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
Review Article

Recent Advances on Antioxidant Identification Based on Machine Learning Methods

Author(s): Pengmian Feng* and Lijing Feng

Volume 21, Issue 10, 2020

Page: [804 - 809] Pages: 6

DOI: 10.2174/1389200221666200719001449

Price: $65

conference banner
Abstract

Antioxidants are molecules that can prevent damages to cells caused by free radicals. Recent studies also demonstrated that antioxidants play roles in preventing diseases. However, the number of known molecules with antioxidant activity is very small. Therefore, it is necessary to identify antioxidants from various resources. In the past several years, a series of computational methods have been proposed to identify antioxidants. In this review, we briefly summarized recent advances in computationally identifying antioxidants. The challenges and future perspectives for identifying antioxidants were also discussed. We hope this review will provide insights into researches on antioxidant identification.

Keywords: Antioxidant, free radical, diseases, sequence encoding scheme, machine learning methods, molecules.

« Previous Next »
[1]
Lobo, V.; Patil, A.; Phatak, A.; Chandra, N. Free radicals, antioxidants and functional foods: impact on human health. Pharmacogn. Rev., 2010, 4(8), 118-126.
[http://dx.doi.org/10.4103/0973-7847.70902] [PMID: 22228951]
[2]
Hajhashemi, V.; Vaseghi, G.; Pourfarzam, M.; Abdollahi, A. Are antioxidants helpful for disease prevention? Res. Pharm. Sci., 2010, 5(1), 1-8.
[PMID: 21589762]
[3]
Pham-Huy, L.A.; He, H.; Pham-Huy, C. Free radicals, antioxidants in disease and health. Int. J. Biomed. Sci., 2008, 4(2), 89-96.
[PMID: 23675073]
[4]
Jain, A.K.; Mehra, N.K.; Swarnakar, N.K. Role of antioxidants for the treatment of cardiovascular diseases: challenges and opportunities. Curr. Pharm. Des., 2015, 21(30), 4441-4455.
[http://dx.doi.org/10.2174/1381612821666150803151758] [PMID: 26234792]
[5]
Toledo-Ibelles, P.; Mas-Oliva, J. Antioxidants in the fight against atherosclerosis: is this a dead end? Curr. Atheroscler. Rep., 2018, 20(7), 36.
[http://dx.doi.org/10.1007/s11883-018-0737-7] [PMID: 29781062]
[6]
Carvalho, A.N.; Firuzi, O.; Gama, M.J.; Horssen, J.V.; Saso, L. Oxidative Stress and Antioxidants in Neurological Diseases: Is There Still Hope? Curr. Drug Targets, 2017, 18(6), 705-718.
[http://dx.doi.org/10.2174/1389450117666160401120514] [PMID: 27033198]
[7]
Pai, V.V.; Shukla, P.; Kikkeri, N.N. Antioxidants in dermatology. Indian Dermatol. Online J., 2014, 5(2), 210-214.
[http://dx.doi.org/10.4103/2229-5178.131127] [PMID: 24860765]
[8]
Fusco, D.; Colloca, G.; Lo Monaco, M.R.; Cesari, M. Effects of antioxidant supplementation on the aging process. Clin. Interv. Aging, 2007, 2(3), 377-387.
[PMID: 18044188]
[9]
Sogut, S.; Zoroglu, S.S.; Ozyurt, H.; Yilmaz, H.R.; Ozugurlu, F.; Sivasli, E.; Yetkin, O.; Yanik, M.; Tutkun, H.; Savas, H.A.; Tarakcioglu, M.; Akyol, O. Changes in nitric oxide levels and antioxidant enzyme activities may have a role in the pathophysiological mechanisms involved in autism. Clin. Chim. Acta, 2003, 331(1-2), 111-117.
[10]
Huang, W.; Deng, Q.C.; Xie, B.J.; Shi, J.; Huang, F.H.; Tian, B.Q.; Huang, Q.D.; Xue, S. Purification and characterization of an antioxidant protein from Ginkgo biloba seeds. Food Res. Int., 2010, 43(1), 86-94.
[http://dx.doi.org/10.1016/j.foodres.2009.08.015]
[11]
Fu, J.; Tang, J.; Wang, Y.; Cui, X.; Yang, Q.; Hong, J.; Li, X.; Li, S.; Chen, Y.; Xue, W.; Zhu, F. Discovery of the consistently well-performed analysis chain for SWATH-MS based pharmacoproteomic quantification. Front. Pharmacol., 2018, 9, 681.
[http://dx.doi.org/10.3389/fphar.2018.00681] [PMID: 29997509]
[12]
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]
[13]
Fernández-Blanco, E.; Aguiar-Pulido, V.; Munteanu, C.R.; Dorado, J. Random Forest classification based on star graph topological indices for antioxidant proteins. J. Theor. Biol., 2013, 317, 331-337.
[http://dx.doi.org/10.1016/j.jtbi.2012.10.006] [PMID: 23116665]
[14]
Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol., 2011, 273(1), 236-247.
[http://dx.doi.org/10.1016/j.jtbi.2010.12.024] [PMID: 21168420]
[15]
Feng, P.M.; Lin, H.; Chen, W. Identification of antioxidants from sequence information using naïve Bayes. Comput. Math. Methods Med., 2013, 2013567529
[http://dx.doi.org/10.1155/2013/567529] [PMID: 24062796]
[16]
UniProt Consortium T. UniProt: the universal protein knowledgebase. Nucleic Acids Res., 2018, 46(5), 2699.
[http://dx.doi.org/10.1093/nar/gky092] [PMID: 29425356]
[17]
Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23), 3150-3152.
[http://dx.doi.org/10.1093/bioinformatics/bts565] [PMID: 23060610]
[18]
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]
[19]
Tan, J.X.; Li, S.H.; Zhang, Z.M.; Chen, C.X.; Chen, W.; Tang, H.; Lin, H. Identification of hormone binding proteins based on machine learning methods. Math. Biosci. Eng., 2019, 16(4), 2466-2480.
[http://dx.doi.org/10.3934/mbe.2019123] [PMID: 31137222]
[20]
Randić, M.; Zupan, J.; Vikić-Topić, D. On representation of proteins by star-like graphs. J. Mol. Graph. Model., 2007, 26(1), 290-305.
[http://dx.doi.org/10.1016/j.jmgm.2006.12.006] [PMID: 17223597]
[21]
Munteanu, C.R.; Magalhães, A.L.; Uriarte, E.; González-Díaz, H. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J. Theor. Biol., 2009, 257(2), 303-311.
[http://dx.doi.org/10.1016/j.jtbi.2008.11.017] [PMID: 19111559]
[22]
Chen, W.; Feng, P.; Liu, T.; Jin, D. Recent advances in machine learning methods for predicting heat shock proteins. Curr. Drug Metab., 2018, 20(3), 224-228.
[PMID: 30378494]
[23]
Chen, W.; Feng, P.; Nie, F. iATP: A sequence based method for identifying anti-tubercular peptides. Med. Chem., 2020, 16(5), 620-625.
[http://dx.doi.org/10.2174/1573406415666191002152441] [PMID: 31339073]
[24]
Ding, H.; Deng, E.Z.; Yuan, L.F.; Liu, L.; Lin, H.; Chen, W.; Chou, K.C. iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res. Int., 2014, 2014286419
[http://dx.doi.org/10.1155/2014/286419] [PMID: 24991545]
[25]
Ding, H.; Li, D. Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids, 2015, 47(2), 329-333.
[http://dx.doi.org/10.1007/s00726-014-1862-4] [PMID: 25385313]
[26]
Chen, W.; Nie, F.; Ding, H. Recent advances of computational methods for identifying bacteriophage virion proteins. Protein Pept. Lett., 2019, 27(4), 259-264.
[PMID: 30968770]
[27]
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, 2013530696
[http://dx.doi.org/10.1155/2013/530696] [PMID: 23762187]
[28]
Ding, H.; Feng, P.M.; Chen, W.; Lin, H. Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. Mol. Biosyst., 2014, 10(8), 2229-2235.
[http://dx.doi.org/10.1039/C4MB00316K] [PMID: 24931825]
[29]
Yang, W.; Zhu, X.J.; Huang, J.; Ding, H.; Lin, H. A brief survey of machine learning methods in protein sub-Golgi localization. Curr. Bioinform., 2019, 14, 234-240.
[http://dx.doi.org/10.2174/1574893613666181113131415]
[30]
Cai, C.Z.; Han, L.Y.; Ji, Z.L.; Chen, X.; Chen, Y.Z. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res., 2003, 31(13), 3692-3697.
[http://dx.doi.org/10.1093/nar/gkg600] [PMID: 12824396]
[31]
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]
[32]
Chao, L.; Wei, L.; Zou, Q. SecProMTB: A SVM-based classifier for secretory proteins of Mycobacterium tuberculosis with imbalanced data set. Proteomics, 2019, 19e1900007
[http://dx.doi.org/10.1002/pmic.201900007]
[33]
Song, L.; Li, D.; Zeng, X.; Wu, Y.; Guo, L.; Zou, Q. nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification. BMC Bioinformatics, 2014, 15, 298.
[http://dx.doi.org/10.1186/1471-2105-15-298] [PMID: 25196432]
[34]
Buchan, D.W.A.; Jones, D.T. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res., 2019, 47(W1), W402-W407.
[http://dx.doi.org/10.1093/nar/gkz297] [PMID: 31251384]
[35]
Zhang, L.; Zhang, C.; Gao, R.; Yang, R.; Song, Q. Sequence based prediction of antioxidant proteins using a classifier selection strategy. PLoS One, 2016, 11(9)e0163274
[http://dx.doi.org/10.1371/journal.pone.0163274] [PMID: 27662651]
[36]
Ehrlich, L.; Reczko, M.; Bohr, H.; Wade, R.C. Prediction of protein hydration sites from sequence by modular neural networks. Protein Eng., 1998, 11(1), 11-19.
[http://dx.doi.org/10.1093/protein/11.1.11] [PMID: 9579655]
[37]
Mirabello, C.; Pollastri, G. Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility. Bioinformatics, 2013, 29(16), 2056-2058.
[http://dx.doi.org/10.1093/bioinformatics/btt344] [PMID: 23772049]
[38]
Dubchak, I.; Muchnik, I.; Holbrook, S.R.; Kim, S.H. Prediction of protein folding class using global description of amino acid sequence. Proc. Natl. Acad. Sci. USA, 1995, 92(19), 8700-8704.
[http://dx.doi.org/10.1073/pnas.92.19.8700] [PMID: 7568000]
[39]
Du, P.F.; Zhao, W.; Miao, Y.Y.; Wei, L.Y.; Wang, L. UltraPse: a universal and extensible software platform for representing biological sequences. Int. J. Mol. Sci., 2017, 18(11)e2400
[http://dx.doi.org/10.3390/ijms18112400] [PMID: 29135934]
[40]
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, 2019.
[http://dx.doi.org/10.1093/bioinformatics/btz689] [PMID: 31504195]
[41]
Xiong, Y.; Wang, Q.; Yang, J.; Zhu, X.; Wei, D.Q. PredT4SE-stack: prediction of bacterial type IV secreted effectors from protein sequences using a stacked ensemble method. Front. Microbiol., 2018, 9, 2571.
[http://dx.doi.org/10.3389/fmicb.2018.02571] [PMID: 30416498]
[42]
Jiao, Y.S.; Du, P.F. Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou’s general pseudo-amino acid compositions. J. Theor. Biol., 2017, 416, 81-87.
[http://dx.doi.org/10.1016/j.jtbi.2016.12.026] [PMID: 28077336]
[43]
Zhao, W.; Li, G.P.; Wang, J.; Zhou, Y.K.; Gao, Y.; Du, P.F. Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions. J. Theor. Biol., 2019, 473, 38-43.
[http://dx.doi.org/10.1016/j.jtbi.2019.04.025] [PMID: 31051179]
[44]
Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 1997, 25(17), 3389-3402.
[http://dx.doi.org/10.1093/nar/25.17.3389] [PMID: 9254694]
[45]
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]
[46]
Chen, W.; Lv, H.; Nie, F.; Lin, H. i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics, 2019, 35(16), 2796-2800.
[http://dx.doi.org/10.1093/bioinformatics/btz015] [PMID: 30624619]
[47]
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]
[48]
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]
[49]
Zhou, Y-K.; Shen, Z-A.; Yu, H.; Luo, T.; Gao, Y.; Du, P-F. Predicting lncRNA–protein interactions with miRNAs as mediators in a heterogeneous network model. Front. Genet., 2020, 10(1341), 1.
[http://dx.doi.org/10.3389/fgene.2019.01341]
[50]
Zhang, L.N.; Zhang, C.J.; Gao, R.; Yang, R.T. Incorporating g-Gap Dipeptide Composition and Position Specific Scoring Matrix for Identifying Antioxidant Proteins. Proceeding of the IEEE 28th Canadian Conference on Electrical and Computer Engineering Halifax, Canada, 2015.
[http://dx.doi.org/10.1109/CCECE.2015.7129155]
[51]
Feng, P.; Chen, W.; Lin, H. Identifying antioxidant proteins by using optimal dipeptide compositions. Interdiscip. Sci., 2016, 8(2), 186-191.
[http://dx.doi.org/10.1007/s12539-015-0124-9] [PMID: 26345449]
[52]
Xiao, X.; Ju, W.F.; Hui, M.J. In iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests. 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017), 2017, pp. 587-593.
[http://dx.doi.org/10.2991/amcce-17.2017.103]
[53]
Xu, L.; Liang, G.; Shi, S.; Liao, C. SeqSVM: a sequence-based support vector machine method for identifying antioxidant proteins. Int. J. Mol. Sci., 2018, 19(6)e1773
[http://dx.doi.org/10.3390/ijms19061773] [PMID: 29914044]
[54]
Zou, Q.; Zeng, J.C.; Cao, L.J.; Ji, R.R. 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]
[55]
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]
[56]
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]
[57]
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]
[58]
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]
[59]
Nie, L.L.; Deng, L.; Fan, C.; Zhan, W.H.; Tang, Y.J. Prediction of protein S-Sulfenylation sites using a deep belief network. Curr. Bioinform., 2018, 13(5), 461-467.
[http://dx.doi.org/10.2174/1574893612666171122152208]
[60]
Lv, Z.; Ao, C.; Zou, Q. Protein function prediction: from traditional classifier to deep learning. Proteomics, 2019, 19(14)e1900119
[http://dx.doi.org/10.1002/pmic.201900119] [PMID: 31187588]
[61]
Shao, L.; Gao, H.; Liu, Z.; Feng, J.; Tang, L.; Lin, H. Identification of antioxidant proteins with deep learning from sequence information. Front. Pharmacol., 2018, 9, 1036.
[http://dx.doi.org/10.3389/fphar.2018.01036] [PMID: 30294271]
[62]
Meng, C.; Jin, S.; Wang, L.; Guo, F.; Zou, Q. AOPs-SVM: a sequence-based classifier of antioxidant proteins using a support vector machine. Front. Bioeng. Biotechnol., 2019, 7, 224.
[http://dx.doi.org/10.3389/fbioe.2019.00224] [PMID: 31620433]
[63]
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]
[64]
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]

Rights & Permissions Print Cite
Article Metrics
11
© 2024 Bentham Science Publishers | Privacy Policy