soolingen, D.; Jensen, P.; Bayona, J. Multidrug-resistant and extensively drug-resistant tuberculosis: a threat to global control of tuberculosis. Lancet, 2010, 375,(9728), 1830-1843.
[http://dx.doi.org/10.1016/S0140-6736(10)60410-2] [PMID: 20488523]
[8]
Abedinzadeh, M.; Gaeini, M.; Sardari, S. Natural antimicrobial peptides against Mycobacterium tuberculosis. J. Antimicrob. Chemother., 2015, 70,(5), 1285-1289.
[http://dx.doi.org/10.1093/jac/dku570] [PMID: 25681127]
[9]
Usmani, S.S.; Bhalla, S.; Raghava, G.P.S. Prediction of antitubercular peptides from sequence information using ensemble classifier and hybrid features. Front. Pharmacol., 2018, 9,, 954.
[http://dx.doi.org/10.3389/fphar.2018.00954] [PMID: 30210341]
[10]
Gogoladze, G.; Grigolava, M.; Vishnepolsky, B.; Chubinidze, M.; Duroux, P.; Lefranc, M.P.; Pirtskhalava, M. DBAASP: database of antimicrobial activity and structure of peptides. FEMS Microbiol. Lett., 2014, 357,(1), 63-68.
[http://dx.doi.org/10.1111/1574-6968.12489] [PMID: 24888447]
[11]
Pan, Y.; Gao, H.; Lin, H.; Liu, Z.; Tang, L.; Li, S. Identification of bacteriophage virion proteins using multinomial naïve bayes with g-gap feature tree. Int. J. Mol. Sci., 2018, 19,(6) E1779
[http://dx.doi.org/10.3390/ijms19061779] [PMID: 29914091]
[12]
Chen, W.; Feng, P.M.; Deng, E.Z.; Lin, H.; Chou, K.C. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal. Biochem., 2014, 462,, 76-83.
[http://dx.doi.org/10.1016/j.ab.2014.06.022] [PMID: 25016190]
[13]
Feng, P.M.; Chen, W.; Lin, H.; Chou, K.C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 2013, 442,(1), 118-125.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID: 23756733]
[14]
Su, Z.D.; Huang, Y.; Zhang, Z.Y.; Zhao, Y.W.; Wang, D.; Chen, W.; Chou, K.C.; Lin, H. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics, 2018, 34,(24), 4196-4204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID: 29931187]
[15]
Zhu, X.J.; Feng, C.Q.; Lai, H.Y.; Chen, W.; Lin, H. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl. Base. Syst., 2019, 163,, 787-793.
[http://dx.doi.org/10.1016/j.knosys.2018.10.007]
[16]
Manavalan, B.; Shin, T.H.; Lee, G. PVP-SVM: Sequence-based prediction of phage virion proteins using a support vector machine. Front. Microbiol., 2018, 9,, 476.
[http://dx.doi.org/10.3389/fmicb.2018.00476] [PMID: 29616000]
[17]
Chen, W.; Feng, P.M.; Lin, H.; Chou, K.C. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed. Res. Int., 2014, 2014, 623149
[http://dx.doi.org/10.1155/2014/623149] [PMID: 24967386]
[18]
Chen, W.; Yang, H.; Feng, P.; Ding, H.; Lin, H. iDNA4mC: identifying DNA N4-methylcytosine sites based on nucleotide chemical properties. Bioinformatics, 2017, 33(22), 3518-3523.
[http://dx.doi.org/10.1093/bioinformatics/btx479] [PMID: 28961687]
[19]
Li, D.; Ju, Y.; Zou, Q. Protein folds prediction with hierarchical structured SVM. Curr. Proteomics, 2016, 13(2), 79-85.
[http://dx.doi.org/10.2174/157016461302160514000940]
[20]
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(1), 3-13.
[http://dx.doi.org/10.2174/1574893611666160608075753]
[21]
Zhang, N.; Sa, Y.; Guo, Y.; Lin, W.; Wang, P.; Feng, Y.M. Discriminating ramos and jurkat cells with image textures from diffraction imaging flow cytometry based on a support vector machine. Curr. Bioinform., 2018, 13(1), 50-56.
[http://dx.doi.org/10.2174/1574893611666160608102537]
[22]
Yang, S.; Gu, J. Feature selection based on mutual information and redundancy-synergy coefficient. J. Zhejiang Univ. Sci., 2004, 5,(11), 1382-1391.
[http://dx.doi.org/10.1631/jzus.2004.1382] [PMID: 15495331]
[23]
Jiao, Y.S.; Du, P.F. Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection. J. Theor. Biol., 2016, 402, 38-44.
[http://dx.doi.org/10.1016/j.jtbi.2016.04.032] [PMID: 27155042]
[24]
Zou, Q.; Zeng, J.C.; Cao, L.J.; Zeng, X.X. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing, 2016, 173, 346-354.
[http://dx.doi.org/10.1016/j.neucom.2014.12.123]
[25]
Zou, Q.; Wan, S.; Ju, Y.; Tang, J.; Zeng, X. Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy. BMC Syst. Biol., 2016, 10(4)(Suppl. 4), 114.
[http://dx.doi.org/10.1186/s12918-016-0353-5] [PMID: 28155714]
[26]
Yang, H.; Lv, H.; Ding, H.; Chen, W.; Lin, H. iRNA-2OM: A sequence-based predictor for identifying 2′-o-methylation sites in homo sapiens. J. Comput. Biol., 2018, 25,(11), 1266-1277.
[http://dx.doi.org/10.1089/cmb.2018.0004] [PMID: 30113871]
[27]
Tang, H.; Zhao, Y.W.; Zou, P.; Zhang, C.M.; Chen, R.; Huang, P.; Lin, H. HBPred: a tool to identify growth hormone-binding proteins. Int. J. Biol. Sci., 2018, 14,(8), 957-964.
[http://dx.doi.org/10.7150/ijbs.24174] [PMID: 29989085]
[28]
Dao, F.Y.; Lv, H.; Wang, F.; Feng, C.Q.; Ding, H.; Chen, W.; Lin, H. Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique. Bioinformatics, 2019, 35,(12), 2075-2083.
[http://dx.doi.org/10.1093/bioinformatics/bty943] [PMID: 30428009]
[29]
Yu, C.Y.; Li, X.X.; Yang, H.; Li, Y.H.; Xue, W.W.; Chen, Y.Z.; Tao, L.; Zhu, F. Assessing the performances of protein function prediction algorithms from the perspectives of identification accuracy and false discovery rate. Int. J. Mol. Sci., 2018, 19,(1), 183.
[http://dx.doi.org/10.3390/ijms19010183] [PMID: 29316706]
[30]
Chen, W.; Feng, P.; Liu, T.; Jin, D. Recent advances in machine learning methods for predicting heat shock proteins. Curr. Drug Metab., 2019, 20,(3), 224-228.
[http://dx.doi.org/10.2174/1389200219666181031105916] [PMID: 30378494]
[31]
Manavalan, B.; Shin, T.H.; Lee, G. DHSpred: support-vector machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest. Oncotarget, 2017, 9(2), 1944-1956.
[PMID: 29416743]
[32]
Feng, P.M.; Ding, H.; Chen, W.; Lin, H. Naïve Bayes classifier with feature selection to identify phage virion proteins. Comput. Math. Methods Med., 2013, 2013, 530696
[http://dx.doi.org/10.1155/2013/530696] [PMID: 23762187]
[33]
Feng, P.M.; Lin, H.; Chen, W. Identification of antioxidants from sequence information using naïve Bayes. Comput. Math. Methods Med., 2013, 2013, 567529
[http://dx.doi.org/10.1155/2013/567529] [PMID: 24062796]
[34]
Feng, C.Q.; Zhang, Z.Y.; Zhu, X.J.; Lin, Y.; Chen, W.; Tang, H.; Lin, H. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics, 2019, 35,(9), 1469-1477.
[http://dx.doi.org/10.1093/bioinformatics/bty827] [PMID: 30247625]
[35]
Du, P.F.; Li, T.T.; Wang, X.; Xu, C. SubChlo-GO: Predicting protein subchloroplast locations with weighted gene ontology scores. Curr. Bioinform., 2013, 8,(2), 193-199.
[http://dx.doi.org/10.2174/1574893611308020007]
[36]
Jiao, Y.; Du, P. Performance measures in evaluating machine learning based bioinformatics predictors for classifications. Quant. Biol., 2016, 4,(4), 320-330.
[http://dx.doi.org/10.1007/s40484-016-0081-2]
[37]
Zhang, T.; Tan, P.; Wang, L.; Jin, N.; Li, Y.; Zhang, L.; Yang, H.; Hu, Z.; Zhang, L.; Hu, C.; Li, C.; Qian, K.; Zhang, C.; Huang, Y.; Li, K.; Lin, H.; Wang, D. RNALocate: a resource for RNA subcellular localizations. Nucleic Acids Res., 2017, 45,(D1), D135-D138.
[PMID: 27543076]
[38]
Yi, Y.; Zhao, Y.; Li, C.; Zhang, L.; Huang, H.; Li, Y.; Liu, L.; Hou, P.; Cui, T.; Tan, P.; Hu, Y.; Zhang, T.; Huang, Y.; Li, X.; Yu, J.; Wang, D. RAID v2.0: an updated resource of RNA-associated interactions across organisms. Nucleic Acids Res., 2017, 45,(D1), D115-D118.
[http://dx.doi.org/10.1093/nar/gkw1052] [PMID: 27899615]
[39]
Liang, Z.Y.; Lai, H.Y.; Yang, H.; Zhang, C.J.; Yang, H.; Wei, H.H.; Chen, X.X.; Zhao, Y.W.; Su, Z.D.; Li, W.C.; Deng, E.Z.; Tang, H.; Chen, W.; Lin, H. Pro54DB: a database for experimentally verified sigma-54 promoters. Bioinformatics, 2017, 33,(3), 467-469.
[PMID: 28171531]
[40]
Feng, P.; Ding, H.; Lin, H.; Chen, W. AOD: the antioxidant protein database. Sci. Rep., 2017, 7,(1), 7449.
[http://dx.doi.org/10.1038/s41598-017-08115-6] [PMID: 28784999]
[41]
Peng, L.; Peng, M.M.; Liao, B.; Huang, G.H.; Li, W.B.; Xie, D.F. The advances and challenges of deep learning application in biological big data processing. Curr. Bioinform., 2018, 13,(4), 352-359.
[http://dx.doi.org/10.2174/1574893612666170707095707]
[42]
Patel, S.; Tripathi, R.; Kumari, V.; Varadwaj, P. DeepInteract: deep neural network based protein-protein interaction prediction tool. Curr. Bioinform., 2017, 12,(6), 551-557.
[http://dx.doi.org/10.2174/1574893611666160815150746]
[43]
Cao, R.Z.; Bhattacharya, D.; Hou, J.; Cheng, J.L. DeepQA: improving the estimation of single protein model quality with deep belief networks. BMC Bioinform, 2016, 17,(1), 495.
[44]
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), 205-21.
[http://dx.doi.org/10.1261/rna.069112] [PMID: 30425123]
[45]
Yu, L.; Sun, X.; Tian, S.W.; Shi, X.Y.; Yan, Y.L. Drug and nondrug classification based on deep learning with various feature selection strategies. Curr. Bioinform., 2018, 13,(3), 253-259.
[http://dx.doi.org/10.2174/1574893612666170125124538]
[46]
Wei, L.; Su, R.; Wang, B.; Li, X.; Zou, Q. Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites. Neurocomputing, 2019, 324,, 3-9.
[http://dx.doi.org/10.1016/j.neucom.2018.04.082]
[47]
Long, H.X.; Wang, M.; Fu, H.Y. Deep convolutional neural networks for predicting hydroxyproline in proteins. Curr. Bioinform., 2017, 12,(3), 233-238.
[http://dx.doi.org/10.2174/1574893612666170221152848]
[48]
Wei, L.; Ding, Y.; Su, R.; Tang, J.; Zou, Q. Prediction of human protein subcellular localization using deep learning. J. Parallel Distrib. Comput., 2018, 117,, 212-217.
[http://dx.doi.org/10.1016/j.jpdc.2017.08.009]

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