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

Kuo-Chen       Chou
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.
« Previous Next »
[1] 
Zhang CT, Chou KC. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci  1992; 1(3): 401-8.
[http://dx.doi.org/10.1002/pro.5560010312] [PMID:  1304347] 
[2] 
Chou KC, Zhang CT. A correlation-coefficient method to predicting protein-structural classes from amino acid compositions. Eur J Biochem  1992; 207(2): 429-3.
[http://dx.doi.org/10.1111/j.1432-1033.1992.tb17067.x] [PMID:  1633801] 
[3] 
Chou KC. Prediction of protein folding types from amino acid composition by correlation angles. Amino Acids  1994; 6(3): 231-46.
[http://dx.doi.org/10.1007/BF00813744] [PMID:  24189732] 
[4] 
Nakashima H, Nishikawa K. Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol  1994; 238(1): 54-61.
[http://dx.doi.org/10.1006/jmbi.1994.1267] [PMID:  8145256] 
[5] 
Chou KC, Zhang CT. Predicting protein folding types by distance functions that make allowances for amino acid interactions. J Biol Chem  1994; 269(35): 22014-20.
[PMID:  8071322] 
[6] 
Chou KC. Does the folding type of a protein depend on its amino acid composition? FEBS Lett  1995; 363(1-2): 127-31.
[http://dx.doi.org/10.1016/0014-5793(95)00245-5] [PMID:  7729532] 
[7] 
Chou KC. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins  1995; 21(4): 319-44.
[http://dx.doi.org/10.1002/prot.340210406] [PMID:  7567954] 
[8] 
Chou KC, Zhang CT. Prediction of protein structural classes. Crit Rev Biochem Mol Biol  1995; 30(4): 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID:  7587280] 
[9] 
Chou KC, Liu WM, Maggiora GM, Zhang CT. Prediction and classification of domain structural classes. Proteins  1998; 31(1): 97-103.
[http://dx.doi.org/10.1002/(SICI)10970134(19980401)31:1<97:AID-PROT8>3.0.CO;2-E] [PMID:  9552161] 
[10] 
Chou KC, Maggiora GM. Domain structural class prediction. Protein Eng  1998; 11(7): 523-38.
[http://dx.doi.org/10.1093/protein/11.7.523] [PMID:  9740370] 
[11] 
Chou KC, Elrod DW. Bioinformatical analysis of G-protein-coupled receptors. J Proteome Res  2002; 1(5): 429-33.
[http://dx.doi.org/10.1021/pr025527k] [PMID:  12645914] 
[12] 
Chou KC, Elrod DW. Using discriminant function for prediction of subcellular location of prokaryotic proteins. Biochem Biophys Res Commun (BBRC)  1998; 252(1): 63-8.
[http://dx.doi.org/10.1006/bbrc.1998.9498] [PMID: 9813147] 
[13] 
Gao Y, Shao S, Xiao X, et al. Using pseudo amino acid composition to predict protein subcellular location: approached with lyapunov index, bessel function, and chebyshev filter. Amino Acids  2005; 28(4): 373-6.
[http://dx.doi.org/10.1007/s00726-005-0206-9] [PMID:  15889221] 
[14] 
Xu Y, Ding J, Wu LY, Chou KC. iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One  2013; 8(2)e55844
[http://dx.doi.org/10.1371/journal.pone.0055844] [PMID:  23409062] 
[15] 
Chou KC, Elrod DW. Protein subcellular location prediction. Protein Eng  1999; 12(2): 107-18.
[http://dx.doi.org/10.1093/protein/12.2.107] [PMID:  10195282] 
[16] 
Chen W, Lin H, Feng PM, Ding C, Zuo YC, Chou KC. iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. PLoS One  2012; 7(10)e47843
[http://dx.doi.org/10.1371/journal.pone.0047843] [PMID:  23144709] 
[17] 
Chou KC. Prediction of G-protein-coupled receptor classes. J Proteome Res  2005; 4(4): 1413-8.
[http://dx.doi.org/10.1021/pr050087t] [PMID:  16083294] 
[18] 
Chou KC, Elrod DW. Prediction of enzyme family classes. J Proteome Res  2003; 2(2): 183-90.
[http://dx.doi.org/10.1021/pr0255710] [PMID:  12716132] 
[19] 
Xiao X, Wang P, Chou KC. Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. J Theor Biol  2008; 254(3): 691-6.
[http://dx.doi.org/10.1016/j.jtbi.2008.06.016] [PMID:  18634802] 
[20] 
Du QS, Jiang ZQ, He WZ, Li DP, Chou KC. Amino acid principal component analysis (AAPCA) and its applications in protein structural class prediction.  J Biomol Struct Dyn [JBSD]  2006; 23(6): 635-40.
[http://dx.doi.org/10.1080/07391102.2006.10507088] [PMID:  16615809] 
[21] 
Xu R, Zhou J, Liu B, et al. Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach.  J Biomol Struct Dyn [JBSD]  2015; 33(8): 1720-30.
[http://dx.doi.org/10.1080/07391102.2014.968624] [PMID:  25252709] 
[22] 
Wang M, Yang J, Xu ZJ, Chou KC. SLLE for predicting membrane protein types. J Theor Biol  2005; 232(1): 7-15.
[http://dx.doi.org/10.1016/j.jtbi.2004.07.023] [PMID:  15498588] 
[23] 
Feng KY, Cai YD, Chou KC. Boosting classifier for predicting protein domain structural class. Biochem Biophys Res Commun [BBRC]  2005; 334(1): 213-7.
[http://dx.doi.org/10.1016/j.bbrc.2005.06.075] [PMID:  15993842] 
[24] 
Cai YD, Chou KC. Artificial neural network model for predicting alpha-turn types. Anal Biochem  1999; 268(2): 407-9.
[http://dx.doi.org/10.1006/abio.1998.2992] [PMID:  10075834] 
[25] 
Thompson TB, Chou KC, Zheng C. Neural network prediction of the HIV-1 protease cleavage sites. J Theor Biol  1995; 177(4): 369-79.
[http://dx.doi.org/10.1006/jtbi.1995.0254] [PMID:  8871474] 
[26] 
Shen HB, Yang J, Chou KC. Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids  2007; 33(1): 57-67.
[http://dx.doi.org/10.1007/s00726-006-0478-8] [PMID:  17235453] 
[27] 
Shen HB, Chou KC. Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers  2007; 85(3): 233-40.
[http://dx.doi.org/10.1002/bip.20640] [PMID:  17120237] 
[28] 
Feng PM, Chen W, Lin H, Chou KC. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal Biochem  2013; 442(1): 118-25.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID:  23756733] 
[29] 
Chen W, Feng PM, Lin H, Chou KC. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res  2013; 41(6)e68. 
[http://dx.doi.org/10.1093/nar/gks1450] [PMID:  23303794] 
[30] 
Xiao X, Wang P, Chou KC. iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix. PLoS One  2012; 7(2)e30869. 
[http://dx.doi.org/10.1371/journal.pone.0030869] [PMID:  22363503] 
[31] 
Lin WZ, Fang JA, Xiao X, Chou KC. iDNA-Prot: identification of DNA binding proteins using random forest with grey model. PLoS One  2011; 6(9)e24756. 
[http://dx.doi.org/10.1371/journal.pone.0024756] [PMID:  21935457] 
[32] 
Kandaswamy KK, Chou KC, Martinetz T, et al. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J Theor Biol  2011; 270(1): 56-62.
[http://dx.doi.org/10.1016/j.jtbi.2010.10.037] [PMID:  21056045] 
[33] 
Cai YD, Chou KC. Predicting subcellular localization of proteins in a hybridization space. Bioinformatics  2004; 20(7): 1151-6.
[http://dx.doi.org/10.1093/bioinformatics/bth054] [PMID:  14764553] 
[34] 
Chou KC, Cai YD. Prediction of protease types in a hybridization space. Biochem Biophys Res Commun  2006; 339(3): 1015-20.
[http://dx.doi.org/10.1016/j.bbrc.2005.10.196] [PMID:  16325146] 
[35] 
Chou KC, Shen HB. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers. J Proteome Res  2006; 5(8): 1888-97.
[http://dx.doi.org/10.1021/pr060167c] [PMID:  16889410] 
[36] 
Chou KC, Shen HB. Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun  2006; 347(1): 150-7.
[http://dx.doi.org/10.1016/j.bbrc.2006.06.059] [PMID:  16808903] 
[37] 
Chou KC, Shen HB. Large-scale predictions of gram-negative bacterial protein subcellular locations. J Proteome Res  2006; 5(12): 3420-8.
[http://dx.doi.org/10.1021/pr060404b] [PMID:  17137343] 
[38] 
Chou KC, Shen HB. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res  2007; 6(5): 1728-34.
[http://dx.doi.org/10.1021/pr060635i] [PMID:  17397210] 
[39] 
Chou KC, Shen HB. Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Commun  2007; 357(3): 633-40.
[http://dx.doi.org/10.1016/j.bbrc.2007.03.162] [PMID:  17434148] 
[40] 
Shen H, Chou KC. Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types. Biochem Biophys Res Commun  2005; 334(1): 288-92.
[http://dx.doi.org/10.1016/j.bbrc.2005.06.087] [PMID:  16002049] 
[41] 
Shen HB, Chou KC. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal Biochem  2009; 394(2): 269-74.
[http://dx.doi.org/10.1016/j.ab.2009.07.046] [PMID:  19651102] 
[42] 
Xiao X, Wang P, Chou KC. GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol Biosyst  2011; 7(3): 911-9.
[http://dx.doi.org/10.1039/C0MB00170H] [PMID:  21180772] 
[43] 
Shen HB, Yang J, Chou KC. Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. J Theor Biol  2006; 240(1): 9-13.
[http://dx.doi.org/10.1016/j.jtbi.2005.08.016] [PMID:  16197963] 
[44] 
Xiao X, Min JL, Wang P, Chou KC. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One  2013; 8(8)e72234. 
[http://dx.doi.org/10.1371/journal.pone.0072234] [PMID:  24015221] 
[45] 
Xiao X, Min JL, Wang P, Chou KC. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. J Theor Biol  2013; 337: 71-9.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.013] [PMID:  23988798] 
[46] 
Xiao X, Wang P, Lin WZ, Jia JH, Chou KC. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem  2013; 436(2): 168-77.
[http://dx.doi.org/10.1016/j.ab.2013.01.019] [PMID:  23395824] 
[47] 
Nakashima H, Nishikawa K, Ooi T. The folding type of a protein is relevant to the amino acid composition. J Biochem  1986; 99(1): 153-62.
[http://dx.doi.org/10.1093/oxfordjournals.jbchem.a135454] [PMID:  3957893] 
[48] 
Klein P, Delisi C. Prediction of protein structural class from the amino acid sequence. Biopolymers  1986; 25(9): 1659-72.
[http://dx.doi.org/10.1002/bip.360250909] [PMID:  3768479] 
[49] 
Klein P. Prediction of protein structural class by discriminant analysis. Biochim Biophys Acta  1986; 874(2): 205-15.
[http://dx.doi.org/10.1016/0167-4838(86)90119-6] [PMID:  3778917] 
[50] 
Chou PY. Prediction of protein structure and the principles of protein conformation. New York: Plenum Press 1989.
[51] 
Metfessel BA, Saurugger PN, Connelly DP, Rich SS. Cross-validation of protein structural class prediction using statistical clustering and neural networks. Protein Sci  1993; 2(7): 1171-82.
[http://dx.doi.org/10.1002/pro.5560020712] [PMID:  8358300] 
[52] 
Cedano J, Aloy P, Pérez-Pons JA, Querol E. Relation between amino acid composition and cellular location of proteins. J Mol Biol  1997; 266(3): 594-600.
[http://dx.doi.org/10.1006/jmbi.1996.0804] [PMID:  9067612] 
[53] 
Zhou GP. An intriguing controversy over protein structural class prediction. J Protein Chem  1998; 17(8): 729-38.
[http://dx.doi.org/10.1023/A:1020713915365] [PMID:  9988519] 
[54] 
Liu WM, Chou KC. Prediction of protein structural classes by modified mahalanobis discriminant algorithm. J Protein Chem  1998; 17(3): 209-17.
[http://dx.doi.org/10.1023/A:1022576400291] [PMID:  9588944] 
[55] 
Chou KC. A key driving force in determination of protein structural classes. Biochem Biophys Res Commun  1999; 264(1): 216-24.
[http://dx.doi.org/10.1006/bbrc.1999.1325] [PMID:  10527868] 
[56] 
Chou KC. Prediction of protein structural classes and subcellular locations. Curr Protein Pept Sci  2000; 1(2): 171-208.
[http://dx.doi.org/10.2174/1389203003381379] [PMID:  12369916] 
[57] 
Zhou GP, Assa-Munt N. Some insights into protein structural class prediction. Proteins  2001; 44(1): 57-9.
[http://dx.doi.org/10.1002/prot.1071] [PMID:  11354006] 
[58] 
Zhou GP, Doctor K. Subcellular location prediction of apoptosis proteins. Proteins  2003; 50(1): 44-8.
[http://dx.doi.org/10.1002/prot.10251] [PMID:  12471598] 
[59] 
Niu B, Cai YD, Lu WC, Li GZ, Chou KC. Predicting protein structural class with AdaBoost Learner. Protein Pept Lett  2006; 13(5): 489-92.
[http://dx.doi.org/10.2174/092986606776819619] [PMID:  16800803] 
[60] 
Jahandideh S, Abdolmaleki P, Jahandideh M, Asadabadi EB. Novel two-stage hybrid neural discriminant model for predicting proteins structural classes. Biophys Chem  2007; 128(1): 87-93.
[http://dx.doi.org/10.1016/j.bpc.2007.03.006] [PMID:  17467878] 
[61] 
Chou KC. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS Struct Fun Gene (Erratum: ibid)  2001; 44,60(43): 246-55.
[62] 
Shen HB, Chou KC. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal Biochem  2008; 373(2): 386-8.
[http://dx.doi.org/10.1016/j.ab.2007.10.012] [PMID:  17976365] 
[63] 
Zhou X, Li Z, Dai Z, Zou X. Predicting promoters by pseudo-trinucleotide compositions based on discrete wavelets transform. J Theor Biol  2013; 319: 1-7.
[http://dx.doi.org/10.1016/j.jtbi.2012.11.024] [PMID:  23211833] 
[64] 
Chen W, Feng PM, Deng EZ, Lin H, Chou KC. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem  2014; 462: 76-83.
[http://dx.doi.org/10.1016/j.ab.2014.06.022] [PMID:  25016190] 
[65] 
Chen W, Feng PM, Lin H, Chou KC. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res Int  2014; 2014623149
[http://dx.doi.org/10.1155/2014/623149] [PMID:  24967386] 
[66] 
Chen W, Lei TY, Jin DC, Lin H, Chou KC. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal Biochem  2014; 456: 53-60.
[http://dx.doi.org/10.1016/j.ab.2014.04.001] [PMID:  24732113] 
[67] 
Feng P, Chen W, Lin H. Prediction of CpG island methylation status by integrating DNA physicochemical properties. Genomics  2014; 104(4): 229-33.
[http://dx.doi.org/10.1016/j.ygeno.2014.08.011] [PMID:  25172426] 
[68] 
Feng P, Jiang N, Liu N. Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions. Sci World J  2014; 2014740506
[http://dx.doi.org/10.1155/2014/740506] [PMID:  25215331] 
[69] 
Guo SH, Deng EZ, Xu LQ, et al. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics  2014; 30(11): 1522-9.
[http://dx.doi.org/10.1093/bioinformatics/btu083] [PMID:  24504871] 
[70] 
Lin H, Deng EZ, Ding H, Chen W, Chou KC. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res  2014; 42(21): 12961-72.
[http://dx.doi.org/10.1093/nar/gku1019] [PMID:  25361964] 
[71] 
Qiu WR, Xiao X, Chou KC. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int J Mol Sci  2014; 15(2): 1746-66.
[http://dx.doi.org/10.3390/ijms15021746] [PMID:  24469313] 
[72] 
Chen W, Zhang X, Brooker J, Lin H, Zhang L, Chou KC. PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics  2015; 31(1): 119-20.
[http://dx.doi.org/10.1093/bioinformatics/btu602] [PMID:  25231908] 
[73] 
Liu B, Liu F, Fang L, Wang X, Chou KC. repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics  2015; 31(8): 1307-9.
[http://dx.doi.org/10.1093/bioinformatics/btu820] [PMID:  25504848] 
[74] 
Liu Z, Xiao X, Qiu WR, Chou KC. iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem  2015; 474: 69-77.
[http://dx.doi.org/10.1016/j.ab.2014.12.009] [PMID:  25596338] 
[75] 
Chen W, Tang H, Ye J, Lin H, Chou KC. iRNA-PseU: identifying rna pseudouridine sites. Mol Ther Nucleic Acids  2016; 5 e332
[PMID:  28427142] 
[76] 
Liu B, Fang L, Liu F, Wang X, Chou KC. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. J Biomol Struct Dyn  2016; 34(1): 223-35.
[http://dx.doi.org/10.1080/07391102.2015.1014422] [PMID:  25645238] 
[77] 
Liu B, Fang L, Long R, Lan X, Chou KC. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics  2016; 32(3): 362-9.
[http://dx.doi.org/10.1093/bioinformatics/btv604] [PMID:  26476782] 
[78] 
Liu B, Liu F, Fang L, Wang X, Chou KC. repRNA: a web server for generating various feature vectors of RNA sequences. Mol Genet Genomics  2016; 291(1): 473-81.
[http://dx.doi.org/10.1007/s00438-015-1078-7] [PMID:  26085220] 
[79] 
Liu B, Long R, Chou KC. iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics  2016; 32(16): 2411-8.
[http://dx.doi.org/10.1093/bioinformatics/btw186] [PMID:  27153623] 
[80] 
Xiao X, Ye HX, Liu Z, Jia JH, Chou KC. iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget  2016; 7(23): 34180-9.
[http://dx.doi.org/10.18632/oncotarget.9057] [PMID:  27147572] 
[81] 
Liu B, Wang S, Long R, Chou KC. iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics  2017; 33(1): 35-41.
[http://dx.doi.org/10.1093/bioinformatics/btw539] [PMID:  27531102] 
[82] 
Liu B, Wu H. 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  2017; 9: 67-91.
[http://dx.doi.org/10.4236/ns.2017.94007] 
[83] 
Liu B, Wu H, Zhang D, Wang X, Chou KC. Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget  2017; 8(8): 13338-43.
[http://dx.doi.org/10.18632/oncotarget.14524] [PMID:  28076851] 
[84] 
Liu B, Yang F, Chou KC. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol Ther Nucleic Acids  2017; 7: 267-77.
[http://dx.doi.org/10.1016/j.omtn.2017.04.008] [PMID:  28624202] 
[85] 
Qiu WR, Jiang SY, Xu ZC, Xiao X, Chou KC. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget  2017; 8(25): 41178-88.
[http://dx.doi.org/10.18632/oncotarget.17104] [PMID:  28476023] 
[86] 
Chen J, Guo M, Wang X, Liu B. A comprehensive review and comparison of different computational methods for protein remote homology detection. Brief Bioinform  2018; 19(2): 231-44.
[http://dx.doi.org/10.1093/bib/bbw108] [PMID:  27881430] 
[87] 
Liu B. BioSeq-Analysis: a platform for DNA, RNA, and protein sequence analysis based on machine learning approaches. Brief Bioinform  2018; 20(4): 1280-94.
[http://dx.doi.org/10.1093/bib/bbx165] [PMID:  29272359] 
[88] 
Liu B, Li K, Huang DS, Chou KC. iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics  2018; 34(22): 3835-42.
[http://dx.doi.org/10.1093/bioinformatics/bty458] [PMID:  29878118] 
[89] 
Liu B, Weng F, Huang DS, Chou KC. iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC. Bioinformatics  2018; 34(18): 3086-93.
[http://dx.doi.org/10.1093/bioinformatics/bty312] [PMID:  29684124] 
[90] 
Liu B, Yang F, Huang DS, Chou KC. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics  2018; 34(1): 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID:  28968797] 
[91] 
Cai YD, Zhou GP, Chou KC. Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition. J Theor Biol  2005; 234(1): 145-9.
[http://dx.doi.org/10.1016/j.jtbi.2004.11.017] [PMID:  15721043] 
[92] 
Cai YD, Chou KC. Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. J Proteome Res  2005; 4(3): 967-71.
[http://dx.doi.org/10.1021/pr0500399] [PMID:  15952744] 
[93] 
Zhou XB, Chen C, Li ZC, Zou XY. Using Chou’s amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J Theor Biol  2007; 248(3): 546-51.
[http://dx.doi.org/10.1016/j.jtbi.2007.06.001] [PMID:  17628605] 
[94] 
Qiu JD, Huang JH, Shi SP, Liang RP. Using the concept of Chou’s pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform. Protein Pept Lett  2010; 17(6): 715-22.
[http://dx.doi.org/10.2174/092986610791190372] [PMID:  19961429] 
[95] 
Wang YC, Wang XB, Yang ZX, Deng NY. Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature. Protein Pept Lett  2010; 17(11): 1441-9.
[http://dx.doi.org/10.2174/0929866511009011441] [PMID:  20666729] 
[96] 
Cai YD, Chou KC. Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. Biochem Biophys Res Commun  2003; 305(2): 407-11.
[http://dx.doi.org/10.1016/S0006-291X(03)00775-7] [PMID:  12745090] 
[97] 
Pan YX, Zhang ZZ, Guo ZM, Feng GY, Huang ZD, He L. Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. J Protein Chem  2003; 22(4): 395-402.
[http://dx.doi.org/10.1023/A:1025350409648] [PMID:  13678304] 
[98] 
Chou KC, Cai YD. Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition 2003; 90(6): 1250-60.
[http://dx.doi.org/10.1002/jcb.10719] 
[99] 
Chou KC, Cai YD. Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo-amino acid composition. J Cell Biochem  2004; 91(6): 1197-203.
[http://dx.doi.org/10.1002/jcb.10790] [PMID:  15048874] 
[100] 
Xiao X, Shao S, Ding Y, Huang Z, Chou KC. Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids  2006; 30(1): 49-54.
[http://dx.doi.org/10.1007/s00726-005-0225-6] [PMID:  16044193] 
[101] 
Shi JY, Zhang SW, Pan Q, Cheng Y-M, Xie J. Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids  2007; 33(1): 69-74.
[http://dx.doi.org/10.1007/s00726-006-0475-y] [PMID:  17235454] 
[102] 
Zhang SW, Zhang YL, Yang HF, Zhao CH, Pan Q. Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies. Amino Acids  2008; 34(4): 565-72.
[http://dx.doi.org/10.1007/s00726-007-0010-9] [PMID:  18074191] 
[103] 
Shi JY, Zhang SW, Pan Q, Zhou GP. Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution. Amino Acids  2008; 35(2): 321-7.
[http://dx.doi.org/10.1007/s00726-007-0623-z] [PMID:  18209947] 
[104] 
Li FM, Li QZ. Predicting protein subcellular location using Chou’s pseudo amino acid composition and improved hybrid approach. Protein Pept Lett  2008; 15(6): 612-6.
[http://dx.doi.org/10.2174/092986608784966930] [PMID:  18680458] 
[105] 
Chen YL, Li QZ. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J Theor Biol  2007; 248(2): 377-81.
[http://dx.doi.org/10.1016/j.jtbi.2007.05.019] [PMID:  17572445] 
[106] 
Jiang X, Wei R, Zhang T, Gu Q. Using the concept of chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept Lett  2008; 15(4): 392-6.
[http://dx.doi.org/10.2174/092986608784246443] [PMID:  18473953] 
[107] 
Lin H, Wang H, Ding H, Chen YL, Li QZ. Prediction of subcellular localization of apoptosis protein using Chou’s pseudo amino acid composition. Acta Biotheor  2009; 57(3): 321-30.
[http://dx.doi.org/10.1007/s10441-008-9067-4] [PMID:  19169652] 
[108] 
Liu T, Zheng X, Wang C, Wang J. Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: an approach from auto covariance transformation. Protein Pept Lett  2010; 17(10): 1263-9.
[http://dx.doi.org/10.2174/092986610792231528] [PMID:  20670213] 
[109] 
Kandaswamy KK, Pugalenthi G, Möller S, et al. Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition. Protein Pept Lett  2010; 17(12): 1473-9.
[http://dx.doi.org/10.2174/0929866511009011473] [PMID:  20666727] 
[110] 
Lin H, Ding H, Guo F-B, Zhang AY, Huang J. Predicting subcellular localization of mycobacterial proteins by using Chou’s pseudo amino acid composition. Protein Pept Lett  2008; 15(7): 739-44.
[http://dx.doi.org/10.2174/092986608785133681] [PMID:  18782071] 
[111] 
Shen HB, Chou KC. Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem Biophys Res Commun  2005; 337(3): 752-6.
[http://dx.doi.org/10.1016/j.bbrc.2005.09.117] [PMID:  16213466] 
[112] 
Li FM, Li QZ. Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids  2008; 34(1): 119-25.
[http://dx.doi.org/10.1007/s00726-007-0545-9] [PMID:  17514493] 
[113] 
Jiang X, Wei R, Zhao Y, Zhang T. Using chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location. Amino Acids  2008; 34(4): 669-75.
[http://dx.doi.org/10.1007/s00726-008-0034-9] [PMID:  18256886] 
[114] 
Du P, Cao S, Li Y. SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. J Theor Biol  2009; 261(2): 330-5.
[http://dx.doi.org/10.1016/j.jtbi.2009.08.004] [PMID:  19679138] 
[115] 
Du P, Li Y. Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics  2006; 7: 518.
[http://dx.doi.org/10.1186/1471-2105-7-518] [PMID:  17134515] 
[116] 
Nanni L, Lumini A. Genetic programming for creating Chou’s pseudo amino acid based features for submitochondria localization. Amino Acids  2008; 34(4): 653-60.
[http://dx.doi.org/10.1007/s00726-007-0018-1] [PMID:  18175047] 
[117] 
Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, Li ML. Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol  2009; 259(2): 366-72.
[http://dx.doi.org/10.1016/j.jtbi.2009.03.028] [PMID:  19341746] 
[118] 
Wang M, Yang J, Liu GP, Xu ZJ, Chou KC. Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. Protein Eng Des Sel  2004; 17(6): 509-16.
[http://dx.doi.org/10.1093/protein/gzh061] [PMID:  15314209] 
[119] 
Cai YD, Chou KC. Predicting membrane protein type by functional domain composition and pseudo-amino acid composition. J Theor Biol  2006; 238(2): 395-400.
[http://dx.doi.org/10.1016/j.jtbi.2005.05.035] [PMID:  16040052] 
[120] 
Liu H, Yang J, Wang M, Xue L, Chou KC. Using fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types. Protein J  2005; 24(6): 385-9.
[http://dx.doi.org/10.1007/s10930-005-7592-4] [PMID:  16323044] 
[121] 
Wang SQ, Yang J, Chou KC. Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. J Theor Biol  2006; 242(4): 941-6.
[http://dx.doi.org/10.1016/j.jtbi.2006.05.006] [PMID:  16806277] 
[122] 
Chou KC, Shen HB. MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem Biophys Res Commun  2007; 360(2): 339-45.
[http://dx.doi.org/10.1016/j.bbrc.2007.06.027] [PMID:  17586467] 
[123] 
Lin H. The modified mahalanobis discriminant for predicting outer membrane proteins by using chou’s pseudo amino acid composition. J Theor Biol  2008; 252(2): 350-6.
[http://dx.doi.org/10.1016/j.jtbi.2008.02.004] [PMID:  18355838] 
[124] 
Gao QB, Ye XF, Jin ZC, He J. Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition. Anal Biochem  2010; 398(1): 52-9.
[http://dx.doi.org/10.1016/j.ab.2009.10.040] [PMID:  19874797] 
[125] 
Diao Y, Ma D, Wen Z, Yin J, Xiang J, Li M. Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity. Amino Acids  2008; 34(1): 111-7.
[http://dx.doi.org/10.1007/s00726-007-0550-z] [PMID:  17520325] 
[126] 
Zhou GP, Cai YD. Predicting protease types by hybridizing gene ontology and pseudo amino acid composition. Proteins  2006; 63(3): 681-4.
[http://dx.doi.org/10.1002/prot.20898] [PMID:  16456852] 
[127] 
Chou KC, Shen HB. ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem Biophys Res Commun  2008; 376(2): 321-5.
[http://dx.doi.org/10.1016/j.bbrc.2008.08.125] [PMID:  18774775] 
[128] 
Xiaohui N, Nana L, Jingbo X, et al. Using the concept of chou’s pseudo amino acid composition to predict protein solubility: an approach with entropies in information theory. J Theor Biol  2013; 332: 211-7.
[http://dx.doi.org/10.1016/j.jtbi.2013.03.010] [PMID:  23524162] 
[129] 
Qiu JD, Huang JH, Liang RP, Lu XQ. Prediction of G-protein-coupled receptor classes based on the concept of chou’s pseudo amino acid composition: an approach from discrete wavelet transform. Anal Biochem  2009; 390(1): 68-73.
[http://dx.doi.org/10.1016/j.ab.2009.04.009] [PMID:  19364489] 
[130] 
Gu Q, Ding YS, Zhang TL. Prediction of G-protein-coupled receptor classes in low homology using Chou’s pseudo amino acid composition with approximate entropy and hydrophobicity patterns. Protein Pept Lett  2010; 17(5): 559-67.
[http://dx.doi.org/10.2174/092986610791112693] [PMID:  19594431] 
[131] 
Gu Q, Ding Y, Zhang T. Prediction of G-protein-coupled receptor classes with pseudo amino acid composition. In:  IEEE Xplore Shanghai, China: iCBBE.  2008.
[http://dx.doi.org/10.1109/ICBBE.2008.215] 
[132] 
Lin WZ, Xiao X, Chou KC. GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis. Protein Eng Des Sel  2009; 22(11): 699-705.
[http://dx.doi.org/10.1093/protein/gzp057] [PMID:  19776029] 
[133] 
Xiao X, Wang P, Chou KC. GPCR-CA: a cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J Comput Chem  2009; 30(9): 1414-23.
[http://dx.doi.org/10.1002/jcc.21163] [PMID:  19037861] 
[134] 
Gao QB, Jin ZC, Ye XF, Wu C, He J. Prediction of nuclear receptors with optimal pseudo amino acid composition. Anal Biochem  2009; 387(1): 54-9.
[http://dx.doi.org/10.1016/j.ab.2009.01.018] [PMID:  19454254] 
[135] 
Mohabatkar H. Prediction of cyclin proteins using chou’s pseudo amino acid composition. Protein Pept Lett  2010; 17(10): 1207-14.
[http://dx.doi.org/10.2174/092986610792231564] [PMID:  20450487] 
[136] 
Yu L, Guo Y, Li Y, et al. SecretP: identifying bacterial secreted proteins by fusing new features into chou’s pseudo-amino acid composition. J Theor Biol  2010; 267(1): 1-6.
[http://dx.doi.org/10.1016/j.jtbi.2010.08.001] [PMID:  20691704] 
[137] 
Esmaeili M, Mohabatkar H, Mohsenzadeh S. Using the concept of chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses. J Theor Biol  2010; 263(2): 203-9.
[http://dx.doi.org/10.1016/j.jtbi.2009.11.016] [PMID:  19961864] 
[138] 
Ding H, Luo L, Lin H. Prediction of cell wall lytic enzymes using chou’s amphiphilic pseudo amino acid composition. Protein Pept Lett  2009; 16(4): 351-5.
[http://dx.doi.org/10.2174/092986609787848045] [PMID:  19356130] 
[139] 
Zhang GY, Li HC, Gao JQ, Fang BS. Predicting lipase types by improved Chou’s pseudo-amino acid composition. Protein Pept Lett  2008; 15(10): 1132-7.
[http://dx.doi.org/10.2174/092986608786071184] [PMID:  19075826] 
[140] 
Mondal S, Bhavna R, Mohan Babu R, Ramakumar S. Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. J Theor Biol  2006; 243(2): 252-60.
[http://dx.doi.org/10.1016/j.jtbi.2006.06.014] [PMID:  16890961] 
[141] 
Lin H, Li QZ. Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified mahalanobis discriminant. Biochem Biophys Res Commun  2007; 354(2): 548-51.
[http://dx.doi.org/10.1016/j.bbrc.2007.01.011] [PMID:  17239817] 
[142] 
Zhang GY, Fang BS. Predicting the cofactors of oxidoreductases based on amino acid composition distribution and chou’s amphiphilic pseudo-amino acid composition. J Theor Biol  2008; 253(2): 310-5.
[http://dx.doi.org/10.1016/j.jtbi.2008.03.015] [PMID:  18471832] 
[143] 
Fang Y, Guo Y, Feng Y, Li M. Predicting DNA-binding proteins: approached from chou’s pseudo amino acid composition and other specific sequence features. Amino Acids  2008; 34(1): 103-9.
[http://dx.doi.org/10.1007/s00726-007-0568-2] [PMID:  17624492] 
[144] 
Xiao X, Shao SH, Huang ZD, Chou KC. Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. J Comput Chem  2006; 27(4): 478-82.
[http://dx.doi.org/10.1002/jcc.20354] [PMID:  16429410] 
[145] 
Chen C, Tian YX, Zou XY, Cai PX, Mo JY. Using pseudo-amino acid composition and support vector machine to predict protein structural class. J Theor Biol  2006; 243(3): 444-8.
[http://dx.doi.org/10.1016/j.jtbi.2006.06.025] [PMID:  16908032] 
[146] 
Chen C, Zhou X, Tian Y, Zou X, Cai P. Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Anal Biochem  2006; 357(1): 116-21.
[http://dx.doi.org/10.1016/j.ab.2006.07.022] [PMID:  16920060] 
[147] 
Zhang TL, Ding YS. Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes. Amino Acids  2007; 33(4): 623-9.
[http://dx.doi.org/10.1007/s00726-007-0496-1] [PMID:  17308864] 
[148] 
Lin H, Li QZ. Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J Comput Chem  2007; 28(9): 1463-6.
[http://dx.doi.org/10.1002/jcc.20554] [PMID:  17330882] 
[149] 
Zhang TL, Ding YS, Chou KC. Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. J Theor Biol  2008; 250(1): 186-93.
[http://dx.doi.org/10.1016/j.jtbi.2007.09.014] [PMID:  17959199] 
[150] 
Ding YS, Zhang TL, Chou KC. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept Lett  2007; 14(8): 811-5.
[http://dx.doi.org/10.2174/092986607781483778] [PMID:  17979824] 
[151] 
Xiao X, Lin WZ, Chou KC. Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. J Comput Chem  2008; 29(12): 2018-24.
[http://dx.doi.org/10.1002/jcc.20955] [PMID:  18381630] 
[152] 
Li ZC, Zhou XB, Dai Z, Zou XY. Prediction of protein structural classes by chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids  2009; 37(2): 415-25.
[http://dx.doi.org/10.1007/s00726-008-0170-2] [PMID:  18726140] 
[153] 
Wu J, Li ML, Yu LZ, Wang C. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition. Protein J  2010; 29(1): 62-7.
[http://dx.doi.org/10.1007/s10930-009-9222-z] [PMID:  20049515] 
[154] 
Zou D, He Z, He J, Xia Y. Supersecondary structure prediction using chou’s pseudo amino acid composition. J Comput Chem  2011; 32(2): 271-8.
[http://dx.doi.org/10.1002/jcc.21616] [PMID:  20652881] 
[155] 
Chen C, Chen L, Zou X, Cai P. Prediction of protein secondary structure content by using the concept of chou’s pseudo amino acid composition and support vector machine. Protein Pept Lett  2009; 16(1): 27-31.
[http://dx.doi.org/10.2174/092986609787049420] [PMID:  19149669] 
[156] 
Chou KC, Cai YD. Predicting protein quaternary structure by pseudo amino acid composition. Proteins  2003; 53(2): 282-9.
[http://dx.doi.org/10.1002/prot.10500] [PMID:  14517979] 
[157] 
Zhang SW, Pan Q, Zhang HC, Shao ZC, Shi JY. Prediction of protein homo-oligomer types by pseudo amino acid composition: approached with an improved feature extraction and naive bayes feature fusion. Amino Acids  2006; 30(4): 461-8.
[http://dx.doi.org/10.1007/s00726-006-0263-8] [PMID:  16773245] 
[158] 
Zhang SW, Chen W, Yang F, Pan Q. Using chou’s pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach. Amino Acids  2008; 35(3): 591-8.
[http://dx.doi.org/10.1007/s00726-008-0086-x] [PMID:  18427713] 
[159] 
Shen HB, Chou KC. QuatIdent: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information. J Proteome Res  2009; 8(3): 1577-84.
[http://dx.doi.org/10.1021/pr800957q] [PMID:  19226167] 
[160] 
Xiao X, Wang P, Chou KC. Quat-2L: a web-server for predicting protein quaternary structural attributes. Mol Divers  2011; 15(1): 149-55.
[http://dx.doi.org/10.1007/s11030-010-9227-8] [PMID:  20148364] 
[161] 
Xiao X, Wang P. Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. J Appl Cryst  2009; 42: 169-73.
[http://dx.doi.org/10.1107/S0021889809002751] 
[162] 
Shen HB, Chou KC. Ensemble classifier for protein fold pattern recognition. Bioinformatics  2006; 22(14): 1717-22.
[http://dx.doi.org/10.1093/bioinformatics/btl170] [PMID:  16672258] 
[163] 
Shen HB, Chou KC. Predicting protein fold pattern with functional domain and sequential evolution information. J Theor Biol  2009; 256(3): 441-6.
[http://dx.doi.org/10.1016/j.jtbi.2008.10.007] [PMID:  18996396] 
[164] 
Georgiou DN, Karakasidis TE, Nieto JJ, Torres A. Use of fuzzy clustering technique and matrices to classify amino acids and its impact to chou’s pseudo amino acid composition. J Theor Biol  2009; 257(1): 17-26.
[http://dx.doi.org/10.1016/j.jtbi.2008.11.003] [PMID:  19056401] 
[165] 
Chou JJ, Matsuo H, Duan H, Wagner G. Solution structure of the RAIDD CARD and model for CARD/CARD interaction in caspase-2 and caspase-9 recruitment. Cell  1998; 94(2): 171-80.
[http://dx.doi.org/10.1016/S0092-8674(00)81417-8] [PMID:  9695946] 
[166] 
Oxenoid K, Dong Y, Cao C, et al. Architecture of the mitochondrial calcium uniporter. Nature  2016; 533(7602): 269-73.
[http://dx.doi.org/10.1038/nature17656] [PMID:  27135929] 
[167] 
Dev J, Park D, Fu Q, et al. Structural basis for membrane anchoring of HIV-1 envelope spike. Science  2016; 353(6295): 172-5.
[http://dx.doi.org/10.1126/science.aaf7066] [PMID:  27338706] 
[168] 
Schnell JR, Chou JJ. Structure and mechanism of the M2 proton channel of influenza A virus. Nature  2008; 451(7178): 591-5.
[http://dx.doi.org/10.1038/nature06531] [PMID:  18235503] 
[169] 
Berardi MJ, Shih WM, Harrison SC, Chou JJ. Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching. Nature  2011; 476(7358): 109-13.
[http://dx.doi.org/10.1038/nature10257] [PMID:  21785437] 
[170] 
Chou JJ, Li S, Klee CB, Bax A. Solution structure of Ca(2+)-calmodulin reveals flexible hand-like properties of its domains. Nat Struct Biol  2001; 8(11): 990-7.
[http://dx.doi.org/10.1038/nsb1101-990] [PMID:  11685248] 
[171] 
OuYang B, Xie S, Berardi MJ, et al. Unusual architecture of the p7 channel from hepatitis C virus. Nature  2013; 498(7455): 521-5.
[http://dx.doi.org/10.1038/nature12283] [PMID:  23739335] 
[172] 
Wang J, Pielak RM, McClintock MA, Chou JJ. Solution structure and functional analysis of the influenza B proton channel. Nat Struct Mol Biol  2009; 16(12): 1267-71.
[http://dx.doi.org/10.1038/nsmb.1707] [PMID:  19898475] 
[173] 
Fu Q, Fu TM, Cruz AC, et al. Structural basis and functional role of intramembrane trimerization of the Fas/CD95 death receptor. Mol Cell  2016; 61(4): 602-13.
[http://dx.doi.org/10.1016/j.molcel.2016.01.009] [PMID:  26853147] 
[174] 
Chou JJ, Li H, Salvesen GS, Yuan J, Wagner G. Solution structure of BID, an intracellular amplifier of apoptotic signaling. Cell  1999; 96(5): 615-24.
[http://dx.doi.org/10.1016/S0092-8674(00)80572-3] [PMID:  10089877] 
[175] 
Oxenoid K, Chou JJ. The structure of phospholamban pentamer reveals a channel-like architecture in membranes. Proc Natl Acad Sci USA  2005; 102(31): 10870-5.
[http://dx.doi.org/10.1073/pnas.0504920102] [PMID:  16043693] 
[176] 
Call ME, Schnell JR, Xu C, Lutz RA, Chou JJ, Wucherpfennig KW. The structure of the zetazeta transmembrane dimer reveals features essential for its assembly with the T cell receptor. Cell  2006; 127(2): 355-68.
[http://dx.doi.org/10.1016/j.cell.2006.08.044] [PMID:  17055436] 
[177] 
Call ME, Wucherpfennig KW, Chou JJ. The structural basis for intramembrane assembly of an activating immunoreceptor complex. Nat Immunol  2010; 11(11): 1023-9.
[http://dx.doi.org/10.1038/ni.1943] [PMID:  20890284] 
[178] 
Gagnon E, Xu C, Yang W, et al. Response multilayered control of T cell receptor phosphorylation. Cell  2010; 142(5): 669-71.
[http://dx.doi.org/10.1016/j.cell.2010.08.019] [PMID:  20813252] 
[179] 
Brüschweiler S, Yang Q, Run C, Chou JJ. Substrate-modulated ADP/ATP-transporter dynamics revealed by NMR relaxation dispersion. Nat Struct Mol Biol  2015; 22(8): 636-41.
[http://dx.doi.org/10.1038/nsmb.3059] [PMID:  26167881] 
[180] 
Cao C, Wang S, Cui T, Su XC, Chou JJ. Ion and inhibitor binding of the double-ring ion selectivity filter of the mitochondrial calcium uniporter. Proc Natl Acad Sci USA  2017; 114(14): e2846-51.
[http://dx.doi.org/10.1073/pnas.1620316114] [PMID:  28325874] 
[181] 
Piai A, Dev J, Fu Q, Chou JJ. Stability and water accessibility of the trimeric membrane anchors of the HIV-1 envelope spikes. J Am Chem Soc  2017; 139(51): 18432-5.
[http://dx.doi.org/10.1021/jacs.7b09352] [PMID:  29193965] 
[182] 
Pan L, Fu TM, Zhao W, et al. Higher-Order clustering of the transmembrane anchor of DR5 drives signaling. Cell  2019; 176 1489: e1414.
[http://dx.doi.org/10.1016/j.cell.2019.02.001] 
[183] 
Carlacci L, Chou KC, Maggiora GM. A heuristic approach to predicting the tertiary structure of bovine somatotropin. Biochemistry  1991; 30(18): 4389-98.
[http://dx.doi.org/10.1021/bi00232a004] [PMID:  2021631] 
[184] 
Chou KC, Jones D, Heinrikson RL. Prediction of the tertiary structure and substrate binding site of caspase-8. FEBS Lett  1997; 419(1): 49-54.
[http://dx.doi.org/10.1016/S0014-5793(97)01246-5] [PMID:  9426218] 
[185] 
Chou KC, Tomasselli AG, Heinrikson RL. Prediction of the tertiary structure of a caspase-9/inhibitor complex. FEBS Lett  2000; 470(3): 249-56.
[http://dx.doi.org/10.1016/S0014-5793(00)01333-8] [PMID:  10745077] 
[186] 
Chou KC, Carlacci L, Maggiora GM, Parodi LA, Schulz MW. An energy-based approach to packing the 7-helix bundle of bacteriorhodopsin. Protein Sci  1992; 1(6): 810-27.
[http://dx.doi.org/10.1002/pro.5560010613] [PMID:  1304922] 
[187] 
Chou KC. Knowledge-based model building of the tertiary structures for lectin domains of the selectin family. J Protein Chem  1996; 15(2): 161-8.
[http://dx.doi.org/10.1007/BF01887396] [PMID:  8924200] 
[188] 
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] 
[216] 
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] 
[218] 
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] 
[235] 
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] 
[237] 
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] 
[243] 
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] 
[261] 
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] 
[264] 
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.
[http://dx.doi.org/10.1002/prot.25689] [PMID:  30958587] 
[267] 
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.
[http://dx.doi.org/10.1093/bib/bby079] [PMID:  30351377] 
[268] 
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
[http://dx.doi.org/10.1093/bib/bby089] [PMID:  30285084] 
[269] 
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] 
[270] 
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] 
[272] 
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.
[http://dx.doi.org/10.18632/oncotarget.13758] [PMID:  27926534] 
[276] 
Ghauri AW, Khan YD, Rasool N, Khan SA, Chou KC. pNitro-Tyr-PseAAC: predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC. Curr Pharm Des  2018; 24(34): 4034-43.
[http://dx.doi.org/10.2174/1381612825666181127101039] [PMID:  30479209] 
[277] 
Li JX, Wang SQ, Du QS, et al. Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans. Curr Pharm Des  2018; 24(34): 4023-33.
[http://dx.doi.org/10.2174/1381612824666181113120948] [PMID:  30421671] 
[278] 
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.
[http://dx.doi.org/10.2174/0929867326666190507082559] [PMID:  31060481] 
[279] 
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.
[http://dx.doi.org/10.2174/1389202920666190325162307] [PMID:  31555063] 
[280] 
Khan YD, Batool A, Rasool N, Khan A. Prediction of nitrosocysteine sites using position and composition variant features. Lett Org Chem  2019; 16: 283-93.
[http://dx.doi.org/10.2174/1570178615666180802122953] 
[281] 
Khan YD, Jamil M, Hussain W, Rasool N, Khan SA, Chou KC. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J Theor Biol  2019; 463: 47-55.
[http://dx.doi.org/10.1016/j.jtbi.2018.12.015] [PMID:  30550863] 
[282] 
Lu Y, Wang S, Wang J, et al. An epidemic avian influenza prediction model based on google trends. Lett Org Chem  2019; 16: 303-10.
[http://dx.doi.org/10.2174/1570178615666180724103325] 
[283] 
Salman; Khan, M Iqbal, N Hussain, T Afzal. A two-level computation model based on deep learning algorithm for identification of piRNA and their functions via Chou’s 5-steps rule. Int J Pept Res Ther  2019; 1-15.
[http://dx.doi.org/10.1007/s10989-019-09887-3] 
[284] 
Song J, Li F, Takemoto K, et al. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J Theor Biol  2018; 443: 125-37.
[http://dx.doi.org/10.1016/j.jtbi.2018.01.023] [PMID:  29408627] 
[285] 
Chen Z, Zhao P, Li F, et al. iFeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics  2018; 34(14): 2499-502.
[http://dx.doi.org/10.1093/bioinformatics/bty140] [PMID:  29528364] 
[286] 
Li F, Li C, Marquez-Lago TT, et al. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics  2018; 34(24): 4223-31.
[http://dx.doi.org/10.1093/bioinformatics/bty522] [PMID:  29947803] 
[287] 
Li F, Wang Y, Li C, et al. Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods. Brief Bioinform 2018. 10.1093/bib/bby077
[http://dx.doi.org/10.1093/bib/bby077] [PMID:  30184176] 
[288] 
Song J, Li F, Leier A, et al. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. Bioinformatics  2018; 34(4): 684-7.
[http://dx.doi.org/10.1093/bioinformatics/btx670] [PMID:  29069280] 
[289] 
Song J, Wang Y, Li F, et al. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform  2019; 20(2): 638-58.
[http://dx.doi.org/10.1093/bib/bby028] [PMID:  29897410] 
[290] 
Wang J, Li J, Yang B, et al. Bastion3: a two-layer approach for identifying type III secreted effectors using ensemble learning. Bioinformatics  2018; 35: 2017-28.
[http://dx.doi.org/10.1093/bioinformatics/bty914] [PMID:  30388198] 
[291] 
Wang J, Yang B, Leier A, et al. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics  2018; 34(15): 2546-55.
[http://dx.doi.org/10.1093/bioinformatics/bty155] [PMID:  29547915] 
[292] 
Zhang S, Yang K, Lei Y, Song K. iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou’s pseudo components. Genomics  2019 Dec; 111(6): 1760-70.
[http://dx.doi.org/10.1016/j.ygeno.2018.11.031] [PMID:  30529702] 
[293] 
Chou KC. Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal Biochem  1996; 233(1): 1-14.
[http://dx.doi.org/10.1006/abio.1996.0001] [PMID:  8789141] 
[294] 
Du QS, Wang SQ, Zhu Y, et al. Polyprotein cleavage mechanism of SARS CoV Mpro and chemical modification of the octapeptide. Peptides  2004; 25(11): 1857-64.
[http://dx.doi.org/10.1016/j.peptides.2004.06.018] [PMID:  15501516] 
[295] 
Du QS, Wang S, Wei DQ, Sirois S. Molecular modelling and chemical modification for finding peptide inhibitor against SARS CoV Mpro. Anal Biochem  2005; 337: 262-70.
[http://dx.doi.org/10.1016/j.ab.2004.10.003] [PMID:  15691506] 
[296] 
Gan YR, Huang H, Huang YD, et al. Synthesis and activity of an octapeptide inhibitor designed for SARS coronavirus main proteinase. Peptides  2006; 27(4): 622-5.
[http://dx.doi.org/10.1016/j.peptides.2005.09.006] [PMID:  16242214] 
[297] 
Du QS, Sun H, Chou KC. Inhibitor design for SARS coronavirus main protease based on “distorted key theory”. Med Chem  2007; 3(1): 1-6.
[http://dx.doi.org/10.2174/157340607779317616] [PMID:  17266617] 
[298] 
Chou KC, Forsén S. Diffusion-controlled effects in reversible enzymatic fast reaction systems-critical spherical shell and proximity rate constant. Biophys Chem  1980; 12(3-4): 255-63.
[http://dx.doi.org/10.1016/0301-4622(80)80002-0] [PMID:  7225518] 
[299] 
Chou KC, Forsén S. Graphical rules for enzyme-catalysed rate laws. Biochem J  1980; 187(3): 829-35.
[http://dx.doi.org/10.1042/bj1870829] [PMID:  7188428] 
[300] 
Forsen S, Zhou GQ. Three schematic rules for deriving apparent rate constants. Chem Scr  1980; 16: 109-13.
[301] 
Chou KC, Li TT, Forsén S. The critical spherical shell in enzymatic fast reaction systems. Biophys Chem  1980; 12(3-4): 265-9.
[http://dx.doi.org/10.1016/0301-4622(80)80003-2] [PMID:  7225519] 
[302] 
Li TT, Forsen S. The flow of substrate molecules in fast enzyme-catalyzed reaction systems. Chem Scr  1980; 16: 192-6.
[303] 
Carter RE, Forsen S. A new graphical method for deriving rate equations for complicated mechanisms. Chem Scr  1981; 18: 82-6.
[304] 
Chou KC, Chen NY, Forsen S. The biological functions of low-frequency phonons: 2. Cooperative effects. Chem Scr  1981; 18: 126-32.
[305] 
Chou KC, Forsen S. Graphical rules of steady-state reaction systems. Can J Chem  1981; 59: 737-55.
[http://dx.doi.org/10.1139/v81-107] 
[306] 
Chou KC. Low-frequency vibrations of helical structures in protein molecules. Biochem J  1983; 209(3): 573-80.
[http://dx.doi.org/10.1042/bj2090573] [PMID:  6870784] 
[307] 
Chou KC. Identification of low-frequency modes in protein molecules. Biochem J  1983; 215(3): 465-9.
[http://dx.doi.org/10.1042/bj2150465] [PMID:  6362659] 
[308] 
Zhou GP, Deng MH. An extension of chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J  1984; 222(1): 169-76.
[http://dx.doi.org/10.1042/bj2220169] [PMID:  6477507] 
[309] 
Chou KC. Biological functions of low-frequency vibrations (phonons). III. Helical structures and microenvironment. Biophys J  1984; 45(5): 881-9.
[http://dx.doi.org/10.1016/S0006-3495(84)84234-4] [PMID:  6428481] 
[310] 
Chou KC. The biological functions of low-frequency vibrations (phonons) 4. Resonance effects and allosteric transition. Biophys Chem  1984; 20(1-2): 61-71.
[http://dx.doi.org/10.1016/0301-4622(84)80005-8] [PMID:  6487745] 
[311] 
Chou KC. Low-frequency vibrations of DNA molecules. Biochem J  1984; 221(1): 27-31.
[http://dx.doi.org/10.1042/bj2210027] [PMID:  6466317] 
[312] 
Chen L, Cai YD, Shi XH, Huang T. Analysis of metabolic pathway using hybrid properties. Protein Pept Lett  2012; 19(1): 99-107.
[http://dx.doi.org/10.2174/092986612798472857] [PMID:  21919854] 
[313] 
Chou KC. Low-frequency motions in protein molecules. Beta-sheet and beta-barrel. Biophys J  1985; 48(2): 289-97.
[http://dx.doi.org/10.1016/S0006-3495(85)83782-6] [PMID:  4052563] 
[314] 
Chou KC. Prediction of a low-frequency mode in bovine pancreatic trypsin inhibitor molecule. Int J Biol Macromol  1985; 7: 77-80.
[http://dx.doi.org/10.1016/0141-8130(85)90035-2] 
[315] 
Chou KC, Kiang YS. The biological functions of low-frequency vibrations (phonons) 5. A phenomenological theory. Biophys Chem  1985; 22(3): 219-35.
[http://dx.doi.org/10.1016/0301-4622(85)80045-4] [PMID:  4052576] 
[316] 
Chou KC. Origin of low-frequency motions in biological macromolecules. A view of recent progress in the quasi-continuity model. Biophys Chem  1986; 25(2): 105-16.
[http://dx.doi.org/10.1016/0301-4622(86)87001-6] [PMID:  3101760] 
[317] 
Chou KC. The biological functions of low-frequency vibrations (phonons) VI. A possible dynamic mechanism of allosteric transition in antibody molecules. Biopolymers  1987; 26(2): 285-95.
[http://dx.doi.org/10.1002/bip.360260209] [PMID:  3828475] 
[318] 
Chou KC. Low-frequency collective motion in biomacromolecules and its biological functions. Biophys Chem  1988; 30(1): 3-48.
[http://dx.doi.org/10.1016/0301-4622(88)85002-6] [PMID:  3046672] 
[319] 
Chou KC, Maggiora GM. The biological functions of low-frequency phonons: 7. The impetus for DNA to accommodate intercalators. Br Polym J  1988; 20: 143-8.
[http://dx.doi.org/10.1002/pi.4980200209] 
[320] 
Chou KC. Low-frequency resonance and cooperativity of hemoglobin. Trends Biochem Sci  1989; 14(6): 212-3.
[http://dx.doi.org/10.1016/0968-0004(89)90026-1] [PMID:  2763333] 
[321] 
Chou KC, Maggiora GM, Mao B. Quasi-continuum models of twist-like and accordion-like low-frequency motions in DNA. Biophys J  1989; 56(2): 295-305.
[http://dx.doi.org/10.1016/S0006-3495(89)82676-1] [PMID:  2775828] 
[322] 
Chou KC. Graphic rules in steady and non-steady state enzyme kinetics. J Biol Chem  1989; 264(20): 12074-9.
[PMID:  2745429] 
[323] 
Chou KC. Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady-state systems. Biophys Chem  1990; 35(1): 1-24.
[http://dx.doi.org/10.1016/0301-4622(90)80056-D] [PMID:  2183882] 
[324] 
Althaus IW, Chou JJ, Gonzales AJ, et al. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J Biol Chem  1993; 268(9): 6119-24.
[PMID:  7681060] 
[325] 
Althaus IW, Gonzales AJ, Chou JJ, et al. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem  1993; 268(20): 14875-80.
[PMID:  7686907] 
[326] 
Althaus IW, Chou JJ, Gonzales AJ, et al. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry  1993; 32(26): 6548-54.
[http://dx.doi.org/10.1021/bi00077a008] [PMID:  7687145] 
[327] 
Althaus IW, Chou JJ, Gonzales AJ, et al. Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia  1994; 50(1): 23-8.
[http://dx.doi.org/10.1007/BF01992044] [PMID:  7507441] 
[328] 
Althaus IW, Chou JJ, Gonzales AJ, et al. Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. Biochem Pharmacol  1994; 47(11): 2017-28.
[http://dx.doi.org/10.1016/0006-2952(94)90077-9] [PMID:  7516658] 
[329] 
Chou KC, Kézdy FJ, Reusser F. Kinetics of processive nucleic acid polymerases and nucleases. Anal Biochem  1994; 221(2): 217-30.
[http://dx.doi.org/10.1006/abio.1994.1405] [PMID:  7529005] 
[330] 
Chou KC, Zhang CT, Maggiora GM. Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers  1994; 34(1): 143-53.
[http://dx.doi.org/10.1002/bip.360340114] [PMID:  8110966] 
[331] 
Althaus IW, Chou KC, Lemay RJ, et al. The benzylthio-pyrimidine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase. Biochem Pharmacol  1996; 51(6): 743-50.
[http://dx.doi.org/10.1016/0006-2952(95)02390-9] [PMID:  8602869] 
[332] 
Liu H, Wang M, Chou KC. Low-frequency Fourier spectrum for predicting membrane protein types. Biochem Biophys Res Commun  2005; 336(3): 737-9.
[http://dx.doi.org/10.1016/j.bbrc.2005.08.160] [PMID:  16140260] 
[333] 
Gordon GA. Designed electromagnetic pulsed therapy: clinical applications. J Cell Physiol  2007; 212(3): 579-82.
[http://dx.doi.org/10.1002/jcp.21025] [PMID:  17577213] 
[334] 
Andraos J. Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Can J Chem  2008; 86(4): 342-57.
[http://dx.doi.org/10.1139/v08-020] 
[335] 
Chou KC, Shen HB. FoldRate: a web-server for predicting protein folding rates from primary sequence. Open Bioinform J  2009; 3: 31-50.
[http://dx.doi.org/10.2174/1875036200903010031] 
[336] 
Shen HB, Song JN. Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J Biomed Sci Eng  (JBiSE)  2009; 2: 136-43.
[337] 
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.
[http://dx.doi.org/10.1016/j.bbrc.2009.10.014] [PMID:  19819222] 
[338] 
Gordon G. Extrinsic electromagnetic fields, low frequency (phonon) vibrations, and control of cell function: a non-linear resonance system. J Biomed Sci Eng  2008; 1: 152-6.
[http://dx.doi.org/10.4236/jbise.2008.13025] 
[339] 
Madkan A, Blank M, Elson E, Geddis MS, Goodman R. Steps to the clinic with ELF EMF. Nat Sci  2009; 1: 157-65.
[http://dx.doi.org/10.4236/ns.2009.13020] 
[340] 
Chou KC. Graphic rule for drug metabolism systems. Curr Drug Metab  2010; 11(4): 369-78.
[http://dx.doi.org/10.2174/138920010791514261] [PMID:  20446902] 
[341] 
Chou KC, Lin WZ, Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat Sci  2011; 3: 862-5.
[http://dx.doi.org/10.4236/ns.2011.310111] 
[342] 
Lian P, Wei DQ, Wang JF, Chou KC. An allosteric mechanism inferred from molecular dynamics simulations on phospholamban pentamer in lipid membranes. PLoS One  2011; 6(4)  e18587
[http://dx.doi.org/10.1371/journal.pone.0018587] [PMID:  21525996] 
[343] 
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.
[http://dx.doi.org/10.2174/157340611794072698] [PMID:  21235516] 
[344] 
Zhou GP. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol  2011; 284(1): 142-8.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.006] [PMID:  21718705] 
[345] 
Li J, Wei DQ, Wang JF, Yu ZT, Chou KC. Molecular dynamics simulations of CYP2E1. Med Chem  2012; 8(2): 208-21.
[http://dx.doi.org/10.2174/157340612800493692] [PMID:  22385180] 
[346] 
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.
[http://dx.doi.org/10.2174/138955712802762275] [PMID:  22420575] 
[347] 
Zhang T, Wei DQ, Chou KC. A pharmacophore model specific to active site of CYP1A2 with a novel molecular modeling explorer and CoMFA. Med Chem  2012; 8(2): 198-207.
[http://dx.doi.org/10.2174/157340612800493601] [PMID:  22385179] 
[348] 
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.
[http://dx.doi.org/10.1016/j.jtbi.2015.04.011] [PMID:  25908206] 
[349] 
Jia J, Liu Z, Xiao X, Liu B, Chou KC. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition. J Biomol Struct Dyn  2016; 34(9): 1946-61.
[http://dx.doi.org/10.1080/07391102.2015.1095116] [PMID:  26375780] 
[350] 
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.
[351] 
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.
[352] 
Chou KC. Gordon Life Science Institute: Its philosophy, achievements, and perspective. Ann Cancer Therapy Pharmacol  2019; 2: 1-26.
Rights & Permissions Print Cite
Article Metrics
15
2
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1381612825666191129164042	Print ISSN 1381-6128
Publisher Name Bentham Science Publisher	Online ISSN 1873-4286
Current Pharmaceutical Design

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

Abstract

"Tuberculosis Prevention, Diagnosis and Drug Discovery"

Current Pharmaceutical challenges in the treatment and diagnosis of neurological dysfunctions

Emerging and re-emerging diseases

Melanoma and Non-Melanoma Skin Cancer Treatment: Standard of Care and Recent Advances

Current Pharmaceutical Design

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

Abstract

Call for Papers in Thematic Issues

"Tuberculosis Prevention, Diagnosis and Drug Discovery"

Current Pharmaceutical challenges in the treatment and diagnosis of neurological dysfunctions

Emerging and re-emerging diseases

Melanoma and Non-Melanoma Skin Cancer Treatment: Standard of Care and Recent Advances

Related Journals

Related Books
