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Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm. (English) Zbl 1406.92190
Summary: Cysteine S-sulfenylation is an important protein post-translational modification, which plays a crucial role in transcriptional regulation, cell signaling, and protein functions. To better elucidate the molecular mechanism of S-sulfenylation, it is important to identify S-sulfenylated substrates and their corresponding S-sulfenylation sites accurately. In this study, a novel bioinformatics tool named Sulf\(_-\)FSVM is proposed to predict S-sulfenylation sites by using multiple feature extraction and fuzzy support vector machine algorithm. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode S-sulfenylation sites. And the maximum relevance minimum redundancy method are adopted to remove the redundant features. On the other hand, a fuzzy support vector machine algorithm is used to handle the class imbalance and noise problem in S-sulfenylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of Sulf\(_-\)FSVM achieves a satisfactory performance with a sensitivity of 73.26%, a specificity of 70.78%, an accuracy of 71.07% and a Matthew’s correlation coefficient of 0.2971. Independent tests also show that Sulf\(_-\)FSVM significantly outperforms existing S-sulfenylation sites predictors. Therefore, Sulf\(_-\)FSVM can be a useful tool for accurate prediction of protein S-sulfenylation sites.
MSC:
92C40 Biochemistry, molecular biology
68T05 Learning and adaptive systems in artificial intelligence
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[1] Ahmad, K.; Waris, M.; Hayat, M., Prediction of protein submitochondrial locations by incorporating dipeptide composition into Chou’s general pseudo amino acid composition, J. Membr. Biol., 249, 293-304, (2016)
[2] Al Maruf, M. A.; Shatabda, S., Irspot-SF: prediction of recombination hotspots by incorporating sequence based features into Chou’s pseudo components, Genomics., (2018), (2018)
[3] Antelmann, H.; Helmann, J. D., Thiol-based redox switches and gene regulation, Antioxid. Redox Signal., 14, 1049-1063, (2011)
[4] Atchley, W. R.; Zhao, J.; Fernandes, A. D.; D¨ruke, T., Solving the protein sequencemetric problem, Proc. Natl. Acad. Sci. U. S. A., 102, 6395-6400, (2005)
[5] Batuwita, R.; Palade, V., Class imbalance learning methods for support vector machines, Imbalanced Learn. Found. Algorithms Appl., 1, 83-99, (2013)
[6] Beltrao, P.; Albanèse, V.; Kenner, L. R.; Swaney, D. L.; Burlingame, A.; Villén, J.; Lim, W. A.; Fraser, J. S.; Frydman, J.; Krogan, N. J., Systematic functional prioritization of protein post-translational modifications, Cell, 150, 413-425, (2012)
[7] Bui, V. M.; Lu, C. T.; Ho, T. T.; Lee, T. Y., MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs, Bioinformatics, 32, 165-172, (2016)
[8] Bui, V. M.; Weng, S. L.; Lu, C. T.; Chang, T. H.; Weng, J. T.; Lee, T. Y., Sohsite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites, BMC Genomics, 17, 59-70, (2016)
[9] Chen, K.; Kurgan, L. A.; Ruan, J., Prediction of flexible/rigid regions from proteinsequences using k-spaced amino acid pairs, BMC Struct. Biol., 7, 25, (2007)
[10] Chen, W.; Ding, H.; Feng, P., Iacp: a sequence-based tool for identifying anticancer peptides, Oncotarget, 7, 16895-16909, (2016)
[11] Chen, W.; Feng, P.; Ding, H.; Lin, H., Irna-methyl: identifying N6-methyladenosine sites using pseudo nucleotide composition, Anal. Biochem., 490, 26-33, (2015)
[12] Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H., Irna-3typea: identifying 3-types of modification at RNA’s adenosine sites, Mol. Ther. Nucleic Acids, 11, 468-474, (2018)
[13] Chen, W.; Lin, H.; Chou, K. C., Pseudo nucleotide composition or pseknc: an effective formulation for analyzing genomic sequences, Mol. BioSyst., 11, 2620-2634, (2015)
[14] Chen, W.; Tang, H.; Ye, J.; Lin, H.; Chou, K. C., Irna-pseu: identifying RNA pseudouridine sites, Mol. Ther.Nucleic Acids, 5, e332, (2016)
[15] Chen, Y. Z.; Tang, Y. R.; Sheng, Z. Y.; Zhang, Z., Prediction of mucin-type O-glycosylation sites in Mammalian proteins using the composition of k-spaced amino acid pairs, BMC Bioinf, 9, 101, (1999)
[16] Cheng, X.; Xiao, X.; Chou, K. C., Ploc-meuk: predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general pseaac, Genomics, 110, 50-58, (2018)
[17] Cheng, X.; Zhao, S. G.; Lin, W. Z., Ploc-manimal: predict subcellular localization of animal proteins with both single and multiple sites, Bioinformatics, 33, 3524-3531, (2017)
[18] Cheng, X.; Zhao, S. G.; Xiao, X., Iatc-misf: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals, Bioinformatics, 33, 341-346, (2017)
[19] Chou, K. C., Prediction of protein cellular attributes using pseudo amino acid composition, Proteins Struct. Funct. Genet., 43, 246-255, (2001)
[20] Chou, K. C., Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21, 10-19, (2005)
[21] Chou, K. C., Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology, Curr. Proteomics, 6, 262-274, (2009)
[22] Chou, K. C., Some remarks on protein attribute prediction and pseudo amino acid composition (50th anniversary year review), J. Theor. Biol., 273, 236-247, (2011) · Zbl 1405.92212
[23] Chou, K. C., Some remarks on predicting multi-label attributes in molecular biosystems, Mol. BioSyst., 9, 1092-1100, (2013)
[24] Chou, K. C., Impacts of bioinformatics to medicinal chemistry. med, Chem, 11, 218-234, (2015)
[25] Chou, K. C., An unprecedented revolution in medicinal chemistry driven by the progress of biological science, Curr. Top. Med. Chem., 17, 2337-2358, (2017)
[26] Deng, L.; Xu, X. J.; Liu, H., Predcso: an ensemble method for the prediction of S-sulfenylation sites in proteins, Mol. Omics, 14, 257-265, (2018)
[27] Ding, Y. S.; Zhang, T. L.; Chou, K. C., Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network, Protein Pept. Lett., 14, 811-815, (2007)
[28] Feng, P.; Ding, H.; Yang, H.; Chen, W., Irna-psecoll: identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into pseknc, Mol. Ther. Nucleic Acids, 7, 155-163, (2017)
[29] Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W., Idna6ma-pseknc: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into pseknc, Genomics., (2018), (2018)
[30] Gupta, V.; Carroll, K. S., Sulfenic acid chemistry, detection and cellular lifetime, Biochim. Biophys. Acta., 1840, 847-875, (2014)
[31] Gupta, M. K.; Niyogi, R.; Misra, M., An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition, SAR QSAR Environ. Res., 24, 597-609, (2013)
[32] Hasan, M. M.; Guo, D.; Kurata, H., Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information, Mol. BioSyst., 13, 2545-2550, (2017)
[33] Hasan, M. M.; Li, J.; Ahmad, S.; Molla, M. I., Predcar-site: carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue, Anal. Biochem., 525, 107-113, (2017)
[34] Hayat, M.; Iqbal, N., Discriminating protein structure classes by incorporating pseudo average chemical shift to Chou’s general pseaac and support vector machine, Comput. Methods Programs Biomed., 116, 184-192, (2014)
[35] Jia, C. Z.; He, W. Y.; Yao, Y. H., OH-PRED: prediction of protein hydroxylation sites by incorporating adapted normal distribution bi-profile Bayes feature extraction and physicochemical properties of amino acids, J. Biomol. Struct. Dyn., 35, 829-835, (2017)
[36] Jia, C. Z.; Zuo, Y., S-sulfpred: a sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique, J. Theor. Biol., 422, 84-89, (2017)
[37] Jia, J.; Liu, Z.; Xiao, X.; Liu, B., Isuc-pseopt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset, Anal. Biochem., 497, 48-56, (2016)
[38] Jia, J.; Liu, Z.; Xiao, X.; Liu, B., Psuc-lys: predict lysine succinylation sites in proteins with pseaac and ensemble random forest approach, J. Theor. Biol., 394, 223-230, (2016) · Zbl 1343.92153
[39] Ju, Z.; Cao, J. Z., Prediction of protein N-formylation using the composition of k-spaced amino acid pairs, Anal. Biochem., 534, 40-45, (2017)
[40] Ju, Z.; Cao, J. Z.; Gu, H., Ilm-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou’s general pseaac, J. Theor. Biol., 385, 50-57, (2015) · Zbl 1343.92157
[41] Ju, Z.; Cao, J. Z.; Gu, H., Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou’s general pseaac, J, Theor. Biol., 397, 145-150, (2016)
[42] Ju, Z.; He, J. J., Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general pseaac, J. Mol. Graphics Modell., 77, 200-204, (2017)
[43] Kawashima, S.; Kanehisa, M., Aaindex: amino acid index database, Nucleic Acids Res, 1, 374, (2000)
[44] Khan, Y. D.; Rasool, N.; Hussain, W.; Khan, S. A., Iphost-pseaac: identify phosphothreonine sites by incorporating sequence statistical moments into pseaac, Anal. Biochem., 550, 109-116, (2018)
[45] Li, B. Q.; Hu, L. L.; Chen, L.; Feng, K. Y.; Cai, Y. D.; Chou, K. C., Prediction of protein domain with mrmr feature selection and analysis, PLoS One, 7, e39308, (2012)
[46] Li, B. Q.; Hu, L. L.; Niu, S.; Cai, Y. D.; Chou, K. C., Predict and analyze S-nitrosylation modification sites with the mrmr and IFS approaches, J. Proteomics, 75, 1654-1665, (2012)
[47] Li, B. Q.; Huang, T.; Liu, L.; Cai, Y. D.; Chou, K. C., Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network, PLoS One, 7, e33393, (2012)
[48] Lin, C. F.; Wang, S. D., Fuzzy support vector machines., IEEE Trans. Neural Netw, 13, 464-471, (2002)
[49] Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L.; Chou, K. C., Pse-in-one: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nucleic Acids Res, 43, W65-W71, (2015)
[50] Liu, Z.; Xiao, X.; Yu, D. J.; Jia, J., Prnam-PC: predicting N-methyladenosine sites in RNA sequences via physical-chemical properties, Anal. Biochem., 497, 60-67, (2016)
[51] Liu, B.; Fang, L.; Long, R.; Lan, X.; Chou, K. C., Ienhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition, Bioinformatics, 32, 362-369, (2016)
[52] Liu, B.; Long, R.; Chou, K. C., Idhs-EL: identifying dnase I hypersensi-tivesites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework, Bioinformatics, 32, 2411-2418, (2016)
[53] Liu, L. M.; Xu, Y.; Chou, K. C., Ipgk-pseaac: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general pseaac, Med. Chem., 13, 552-559, (2017)
[54] Liu, B.; Wang, S.; Long, R.; Chou, K. C., Irspot-EL: identify recombination spots with an ensemble learning approach, Bioinformatics, 33, 35-41, (2017)
[55] Liu, B.; Yang, F.; Chou, K. C., 2L-pirna: a two-layer ensemble classifier for identifying piwi-interacting RNAs and their function, Mol. Ther.-Nucleic Acids, 7, 267-277, (2017)
[56] Liu, B.; Wu, H.; Chou, K. C., Pse-in-one 2.0: an improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nat. Sci., 9, 67-91, (2017)
[57] Liu, B.; Li, K.; Huang, D. S.; Chou, K. C., Ienhancer-EL: identifying enhancers and their strength with ensemble learning approach, Bioinformatics., (2018), (2018)
[58] Liu, B.; Weng, F.; Huang, D. S.; Chou, K. C., Iro-3wpseknc: identify DNA replication origins by three-window-based pseknc, Bioinformatics., (2018), (2018)
[59] Liu, B.; Yang, F.; Huang, D. S.; Chou, K. C., Ipromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based pseknc, Bioinformatics, 34, 33-40, (2018)
[60] Meher, P. K.; Sahu, T. K.; Saini, V.; Rao, A. R., Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general pseaac, Sci. Rep., 7, 42362, (2017)
[61] Peng, H.; Long, F.; Ding, C., Feature selection based on mutual information criteria of MAX-dependency, MAX-relevance, and MIN-redundancy, IEEE Trans. Pattern Anal. Mach. Intell., 27, 1226-1238, (2005)
[62] Qiu, W. R.; Jiang, S. Y.; Sun, B. Q., Irna-2methyl: identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general pseknc and ensemble classifier, Med. Chem., 13, 734-743, (2017)
[63] Qiu, W. R.; Sun, B. Q.; Xiao, X., Iphos-pseevo: identifying human phosphorylated proteins by incorporating evolutionary information into general pseaac via grey system theory, Mol. Inf., 36, (2017)
[64] Qiu, W. R.; Xiao, X.; Xu, Z. C., Iphos-pseen: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier, Oncotarget, 7, 51270-51283, (2016)
[65] Qiu, W. R.; Sun, B. Q.; Xiao, X.; Xu, Z. C., Iptm-mlys: identifying multiple lysine PTM sites and their different types, Bioinformatics, 32, 3116-3123, (2016), (2016)
[66] Qiu, W. R.; Xiao, X.; Lin, W. Z., Imethyl-pseaac: identification of protein methylation sites via a pseudo amino acid composition approach, Biomed. Res. Int., (2014)
[67] Sakka, M.; Tzortzis, G.; Mantzaris, M. D.; Bekas, N.; Kellici, T. F.; Likas, A.; Galaris, D.; Gerothanassis, I. P.; Tzakos, A. G., PRESS: protein S-sulfenylation server, Bioinformatics, 32, 2710-2712, (2016)
[68] Sharma, R.; Dehzangi, A.; Lyons, J.; Paliwal, K.; Tsunoda, T.; Sharma, A., Predict Gram-positive and Gram-negative subcellular localization via incorporating evolutionary information and physicochemical features into Chou’s general pseaac, IEEE Trans. Nanobiosci., 14, 915-926, (2015)
[69] Shen, H. B.; Yang, J.; Chou, K. C., Fuzzy KNN for predicting membrane protein types from pseudo amino acid composition, J. Theor. Biol., 240, 9-13, (2006)
[70] 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), (2018)
[71] Vacic, V.; Iakoucheva, L. M.; Radivojac, P., Two sample logo: a graphical representation of the differences between two sets of sequence alignments, Bioinformatics, 22, 1536-1537, (2006)
[72] Veropoulos, K.; Campbell, C.; Cristianini, N., Controlling the sensitivity of support vector machines, (Proceedings of the International Joint Conference on Artificial Intelligence, (1999)), 55-60
[73] Wang, X.; Yan, R.; Li, J.; Song, J., SOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites, Mol. BioSyst., 12, 2849-2858, (2016)
[74] Xu, Y.; Ding, J.; Wu, L. Y., Isulf-cys: prediction of S-sulfenylation sites in proteins with physicochemical properties of amino acids, Plos One, 11, (2016)
[75] Xu, Y.; Shao, X. J.; Wu, L. Y.; Deng, N. Y., Isno-aapair: incorporating amino acid pairwise coupling into pseaac for predicting cysteine S-nitrosylation sites in proteins, PeerJ, 1, e171, (2013)
[76] Xu, Y.; Wen, X.; Wen, L. S.; Wu, L. Y., Initro-tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition, PLoS ONE, 9, (2014)
[77] Yang, H.; Qiu, W. R.; Liu, G.; Guo, F. B.; Chen, W.; Chou, K. C.; Lin, H., Irspot-pse6NC: identifying recombination spots in saccharomyces cerevisiae by incorporating hexamer composition into general pseknc, Int. J. Biol. Sci., 14, 883-891, (2018)
[78] Yang, J.; Gupta, V.; Tallman, K. A.; Porter, N. A.; Carroll, K. S.; Liebler, D. C., Global, in situ, site-specific analysis of protein S-sulfenylation, Nat. Protoc., 10, 1022-1037, (2015)
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