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

Kuo-Chen       Chou
doi:10.2174/1381612825666191129164042
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, Watenpaugh KD, Heinrikson RL. A model of the complex between cyclin-dependent kinase 5 and the activation domain of neuronal Cdk5 activator. Biochem Biophys Res Commun  1999; 259(2): 420-8.
[http://dx.doi.org/10.1006/bbrc.1999.0792] [PMID:  10362524] 
[189] 
Chou KC, Howe WJ. Prediction of the tertiary structure of the beta-secretase zymogen. Biochem Biophys Res Commun  2002; 292(3): 702-8.
[http://dx.doi.org/10.1006/bbrc.2002.6686] [PMID:  11922623] 
[190] 
Chou KC. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J Proteome Res  2005; 4(5): 1681-6.
[http://dx.doi.org/10.1021/pr050145a] [PMID:  16212421] 
[191] 
Chou KC. Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5. Biochem Biophys Res Commun  2004; 316(3): 636-42.
[http://dx.doi.org/10.1016/j.bbrc.2004.02.098] [PMID:  15033447] 
[192] 
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] 
[193] 
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] 
[194] 
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] 
[195] 
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] 
[196] 
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] 
[197] 
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] 
[198] 
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] 
[199] 
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] 
[200] 
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] 
[201] 
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] 
[202] 
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
[http://dx.doi.org/10.1371/journal.pone.0038546] [PMID:  22685582] 
[203] 
Chou KC. Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol  2011; 273(1): 236-47.
[http://dx.doi.org/10.1016/j.jtbi.2010.12.024] [PMID:  21168420] 
[204] 
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.
[http://dx.doi.org/10.1016/j.bbrc.2006.08.030] 
[205] 
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] 
[206] 
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] 
[207] 
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.
[http://dx.doi.org/10.1038/nprot.2007.494] [PMID:  18274516] 
[208] 
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] 
[209] 
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
[http://dx.doi.org/10.1371/journal.pone.0018258] [PMID:  21483473] 
[210] 
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] 
[211] 
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.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.005] [PMID:  21684290] 
[212] 
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] 
[213] 
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.
[http://dx.doi.org/10.2174/092986612798472839] [PMID:  21919865] 
[214] 
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.
[http://dx.doi.org/10.1039/c3mb25466f] [PMID:  23370050] 
[215] 
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.
[http://dx.doi.org/10.1039/C7MB00267J] [PMID:  28702580] 
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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.
[217] 
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.
[http://dx.doi.org/10.1093/bioinformatics/btx476] [PMID:  29036535] 
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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.
[http://dx.doi.org/10.4236/ns.2017.99032] 
[219] 
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.
[http://dx.doi.org/10.1016/j.ygeno.2017.08.005] [PMID:  28818512] 
[220] 
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.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.002] [PMID:  28989035] 
[221] 
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.
[http://dx.doi.org/10.1093/bioinformatics/btx711] [PMID:  29106451] 
[222] 
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.
[http://dx.doi.org/10.1016/j.jtbi.2018.09.005] [PMID:  30201434] 
[223] 
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.
[http://dx.doi.org/10.2174/1381612824666181119145030] [PMID:  30451108] 
[224] 
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.
[http://dx.doi.org/10.1016/j.ygeno.2018.08.007] [PMID:  30179658] 
[225] 
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.
[http://dx.doi.org/10.2174/1573406415666181218102517] [PMID:  30569871] 
[226] 
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.
[http://dx.doi.org/10.2174/1573406415666181217114710] [PMID:  30556503] 
[227] 
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.
[http://dx.doi.org/10.1093/bioinformatics/bty628] [PMID:  30010789] 
[228] 
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.
[http://dx.doi.org/10.1016/j.ygeno.2018.05.017] [PMID:  29842950] 
[229] 
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.
[http://dx.doi.org/10.1093/protein/gzt042] [PMID:  24048266] 
[230] 
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
[http://dx.doi.org/10.7717/peerj.171] [PMID:  24109555] 
[231] 
Jia C, Lin X, Wang Z. Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile bayes and chou’s pseudo amino acid composition. Int J Mol Sci  2014; 15(6): 10410-23.
[http://dx.doi.org/10.3390/ijms150610410] [PMID:  24918295] 
[232] 
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
[http://dx.doi.org/10.1155/2014/947416] [PMID:  24977164] 
[233] 
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.
[http://dx.doi.org/10.3390/ijms15057594] [PMID:  24857907] 
[234] 
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
[http://dx.doi.org/10.1371/journal.pone.0105018] [PMID:  25121969] 
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Zhang J, Zhao X, Sun P, Ma Z. PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou’s PseAAC. Int J Mol Sci  2014; 15(7): 11204-19.
[http://dx.doi.org/10.3390/ijms150711204] [PMID:  24968264] 
[236] 
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.
[http://dx.doi.org/10.1016/j.ab.2015.08.021] [PMID:  26314792] 
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Qiu WR, Xiao X, Lin WZ. iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model J Biomol Struct Dynamics (JBSD)  2015; 33: 1731-42.
[238] 
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.
[http://dx.doi.org/10.1016/j.ab.2015.12.009] [PMID:  26723495] 
[239] 
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.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.020] [PMID:  26807806] 
[240] 
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.
[http://dx.doi.org/10.18632/oncotarget.9148] [PMID:  27153555] 
[241] 
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.
[http://dx.doi.org/10.1093/bioinformatics/btw387] [PMID:  27354696] 
[242] 
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.
[http://dx.doi.org/10.1016/j.jtbi.2016.02.020] [PMID:  26908349] 
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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.
[http://dx.doi.org/10.1016/j.ab.2015.12.017] [PMID:  26748145] 
[244] 
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.
[http://dx.doi.org/10.18632/oncotarget.10027] [PMID:  27322424] 
[245] 
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.
[http://dx.doi.org/10.1093/bioinformatics/btw380] [PMID:  27334473] 
[246] 
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.
[http://dx.doi.org/10.18632/oncotarget.9987] [PMID:  27323404] 
[247] 
Xu Y, Chou KC. Recent progress in predicting posttranslational modification sites in proteins. Curr Top Med Chem  2016; 16(6): 591-603.
[http://dx.doi.org/10.2174/1568026615666150819110421] [PMID:  26286211] 
[248] 
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.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID:  28624191] 
[249] 
Ju Z, He JJ. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into chou’s general PseAAC. J Mol Graph Model  2017; 77: 200-4.
[http://dx.doi.org/10.1016/j.jmgm.2017.08.020] [PMID:  28886434] 
[250] 
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.
[http://dx.doi.org/10.2174/1573406413666170515120507] [PMID:  28521678] 
[251] 
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.
[http://dx.doi.org/10.2174/1573406413666170623082245] [PMID:  28641529] 
[252] 
Qiu WR, Sun BQ, Xiao X, Xu D. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Molecular Informatics  2017; 36 1600010
[253] 
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.
[http://dx.doi.org/10.2174/1573406413666170419150052] [PMID:  28425870] 
[254] 
Akbar S, Hayat M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into chou’s PseAAC to formulate RNA sequences. J Theor Biol  2018; 455: 205-11.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.018] [PMID:  30031793] 
[255] 
Chen W, Ding H, Zhou X, Lin H, Chou KC. iRNA(m6A)-PseDNC: identifying N6-methyladenosine sites using pseudo dinucleotide composition. Anal Biochem  2018; 561-562: 59-65.
[http://dx.doi.org/10.1016/j.ab.2018.09.002] [PMID:  30201554] 
[256] 
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.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID:  29858081] 
[257] 
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.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID:  29360500] 
[258] 
Ju Z, Wang SY. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into chou’s general pseudo amino acid composition. Gene  2018; 664: 78-83.
[http://dx.doi.org/10.1016/j.gene.2018.04.055] [PMID:  29694908] 
[259] 
Khan YD, Rasool N, Hussain W, Khan SA, Chou KC. iPhosT-PseAAC: identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal Biochem  2018; 550: 109-16.
[http://dx.doi.org/10.1016/j.ab.2018.04.021] [PMID:  29704476] 
[260] 
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.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.008] [PMID:  29107015] 
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Sabooh MF, Iqbal N, Khan M, Khan M, Maqbool HF. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J Theor Biol  2018; 452: 1-9.
[http://dx.doi.org/10.1016/j.jtbi.2018.04.037] [PMID:  29727634] 
[262] 
Khan YD, Rasool N, Hussain W, Khan SA, Chou KC. iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol Biol Rep  2018; 45(6): 2501-9.
[http://dx.doi.org/10.1007/s11033-018-4417-z] [PMID:  30311130] 
[263] 
Hussain W, Khan YD, Rasool N, Khan SA, Chou KC. 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  2019; 568: 14-23.
[http://dx.doi.org/10.1016/j.ab.2018.12.019] [PMID:  30593778] 
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Li F, Zhang Y, Purcell AW, et al. Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics  2019; 20(1): 112.
[http://dx.doi.org/10.1186/s12859-019-2700-1] [PMID:  30841845] 
[265] 
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  2019; 461: 51-8.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.046] [PMID:  30365947] 
[266] 
Shyamili VK, Vellaichamy A. Sequence and structure-based characterization of human and yeast ubiquitination sites by using chou’s sample formulation. Proteins  2019; 87(8): 646-57.
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Zhang Y, Xie R, Wang J, et al. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief Bioinform 2018. doi: 10.1093/bib/bby079.
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Chen Z, Liu X, Li F, et al. Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief Bioinform 2018. 10.1093/bib/bby089
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Awais M, Hussain W, Khan YD, Rasool N, Khan SA. iPhosHPseAAC: identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the chou's 5-step rule and general pseudo amino acid composition . IEEE/ACM Trans Comput Biol Bioinform 2019. doi: 10.1109/TCBB.2019.2919025.
[http://dx.doi.org/10.1109/TCBB.2019.2919025] 
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Hussain W, Khan YD, Rasool N, Khan SA, Chou KC. SPrenylC-PseAAC: a sequence-based model developed via chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. J Theor Biol  2019; 468: 1-11.
[http://dx.doi.org/10.1016/j.jtbi.2019.02.007] [PMID:  30768975] 
[271] 
Chou KC. Progresses in predicting post-translational modification. Int J Pept Res Ther  2019; 1-16.
[http://dx.doi.org/10.1007/s10989-019-09893-5] 
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Ding H, Deng EZ, Yuan LF, et al. 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] 
[273] 
Liu B, Fang L, Liu F, Wang X, Chen J, Chou KC. Identification of real microRNA precursors with a pseudo structure status composition approach. PLoS One  2015; 10(3)e0121501
[http://dx.doi.org/10.1371/journal.pone.0121501] [PMID:  25821974] 
[274] 
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.
[http://dx.doi.org/10.1080/07391102.2014.998710] [PMID:  25513722] 
[275] 
Chen W, Feng P, Yang H, Ding H, Lin H, Chou KC. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget  2017; 8(3): 4208-17.
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Liu H, Wang M, Chou KC. Low-frequency Fourier spectrum for predicting membrane protein types. Biochem Biophys Res Commun  2005; 336(3): 737-9.
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Wang JF, Chou KC. Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations. Biochem Biophys Res Commun  2009; 390(3): 608-12.
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Liao QH, Gao QZ, Wei J, Chou KC. Docking and molecular dynamics study on the inhibitory activity of novel inhibitors on epidermal growth factor receptor (EGFR). Med Chem  2011; 7(1): 24-31.
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Wang JF, Chou KC. Recent advances in computational studies on influenza a virus M2 proton channel. Mini Rev Med Chem  2012; 12(10): 971-8.
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Jia J, Liu Z, Xiao X, Liu B, Chou KC. iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J Theor Biol  2015; 377: 47-56.
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Chou KC. Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications. Trends in Artificial Inttelengence  2019; 3: 60-74.
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Chou KC. An insightful recollection since the birth of Gordon Life Science Institute about 17 years ago. Adv Sci Eng Res  2019; 4: 31-6.
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DOI https://dx.doi.org/10.2174/1381612825666191129164042	Print ISSN 1381-6128
Publisher Name Bentham Science Publisher	Online ISSN 1873-4286
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