Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis

Kuo-Chen       Chou
Abstract

Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer’s, and Parkinson’s. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.
Keywords: Five-steps rules, Protein structural classes, Protein subcellular localization, Post-translational modifications, Webserver, Global and local metrics.
« Previous Next »
Graphical Abstract

[1] 
Chou, J.J.; 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), 171-180.
[http://dx.doi.org/10.1016/S0092-8674(00)81417-8] [PMID:  9695946] 
[2] 
Oxenoid, K.; Dong, Y.; Cao, C.; Cui, T.; Sancak, Y.; Markhard, A.L.; Grabarek, Z.; Kong, L.; Liu, Z.; Ouyang, B.; Cong, Y.; Mootha, V.K.; Chou, J.J. Architecture of the mitochondrial calcium uniporter. Nature,  2016, 533(7602), 269-273.
[http://dx.doi.org/10.1038/nature17656] [PMID:  27135929] 
[3] 
Dev, J.; Park, D.; Fu, Q.; Chen, J.; Ha, H.J.; Ghantous, F.; Herrmann, T.; Chang, W.; Liu, Z.; Frey, G.; Seaman, M.S.; Chen, B.; Chou, J.J. Structural basis for membrane anchoring of HIV-1 envelope spike. Science,  2016, 353(6295), 172-175.
[http://dx.doi.org/10.1126/science.aaf7066] [PMID:  27338706] 
[4] 
Schnell, J.R.; Chou, J.J. Structure and mechanism of the M2 proton channel of influenza A virus. Nature,  2008, 451(7178), 591-595.
[http://dx.doi.org/10.1038/nature06531] [PMID:  18235503] 
[5] 
Berardi, M.J.; Shih, W.M.; Harrison, S.C.; Chou, J.J. Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching. Nature,  2011, 476(7358), 109-113.
[http://dx.doi.org/10.1038/nature10257] [PMID:  21785437] 
[6] 
Chou, J.J.; Li, S.; Klee, C.B.; Bax, A. Solution structure of Ca(2+)-calmodulin reveals flexible hand-like properties of its domains. Nat. Struct. Biol.,  2001, 8(11), 990-997.
[http://dx.doi.org/10.1038/nsb1101-990] [PMID:  11685248] 
[7] 
OuYang, B.; Xie, S.; Berardi, M.J.; Zhao, X.; Dev, J.; Yu, W.; Sun, B.; Chou, J.J. Unusual architecture of the p7 channel from hepatitis C virus. Nature,  2013, 498(7455), 521-525.
[http://dx.doi.org/10.1038/nature12283] [PMID:  23739335] 
[8] 
Wang, J.; Pielak, R.M.; McClintock, M.A.; Chou, J.J. Solution structure and functional analysis of the influenza B proton channel. Nat. Struct. Mol. Biol.,  2009, 16(12), 1267-1271.
[http://dx.doi.org/10.1038/nsmb.1707] [PMID:  19898475] 
[9] 
Fu, Q.; Fu, T.M.; Cruz, A.C.; Sengupta, P.; Thomas, S.K.; Wang, S.; Siegel, R.M.; Wu, H.; Chou, J.J. Structural basis and functional role of intramembrane trimerization of the Fas/CD95 death receptor. Mol. Cell,  2016, 61(4), 602-613.
[http://dx.doi.org/10.1016/j.molcel.2016.01.009] [PMID:  26853147] 
[10] 
Chou, J.J.; Li, H.; Salvesen, G.S.; Yuan, J.; Wagner, G. Solution structure of BID, an intracellular amplifier of apoptotic signaling. Cell,  1999, 96(5), 615-624.
[http://dx.doi.org/10.1016/S0092-8674(00)80572-3] [PMID:  10089877] 
[11] 
Oxenoid, K.; Chou, J.J. The structure of phospholamban pentamer reveals a channel-like architecture in membranes. Proc. Natl. Acad. Sci. USA,  2005, 102(31), 10870-10875.
[http://dx.doi.org/10.1073/pnas.0504920102] [PMID:  16043693] 
[12] 
Call, M.E.; Schnell, J.R.; Xu, C.; Lutz, R.A.; Chou, J.J.; Wucherpfennig, K.W. The structure of the zetazeta transmembrane dimer reveals features essential for its assembly with the T cell receptor. Cell,  2006, 127(2), 355-368.
[http://dx.doi.org/10.1016/j.cell.2006.08.044] [PMID:  17055436] 
[13] 
Call, M.E.; Wucherpfennig, K.W.; Chou, J.J. The structural basis for intramembrane assembly of an activating immunoreceptor complex. Nat. Immunol.,  2010, 11(11), 1023-1029.
[http://dx.doi.org/10.1038/ni.1943] [PMID:  20890284] 
[14] 
Gagnon, E.; Xu, C.; Yang, W.; Chu, H.H.; Call, M.E.; Chou, J.J.; Wucherpfennig, K.W. Response multilayered control of T cell receptor phosphorylation. Cell,  2010, 142(5), 669-671.
[http://dx.doi.org/10.1016/j.cell.2010.08.019] [PMID:  20813252] 
[15] 
Brüschweiler, S.; Yang, Q.; Run, C.; Chou, J.J. Substrate-modulated ADP/ATP-transporter dynamics revealed by NMR relaxation dispersion. Nat. Struct. Mol. Biol.,  2015, 22(8), 636-641.
[http://dx.doi.org/10.1038/nsmb.3059] [PMID:  26167881] 
[16] 
Cao, C.; Wang, S.; Cui, T.; Su, X.C.; Chou, J.J. 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-E2851.
[http://dx.doi.org/10.1073/pnas.1620316114] [PMID:  28325874] 
[17] 
Piai, A.; Dev, J.; Fu, Q.; Chou, J.J. Stability and water accessibility of the trimeric membrane anchors of the HIV-1 envelope spikes. J. Am. Chem. Soc.,  2017, 139(51), 18432-18435.
[http://dx.doi.org/10.1021/jacs.7b09352] [PMID:  29193965] 
[18] 
Pan, L.; Fu, T.M.; Zhao, W.; Zhao, L.; Chen, W.; Qiu, C.; Liu, W.; Liu, Z.; Piai, A.; Fu, Q.; Chen, S.; Wu, H.; Chou, J.J. Higher-order clustering of the transmembrane anchor of DR5 drives signaling., 2019.
[19] 
Chou, K.C.; Tomasselli, A.G.; Heinrikson, R.L. Prediction of the tertiary structure of a caspase-9/inhibitor complex. FEBS Lett.,  2000, 470(3), 249-256.
[http://dx.doi.org/10.1016/S0014-5793(00)01333-8] [PMID:  10745077] 
[20] 
Chou, K.C.; Jones, D.; Heinrikson, R.L. 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] 
[21] 
Chou, K.C. Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochem. Biophys. Res. Commun.,  2004, 319(2), 433-438. [BBRC]
[http://dx.doi.org/[http://10.1016/j.bbrc.2004.05.016] [PMID:  15178425] 
[22] 
Chou, K.C. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J. Proteome Res.,  2005, 4(5), 1681-1686.
[http://dx.doi.org/10.1021/pr050145a] [PMID:  16212421] 
[23] 
Chou, K.C.; Howe, W.J. Prediction of the tertiary structure of the beta-secretase zymogen. Biochem. Biophys. Res. Commun.,  2002, 292(3), 702-708. [BBRC]. 
[http://dx.doi.org/10.1006/bbrc.2002.6686] [PMID:  11922623] 
[24] 
Chou, K.C. Insights from modeling the tertiary structure of human BACE2. J. Proteome Res.,  2004, 3(5), 1069-1072.
[http://dx.doi.org/10.1021/pr049905s] [PMID:  15473697] 
[25] 
Chou, K.C. Insights from modeling three-dimensional structures of the human potassium and sodium channels. J. Proteome Res.,  2004, 3(4), 856-861.
[http://dx.doi.org/10.1021/pr049931q] [PMID:  15359741] 
[26] 
Chou, K.C. 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] 
[27] 
Chou, K.C. Insights from modeling the 3D structure of DNA-CBF3b complex. J. Proteome Res.,  2005, 4(5), 1657-1660.
[http://dx.doi.org/10.1021/pr050135+] [PMID:  16212418] 
[28] 
Wang, S.Q.; Du, Q.S.; Chou, K.C. Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases. Biochem. Biophys. Res. Commun.,  2007, 354(3), 634-640.
[http://dx.doi.org/10.1016/j.bbrc.2006.12.235] [PMID:  17266937] 
[29] 
Wang, S.Q.; Du, Q.S.; Huang, R.B.; Zhang, D.W.; Chou, K.C. Insights from investigating the interaction of oseltamivir (Tamiflu) with neuraminidase of the 2009 H1N1 swine flu virus. Biochem. Biophys. Res. Commun.,  2009, 386(3), 432-436.
[http://dx.doi.org/10.1016/j.bbrc.2009.06.016] [PMID:  19523442] 
[30] 
Li, X.B.; Wang, S.Q.; Xu, W.R.; Wang, R.L.; Chou, K.C. 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] 
[31] 
Ma, Y.; Wang, S.Q.; Xu, W.R.; Wang, R.L.; Chou, K.C. 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] 
[32] 
Chen, W.; Feng, P.M.; Lin, H.; Chou, K.C. 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] 
[33] 
Feng, P.M.; Chen, W.; Lin, H.; Chou, K.C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem.,  2013, 442(1), 118-125.
[http://dx.doi.org/10.1016/j.ab.2013.05.024] [PMID:  23756733] 
[34] 
Lin, H.; Deng, E.Z.; Ding, H.; Chen, W.; Chou, K.C. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res.,  2014, 42(21), 12961-12972.
[http://dx.doi.org/10.1093/nar/gku1019] [PMID:  25361964] 
[35] 
Chen, W.; Feng, P.M.; Deng, E.Z.; Lin, H.; Chou, K.C. 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] 
[36] 
Ding, H.; Deng, E.Z.; Yuan, L.F.; Liu, L.; Lin, H.; Chen, W.; Chou, K.C. 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] 
[37] 
Liu, B.; Fang, L.; Wang, S.; Wang, X.; Li, H.; Chou, K.C. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J. Theor. Biol.,  2015, 385, 153-159.
[http://dx.doi.org/10.1016/j.jtbi.2015.08.025] [PMID:  26362104] 
[38] 
Liu, Z.; Xiao, X.; Qiu, W.R.; Chou, K.C. 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] 
[39] 
Xiao, X.; Min, J.L.; Lin, W.Z.; Liu, Z.; Cheng, X.; Chou, K.C. 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), 2221-2233.  [JBSD]
[http://dx.doi.org/10.1080/07391102.2014.998710] [PMID:  25513722] 
[40] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. 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] 
[41] 
Jia, J.; Zhang, L.; Liu, Z.; Xiao, X.; Chou, K.C. pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC. Bioinformatics,  2016, 32(20), 3133-3141.
[http://dx.doi.org/10.1093/bioinformatics/btw387] [PMID:  27354696] 
[42] 
Liu, B.; Fang, L.; Long, R.; Lan, X.; Chou, K.C. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics,  2016, 32(3), 362-369.
[http://dx.doi.org/10.1093/bioinformatics/btv604] [PMID:  26476782] 
[43] 
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget,  2017, 8(3), 4208-4217.
[http://dx.doi.org/10.18632/oncotarget.13758] [PMID:  27926534] 
[44] 
Chen, W.; Ding, H.; Zhou, X.; Lin, H.; Chou, K.C. 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] 
[45] 
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Mol. Ther. Nucleic Acids,  2018, 11, 468-474.
[http://dx.doi.org/10.1016/j.omtn.2018.03.012] [PMID:  29858081] 
[46] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Jia, J.H.; Chou, K.C. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier. Genomics,  2018, 110(5), 239-246.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.008] [PMID:  29107015] 
[47] 
Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chen, W.; Chou, K.C. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics,  2019, 111(1), 96-102.
[http://dx.doi.org/10.1016/j.ygeno.2018.01.005] [PMID:  29360500] 
[48] 
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal. Biochem.,  2019, 568, 14-23.
[http://dx.doi.org/10.1016/j.ab.2018.12.019] [PMID:  30593778] 
[49] 
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. 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] 
[50] 
Jia, J.; Li, X.; Qiu, W.; Xiao, X.; Chou, K.C. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J. Theor. Biol.,  2019, 460, 195-203.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.021] [PMID:  30312687] 
[51] 
Khan, Y.D.; Jamil, M.; Hussain, W.; Rasool, N.; Khan, S.A.; Chou, K.C. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J. Theor. Biol.,  2019, 463, 47-55.
[http://dx.doi.org/10.1016/j.jtbi.2018.12.015] [PMID:  30550863] 
[52] 
Lu, Y.; Wang, S.; Wang, J.; Zhou, G.; Zhang, Q.; Zhou, X.; Niu, B.; Chen, Q. An Epidemic Avian Influenza Prediction Model Based on Google Trends. Lett. Org. Chem.,  2019, 16, 303-310.
[http://dx.doi.org/10.2174/1570178615666180724103325] 
[53] 
Khan, Y.D.; Batool, A.; Rasool, N.; Khan, A. Prediction of nitrosocysteine sites using position and composition variant features. Lett. Org. Chem.,  2019, 16, 283-293.
[http://dx.doi.org/10.2174/1570178615666180802122953] 
[54] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr. Pharm. Des.,  2018, 24(34), 4013-4022.
[http://dx.doi.org/10.2174/1381612824666181119145030] [PMID:  30451108] 
[55] 
Li, J.X.; Wang, S.Q.; Du, Q.S.; Wei, H.; Li, X.M.; Meng, J.Z.; Wang, Q.Y.; Xie, N.Z.; Huang, R.B.; Chou, K.C. Simulated protein thermal detection (SPTD) for enzyme thermostability study and an application example for pullulanase from Bacillus deramificans. Curr. Pharm. Des.,  2018, 24(34), 4023-4033.
[http://dx.doi.org/10.2174/1381612824666181113120948] [PMID:  30421671] 
[56] 
Ghauri, A.W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou’s general PseAAC. Curr. Pharm. Des.,  2018, 24(34), 4034-4043.
[http://dx.doi.org/10.2174/1381612825666181127101039] [PMID:  30479209] 
[57] 
Chou, K.C. Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr. Med. Chem., 2019. [ePub Ahead of print
[http://dx.doi.org/10.2174/0929867326666190507082559] [PMID:  31060481] 
[58] 
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K.C. pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics,  2019, 111(4), 886-892.
[http://dx.doi.org/10.1016/j.ygeno.2018.05.017] [PMID:  29842950] 
[59] 
Zhang, M.; Li, F.; Marquez-Lago, T.T.; Leier, A.; Fan, C.; Kwoh, C.K.; Chou, K.C.; Song, J.; Jia, C. MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics,  2019, 35(17), 2957-2965.
[http://dx.doi.org/10.1093/bioinformatics/btz016] [PMID:  30649179] 
[60] 
Chen, Z.; Zhao, P.; Li, F.; Marquez-Lago, T.T.; Leier, A.; Revote, J.; Zhu, Y.; Powell, D.R.; Akutsu, T.; Webb, G.I.; Chou, K.C.; Smith, A.I.; Daly, R.J.; Li, J.; Song, J. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Brief. Bioinform., 2019.bbz041
[http://dx.doi.org/10.1093/bib/bbz041] [PMID:  31067315] 
[61] 
Zhang, Y.; Xie, R.; Wang, J.; Leier, A.; Marquez-Lago, T.T.; Akutsu, T.; Webb, G.I.; Chou, K.C.; Song, J. Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework. Brief. Bioinform., 2018. [Epub Ahead of Print
[http://dx.doi.org/10.1093/bib/bby079] [PMID:  30351377] 
[62] 
Song, J.; Wang, Y.; Li, F.; Akutsu, T.; Rawlings, N.D.; Webb, G.I.; Chou, K.C. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief. Bioinform.,  2019, 20(2), 638-658.
[http://dx.doi.org/10.1093/bib/bby028] [PMID:  29897410] 
[63] 
Song, J.; Li, F.; Takemoto, K.; Haffari, G.; Akutsu, T.; Chou, K.C.; Webb, G.I. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J. Theor. Biol.,  2018, 443, 125-137.
[http://dx.doi.org/10.1016/j.jtbi.2018.01.023] [PMID:  29408627] 
[64] 
Li, F.; Wang, Y.; Li, C.; Marquez-Lago, T.T.; Leier, A.; Rawlings, N.D.; Haffari, G.; Revote, J.; Akutsu, T.; Chou, K.C.; Purcell, A.W.; Pike, R.N.; Webb, G.I.; Ian Smith, A.; Lithgow, T.; Daly, R.J.; Whisstock, J.C.; Song, J. Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods. Brief. Bioinform., 2018. [Epub Ahead of Print
[http://dx.doi.org/10.1093/bib/bby077] [PMID:  30184176] 
[65] 
Li, F.; Li, C.; Marquez-Lago, T.T.; Leier, A.; Akutsu, T.; Purcell, A.W.; Ian Smith, A.; Lithgow, T.; Daly, R.J.; Song, J.; Chou, K.C. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics,  2018, 34(24), 4223-4231.
[http://dx.doi.org/10.1093/bioinformatics/bty522] [PMID:  29947803] 
[66] 
Kabir, M.; Ahmad, S.; Iqbal, M.; Hayat, M. iNR-2L: A two-level
sequence-based predictor developed via Chou’s 5-steps rule and
general PseAAC for identifying nuclear receptors and their families. Genomics,  2019, pii. S0888-7543(18), 30694-3.
[http://dx.doi.org/10.1016/j.ygeno.2019.02.006] [PMID:  30779939] 
[67] 
Le, N.Q.K. iN6-methylat (5-step): identifying DNA N(6)-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule. Mol. Gen. Genet.,  2019, 294(5), 1173-1182.
[http://dx.doi.org/10.1007/s00438-019-01570-y] 
[68] 
Le, N.Q.K.; Yapp, E.K.Y.; Ho, Q.T.; Nagasundaram, N.; Ou, Y.Y.; Yeh, H.Y. iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou’s 5-step rule and word embedding. Anal. Biochem.,  2019, 571, 53-61.
[http://dx.doi.org/10.1016/j.ab.2019.02.017] [PMID:  30822398] 
[69] 
Le, N.Q.K.; Yapp, E.K.Y.; Ou, Y.Y.; Yeh, H.Y. iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou’s 5-step rule. Anal. Biochem.,  2019, 575, 17-26.
[http://dx.doi.org/10.1016/j.ab.2019.03.017] [PMID:  30930199] 
[70] 
Ning, Q.; Ma, Z.; Zhao, X. dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components. J. Theor. Biol.,  2019, 470, 43-49.
[http://dx.doi.org/10.1016/j.jtbi.2019.03.011] [PMID:  30880183] 
[71] 
Tahir, M.; Tayara, H.; Chong, K.T. iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou’s 5-step rule. CHEMOLAB,  2019, 189, 96-101.
[http://dx.doi.org/10.1016/j.chemolab.2019.04.007] 
[72] 
Ehsan, A.; Mahmood, M.K.; Khan, Y.D.; Barukab, O.M.; Khan, S.A.; Chou, K.C. 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), 124-133.
[http://dx.doi.org/10.2174/1389202920666190325162307] [PMID:  31555063] 
[73] 
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, 51-58.
[http://dx.doi.org/10.1016/j.jtbi.2018.10.046] [PMID:  30365947] 
[74] 
Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol.,  2011, 273(1), 236-247.
[http://dx.doi.org/10.1016/j.jtbi.2010.12.024] [PMID:  21168420] 
[75] 
Zhai, X.; Chen, M.; Lu, W. Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods. Comput. Mater. Sci.,  2018, 151, 41-48.
[http://dx.doi.org/10.1016/j.commatsci.2018.04.031] 
[76] 
Chou, K.C.; Zhang, C.T. 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] 
[77] 
Zhang, C.T.; Chou, K.C. Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. Biophys. J.,  1992, 63(6), 1523-1529.
[http://dx.doi.org/10.1016/S0006-3495(92)81728-9] [PMID:  1297323] 
[78] 
Chou, J.J.; Zhang, C.T. A joint prediction of the folding types of 1490 human proteins from their genetic codons. J. Theor. Biol.,  1993, 161(2), 251-262.
[http://dx.doi.org/10.1006/jtbi.1993.1053] [PMID:  8331952] 
[79] 
Chou, K.C. Prediction of protein folding types from amino acid composition by correlation angles. Amino Acids,  1994, 6(3), 231-246.
[http://dx.doi.org/10.1007/BF00813744] [PMID:  24189732] 
[80] 
Chou, K.C.; Zhang, C.T. A new approach to predicting protein folding types. J. Protein Chem.,  1993, 12(2), 169-178.
[http://dx.doi.org/10.1007/BF01026038] [PMID:  8489703] 
[81] 
Chou, K.C.; Zhang, C.T. Predicting protein folding types by distance functions that make allowances for amino acid interactions. J. Biol. Chem.,  1994, 269(35), 22014-22020.
[PMID:  8071322] 
[82] 
Zhang, C.T.; Chou, K.C. Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. II. Correlative effect. J. Protein Chem.,  1995, 14(4), 251-258.
[http://dx.doi.org/10.1007/BF01886766] [PMID:  7662113] 
[83] 
Zhang, C.T.; Chou, K.C. An eigenvalue-eigenvector approach to predicting protein folding types. J. Protein Chem.,  1995, 14(5), 309-326.
[http://dx.doi.org/10.1007/BF01886788] [PMID:  8590599] 
[84] 
Kikuchi, T. Discrimination of folding types of globular proteins based on average distance maps constructed from their sequences. J. Protein Chem.,  1993, 12(5), 515-523.
[http://dx.doi.org/10.1007/BF01025116] [PMID:  8141996] 
[85] 
Chou, K.C. Does the folding type of a protein depend on its amino acid composition? FEBS Lett.,  1995, 363(1-2), 127-131.
[http://dx.doi.org/10.1016/0014-5793(95)00245-5] [PMID:  7729532] 
[86] 
Zhang, C.T.; Chou, K.C. An analysis of protein folding type prediction by seed-propagated sampling and jackknife test. J. Protein Chem.,  1995, 14(7), 583-593.
[http://dx.doi.org/10.1007/BF01886884] [PMID:  8561854] 
[87] 
Zhang, C.T.; Chou, K.C.; Maggiora, G.M. Predicting protein structural classes from amino acid composition: application of fuzzy clustering. Protein Eng.,  1995, 8(5), 425-435.
[http://dx.doi.org/10.1093/protein/8.5.425] [PMID:  8532663] 
[88] 
Devillers, J. In neural networks in qsar and drug design, 1st Ed.;; Elsevier: Amsterdam, 1996. 
[http://dx.doi.org/10.1016/B978-0-12-213815-7.X5000-6] 
[89] 
Liu, W.M.; Chou, K.C. Prediction of protein structural classes by modified mahalanobis discriminant algorithm. J. Protein Chem.,  1998, 17(3), 209-217.
[http://dx.doi.org/10.1023/A:1022576400291] [PMID:  9588944] 
[90] 
Chou, K.C. A key driving force in determination of protein structural classes. Biochem. Biophys. Res. Commun.,  1999, 264(1), 216-224.
[http://dx.doi.org/10.1006/bbrc.1999.1325] [PMID:  10527868] 
[91] 
Chou, K.C. 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] 
[92] 
Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Prediction of protein structural classes by support vector machines. Comput. Chem.,  2002, 26(3), 293-296.
[http://dx.doi.org/10.1016/S0097-8485(01)00113-9] [PMID:  11868916] 
[93] 
Shen, H.B.; Yang, J.; Liu, X.J.; Chou, K.C. Using supervised fuzzy clustering to predict protein structural classes. Biochem. Biophys. Res. Commun.,  2005, 334(2), 577-581.
[http://dx.doi.org/10.1016/j.bbrc.2005.06.128] [PMID:  16023077] 
[94] 
Du, Q.S.; Jiang, Z.Q.; He, W.Z.; Li, D.P.; Chou, K.C. Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction. J. Biomol. Struct. Dyn.,  2006, 23(6), 635-640.
[http://dx.doi.org/10.1080/07391102.2006.10507088] [PMID:  16615809] 
[95] 
Xiao, X.; Wang, P.; Chou, K.C. Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. J. Theor. Biol.,  2008, 254(3), 691-696.
[http://dx.doi.org/10.1016/j.jtbi.2008.06.016] [PMID:  18634802] 
[96] 
Zhang, T.L.; Ding, Y.S.; Chou, K.C. Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. J. Theor. Biol.,  2008, 250(1), 186-193.
[http://dx.doi.org/10.1016/j.jtbi.2007.09.014] [PMID:  17959199] 
[97] 
Chou, K.C.; Liu, W.M.; Maggiora, G.M.; Zhang, C.T. Prediction and classification of domain structural classes. Proteins,  1998, 31(1), 97-103.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19980401)31:1<97:AID-PROT8>3.0.CO;2-E] [PMID:  9552161] 
[98] 
Chen, C.; Tian, Y.X.; Zou, X.Y.; Cai, P.X.; Mo, J.Y. Using pseudo-amino acid composition and support vector machine to predict protein structural class. J. Theor. Biol.,  2006, 243(3), 444-448.
[http://dx.doi.org/10.1016/j.jtbi.2006.06.025] [PMID:  16908032] 
[99] 
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), 116-121.
[http://dx.doi.org/10.1016/j.ab.2006.07.022] [PMID:  16920060] 
[100] 
Xiao, X.; Shao, S.H.; Huang, Z.D.; Chou, K.C. Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. J. Comput. Chem.,  2006, 27(4), 478-482.
[http://dx.doi.org/10.1002/jcc.20354] [PMID:  16429410] 
[101] 
Lin, H.; Li, Q.Z. Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J. Comput. Chem.,  2007, 28(9), 1463-1466.
[http://dx.doi.org/10.1002/jcc.20554] [PMID:  17330882] 
[102] 
Zhang, T.L.; Ding, Y.S. Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes. Amino Acids,  2007, 33(4), 623-629.
[http://dx.doi.org/10.1007/s00726-007-0496-1] [PMID:  17308864] 
[103] 
Xiao, X.; Lin, W.Z.; Chou, K.C. Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. J. Comput. Chem.,  2008, 29(12), 2018-2024.
[http://dx.doi.org/10.1002/jcc.20955] [PMID:  18381630] 
[104] 
Li, Z.C.; Zhou, X.B.; Dai, Z.; Zou, X.Y. 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), 415-425.
[http://dx.doi.org/10.1007/s00726-008-0170-2] [PMID:  18726140] 
[105] 
Sahu, S.S.; Panda, G. A novel feature representation method based on Chou’s pseudo amino acid composition for protein structural class prediction. Comput. Biol. Chem.,  2010, 34(5-6), 320-327.
[http://dx.doi.org/10.1016/j.compbiolchem.2010.09.002] [PMID:  21106461] 
[106] 
Wu, J.; Li, M.L.; Yu, L.Z.; 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), 62-67.
[http://dx.doi.org/10.1007/s10930-009-9222-z] [PMID:  20049515] 
[107] 
Chen, C.; Shen, Z.B.; Zou, X.Y. Dual-layer wavelet SVM for predicting protein structural class via the general form of Chou’s pseudo amino acid composition. Protein Pept. Lett.,  2012, 19(4), 422-429.
[http://dx.doi.org/10.2174/092986612799789332] [PMID:  22185506] 
[108] 
Kong, L.; Zhang, L.; Lv, J. Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2014, 344, 12-18.
[http://dx.doi.org/10.1016/j.jtbi.2013.11.021] [PMID:  24316044] 
[109] 
Zhang, L.; Zhao, X.; Kong, L. Predict protein structural class for low-similarity sequences by evolutionary difference information into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2014, 355, 105-110.
[http://dx.doi.org/10.1016/j.jtbi.2014.04.008] [PMID:  24735902] 
[110] 
Liang, Y.; Zhang, S. Predict protein structural class by incorporating two different modes of evolutionary information into Chou’s general pseudo amino acid composition. J. Mol. Graph. Model.,  2017, 78, 110-117.
[http://dx.doi.org/10.1016/j.jmgm.2017.10.003] [PMID:  29055184] 
[111] 
Yu, B.; Lou, L.; Li, S.; Zhang, Y.; Qiu, W.; Wu, X.; Wang, M.; Tian, B. Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising. J. Mol. Graph. Model.,  2017, 76, 260-273.
[http://dx.doi.org/10.1016/j.jmgm.2017.07.012] [PMID:  28743071] 
[112] 
Liao, B.; Xiang, Q.; Li, D. Incorporating secondary features into the general form of Chou’s PseAAC for predicting protein structural class. Protein Pept. Lett.,  2012, 19(11), 1133-1138.
[http://dx.doi.org/10.2174/092986612803217051] [PMID:  22185510] 
[113] 
Qin, Y.F.; Wang, C.H.; Yu, X.Q.; Zhu, J.; Liu, T.G.; Zheng, X.Q. Predicting protein structural class by incorporating patterns of over-represented k-mers into the general form of Chou’s PseAAC. Protein Pept. Lett.,  2012, 19(4), 388-397.
[http://dx.doi.org/10.2174/092986612799789350] [PMID:  22316305] 
[114] 
Zhang, S.L. Accurate prediction of protein structural classes by incorporating PSSS and PSSM into Chou’s general PseAAC. Chemom. Intell. Lab. Syst.,  2015, 142, 28-35.
[http://dx.doi.org/10.1016/j.chemolab.2015.01.004] 
[115] 
Tripathi, P.; Pandey, P.N. A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou’s pseudo amino acid composition. J. Theor. Biol.,  2017, 424, 49-54.
[http://dx.doi.org/10.1016/j.jtbi.2017.04.027] [PMID:  28476562] 
[116] 
Contreras-Torres, E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J. Theor. Biol.,  2018, 454, 139-145.
[http://dx.doi.org/10.1016/j.jtbi.2018.05.033] [PMID:  29870696] 
[117] 
Carlacci, L.; Chou, K.C.; Maggiora, G.M. A heuristic approach to predicting the tertiary structure of bovine somatotropin. Biochemistry,  1991, 30(18), 4389-4398.
[http://dx.doi.org/10.1021/bi00232a004] [PMID:  2021631] 
[118] 
Zhang, C.T.; Chou, K.C. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci.,  1992, 1(3), 401-408.
[http://dx.doi.org/10.1002/pro.5560010312] [PMID:  1304347] 
[119] 
Chou, K.C.; Elrod, D.W. Bioinformatical analysis of G-protein-coupled receptors. J. Proteome Res.,  2002, 1(5), 429-433.
[http://dx.doi.org/10.1021/pr025527k] [PMID:  12645914] 
[120] 
Chou, K.C.; Cai, Y.D. Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition. J. Cell. Biochem.,  2003, 90, 1250-1260.
[http://dx.doi.org/10.1002/jcb.10719] 
[121] 
Hu, L.; Huang, T.; Shi, X.; Lu, W.C.; Cai, Y.D.; Chou, K.C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One,  2011, 6(1)e14556
[http://dx.doi.org/10.1371/journal.pone.0014556] [PMID:  21283518] 
[122] 
Cai, Y.D.; Zhou, G.P.; Chou, K.C. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J.,  2003, 84(5), 3257-3263.
[http://dx.doi.org/10.1016/S0006-3495(03)70050-2] [PMID:  12719255] 
[123] 
Cai, Y.D.; Feng, K.Y.; Lu, W.C.; Chou, K.C. Using LogitBoost classifier to predict protein structural classes. J. Theor. Biol.,  2006, 238(1), 172-176.
[http://dx.doi.org/10.1016/j.jtbi.2005.05.034] [PMID:  16043193] 
[124] 
Chou, K.C. Impacts of bioinformatics to medicinal chemistry. Med. Chem.,  2015, 11(3), 218-234.
[http://dx.doi.org/10.2174/1573406411666141229162834] [PMID:  25548930] 
[125] 
Chou, K.C. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins,  2001, 43, 246-255.
[126] 
Chou, K.C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics,  2005, 21(1), 10-19.
[http://dx.doi.org/10.1093/bioinformatics/bth466] [PMID:  15308540] 
[127] 
Cai, Y.D.; Chou, K.C. 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), 407-411.
[http://dx.doi.org/10.1016/S0006-291X(03)00775-7] [PMID:  12745090] 
[128] 
Chou, K.C.; Cai, Y.D. Predicting protein quaternary structure by pseudo amino acid composition. Proteins,  2003, 53(2), 282-289.
[http://dx.doi.org/10.1002/prot.10500] [PMID:  14517979] 
[129] 
Chou, K.C.; Cai, Y.D. Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo-amino acid composition. J. Cell. Biochem.,  2004, 91(6), 1197-1203.
[http://dx.doi.org/10.1002/jcb.10790] [PMID:  15048874] 
[130] 
Cai, Y.D.; Chou, K.C. Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. J. Proteome Res.,  2005, 4(3), 967-971.
[http://dx.doi.org/10.1021/pr0500399] [PMID:  15952744] 
[131] 
Cai, Y.D.; Zhou, G.P.; Chou, K.C. Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition. J. Theor. Biol.,  2005, 234(1), 145-149.
[http://dx.doi.org/10.1016/j.jtbi.2004.11.017] [PMID:  15721043] 
[132] 
Cai, Y.D.; Chou, K.C. 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] 
[133] 
Chou, K.C.; Cai, Y.D. Prediction of protease types in a hybridization space. Biochem. Biophys. Res. Commun.,  2006, 339(3), 1015-1020. [BBRC]
[http://dx.doi.org/10.1016/j.bbrc.2005.10.196] [PMID:  16325146] 
[134] 
Chou, K.C.; Cai, Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome Res.,  2006, 5(2), 316-322.
[http://dx.doi.org/10.1021/pr050331g] [PMID:  16457597] 
[135] 
Chou, K.C.; Cai, Y.D.; Zhong, W.Z. Predicting networking couples for metabolic pathways of Arabidopsis. BMC Bioinformatics,  2010, 11, 293.
[136] 
He, Z.; Zhang, J.; Shi, X.H.; Hu, L.L.; Kong, X.; Cai, Y.D.; Chou, K.C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS One,  2010, 5(3)e9603
[http://dx.doi.org/10.1371/journal.pone.0009603] [PMID:  20300175] 
[137] 
Huang, T.; Shi, X.H.; Wang, P.; He, Z.; Feng, K.Y.; Hu, L.; Kong, X.; Li, Y.X.; Cai, Y.D.; Chou, K.C. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS One,  2010, 5(6)e10972
[http://dx.doi.org/10.1371/journal.pone.0010972] [PMID:  20532046] 
[138] 
Li, B.Q.; Hu, L.L.; Niu, S.; Cai, Y.D.; Chou, K.C. Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches. J. Proteomics,  2012, 75(5), 1654-1665.
[http://dx.doi.org/10.1016/j.jprot.2011.12.003] [PMID:  22178444] 
[139] 
Zheng, L.L.; Li, Y.X.; Ding, J.; Guo, X.K.; Feng, K.Y.; Wang, Y.J.; Hu, L.L.; Cai, Y.D.; Hao, P.; Chou, K.C. A comparison of computational methods for identifying virulence factors. PLoS One,  2012, 7(8)e42517
[http://dx.doi.org/10.1371/journal.pone.0042517] [PMID:  22880014] 
[140] 
Liu, B.; Zhang, D.; Xu, R.; Xu, J.; Wang, X.; Chen, Q.; Dong, Q.; Chou, K.C. Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection. Bioinformatics,  2014, 30(4), 472-479.
[http://dx.doi.org/10.1093/bioinformatics/btt709] [PMID:  24318998] 
[141] 
Xu, R.; Zhou, J.; Liu, B.; He, Y.; Zou, Q.; Wang, X.; Chou, K.C. Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach. J. Biomol. Struct. Dyn.,  2015, 33(8), 1720-1730.
[http://dx.doi.org/10.1080/07391102.2014.968624] [PMID:  25252709] 
[142] 
Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. J. Theor. Biol.,  2015, 364, 284-294.
[http://dx.doi.org/10.1016/j.jtbi.2014.09.029] [PMID:  25264267] 
[143] 
Behbahani, M.; Mohabatkar, H.; Nosrati, M. Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou’s general pseudo amino acid composition. J. Theor. Biol.,  2016, 411, 1-5.
[http://dx.doi.org/10.1016/j.jtbi.2016.09.001] [PMID:  27615149] 
[144] 
Kabir, M.; Hayat, M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol. Genet. Genomics,  2016, 291(1), 285-296.
[http://dx.doi.org/10.1007/s00438-015-1108-5] [PMID:  26319782] 
[145] 
Meher, P.K.; Sahu, T.K.; Saini, V.; Rao, A.R. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep.,  2017, 7, 42362.
[http://dx.doi.org/10.1038/srep42362] [PMID:  28205576] 
[146] 
Ju, Z.; He, J.J. Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou’s PseAAC. J. Mol. Graph. Model.,  2017, 76, 356-363.
[http://dx.doi.org/10.1016/j.jmgm.2017.07.022] [PMID:  28763688] 
[147] 
Yu, B.; Li, S.; Qiu, W.Y.; Chen, C.; Chen, R.X.; Wang, L.; Wang, M.H.; Zhang, Y. Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget,  2017, 8(64), 107640-107665.
[http://dx.doi.org/10.18632/oncotarget.22585] [PMID:  29296195] 
[148] 
Zhang, S.; Yang, K.; Lei, Y.; Song, K. iRSpot-DTS: Predict recombination
   spots by incorporating the dinucleotide-based sparecross
  covariance information into Chou’s pseudo components. Genomics,  2018, S0888-7543(18), 30491-304999.
[http://dx.doi.org/10.1016/j.ygeno.2018.11.031] [PMID: 30529702] 
[149] 
Ahmad, J.; Hayat, M. MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J. Theor. Biol.,  2019, 463, 99-109.
[http://dx.doi.org/10.1016/j.jtbi.2018.12.017] [PMID:  30562500] 
[150] 
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, 205-211.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.018] [PMID:  30031793] 
[151] 
Zhang, L.; Kong, L. iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou’s pseudo components. J. Theor. Biol.,  2018, 441, 1-8.
[http://dx.doi.org/10.1016/j.jtbi.2017.12.025] [PMID:  29305179] 
[152] 
Zhang, S.; Liang, Y. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J. Theor. Biol.,  2018, 457, 163-169.
[http://dx.doi.org/10.1016/j.jtbi.2018.08.042] [PMID:  30179589] 
[153] 
Zhang, L.; Kong, L. iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou’s pseudo components. Genomics,  2019, 111(3), 457-464.
[http://dx.doi.org/10.1016/j.ygeno.2018.03.003] [PMID:  29548799] 
[154] 
Tahir, M.; Hayat, M.; Khan, S.A. iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition. Mol. Genet. Genomics,  2019, 294(1), 199-210.
[http://dx.doi.org/10.1007/s00438-018-1498-2] [PMID:  30291426] 
[155] 
Chou, K.C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem.,  2017, 17(21), 2337-2358.
[http://dx.doi.org/10.2174/1568026617666170414145508] [PMID:  28413951] 
[156] 
Shen, H.B.; Chou, K.C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem.,  2008, 373(2), 386-388.
[http://dx.doi.org/10.1016/j.ab.2007.10.012] [PMID:  17976365] 
[157] 
Du, P.; Wang, X.; Xu, C.; Gao, Y. PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions. Anal. Biochem.,  2012, 425(2), 117-119.
[http://dx.doi.org/10.1016/j.ab.2012.03.015] [PMID:  22459120] 
[158] 
Cao, D.S.; Xu, Q.S.; Liang, Y.Z. propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics,  2013, 29(7), 960-962.
[http://dx.doi.org/10.1093/bioinformatics/btt072] [PMID:  23426256] 
[159] 
Du, P.; Gu, S.; Jiao, Y. PseAAC-General: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci.,  2014, 15(3), 3495-3506.
[http://dx.doi.org/10.3390/ijms15033495] [PMID:  24577312] 
[160] 
Chou, K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics,  2009, 6, 262-274.
[http://dx.doi.org/10.2174/157016409789973707] 
[161] 
Chen, W.; Lei, T.Y.; Jin, D.C.; Lin, H.; Chou, K.C. 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] 
[162] 
Chen, W.; Lin, H.; Chou, K.C. Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences. Mol. Biosyst.,  2015, 11(10), 2620-2634.
[http://dx.doi.org/10.1039/C5MB00155B] [PMID:  26099739] 
[163] 
Liu, B.; Yang, F.; Huang, D.S.; Chou, K.C. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics,  2018, 34(1), 33-40.
[http://dx.doi.org/10.1093/bioinformatics/btx579] [PMID:  28968797] 
[164] 
Tahir, M.; Tayara, H.; Chong, K.T. iRNA-PseKNC(2methyl): Identify RNA 2′-O-methylation sites by convolution neural network and Chou’s pseudo components. J. Theor. Biol.,  2019, 465, 1-6.
[http://dx.doi.org/10.1016/j.jtbi.2018.12.034] [PMID:  30590059] 
[165] 
Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L.; Chou, K.C. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res.,  2015, 43(W1)W65-71
[http://dx.doi.org/10.1093/nar/gkv458] [PMID:  25958395] 
[166] 
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] 
[167] 
Chou, K.C.; Cai, Y.D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem.,  2002, 277(48), 45765-45769.
[http://dx.doi.org/10.1074/jbc.M204161200] [PMID:  12186861] 
[168] 
Chou, K.C.; Shen, H.B. Recent progress in protein subcellular location prediction. Anal. Biochem.,  2007, 370(1), 1-16.
[http://dx.doi.org/10.1016/j.ab.2007.07.006] [PMID:  17698024] 
[169] 
Shen, H.B.; Chou, K.C. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal. Biochem.,  2009, 394(2), 269-274.
[http://dx.doi.org/10.1016/j.ab.2009.07.046] [PMID:  19651102] 
[170] 
Shen, H.B.; Chou, K.C. Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins. Protein Pept. Lett.,  2009, 16(12), 1478-1484.
[http://dx.doi.org/10.2174/092986609789839322] [PMID:  20001911] 
[171] 
Chou, K.C.; Shen, H.B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One,  2010, 5(4)e9931
[http://dx.doi.org/10.1371/journal.pone.0009931] [PMID:  20368981] 
[172] 
Chou, K.C.; Shen, H.B. Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One,  2010, 5(6)e11335
[http://dx.doi.org/10.1371/journal.pone.0011335] [PMID:  20596258] 
[173] 
Shen, H.B.; Chou, K.C. Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. J. Theor. Biol.,  2010, 264(2), 326-333.
[http://dx.doi.org/10.1016/j.jtbi.2010.01.018] [PMID:  20093124] 
[174] 
Shen, H.B.; Chou, K.C. Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J. Biomol. Struct. Dyn.,  2010, 28(2), 175-186.
[http://dx.doi.org/10.1080/07391102.2010.10507351] [PMID:  20645651] 
[175] 
Chou, K.C.; Wu, Z.C.; 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] 
[176] 
Wu, Z.C.; Xiao, X.; Chou, K.C. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol. Biosyst.,  2011, 7(12), 3287-3297.
[http://dx.doi.org/10.1039/c1mb05232b] [PMID:  21984117] 
[177] 
Xiao, X.; Wu, Z.C.; Chou, K.C. iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol.,  2011, 284(1), 42-51.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.005] [PMID:  21684290] 
[178] 
Chou, K.C.; Wu, Z.C.; Xiao, X. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. Biosyst.,  2012, 8(2), 629-641.
[http://dx.doi.org/10.1039/C1MB05420A] [PMID:  22134333] 
[179] 
Xiao, X.; Wu, Z.C.; Chou, K.C. A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PLoS One,  2011, 6(6)e20592
[http://dx.doi.org/10.1371/journal.pone.0020592] [PMID:  21698097] 
[180] 
Wu, Z.C.; Xiao, X.; Chou, K.C. 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] 
[181] 
Lin, W.Z.; Fang, J.A.; Xiao, X.; Chou, K.C. iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Mol. Biosyst.,  2013, 9(4), 634-644.
[http://dx.doi.org/10.1039/c3mb25466f] [PMID:  23370050] 
[182] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol. Biosyst.,  2017, 13(9), 1722-1727.
[http://dx.doi.org/10.1039/C7MB00267J] [PMID:  28702580] 
[183] 
Cheng, X.; Xiao, X. pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene,  2017, 628, 315-321.
[184] 
Cheng, X.; Zhao, S.G.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics,  2017, 33(22), 3524-3531.
[http://dx.doi.org/10.1093/bioinformatics/btx476] [PMID:  29036535] 
[185] 
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, 331-349.
[http://dx.doi.org/10.4236/ns.2017.99032] 
[186] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics,  2018, 110(1), 50-58.
[http://dx.doi.org/10.1016/j.ygeno.2017.08.005] [PMID:  28818512] 
[187] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics,  2017, 110, 231-239.
[http://dx.doi.org/10.1016/j.ygeno.2017.10.002] [PMID:  28989035] 
[188] 
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics,  2018, 34(9), 1448-1456.
[http://dx.doi.org/10.1093/bioinformatics/btx711] [PMID:  29106451] 
[189] 
Cheng, X.; Xiao, X.; Chou, K.C. 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] 
[190] 
Chou, K.C.; Cheng, X.; Xiao, X. pLoc_bal-mHum: Predict subcellular
  localization of human proteins by PseAAC and quasibalancing
  training dataset. Genomics,  2018, S0888-7543(18), 30276-3.
[http://dx.doi.org/10.1016/j.ygeno.2018.08.007] [PMID:  30179658] 
[191] 
Cheng, X.; Lin, W.Z.; Xiao, X.; Chou, K.C. 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] 
[192] 
Chou, K.C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. Biosyst.,  2013, 9(6), 1092-1100.
[http://dx.doi.org/10.1039/c3mb25555g] [PMID:  23536215] 
[193] 
Shen, H.; Chou, K.C. 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), 288-292.
[http://dx.doi.org/10.1016/j.bbrc.2005.06.087] [PMID:  16002049] 
[194] 
Chou, K.C.; Shen, H.B. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J. Proteome Res.,  2007, 6(5), 1728-1734.
[http://dx.doi.org/10.1021/pr060635i] [PMID:  17397210] 
[195] 
Shen, H.B.; Chou, K.C. QuatIdent: a web server for identifying protein quaternary structural attribute by fusing functional domain and sequential evolution information. J. Proteome Res.,  2009, 8(3), 1577-1584.
[http://dx.doi.org/10.1021/pr800957q] [PMID:  19226167] 
[196] 
Xu, Y.; Ding, J.; Wu, L.Y.; Chou, K.C. 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] 
[197] 
Chou, K.C. Prediction of protein signal sequences and their cleavage sites. Proteins,  2001, 42(1), 136-139.
[http://dx.doi.org/10.1002/1097-0134(20010101)42:1<136:AID-PROT130>3.0.CO;2-F] [PMID:  11093267] 
[198] 
Chou, K.C. Using subsite coupling to predict signal peptides. Protein Eng.,  2001, 14(2), 75-79.
[http://dx.doi.org/10.1093/protein/14.2.75] [PMID:  11297664] 
[199] 
Chou, K.C. Prediction of signal peptides using scaled window. Peptides,  2001, 22(12), 1973-1979.
[http://dx.doi.org/10.1016/S0196-9781(01)00540-X] [PMID:  11786179] 
[200] 
Chou, K.C.; Zhang, C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol.,  1995, 30(4), 275-349.
[http://dx.doi.org/10.3109/10409239509083488] [PMID:  7587280] 
[201] 
Chou, K.C.; Shen, H.B. Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc.,  2008, 3(2), 153-162.
[http://dx.doi.org/10.1038/nprot.2007.494] [PMID:  18274516] 
[202] 
Chou, K.C.; Shen, H.B. Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat. Sci.,  2010, 2, 1090-1103.
[http://dx.doi.org/10.4236/ns.2010.210136] 
[203] 
Zhou, G.P.; Assa-Munt, N. Some insights into protein structural class prediction. Proteins,  2001, 44(1), 57-59.
[http://dx.doi.org/10.1002/prot.1071] [PMID:  11354006] 
[204] 
Mohabatkar, H. Prediction of cyclin proteins using Chou’s pseudo amino acid composition. Protein Pept. Lett.,  2010, 17(10), 1207-1214.
[http://dx.doi.org/10.2174/092986610792231564] [PMID:  20450487] 
[205] 
Zhou, G.P.; Doctor, K. Subcellular location prediction of apoptosis proteins. Proteins,  2003, 50(1), 44-48.
[http://dx.doi.org/10.1002/prot.10251] [PMID:  12471598] 
[206] 
Zia-Ur-Rehman. Khan, A. Identifying GPCRs and their types with Chou’s pseudo amino acid composition: an approach from multi-scale energy representation and position specific scoring matrix. Protein Pept. Lett.,  2012, 19(8), 890-903.
[http://dx.doi.org/10.2174/092986612801619589] [PMID:  22316312] 
[207] 
Fan, G.L.; Li, Q.Z. Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol.,  2013, 334, 45-51.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.003] [PMID:  23770403] 
[208] 
Huang, C.; Yuan, J.Q. Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions. J. Theor. Biol.,  2013, 335, 205-212.
[http://dx.doi.org/10.1016/j.jtbi.2013.06.034] [PMID:  23850480] 
[209] 
Hajisharifi, Z.; Piryaiee, M.; Mohammad Beigi, M.; Behbahani, M.; Mohabatkar, H. Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test. J. Theor. Biol.,  2014, 341, 34-40.
[http://dx.doi.org/10.1016/j.jtbi.2013.08.037] [PMID:  24035842] 
[210] 
Xie, H.L.; Fu, L.; Nie, X.D. 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), 735-742.
[http://dx.doi.org/10.1093/protein/gzt042] [PMID:  24048266] 
[211] 
Xu, Y.; Shao, X.J.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ,  2013, 1e171
[http://dx.doi.org/10.7717/peerj.171] [PMID:  24109555] 
[212] 
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), 10410-10423.
[http://dx.doi.org/10.3390/ijms150610410] [PMID:  24918295] 
[213] 
Qiu, W.R.; Xiao, X.; Lin, W.Z.; Chou, K.C. iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach. BioMed Res. Int.,  2014, 2014947416
[http://dx.doi.org/10.1155/2014/947416] [PMID:  24977164] 
[214] 
Xu, Y.; Wen, X.; Shao, X.J.; Deng, N.Y.; Chou, K.C. iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition. Int. J. Mol. Sci.,  2014, 15(5), 7594-7610.
[http://dx.doi.org/10.3390/ijms15057594] [PMID:  24857907] 
[215] 
Xu, Y.; Wen, X.; Wen, L.S.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition. PLoS One,  2014, 9(8)e105018
[http://dx.doi.org/10.1371/journal.pone.0105018] [PMID:  25121969] 
[216] 
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), 11204-11219.
[http://dx.doi.org/10.3390/ijms150711204] [PMID:  24968264] 
[217] 
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K.C. 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] 
[218] 
Qiu, W.R.; Xiao, X.; Lin, W.Z. iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model. J. Biomol. Struct. Dyn. (JBSD) ,  2015, 33, 1731-1742.
[219] 
Chen, W.; Tang, H.; Ye, J.; Lin, H.; Chou, K.C. iRNA-PseU: Identifying RNA pseudouridine sites. Mol. Ther. Nucleic Acids,  2016, 5e332
[PMID:  28427142] 
[220] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol.,  2016, 394, 223-230.
[http://dx.doi.org/10.1016/j.jtbi.2016.01.020] [PMID:  26807806] 
[221] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget,  2016, 7(23), 34558-34570.
[http://dx.doi.org/10.18632/oncotarget.9148] [PMID:  27153555] 
[222] 
Ju, Z.; Cao, J.Z.; Gu, H. Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. J. Theor. Biol.,  2016, 397, 145-150.
[http://dx.doi.org/10.1016/j.jtbi.2016.02.020] [PMID:  26908349] 
[223] 
Liu, Z.; Xiao, X.; Yu, D.J.; Jia, J.; Qiu, W.R.; Chou, K.C. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal. Biochem.,  2016, 497, 60-67.
[http://dx.doi.org/10.1016/j.ab.2015.12.017] [PMID:  26748145] 
[224] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Chou, K.C. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget,  2016, 7(28), 44310-44321.
[http://dx.doi.org/10.18632/oncotarget.10027] [PMID:  27322424] 
[225] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics,  2016, 32(20), 3116-3123.
[http://dx.doi.org/10.1093/bioinformatics/btw380] [PMID:  27334473] 
[226] 
Qiu, W.R.; Xiao, X.; Xu, Z.C.; Chou, K.C. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget,  2016, 7(32), 51270-51283.
[http://dx.doi.org/10.18632/oncotarget.9987] [PMID:  27323404] 
[227] 
Xu, Y.; Chou, K.C. 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] 
[228] 
Feng, P.; Ding, H.; Yang, H.; Chen, W.; Lin, H.; Chou, K.C. iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Mol. Ther. Nucleic Acids,  2017, 7, 155-163.
[http://dx.doi.org/10.1016/j.omtn.2017.03.006] [PMID:  28624191] 
[229] 
Ju, Z.; He, J.J. Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou’s general PseAAC. J. Mol. Graph. Model.,  2017, 77, 200-204.
[http://dx.doi.org/10.1016/j.jmgm.2017.08.020] [PMID:  28886434] 
[230] 
Liu, L.M.; Xu, Y.; Chou, K.C. iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general PseAAC. Med. Chem.,  2017, 13(6), 552-559.
[http://dx.doi.org/10.2174/1573406413666170515120507] [PMID:  28521678] 
[231] 
Qiu, W.R.; Jiang, S.Y.; Sun, B.Q.; Xiao, X.; Cheng, X.; Chou, K.C. iRNA-2methyl: identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier. Med. Chem.,  2017, 13(8), 734-743.
[http://dx.doi.org/10.2174/1573406413666170623082245] [PMID:  28641529] 
[232] 
Qiu, W.R.; Jiang, S.Y.; Xu, Z.C.; Xiao, X.; Chou, K.C. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition. Oncotarget,  2017, 8(25), 41178-41188.
[http://dx.doi.org/10.18632/oncotarget.17104] [PMID:  28476023] 
[233] 
Qiu, W.R.; Sun, B.Q.; Xiao, X.; Xu, D. iPhos-PseEvo: Identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory. Mol. Inform.,  2017, 36, 5-6.
[234] 
Xu, Y.; Wang, Z.; Li, C.; Chou, K.C. iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC. Med. Chem.,  2017, 13(6), 544-551.
[http://dx.doi.org/10.2174/1573406413666170419150052] [PMID:  28425870] 
[235] 
Chandra, A.; Sharma, A.; Dehzangi, A.; Ranganathan, S.; Jokhan, A.; Chou, K.C.; Tsunoda, T. PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Sci. Rep.,  2018, 8(1), 17923.
[http://dx.doi.org/10.1038/s41598-018-36203-8] [PMID:  30560923] 
[236] 
Ju, Z.; Wang, S.Y. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene,  2018, 664, 78-83.
[http://dx.doi.org/10.1016/j.gene.2018.04.055] [PMID:  29694908] 
[237] 
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K.C. iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC. Anal. Biochem.,  2018, 550, 109-116.
[http://dx.doi.org/10.1016/j.ab.2018.04.021] [PMID:  29704476] 
[238] 
Khan, Y.D.; Rasool, N.; Hussain, W.; Khan, S.A.; Chou, K.C. iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Mol. Biol. Rep.,  2018, 45(6), 2501-2509.
[http://dx.doi.org/10.1007/s11033-018-4417-z] [PMID:  30311130] 
[239] 
Sabooh, M.F.; Iqbal, N.; Khan, M.; Khan, M.; Maqbool, H.F. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC. J. Theor. Biol.,  2018, 452, 1-9.
[http://dx.doi.org/10.1016/j.jtbi.2018.04.037] [PMID:  29727634] 
[240] 
Li, F.; Zhang, Y.; Purcell, A.W.; Webb, G.I.; Chou, K.C.; Lithgow, T.; Li, C.; Song, J. 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] 
[241] 
Chou, K.C.; Shen, H.B. Recent advances in developing web-servers for predicting protein attributes. Nat. Sci.,  2009, 1, 63-92.
[http://dx.doi.org/10.4236/ns.2009.12011] 
[242] 
Liu, B.; Xu, J.; Lan, X.; Xu, R.; Zhou, J.; Wang, X.; Chou, K.C. iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS One,  2014, 9(9)e106691
[http://dx.doi.org/10.1371/journal.pone.0106691] [PMID:  25184541] 
[243] 
Fan, Y.N.; Xiao, X.; Min, J.L.; Chou, K.C. iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking. Int. J. Mol. Sci.,  2014, 15(3), 4915-4937.
[http://dx.doi.org/10.3390/ijms15034915] [PMID:  24651462] 
[244] 
Guo, S.H.; Deng, E.Z.; Xu, L.Q.; Ding, H.; Lin, H.; Chen, W.; Chou, K.C. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics,  2014, 30(11), 1522-1529.
[http://dx.doi.org/10.1093/bioinformatics/btu083] [PMID:  24504871] 
[245] 
Qiu, W.R.; Xiao, X.; Chou, K.C. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci.,  2014, 15(2), 1746-1766.
[http://dx.doi.org/10.3390/ijms15021746] [PMID:  24469313] 
[246] 
Chen, W.; Feng, P.M.; Lin, H.; Chou, K.C. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res. Int.,  2014, 2014623149
[http://dx.doi.org/10.1155/2014/623149] [PMID:  24967386] 
[247] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. 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] 
[248] 
Liu, B.; Fang, L.; Liu, F.; Wang, X.; Chen, J.; Chou, K.C. 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] 
[249] 
Xiao, X.; Ye, H.X.; Liu, Z.; Jia, J.H.; Chou, K.C. iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget,  2016, 7(23), 34180-34189.
[http://dx.doi.org/10.18632/oncotarget.9057] [PMID:  27147572] 
[250] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. iPPBS-Opt: A sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets. Molecules,  2016, 21(1)E95
[http://dx.doi.org/10.3390/molecules21010095] [PMID:  26797600] 
[251] 
Zhang, C.J.; Tang, H.; Li, W.C.; Lin, H.; Chen, W.; Chou, K.C. iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition. Oncotarget,  2016, 7(43), 69783-69793.
[http://dx.doi.org/10.18632/oncotarget.11975] [PMID:  27626500] 
[252] 
Liu, B.; Fang, L.; Liu, F.; Wang, X.; Chou, K.C. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. J. Biomol. Struct. Dyn.,  2016, 34(1), 223-235.
[http://dx.doi.org/10.1080/07391102.2015.1014422] [PMID:  25645238] 
[253] 
Liu, B.; Long, R.; Chou, K.C. 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), 2411-2418.
[http://dx.doi.org/10.1093/bioinformatics/btw186] [PMID:  27153623] 
[254] 
Jia, J.; Liu, Z.; Xiao, X.; Liu, B.; Chou, K.C. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition. J. Biomol. Struct. Dyn.,  2016, 34(9), 1946-1961. [JBSD]
[http://dx.doi.org/10.1080/07391102.2015.1095116] [PMID: 26375780] 
[255] 
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget,  2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID:  26942877] 
[256] 
Chen, J.; Long, R.; Wang, X.L.; Liu, B.; Chou, K.C. dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation. Sci. Rep.,  2016, 6, 32333.
[http://dx.doi.org/10.1038/srep32333] [PMID:  27581095] 
[257] 
Liu, B.; Wu, H.; Zhang, D.; Wang, X.; Chou, K.C. Pse-Analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget,  2017, 8(8), 13338-13343.
[http://dx.doi.org/10.18632/oncotarget.14524] [PMID:  28076851] 
[258] 
Wang, J.; Yang, B.; Revote, J.; Leier, A.; Marquez-Lago, T.T.; Webb, G.; Song, J.; Chou, K.C.; Lithgow, T. POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics,  2017, 33(17), 2756-2758.
[http://dx.doi.org/10.1093/bioinformatics/btx302] [PMID:  28903538] 
[259] 
Liu, B.; Wang, S.; Long, R.; Chou, K.C. 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] 
[260] 
Cheng, X.; Zhao, S.G.; Xiao, X. iATC-mISF: a multi-label classifier
 for predicting the classes of anatomical therapeutic chemicals. Bioinformatics (Corrigendum, ibid., 2017, Vol.33, 2610),  2017, 33, 341-346.
[261] 
Cheng, X.; Zhao, S.G.; Xiao, X.; Chou, K.C. iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget,  2017, 8(35), 58494-58503.
[http://dx.doi.org/10.18632/oncotarget.17028] [PMID:  28938573] 
[262] 
Liu, B.; Yang, F.; Chou, K.C. 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Mol. Ther. Nucleic Acids,  2017, 7, 267-277.
[http://dx.doi.org/10.1016/j.omtn.2017.04.008] [PMID:  28624202] 
[263] 
Wang, J.; Yang, B.; Leier, A.; Marquez-Lago, T.T.; Hayashida, M.; Rocker, A.; Zhang, Y.; Akutsu, T.; Chou, K.C.; Strugnell, R.A.; Song, J.; Lithgow, T. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics,  2018, 34(15), 2546-2555.
[http://dx.doi.org/10.1093/bioinformatics/bty155] [PMID:  29547915] 
[264] 
Liu, B.; Li, K.; Huang, D.S.; Chou, K.C. iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics,  2018, 34(22), 3835-3842.
[http://dx.doi.org/10.1093/bioinformatics/bty458] [PMID:  29878118] 
[265] 
Chen, Z.; Zhao, P.; Li, F.; Leier, A.; Marquez-Lago, T.T.; Wang, Y.; Webb, G.I.; Smith, A.I.; Daly, R.J.; Chou, K.C.; Song, J. iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics,  2018, 34(14), 2499-2502.
[http://dx.doi.org/10.1093/bioinformatics/bty140] [PMID:  29528364] 
[266] 
Su, Z.D.; Huang, Y.; Zhang, Z.Y.; Zhao, Y.W.; Wang, D.; Chen, W.; Chou, K.C.; Lin, H. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics,  2018, 34(24), 4196-4204.
[http://dx.doi.org/10.1093/bioinformatics/bty508] [PMID:  29931187] 
[267] 
Liu, B.; Weng, F.; Huang, D.S.; Chou, K.C. iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC. Bioinformatics,  2018, 34(18), 3086-3093.
[http://dx.doi.org/10.1093/bioinformatics/bty312] [PMID:  29684124] 
[268] 
Yang, H.; Qiu, W.R.; Liu, G.; Guo, F.B.; Chen, W.; Chou, K.C.; Lin, H. iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int. J. Biol. Sci.,  2018, 14(8), 883-891.
[http://dx.doi.org/10.7150/ijbs.24616] [PMID:  29989083] 
[269] 
Song, J.; Li, F.; Leier, A.; Marquez-Lago, T.T.; Akutsu, T.; Haffari, G.; Chou, K.C.; Webb, G.I.; Pike, R.N.; Hancock, J. PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. Bioinformatics,  2018, 34(4), 684-687.
[http://dx.doi.org/10.1093/bioinformatics/btx670] [PMID:  29069280] 
[270] 
Chou, K.C. Progress in protein structural class prediction and its impact to bioinformatics and proteomics. Curr. Protein Pept. Sci.,  2005, 6(5), 423-436.
[http://dx.doi.org/10.2174/138920305774329368] [PMID:  16248794] 
[271] 
Liu, B.; Liu, F.; Fang, L.; Wang, X.; Chou, K.C. 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), 1307-1309.
[http://dx.doi.org/10.1093/bioinformatics/btu820] [PMID:  25504848] 
[272] 
Liu, B.; Liu, F.; Fang, L.; Wang, X.; Chou, K.C. repRNA: a web server for generating various feature vectors of RNA sequences. Mol. Genet. Genomics,  2016, 291(1), 473-481.
[http://dx.doi.org/10.1007/s00438-015-1078-7] [PMID:  26085220] 
[273] 
Shyamili, V.K.; Vellaichamy, A. Sequence and structure-based characterization of human and yeast ubiquitination sites by using Chou’s sample formulation. Proteins,  2019, 87(4)
[http://dx.doi.org/10.1002/prot.25689] [PMID:  30958587] 
[274] 
Awais, M.; Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A. iPhosH-
 PseAAC: 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. [Epub ahead of
 print]
[http://dx.doi.org/10.1109/TCBB.2019.2919025] 
[275] 
Lu, F.; Zhu, M.; Lin, Y.; Zhong, H.; Cai, L.; He, L.; Chou, K.C. The preliminary efficacy evaluation of the CTLA-4-Ig treatment against Lupus nephritis through in-silico analyses. J. Theor. Biol.,  2019, 471, 74-81.
[http://dx.doi.org/10.1016/j.jtbi.2019.03.017] [PMID:  30928350] 
[276] 
Niu, B.; Liang, C.; Lu, Y.; Zhao, M.; Chen, Q.; Zhang, Y.; Zheng, L.; Chou, K.C. Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks. Genomics,  2019, S0888-7543(19), 30174-0.,  
[http://dx.doi.org/10.1016/j.ygeno.2019.05.024] [PMID: 31150762] 
[277] 
Chou, K.C.; Forsén, S. Diffusion-controlled effects in reversible enzymatic fast reaction systems--critical spherical shell and proximity rate constant. Biophys. Chem.,  1980, 12(3-4), 255-263.
[http://dx.doi.org/10.1016/0301-4622(80)80002-0] [PMID:  7225518] 
[278] 
Chou, K.C.; Li, T.T.; Forsén, S. The critical spherical shell in enzymatic fast reaction systems. Biophys. Chem.,  1980, 12(3-4), 265-269.
[http://dx.doi.org/10.1016/0301-4622(80)80003-2] [PMID:  7225519] 
[279] 
Li, T.T.; Forsen, S. The flow of substrate molecules in fast enzyme-catalyzed reaction systems. Chem. Scr.,  1980, 16, 192-196.
[280] 
Chou, K.C.; Forsén, S. Graphical rules for enzyme-catalysed rate laws. Biochem. J.,  1980, 187(3), 829-835.
[http://dx.doi.org/10.1042/bj1870829] [PMID:  7188428] 
[281] 
Forsen, S.; Zhou, G.Q. Three schematic rules for deriving apparent rate constants. Chem. Scr.,  1980, 16, 109-113.
[282] 
Carter, R.E.; Forsen, S. A new graphical method for deriving rate equations for complicated mechanisms. Chem. Scr.,  1981, 18, 82-86.
[283] 
Chou, K.C.; Forsen, S. Graphical rules of steady-state reaction systems. Can. J. Chem.,  1981, 59, 737-755.
[http://dx.doi.org/10.1139/v81-107] 
[284] 
Chen, N.Y.; Forsen, S. The biological functions of low-frequency phonons: 2. Cooperative effects. Chem. Scr.,  1981, 18, 126-132.
[285] 
Chou, K.C.; Chen, N.Y. The biological functions of low-frequency phonons. Sci. Sin.,  1977, 20, 447-457.
[PMID:  6487745] 
[286] 
Chou, K.C.; Jiang, S.P.; Liu, W.M.; Fee, C.H. Graph theory of enzyme kinetics: 1. Steady-state reaction system. Sci. Sin.,  1979, 22, 341-358.
[287] 
Chou, K.C. Low-frequency vibrations of helical structures in protein molecules. Biochem. J.,  1983, 209(3), 573-580.
[http://dx.doi.org/10.1042/bj2090573] [PMID:  6870784] 
[288] 
Chou, K.C. Identification of low-frequency modes in protein molecules. Biochem. J.,  1983, 215(3), 465-469.
[http://dx.doi.org/10.1042/bj2150465] [PMID:  6362659] 
[289] 
Zhou, G.P.; Deng, M.H. An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J.,  1984, 222(1), 169-176.
[http://dx.doi.org/10.1042/bj2220169] [PMID:  6477507] 
[290] 
Chou, K.C. Biological functions of low-frequency vibrations (phonons). Helical structures and microenvironment. Biophys. J.,  1984, 45(5), 881-889.
[http://dx.doi.org/10.1016/S0006-3495(84)84234-4] [PMID:  6428481] 
[291] 
Chou, K.C. The biological functions of low-frequency vibrations (phonons). 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] 
[292] 
Chou, K.C. Low-frequency vibrations of DNA molecules. Biochem. J.,  1984, 221(1), 27-31.
[http://dx.doi.org/10.1042/bj2210027] [PMID:  6466317] 
[293] 
Chou, K.C. Prediction of a low-frequency mode in BPTI. Int. J. Biol. Macromol.,  1985, 7, 77-80.
[http://dx.doi.org/10.1016/0141-8130(85)90035-2] 
[294] 
Chou, K.C. Low-frequency motions in protein molecules. Beta-sheet and beta-barrel. Biophys. J.,  1985, 48(2), 289-297.
[http://dx.doi.org/10.1016/S0006-3495(85)83782-6] [PMID:  4052563] 
[295] 
Chou, K.C. 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] 
[296] 
Chou, K.C.; Kiang, Y.S. The biological functions of low-frequency vibrations (phonons). A phenomenological theory. Biophys. Chem.,  1985, 22(3), 219-235.
[http://dx.doi.org/10.1016/0301-4622(85)80045-4] [PMID:  4052576] 
[297] 
Chou, K.C. Origin of low-frequency motions in biological macromolecules. A view of recent progress in the quasi-continuity model. Biophys. Chem.,  1986, 25(2), 105-116.
[http://dx.doi.org/10.1016/0301-4622(86)87001-6] [PMID:  3101760] 
[298] 
Chou, K.C. The biological functions of low-frequency vibrations (phonons). A possible dynamic mechanism of allosteric transition in antibody molecules. Biopolymers,  1987, 26(2), 285-295.
[http://dx.doi.org/10.1002/bip.360260209] [PMID:  3828475] 
[299] 
Chou, K.C. 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] 
[300] 
Maggiora, G.M. The biological functions of low-frequency phonons: The impetus for DNA to accommodate intercalators. Br. Polym. J.,  1988, 20, 143-148.
[http://dx.doi.org/10.1002/pi.4980200209] 
[301] 
Chou, K.C. Low-frequency resonance and cooperativity of hemoglobin. Trends Biochem. Sci.,  1989, 14(6), 212-213.
[http://dx.doi.org/10.1016/0968-0004(89)90026-1] [PMID:  2763333] 
[302] 
Chou, K.C.; Maggiora, G.M.; 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] 
[303] 
Chou, K.C. Graphic rules in steady and non-steady state enzyme kinetics. J. Biol. Chem.,  1989, 264(20), 12074-12079.
[PMID:  2745429] 
[304] 
Chou, K.C. 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] 
[305] 
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem.,  1993, 268(9), 6119-6124.
[PMID:  7681060] 
[306] 
Althaus, I.W.; Gonzales, A.J.; Chou, J.J.; Romero, D.L.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Resnick, L.; Busso, M.E.; So, A.G. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem.,  1993, 268(20), 14875-14880.
[PMID:  7686907] 
[307] 
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Palmer, J.R.; Thomas, R.C.; Aristoff, P.A. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry,  1993, 32(26), 6548-6554.
[http://dx.doi.org/10.1021/bi00077a008] [PMID:  7687145] 
[308] 
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; LeMay, R.J.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Thomas, R.C.; Aristoff, P.A. Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia,  1994, 50(1), 23-28.
[http://dx.doi.org/10.1007/BF01992044] [PMID:  7507441] 
[309] 
Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Deibel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Thomas, R.C.; Aristoff, P.A.; Tarpley, W.G. Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. Biochem. Pharmacol.,  1994, 47(11), 2017-2028.
[http://dx.doi.org/10.1016/0006-2952(94)90077-9] [PMID:  7516658] 
[310] 
Chou, K.C.; Kézdy, F.J.; Reusser, F. Kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem.,  1994, 221(2), 217-230.
[http://dx.doi.org/10.1006/abio.1994.1405] [PMID:  7529005] 
[311] 
Chou, K.C.; Zhang, C.T.; Maggiora, G.M. Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers,  1994, 34(1), 143-153.
[http://dx.doi.org/10.1002/bip.360340114] [PMID:  8110966] 
[312] 
Althaus, I.W.; Chou, K.C.; Lemay, R.J.; Franks, K.M.; Deibel, M.R.; Kezdy, F.J.; Resnick, L.; Busso, M.E.; So, A.G.; Downey, K.M.; Romero, D.L.; Thomas, R.C.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. The benzylthio-pyrimidine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase. Biochem. Pharmacol.,  1996, 51(6), 743-750.
[http://dx.doi.org/10.1016/0006-2952(95)02390-9] [PMID:  8602869] 
[313] 
Liu, H.; Wang, M.; Chou, K.C. Low-frequency Fourier spectrum for predicting membrane protein types. Biochem. Biophys. Res. Commun.,  2005, 336(3), 737-739.
[http://dx.doi.org/10.1016/j.bbrc.2005.08.160] [PMID:  16140260] 
[314] 
Gordon, G.A. Designed electromagnetic pulsed therapy: clinical applications. J. Cell. Physiol.,  2007, 212(3), 579-582.
[http://dx.doi.org/10.1002/jcp.21025] [PMID:  17577213] 
[315] 
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, 342-357.
[http://dx.doi.org/10.1139/v08-020] 
[316] 
Chou, K.C.; Shen, H.B. 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] 
[317] 
Shen, H.B.; Song, J.N. Prediction of protein folding rates from primary sequence by fusing multiple sequential features. J. Biomed. Sci. Eng.,  2009, 2, 136-143.
[318] 
Wang, J.F.; Chou, K.C. Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations. Biochem. Biophys. Res. Commun.,  2009, 390(3), 608-612.
[http://dx.doi.org/10.1016/j.bbrc.2009.10.014] [PMID:  19819222] 
[319] 
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, 152-156.
[http://dx.doi.org/10.4236/jbise.2008.13025] 
[320] 
Madkan, A.; Blank, M.; Elson, E.; Geddis, M.S.; Goodman, R. Steps to the clinic with ELF EMF. Nat. Sci.,  2009, 1, 157-165.
[http://dx.doi.org/10.4236/ns.2009.13020] 
[321] 
Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab.,  2010, 11(4), 369-378.
[http://dx.doi.org/10.2174/138920010791514261] [PMID:  20446902] 
[322] 
Lin, W.Z.; Xiao, X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat. Sci.,  2011, 3, 862-865.
[323] 
Lian, P.; Wei, D.Q.; Wang, J.F.; Chou, K.C. 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] 
[324] 
Liao, Q.H.; Gao, Q.Z.; Wei, J.; Chou, K.C. 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] 
[325] 
Zhou, G.P. 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), 142-148.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.006] [PMID:  21718705] 
[326] 
Li, J.; Wei, D.Q.; Wang, J.F.; Yu, Z.T.; Chou, K.C. Molecular dynamics simulations of CYP2E1. Med. Chem.,  2012, 8(2), 208-221.
[http://dx.doi.org/10.2174/157340612800493692] [PMID:  22385180] 
[327] 
Wang, J.F.; Chou, K.C. Recent advances in computational studies on influenza a virus M2 proton channel. Mini Rev. Med. Chem.,  2012, 12(10), 971-978.
[http://dx.doi.org/10.2174/138955712802762275] [PMID:  22420575] 
[328] 
Zhang, T.; Wei, D.Q.; Chou, K.C. 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] 
[329] 
Chou, K.C. Progresses in predicting post-translational modification. Int. J. Pept. Res. Ther., 2019.
[http://dx.doi.org/10.1007/s10989-019-09893-5] 
[330] 
Chou, K.C. Steps to the clinic with ELF EMF. Natural Science ,  2019, 1, 157-165.
[http://dx.doi.org/10.4236/ns.2009.13020] 
[331] 
Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab.,  2019, 11, 369-378.
[332] 
Chou, K.C. Recent progresses in predicting protein subcellular localization with artificial intelligence tools developed via the 5-steps rule. Genomics, 2019. in press
[333] 
Chou, K.C. An insightful 10-year recollection since the emergence of the 5-steps rule. Current. Pharmaceutical Design, 2019. in press
[334] 
Chou, K.C. Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis. Proteomics, 2019. in press
[335] 
Chou, K.C. An insightful recollection since the distorted key theory was born about 23 years ago. Int. J. Pept. Res. Ther., 2019. in press
[http://dx.doi.org/10.1016/j.ygeno.2019.09.001] 
[336] 
Chou, K.C. Proposing pseudo amino acid components is an important milestone for proteome and genome analyses. Intl. J. Pept.
Res. Therap.(IJPRT),, 2019. [in press] 
[http://dx.doi.org/10.1007/s10989-019-09910-7] 
[337] 
Chou, K.C. Two kinds of metrics for computational biology. Genomics,  in press
Rights & Permissions Print Cite
Article Metrics
22
2
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1568026619666191018100141	Print ISSN 1568-0266
Publisher Name Bentham Science Publisher	Online ISSN 1873-4294
Current Topics in Medicinal Chemistry

Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis

Abstract

Graphical Abstract

Chemistry Based on Natural Products for Therapeutic Purposes

Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design

Drug Discovery in the Age of Artificial Intelligence

From Biodiversity to Chemical Diversity: Focus of Flavonoids

Current Topics in Medicinal Chemistry

Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis

Abstract

Graphical Abstract

Call for Papers in Thematic Issues

Chemistry Based on Natural Products for Therapeutic Purposes

Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design

Drug Discovery in the Age of Artificial Intelligence

From Biodiversity to Chemical Diversity: Focus of Flavonoids

Related Journals

Related Books

Related Articles
