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SPrenylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. (English) Zbl 1411.92233
Summary: The protein prenylation (or S-prenylation) is one of the most essential modifications, required for the association of membrane of a plethora of signalling proteins with the key biological process such as protein trafficking, cell growth, proliferation and differentiation. Due to the ubiquitous nature of S-prenylation and its role in cellular functions, any defect in the biosynthesis or regulation of the isoprenoid leads to the occurrence of a variety of diseases including neurodegenerative disorders, metabolic issues, cardiovascular diseases and one of the most fatal diseases, cancer. This depicts the strong biological significance of S-prenylation, thus, the timely and accurate identification of S-prenylation sites is crucial and may provide with possible ways to understand the mechanism of this modification in proteins. To avoid laborious, resource demanding and expensive experimental techniques of identifying S-prenylation sites, here, we propose a novel predictor namely SPrenylC-PseAAC by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. A 2-tier classification was performed i.e., at first level, identification of prenylation and non-prenylation sites is performed, while at the second level, identification of S-farnesylation and S-geranylgeranylation sites is performed. Using jackknife, perdition model validation gave 95.31% accuracy for tier-1 classification and 91.42% for tier 2 classification, while for 10-fold cross-validation, it gave 93.68% accuracy for tier-1 classification and 89.70% for tier 2 classification. Thus the proposed predictor can help in predicting the Prenylation sites in an efficient and accurate way. The SPrenylC-PseAAC is available at (http://biopred.org/prenyl).

MSC:
92D20 Protein sequences, DNA sequences
68T05 Learning and adaptive systems in artificial intelligence
62P10 Applications of statistics to biology and medical sciences; meta analysis
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[1] Akbar, S.; Hayat, M., iMethyl-STTNC: identification of N(6)-methyladenosine sites by extending the Idea of SAAC into Chou’s PseAAC to formulate RNA sequences, J. Theor. Biol., 455, 205-211, (2018) · Zbl 1406.92448
[2] Akmal, M. A.; Rasool, N.; Khan, Y. D., Prediction of N-linked glycosylation sites using position relative features and statistical moments, PLoS One, 12, (2017)
[3] Arif, M.; Hayat, M.; Jan, Z., iMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition, J. Theor. Biol., 442, 11-21, (2018) · Zbl 1397.92180
[4] Blanden, M. J.; Suazo, K. F.; Hildebrandt, E. R.; Hardgrove, D. S.; Patel, M.; Saunders, W. P.; Distefano, M. D.; Schmidt, W. K.; Hougland, J. L., Efficient farnesylation of an extended C-terminal C (x) 3X sequence motif expands the scope of the prenylated proteome, J. Biol. Chem. jbc, M117, (2017)
[5] Butt, A. H.; Rasool, N.; Khan, Y. D., A treatise to computational approaches towards prediction of membrane protein and its subtypes, J. Membr. Biol., 250, 55-76, (2017)
[6] Butt, A. H.; Khan, S. A.; Jamil, H.; Rasool, N.; Khan, Y. D., A prediction model for membrane proteins using moments based features, BioMed Res. Int., 2016, 1-7, (2016)
[7] Cai, L.; Huang, T.; Su, J.; Zhang, X.; Chen, W.; Zhang, F.; He, L.; Chou, K.-C., Implications of newly identified brain eQTL genes and their interactors in Schizophrenia, Mol. Ther. Nucleic Acids, 12, 433-442, (2018)
[8] Cai, Y. D.; Chou, K. C., Predicting subcellular localization of proteins in a hybridization space, Bioinformatics, 20, 1151-1156, (2004), bth054 [pii] doi:10.1093/bioinformatics/bth054
[9] Cao, D. S.; Xu, Q. S.; Liang, Y. Z., propy: a tool to generate various modes of Chou’s PseAAC, Bioinformatics, 29, 960-962, (2013), btt072 [pii]
[10] Chandra, A.; Sharma, A.; Dehzangi, A.; Ranganathan, S.; Jokhan, A.; Chou, K.-C.; Tsunoda, T., PhoglyStruct: prediction of phosphoglycerylated lysine residues using structural properties of amino acids, Sci. Rep., 8, 17923, (2018)
[11] Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K. C., iACP: a sequence-based tool for identifying anticancer peptides, Oncotarget, 7, 16895-16909, (2016)
[12] Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K.-C., Using deformation energy to analyze nucleosome positioning in genomes, Genomics, 107, 69-75, (2016)
[13] Chen, W.; Tang, H.; Ye, J.; Lin, H.; Chou, K. C., iRNA-PseU: identifying RNA pseudouridine sites, Mol. Ther. Nucleic Acids, 5, e332, (2016)
[14] Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.-C., iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences, Oncotarget, 8, 4208, (2017)
[15] Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K. C., iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences, Oncotarget, 8, 4208-4217, (2017)
[16] Cheng, X.; Xiao, X.; Chou, K.-C., pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC, Gene, 628, 315-321, (2017)
[17] Cheng, X.; Xiao, X.; Chou, K.-C., pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC, Mol. Biosyst., 13, 1722-1727, (2017)
[18] 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, 1, 50-58, (2017)
[19] Cheng, X.; Xiao, X.; Chou, K.-C., pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset, Curr. Pharm. Des., 24, 4013-4022, (2018)
[20] Cheng, X.; Zhao, S.-G.; Xiao, X.; Chou, K.-C., iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals, Bioinformatics, 33, 341-346, (2016)
[21] Cheng, X.; Zhao, S.-G.; Lin, W.-Z.; Xiao, X.; Chou, K.-C., pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites, Bioinformatics, 33, 3524-3531, (2017)
[22] Chou, K.-C., Using subsite coupling to predict signal peptides, Protein Eng., 14, 75-79, (2001)
[23] Chou, K.-C., Prediction of signal peptides using scaled window, Peptides, 22, 1973-1979, (2001)
[24] Chou, K.-C., Some remarks on protein attribute prediction and pseudo amino acid composition, J. Theor. Biol., 273, 236-247, (2011) · Zbl 1405.92212
[25] Chou, K.-C., Some remarks on predicting multi-label attributes in molecular biosystems, Mol. Biosyst., 9, 1092-1100, (2013)
[26] Chou, K.-C.; Zhang, C.-T., Prediction of protein structural classes, Crit. Rev. Biochem. Mol. Biol., 30, 275-349, (1995)
[27] Chou, K.-C.; Wu, Z.-C.; Xiao, X., iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites, Mol. Biosyst., 8, 629-641, (2012)
[28] Chou, K.-C.; Cheng, X.; Xiao, X., pLoc_bal-mHum: predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset, Genomics, (2018)
[29] Chou, K. C., Prediction of protein cellular attributes using pseudo amino acid composition, Proteins Struct. Funct. Genet., 43, 246-255, (2001), (Erratum: ibid., 2001, Vol.44, 60)[pii] 10.1002/prot.1035
[30] Chou, K. C., Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology, Curr. Proteomics, 6, 262-274, (2009)
[31] Chou, K. C., Impacts of bioinformatics to medicinal chemistry, Med. Chem., 11, 218-234, (2015)
[32] Chou, K. C., An unprecedented revolution in medicinal chemistry driven by the progress of biological science, Curr. Top. Med. Chem., 17, 2337-2358, (2017)
[33] Chou, K. C.; Elrod, D. W., Bioinformatical analysis of G-protein-coupled receptors, J. Proteome Res., 1, 429-433, (2002)
[34] Chou, K. C.; Cai, Y. D., Prediction of protease types in a hybridization space, Biochem. Biophys. Res. Comm. (BBRC), 339, 1015-1020, (2006)
[35] Chou, K. C.; Shen, H. B., Recent advances in developing web-servers for predicting protein attributes, Nat. Sci., 1, 63-92, (2009)
[36] Contreras-Torres, E., Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC, J. Theor. Biol., 454, 139-145, (2018) · Zbl 1406.92452
[37] Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A., Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chourts general PseAAC, J. Theor. Biol., 364, 284-294, (2015) · Zbl 1405.92092
[38] Dou, Y.; Yao, B.; Zhang, C., PhosphoSVM: prediction of phosphorylation sites by integrating various protein sequence attributes with a support vector machine, Amino Acids, 46, 1459-1469, (2014)
[39] Downward, J., Targeting RAS signalling pathways in cancer therapy, Nat. Rev. Cancer, 3, 11, (2003)
[40] Du, P.; Gu, S.; Jiao, Y., PseAAC-General: fast building various modes of general form of Chou’s pseudo amino acid composition for large-scale protein datasets, Int. J. Mol. Sci., 15, 3495-3506, (2014)
[41] Du, P.; Wang, X.; Xu, C.; Gao, Y., PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo amino acid compositions, Anal. Biochem., 425, 117-119, (2012), [pii] 10.1016/j.ab.2012.03.015
[42] 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., 8, 1039, (2018)
[43] Feng, K.-Y.; Cai, Y.-D.; Chou, K.-C., Boosting classifier for predicting protein domain structural class, Biochem. Biophys. Res. Commun., 334, 213-217, (2005)
[44] Feng, P.-M.; Lin, H.; Chen, W., Identification of antioxidants from sequence information using Naive Bayes, Comput. Math. Methods Med., 2013, 567529, (2013)
[45] Feng, P.-M.; Ding, H.; Chen, W.; Lin, H., Naive Bayes classifier with feature selection to identify phage virion proteins, Comput. math. Methods Med., 2013, 530696, (2013) · Zbl 1275.92017
[46] Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K. C., 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)
[47] Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K. C., iDNA6mA-PseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC, Genomics, (2018)
[48] Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W., CD-HIT: accelerated for clustering the next-generation sequencing data, Bioinformatics, 28, 3150-3152, (2012)
[49] Ghauri, A.; Khan, Y.; Rasool, N.; Khan, S.; Chou, K., pNitro-Tyr-PseAAC: predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC, Current Pharm. Des, 24, 34, 4034-4043, (2018)
[50] Higgins, J. B.; Casey, P. J., The role of prenylation in G-protein assembly and function, Cell. Signal., 8, 433-437, (1996)
[51] Hussain, W.; Khan, Y. D.; Rasool, N.; Khan, S. A.; Chou, K.-C., SPalmitoylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins, Anal. Biochem., 568, 14-23, (2019)
[52] Javed, F.; Hayat, M., Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou’s PseAAC, Genomics, (2018)
[53] Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.-C., 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
[54] Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K. C., 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
[55] Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.-C., Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition, J. Biomol. Struct. Dyn., 34, 1946-1961, (2016)
[56] Jia, J.; Li, X.; Qiu, W.; Xiao, X.; Chou, K.-C., iPPI-PseAAC (CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC, J. Theor. Biol., 460, 195-203, (2019) · Zbl 1406.92189
[57] Jiang, L.; Zhang, J.; Xuan, P.; Zou, Q., BP neural network could help improve pre-miRNA identification in various species, BioMed Res. Int., 2016, 1-8, (2016)
[58] 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. Graph. Model., 77, 200-204, (2017)
[59] Ju, Z.; Wang, S. Y., Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition, Gene, 664, 78-83, (2018)
[60] Ju, Z.; Cao, J.-Z.; Gu, H., Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chouacid pairs into Ch, J. Theor. Biol., 397, 145-150, (2016)
[61] Khan, Y. D.; Ahmad, F.; Anwar, M. W., A neuro-cognitive approach for iris recognition using back propagation, World Appl. Sci. J., 16, 678-685, (2012)
[62] Khan, Y. D.; Ahmed, F.; Khan, S. A., Situation recognition using image moments and recurrent neural networks, Neural Comput. Appl., 24, 1519-1529, (2014)
[63] Khan, Y. D.; Khan, S. A.; Ahmad, F.; Islam, S., Iris recognition using image moments and k-means algorithm, Sci. World J., 2014, (2014)
[64] Khan, Y. D.; Rasool, N.; Hussain, W.; Khan, S. A.; Chou, K.-C., iPhosT-PseAAC: identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC, Anal. Biochem., 550, 109-116, (2018)
[65] Khan, Y. D.; Jamil, M.; Hussain, W.; Rasool, N.; Khan, S. A.; Chou, K.-C., pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments, J. Theor. Biol, (2018) · Zbl 1406.92460
[66] Khan, Y. D.; Khan, N. S.; Farooq, S.; Abid, A.; Khan, S. A.; Ahmad, F.; Mahmood, M. K., An efficient algorithm for recognition of human actions, Sci. World J., 2014, (2014)
[67] Krishnan, M. S., Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains, J. Theor. Biol., 445, 62-74, (2018)
[68] Kumar, R.; Srivastava, A.; Kumari, B.; Kumar, M., Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine, J. Theor. Biol., 365, 96-103, (2015) · Zbl 1314.92055
[69] Larijani, B.; Hume, A. N.; Tarafder, A. K.; Seabra, M. C., Multiple factors contribute to inefficient prenylation of Rab27a in Rab prenylation diseases, J. Biol. Chem, 278, 47, 46798-46804, (2003)
[70] Liang, Y.; Zhang, S., Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence, J. Theor. Biol., 454, 22-29, (2018) · Zbl 1406.92196
[71] Lin, H.; Deng, E. Z.; Ding, H.; Chen, W.; Chou, K. C., iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition, Nucleic Acids Res., 42, 12961-12972, (2014)
[72] Lin, H.; Ding, C.; Song, Q.; Yang, P.; Ding, H.; Deng, K.-J.; Chen, W., The prediction of protein structural class using averaged chemical shifts, J. Biomol. Struct. Dyn., 29, 1147-1153, (2012)
[73] Lin, W.-Z.; Fang, J.-A.; Xiao, X.; Chou, K.-C., iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins, Mol. Biosyst., 9, 634-644, (2013)
[74] Lin, W. Z.; Fang, J. A.; Xiao, X.; Chou, K. C., iDNA-Prot: identification of DNA binding proteins using random forest with grey model, PLoS One, 6, e24756, (2011), [pii] 10.1371/journal.pone.0024756
[75] 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)
[76] 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)
[77] Liu, B.; Wang, S.; Long, R.; Chou, K.-C., iRSpot-EL: identify recombination spots with an ensemble learning approach, Bioinformatics, 33, 35-41, (2016)
[78] Liu, B.; Wang, S.; Long, R.; Chou, K. C., iRSpot-EL: identify recombination spots with an ensemble learning approach, Bioinformatics, 33, 35-41, (2017)
[79] 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)
[80] 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, (2015)
[81] 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)
[82] 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)
[83] 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)
[84] Lo, C.-H.; Don, H.-S., 3-D moment forms: their construction and application to object identification and positioning, IEEE Trans. Pattern Anal. Mach. Intell., 11, 1053-1064, (1989)
[85] Mei, J.; Zhao, J., Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers, Sci. Rep., 8, 2359, (2018)
[86] Mei, J.; Zhao, J., Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features, J. Theor. Biol., 427, 147-153, (2018)
[87] Mei, J.; Fu, Y.; Zhao, J., Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition, J. Theor. Biol., 456, 41-48, (2018) · Zbl 1406.92198
[88] Mondal, S.; Pai, P. P., Chou s pseudo amino acid composition improves sequencebased antifreeze protein prediction, J. Theor. Biol., 356, 30-35, (2014)
[89] Nanni, L.; Brahnam, S.; Lumini, A., Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition, J. Theor. Biol., 360, 109-116, (2014) · Zbl 1343.92387
[90] Qiu, W.-R.; Xiao, X.; Chou, K.-C., iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components, Int. J. Mol. Sci., 15, 1746-1766, (2014)
[91] Qiu, W.-R.; Xiao, X.; Lin, W.-Z.; Chou, K.-C., iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach, BioMed Res. Int., 2014, 1-8, (2014)
[92] Qiu, W.-R.; Xiao, X.; Lin, W.-Z.; Chou, K.-C., iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model, J. Biomol. Struct. Dyn., 33, 1731-1742, (2015)
[93] Qiu, W.-R.; Xiao, X.; Xu, Z.-C.; Chou, K.-C., iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier, Oncotarget, 7, 51270, (2016)
[94] Qiu, W.-R.; Sun, B.-Q.; Xiao, X.; Xu, Z.-C.; Chou, K.-C., iPTM-mLys: identifying multiple lysine PTM sites and their different types, Bioinformatics, 32, 3116-3123, (2016)
[95] Qiu, W.-R.; Sun, B.-Q.; Xiao, X.; Xu, Z.-C.; Chou, K.-C., iHyd-PseCp: identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC, Oncotarget, 7, 44310, (2016)
[96] Qiu, W.-R.; Jiang, S.-Y.; Xu, Z.-C.; Xiao, X.; Chou, K.-C., iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition, Oncotarget, 8, 41178, (2017)
[97] Qiu, W.-R.; Jiang, S.-Y.; Sun, B.-Q.; Xiao, X.; Cheng, X.; Chou, K.-C., 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)
[98] Qiu, W.; Li, S.; Cui, X.; Yu, Z.; Wang, M.; Du, J.; Peng, Y.; Yu, B., Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition, J. Theor. Biol., 450, 86-103, (2018) · Zbl 1397.92228
[99] Qiu, W. R.; Sun, B. Q.; Xiao, X.; Xu, D.; Chou, K. C., iPhos‐PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory, Mol. Inf., 36, (2017)
[100] Rahman, S. M.; Shatabda, S.; Saha, S.; Kaykobad, M.; Sohel Rahman, M., DPP-PseAAC: a DNA-binding protein prediction model using Chou’s general PseAAC, J Theor Biol, 452, 22-34, (2018)
[101] Sabooh, M. F.; Iqbal, N.; Khan, M.; Khan, M.; Maqbool, H. F., Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC, J. Theor. Biol., 452, 1-9, (2018) · Zbl 1397.92232
[102] Sankari, E. S.; Manimegalai, D. D., Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC, J. Theor. Biol., 455, 319-328, (2018) · Zbl 1406.92470
[103] Shen, H.-B.; Chou, K.-C., Signal-3L: a 3-layer approach for predicting signal peptides, Biochem. Biophys. Res. Commun., 363, 297-303, (2007)
[104] Shen, H.-B.; Yang, J.; Chou, K.-C., Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction, Amino Acids, 33, 57-67, (2007)
[105] Shen, N.; Gong, T.; Wang, J.-D.; Meng, F.-L.; Qiao, L.; Yang, R.-L.; Xue, B.; Pan, F.-Y.; Zhou, X.-J.; Chen, H.-Q., Cigarette smoke-induced pulmonary inflammatory responses are mediated by EGR-1/GGPPS/MAPK signaling, Am. J. Pathol., 178, 110-118, (2011)
[106] Song, J.; Wang, Y.; Li, F.; Akutsu, T.; Rawlings, N. D.; Webb, G. I.; Chou, K.-C., iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites, Brief. Bioinform, (2018)
[107] Song, J.; Li, F.; Takemoto, K.; Haffari, G.; Akutsu, T.; Chou, K.-C.; Webb, G. I., PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework, J. Theor. Biol., 443, 125-137, (2018)
[108] Srivastava, A.; Kumar, R.; Kumar, M., BlaPred: predicting and classifying beta-lactamase using a 3-tier prediction system via Chou’s general PseAAC, J. Theor. Biol., (2018) · Zbl 1406.92215
[109] Stevens, T. J.; Arkin, I. T., Do more complex organisms have a greater proportion of membrane proteins in their genomes?, Proteins Struct. Funct. Bioinf., 39, 417-420, (2000)
[110] Terry, K. L.; Casey, P. J.; Beese, L. S., Conversion of protein farnesyltransferase to a geranylgeranyltransferase, Biochemistry, 45, 9746-9755, (2006)
[111] Timothy, A.; Casey, P. J., Signalling functions and biochemical properties of pertussis toxin-resistant G-proteins, Biochem. J., 321, 561-571, (1997)
[112] Vranová, E.; Coman, D.; Gruissem, W., Network analysis of the MVA and MEP pathways for isoprenoid synthesis, Annu. Rev. Plant Biol., 64, 665-700, (2013)
[113] Wang, L.; Zhang, R.; Mu, Y., Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC, J. Theor. Biol., 461, 51-58, (2019) · Zbl 1406.92221
[114] Wu, Z.-C.; Xiao, X.; Chou, K.-C., iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites, Mol. Biosyst., 7, 3287-3297, (2011)
[115] Xiao, X.; Wu, Z.-C.; Chou, K.-C., iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites, J. Theor. Biol., 284, 42-51, (2011) · Zbl 1397.92238
[116] Xiao, X.; Wang, P.; Lin, W.-Z.; Jia, J.-H.; Chou, K.-C., iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types, Anal. Biochem., 436, 168-177, (2013)
[117] Xiao, X.; Ye, H.-X.; Liu, Z.; Jia, J.-H.; Chou, K.-C., iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition, Oncotarget, 7, 34180, (2016)
[118] Xie, Y.; Zheng, Y.; Li, H.; Luo, X.; He, Z.; Cao, S.; Shi, Y.; Zhao, Q.; Xue, Y.; Zuo, Z., GPS-Lipid: a robust tool for the prediction of multiple lipid modification sites, Sci. Rep., 6, 28249, (2016)
[119] Xu, N.; Shen, N.; Wang, X.; Jiang, S.; Xue, B.; Li, C., Protein prenylation and human diseases: a balance of protein farnesylation and geranylgeranylation, Sci. China Life Sci., 58, 328-335, (2015)
[120] Xu, Y.; Wang, Z.; Li, C.; Chou, K.-C., iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC, Med. Chem., 13, 544-551, (2017)
[121] Xu, Y.; Shao, X. J.; Wu, L. Y.; Deng, N. Y.; Chou, K. C., iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins, PeerJ, 1, e171, (2013)
[122] Xu, Y.; Wen, X.; Shao, X.-J.; Deng, N.-Y.; Chou, K.-C., iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition, Int. J. Mol. Sci., 15, 7594-7610, (2014)
[123] Xu, Y.; Wen, X.; Wen, L. S.; Wu, L. Y.; Deng, N. Y.; Chou, K. C., iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition, PLoS One, 9, (2014)
[124] Zhang, C. J.; Tang, H.; Li, W. C.; Lin, H.; Chen, W.; Chou, K. C., iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition, Oncotarget, 7, 69783-69793, (2016)
[125] Zhang, L.; Kong, L., iRSpot-ADPM: identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components, J. Theor. Biol., 441, 1-8, (2018)
[126] Zhang, S.; Liang, Y., Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC, J. Theor. Biol., (2018) · Zbl 1406.92230
[127] Zhang, S.; Duan, X., Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC, J. Theor. Biol., 437, 239-250, (2018) · Zbl 1394.92047
[128] Zhao, W.; Wang, L.; Zhang, T. X.; Zhao, Z. N.; Du, P. F., A brief review on software tools in generating Chou’s pseudo-factor representations for all types of biological sequences, Protein Pept. Lett., (2018)
[129] Zhou, G. P.; Doctor, K., Subcellular location prediction of apoptosis proteins, Proteins Struct. Funct. Bioinf., 50, 44-48, (2003)
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