An Insightful 10-year Recollection Since the Emergence of the 5-steps Rule

Author(s): Kuo-Chen Chou*.

Journal Name: Current Pharmaceutical Design

Volume 25 , Issue 40 , 2019

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Abstract:

Objective: One of the most challenging and also the most difficult problems is how to formulate a biological sequence with a vector but considerably keep its sequence order information.

Methods: To address such a problem, the approach of Pseudo Amino Acid Components or PseAAC has been developed.

Results and Conclusion: It has become increasingly clear via the 10-year recollection that the aforementioned proposal has been indeed very powerful.

Keywords: PseAAC, PseKNC, 5-steps rule, byproducts, distorted key theory, biological sequence.

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Chou KC, Wei DQ, Zhong WZ. Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. Biochem Biophys Res Commun 2003; 308(1): 148-51.
[http://dx.doi.org/10.1016/S0006-291X(03)01342-1] [PMID: 12890493]
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Chou KC. Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochem Biophys Res Commun 2004; 319(2): 433-8.
[http://dx.doi.org/10.1016/j.bbrc.2004.05.016] [PMID: 15178425]
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Chou KC. Insights from modeling the tertiary structure of human BACE2. J Proteome Res 2004; 3(5): 1069-72.
[http://dx.doi.org/10.1021/pr049905s] [PMID: 15473697]
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Chou KC. Molecular therapeutic target for type-2 diabetes. J Proteome Res 2004; 3(6): 1284-8.
[http://dx.doi.org/10.1021/pr049849v] [PMID: 15595739]
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Chou KC. Insights from modeling three-dimensional structures of the human potassium and sodium channels. J Proteome Res 2004; 3(4): 856-61.
[http://dx.doi.org/10.1021/pr049931q] [PMID: 15359741]
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Chou KC. Insights from modeling the 3D structure of DNA-CBF3b complex. J Proteome Res 2005; 4(5): 1657-60.
[http://dx.doi.org/10.1021/pr050135+] [PMID: 16212418]
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Chou KC. Modeling the tertiary structure of human cathepsin-E. Biochem Biophys Res Commun 2005; 331(1): 56-60.
[http://dx.doi.org/10.1016/j.bbrc.2005.03.123] [PMID: 15845357]
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Wang SQ, Du QS, Chou KC. Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases. Biochem Biophys Res Commun 2007; 354(3): 634-40.
[http://dx.doi.org/10.1016/j.bbrc.2006.12.235] [PMID: 17266937]
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Wang SQ, Du QS, Huang RB, Zhang DW, Chou KC. Insights from investigating the interaction of oseltamivir (Tamiflu) with neuraminidase of the 2009 H1N1 swine flu virus. Biochem Biophys Res Commun 2009; 386(3): 432-6.
[http://dx.doi.org/10.1016/j.bbrc.2009.06.016] [PMID: 19523442]
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Li XB, Wang SQ, Xu WR, Wang RL, Chou KC. Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method. PLoS One 2011; 6(11) e28111
[http://dx.doi.org/10.1371/journal.pone.0028111] [PMID: 22140516]
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Ma Y, Wang SQ, Xu WR, Wang RL, Chou KC. Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach. PLoS One 2012; 7(6) e38546
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Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 2011; 273(1): 236-47.
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Chou KC, Shen HB. Addendum to “Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization”. Biochem Biophys Res Commun 2006; 348: 1479.
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Shen HB, Chou KC. Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins. Protein Eng Des Sel 2007; 20(1): 39-46.
[http://dx.doi.org/10.1093/protein/gzl053] [PMID: 17244638]
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Shen HB, Chou KC. Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng Des Sel 2007; 20(11): 561-7.
[http://dx.doi.org/10.1093/protein/gzm057] [PMID: 17993650]
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Chou KC, Shen HB. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat Protoc 2008; 3(2): 153-62.
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Chou KC, Shen HB. Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Sci 2010; 2: 1090-103.
[http://dx.doi.org/10.4236/ns.2010.210136]
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Chou KC, Wu ZC, Xiao X. iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS One 2011; 6(3)e18258
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Wu ZC, Xiao X, Chou KC. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol Biosyst 2011; 7(12): 3287-97.
[http://dx.doi.org/10.1039/c1mb05232b] [PMID: 21984117]
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Xiao X, Wu ZC, Chou KC. iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol 2011; 284(1): 42-51.
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Chou KC, Wu ZC, Xiao X. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol Biosyst 2012; 8(2): 629-41.
[http://dx.doi.org/10.1039/C1MB05420A] [PMID: 22134333]
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Wu ZC, Xiao X, Chou KC. iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins. Protein Pept Lett 2012; 19(1): 4-14.
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Lin WZ, Fang JA, Xiao X, Chou KC. iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol Biosyst 2013; 9(4): 634-44.
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Cheng X, Xiao X, Chou KC. pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol Biosyst 2017; 13(9): 1722-7.
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Cheng X, Xiao X. pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene (Erratum: ibid) 2018; 628: 315-21.
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Cheng X, Zhao SG, Lin WZ, Xiao X, Chou KC. pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics 2017; 33(22): 3524-31.
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Xiao X, Cheng X, Su S, Nao Q. pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat Sci 2017; 9: 331-49.
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Cheng X, Xiao X, Chou KC. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 2018; 110(1): 50-8.
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Cheng X, Xiao X, Chou KC. pLoc-mGneg: predict subcellular localization of gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 2017; 110: 231-9.
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Cheng X, Xiao X, Chou KC. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 2018; 34(9): 1448-56.
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Cheng X, Xiao X, Chou KC. pLoc_bal-mGneg: predict subcellular localization of gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J Theor Biol 2018; 458: 92-102.
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Cheng X, Xiao X, Chou KC. pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr Pharm Des 2018; 24(34): 4013-22.
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Chou KC, Cheng X, Xiao X. pLoc_bal-mHum: predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics 2018; 111(6): 1274-82.
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Chou KC, Cheng X, Xiao X. pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med Chem 2019; 15(5): 472-85.
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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by chou’s general PseAAC and IHTS treatment to balance training dataset. Med Chem 2019; 15(5): 496-509.
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Cheng X, Lin WZ, Xiao X, Chou KC. pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 2019; 35(3): 398-406.
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Xiao X, Cheng X, Chen G, Mao Q, Chou KC. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics 2019; 111(4): 886-92.
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Xie HL, Fu L, Nie XD. Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of chou’s PseAAC. Protein Eng Des Sel 2013; 26(11): 735-42.
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Xu Y, Shao XJ, Wu LY, Deng NY, Chou KC. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 2013; 1 e171
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Qiu WR, Xiao X, Lin WZ, Chou KC. iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach. BioMed Res Int 2014.2014947416
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Xu Y, Wen X, Shao XJ, Deng NY, Chou KC. iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int J Mol Sci 2014; 15(5): 7594-610.
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Xu Y, Wen X, Wen LS, Wu LY, Deng NY, Chou KC. iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One 2014; 9(8) e105018
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Chen W, Feng P, Ding H, Lin H, Chou KC. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 2015; 490: 26-33.
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Jia J, Liu Z, Xiao X, Liu B, Chou KC. iSuc-PseOpt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal Biochem 2016; 497: 48-56.
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Jia J, Liu Z, Xiao X, Liu B, Chou KC. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol 2016; 394: 223-30.
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Jia J, Liu Z, Xiao X, Liu B, Chou KC. iCar-PseCp: identify carbonylation sites in proteins by monte carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget 2016; 7(23): 34558-70.
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Jia J, Zhang L, Liu Z, Xiao X, Chou KC. pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics 2016; 32(20): 3133-41.
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Ju Z, Cao JZ, Gu H. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into chou׳s general PseAAC. J Theor Biol 2016; 397: 145-50.
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Liu Z, Xiao X, Yu DJ, Jia J, Qiu WR, Chou KC. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal Biochem 2016; 497: 60-7.
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC. iHyd-PseCp: identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget 2016; 7(28): 44310-21.
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Chou KC. iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics 2016; 32(20): 3116-23.
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Qiu WR, Xiao X, Xu ZC, Chou KC. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget 2016; 7(32): 51270-83.
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Xu Y, Chou KC. Recent progress in predicting posttranslational modification sites in proteins. Curr Top Med Chem 2016; 16(6): 591-603.
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Feng P, Ding H, Yang H, Chen W, Lin H, Chou KC. iRNA-PseColl: identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol Ther Nucleic Acids 2017; 7: 155-63.
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Liu LM, Xu Y, Chou KC. 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 2017; 13(6): 552-9.
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Qiu WR, Jiang SY, Sun BQ, Xiao X, Cheng X, Chou KC. iRNA-2methyl: identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier. Med Chem 2017; 13(8): 734-43.
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Xu Y, Wang Z, Li C, Chou KC. iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med Chem 2017; 13(6): 544-51.
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Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Mol Ther Nucleic Acids 2018; 11: 468-74.
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Feng P, Yang H, Ding H, Lin H, Chen W, Chou KC. iDNA6mA-PseKNC: identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 2019; 111(1): 96-102.
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Qiu WR, Sun BQ, Xiao X, Xu ZC, Jia JH, Chou KC. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics 2018; 110(5): 239-46.
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Xiao X, Min JL, Lin WZ, Liu Z, Cheng X, Chou KC. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J Biomol Struct Dyn 2015; 33(10): 2221-33.
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Chou KC. Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr Med Chem 2019; 26: 4918-43.
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Ehsan A, Mahmood MK, Khan YD, Barukab OM, Khan SA, Chou KC. iHyd-PseAAC (EPSV): Identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via Chou’s 5-step rule and general pseudo amino acid composition. Curr Genomics 2019; 20(2): 124-33.
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