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Current Proteomics

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ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

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Mini-Review Article

Distorted Key Theory and its Implication for Drug Development

Author(s): Kuo-Chen Chou*

Volume 17, Issue 4, 2020

Page: [311 - 323] Pages: 13

DOI: 10.2174/1570164617666191025101914

Price: $65

Abstract

During the last three decades or so, many efforts have been made to study the protein cleavage sites by some disease-causing enzyme, such as HIV (Human Immunodeficiency Virus) protease and SARS (Severe Acute Respiratory Syndrome) coronavirus main proteinase. It has become increasingly clear via this mini-review that the motivation driving the aforementioned studies is quite wise, and that the results acquired through these studies are very rewarding, particularly for developing peptide drugs.

Keywords: HIV, SARS, protein cleavage sites, lock and key, induced fit theory, rack mechanism, peptide drugs.

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Graphical Abstract

[1]
Chou, K.C.; Zhang, C.T. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS Res. Hum. Retroviruses, 1992, 8, 1967-1976.
[http://dx.doi.org/10.1089/aid.1992.8.1967] [PMID: 1493047]
[2]
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]
[3]
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]
[4]
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]
[5]
Chou, J.J. Predicting cleavability of peptide sequences by HIV protease via correlation-angle approach. J. Protein Chem., 1993, 12(3), 291-302.
[http://dx.doi.org/10.1007/BF01028191] [PMID: 8397787]
[6]
Chou, K.C. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J. Biol. Chem., 1993, 268(23), 16938-16948.
[PMID: 8349584]
[7]
Chou, K.C.; Zhang, C.T. Studies on the specificity of HIV protease: an application of Markov chain theory. J. Protein Chem., 1993, 12(6), 709-724.
[http://dx.doi.org/10.1007/BF01024929] [PMID: 8136021]
[8]
Chou, K.C.; Zhang, C.T.; Kézdy, F.J. A vector projection approach to predicting HIV protease cleavage sites in proteins. Proteins, 1993, 16(2), 195-204.
[http://dx.doi.org/10.1002/prot.340160206] [PMID: 8332607]
[9]
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]
[10]
Zhang, C.T.; Chou, K.C. An alternate-subsite-coupled model for predicting HIV protease cleavage sites in proteins. Protein Eng., 1994, 7(1), 65-73.
[http://dx.doi.org/10.1093/protein/7.1.65] [PMID: 8140096]
[11]
Thompson, T.B.; Chou, K.C.; Zheng, C. Neural network prediction of the HIV-1 protease cleavage sites. J. Theor. Biol., 1995, 177(4), 369-379.
[http://dx.doi.org/10.1006/jtbi.1995.0254] [PMID: 8871474]
[12]
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]
[13]
Chou, K.C.; Tomasselli, A.L.; Reardon, I.M.; Heinrikson, R.L. Predicting HIV protease cleavage sites in proteins by a discriminant function method. Proteins, 1996, 24, 51-72.
[http://dx.doi.org/10.1002/(SICI)1097-0134(199601)24:1<51::AIDPROT4>3.0.CO;2-R]] [PMID: 8628733]
[14]
Cai, Y.D.; Chou, K.C. Artificial neural network model for HIV protease cleavage sites in proteins. Adv. Eng. Softw., 1998, 29, 119-128.
[http://dx.doi.org/10.1016/S0965-9978(98)00046-5]
[15]
Cai, Y.D.; Yu, H.; Chou, K.C. Using neural network for prediction of HIV protease cleavage sites in proteins. J. Protein Chem., 1998, 17, 607-615.
[http://dx.doi.org/10.1007/BF02780962] [PMID: 9853675]
[16]
Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support Vector Machines for predicting HIV protease cleavage sites in protein. J. Comput. Chem., 2002, 23(2), 267-274.
[http://dx.doi.org/10.1002/jcc.10017] [PMID: 11924738]
[17]
Sirois, S.; Sing, T.; Chou, K.C. HIV-1 gp120 V3 loop for structure-based drug design. Curr. Protein Pept. Sci., 2005, 6(5), 413-422.
[http://dx.doi.org/10.2174/138920305774329359] [PMID: 16248793]
[18]
Sirois, S.; Tsoukas, C.M.; Chou, K.C.; Wei, D.; Boucher, C.; Hatzakis, G.E. Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity. Med. Chem., 2005, 1(2), 173-184.
[http://dx.doi.org/10.2174/1573406053175238] [PMID: 16787312]
[19]
Gao, W.N.; Wei, D.Q.; Li, Y.; Gao, H.; Xu, W.R.; Li, A.X.; Chou, K.C. Agaritine and its derivatives are potential inhibitors against HIV proteases. Med. Chem., 2007, 3(3), 221-226.
[http://dx.doi.org/10.2174/157340607780620644] [PMID: 17504192]
[20]
Sirois, S.; Touaibia, M.; Chou, K.C.; Roy, R. Glycosylation of HIV-1 gp120 V3 loop: towards the rational design of a synthetic carbohydrate vaccine. Curr. Med. Chem., 2007, 14(30), 3232-3242.
[http://dx.doi.org/10.2174/092986707782793826] [PMID: 18220757]
[21]
Shen, H.B.; Chou, K.C. HIVcleave: A web-server for predicting HIV protease cleavage sites in proteins. Anal. Biochem., 2008, 375, 388-390.
[http://dx.doi.org/10.1016/j.ab.2008.01.012] [PMID: 18249180]
[22]
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]
[23]
Chen, B.; Chou, J.J. Structure of the transmembrane domain of HIV-1 envelope glycoprotein. FEBS J., 2017, 284(8), 1171-1177.
[http://dx.doi.org/10.1111/febs.13954] [PMID: 27868386]
[24]
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]
[25]
Fu, Q.; Shaik, M.M.; Cai, Y.; Ghantous, F.; Piai, A.; Peng, H.; Rits-Volloch, S.; Liu, Z.; Harrison, S.C.; Seaman, M.S.; Chen, B.; Chou, J.J. Structure of the membrane proximal external region of HIV-1 envelope glycoprotein. Proc. Natl. Acad. Sci. USA, 2018, 115(38), E8892-E8899.
[http://dx.doi.org/10.1073/pnas.1807259115] [PMID: 30185554]
[26]
Anand, K.; Ziebuhr, J.; Wadhwani, P.; Mesters, J.R.; Hilgenfeld, R. Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs. Science, 2003, 300(5626), 1763-1767.
[http://dx.doi.org/10.1126/science.1085658] [PMID: 12746549]
[27]
Chou, K.C.; Wei, D.Q.; Zhong, W.Z. Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. Biochem. Biophys. Res. Commun. [BBRC], 2003, 308(1), 148-151.
[http://dx.doi.org/10.1016/S0006-291X(03)01342-1] [PMID: 12890493]
[28]
Du, Q.S.; Wang, S.Q.; Zhu, Y.; Wei, D.Q.; Guo, H.; Sirois, S.; Chou, K.C. Polyprotein cleavage mechanism of SARS CoV Mpro and chemical modification of the octapeptide. Peptides, 2004, 25(11), 1857-1864.
[http://dx.doi.org/10.1016/j.peptides.2004.06.018] [PMID: 15501516]
[29]
Sirois, S.; Wei, D.Q.; Du, Q.; Chou, K.C. Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points. J. Chem. Inf. Comput. Sci., 2004, 44(3), 1111-1122.
[http://dx.doi.org/10.1021/ci034270n] [PMID: 15154780]
[30]
Du, Q.S.; Wang, S.; Wei, D.Q.; Sirois, S.; Chou, K.C. Molecular modelling and chemical modification for finding peptide inhibitor against SARS CoV Mpro. Anal. Biochem., 2005, 337, 262-270.
[http://dx.doi.org/10.1016/j.ab.2004.10.003] [PMID: 15691506]
[31]
Du, Q.; Wang, S.; Jiang, Z.; Gao, W.; Li, Y.; Wei, D.; Chou, K.C. Application of bioinformatics in search for cleavable peptides of SARS-CoV M(pro) and chemical modification of octapeptides. Med. Chem., 2005, 1(3), 209-213.
[http://dx.doi.org/10.2174/1573406053765468] [PMID: 16787316]
[32]
Wang, M.; Yao, J.S.; Huang, Z.D.; Xu, Z.J.; Liu, G.P.; Zhao, H.Y.; Wang, X.Y.; Yang, J.; Zhu, Y.S.; Chou, K.C. A new nucleotide-composition based fingerprint of SARS-CoV with visualization analysis. Med. Chem., 2005, 1(1), 39-47.
[http://dx.doi.org/10.2174/1573406053402505] [PMID: 16789884]
[33]
Wei, D.Q.; Chou, K.C.; Gan, Y.R.; Du, Q.S. Polypeptide and its derivatives as inhibitors against SARS.Patent Application No: CN 1560074A; China, 2005.
[34]
Chou, K.C.; Wei, D.Q.; Du, Q.S.; Sirois, S.; Zhong, W.Z. Progress in computational approach to drug development against SARS. Curr. Med. Chem., 2006, 13(27), 3263-3270.
[http://dx.doi.org/10.2174/092986706778773077] [PMID: 17168850]
[35]
Gao, L.; Ding, Y.S.; Dai, H.; Shao, S.H.; Huang, Z.D.; Chou, K.C. A novel fingerprint map for detecting SARS-CoV. J. Pharm. Biomed. Anal., 2006, 41(1), 246-250.
[http://dx.doi.org/10.1016/j.jpba.2005.09.031] [PMID: 16289934]
[36]
Wei, D.Q.; Zhang, R.; Du, Q.S.; Gao, W.N.; Li, Y.; Gao, H.; Wang, S.Q.; Zhang, X.; Li, A.X.; Sirois, S.; Chou, K.C. Anti-SARS drug screening by molecular docking. Amino Acids, 2006, 31(1), 73-80.
[http://dx.doi.org/10.1007/s00726-006-0361-7] [PMID: 16715412]
[37]
Zhang, R.; Wei, D.Q.; Du, Q.S.; Chou, K.C. Molecular modeling studies of peptide drug candidates against SARS. Med. Chem., 2006, 2(3), 309-314.
[http://dx.doi.org/10.2174/157340606776930736] [PMID: 16948478]
[38]
Du, Q.S.; Sun, H.; Chou, K.C. 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]
[39]
Wang, S.Q.; Du, Q.S.; Zhao, K.; Li, A.X.; Wei, D.Q.; Chou, K.C. Virtual screening for finding natural inhibitor against cathepsin-L for SARS therapy. Amino Acids, 2007, 33(1), 129-135.
[http://dx.doi.org/10.1007/s00726-006-0403-1] [PMID: 16998715]
[40]
Chou, K.C.; Wei, D.Q.; Du, Q.S.; Sirois, S.; Shen, H.B.; Zhong, W.Z. Proteases in Biology and Disease: Viral proteases and antiviral protease inhibitor therapy; Lendeckel, U.; Hooper, N.M., Eds.; Springer Science: Media B.V, 2009, p. 8.
[41]
Voet, D.; Voet, J.G.; Pratt, C.W. Fundamentals of Biochemistry; John Wiley & Sons: New York, 2002, p. 1184.
[42]
Chou, K.C.; Chen, N.Y. The biological functions of low-frequency phonons. Sci. Sin., 1977, 20, 447-457.
[PMID: 6487745]
[43]
Chou, K.C. 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]
[44]
Zhang, C.T.; Chou, K.C. Graphic analysis of codon usage strategy in 1490 human proteins. J. Protein Chem., 1993, 12(3), 329-335.
[http://dx.doi.org/10.1007/BF01028195] [PMID: 8397791]
[45]
Zhang, C.T.; Chou, K.C. Analysis of codon usage in 1562 E. coli protein coding sequences. J. Mol. Biol., 1994, 238, 1-8.
[http://dx.doi.org/10.1006/jmbi.1994.1263] [PMID: 8145249]
[46]
Chou, K.C. Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr. Med. Chem., 2019, 26, 4918-4943.
[http://dx.doi.org/10.2174/0929867326666190507082559] [PMID: 31060481]
[47]
Chou, K.C. Progresses in predicting post-translational modification. Int. J. Pept. Res. Ther.[IJPRT], 2019.
[http://dx.doi.org/10.1007/s10989-019-09893-5]
[48]
Chou, K.C. An insightful recollection since the distorted key theory was born about 23 years ago. Genomics, 2019, S0888-7543(19), 30554-3.
[http://dx.doi.org/10.1016/j.ygeno.2019.09.001] [PMID: 31494196]
[49]
Chou, K.C. Proposing pseudo amino acid components is an important milestone for proteome and genome analyses. Int. J. Pept. Res. Ther., 2020, 26, 1085-1098.
[http://dx.doi.org/10.1007/s10989-019-09910-7]
[50]
Chou, K.C. An insightful recollection for predicting protein subcellular locations in multi-label systems. Genomics, 2019, 7543(19), 30460-30464.
[http://dx.doi.org/10.1016/j.ygeno.2019.08.008] [PMID: 31476433]
[51]
Chou, K.C. An insightful recollection for predicting protein subcellular locations in multi-label systems. Genomics, 2019. [Epub ahead of print]
[http://dx.doi.org/10.1016/j.ygeno.2019.08.008] [PMID: 31476433]
[52]
Chou, K.C. Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications. Trends in Artificial Inttelengence, 2019, 3, 60-74.
[53]
Chou, K.C. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60), 2001, 43, 246-255.
[54]
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]
[55]
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]
[56]
Liu, B.; Wu, H.; Chou, K.C. Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nat. Sci., 2017, 9, 67-91.
[http://dx.doi.org/10.4236/ns.2017.94007]
[57]
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]
[58]
Guo, Z.M. Prediction of membrane protein types by using pattern recognition method based on pseudo amino acid composition; Master Thesis, Bio-X Life Science Research Center:. Shanghai Jiaotong University, 2002.
[59]
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]
[60]
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]
[61]
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.
[62]
Pan, Y.X.; Zhang, Z.Z.; Guo, Z.M.; Feng, G.Y.; Huang, Z.D.; 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]
[63]
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]
[64]
Wang, M.; Yang, J.; Liu, G.P.; Xu, Z.J.; Chou, K.C. Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. Protein Eng. Des. Sel., 2004, 17(6), 509-516.
[http://dx.doi.org/10.1093/protein/gzh061] [PMID: 15314209]
[65]
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]
[66]
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]
[67]
Gao, Y.; Shao, S.; Xiao, X.; Ding, Y.; Huang, Y.; Huang, Z.; Chou, K.C. Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids, 2005, 28(4), 373-376.
[http://dx.doi.org/10.1007/s00726-005-0206-9] [PMID: 15889221]
[68]
Liu, H.; Yang, J.; Wang, M.; Xue, L.; Chou, K.C. Using fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types. Protein J., 2005, 24(6), 385-389.
[http://dx.doi.org/10.1007/s10930-005-7592-4] [PMID: 16323044]
[69]
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]
[70]
Shen, H.B.; Chou, K.C. Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun., 2005, 337(3), 752-756.
[http://dx.doi.org/10.1016/j.bbrc.2005.09.117] [PMID: 16213466]
[71]
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]
[72]
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]
[73]
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]
[74]
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]
[75]
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), 252-260.
[http://dx.doi.org/10.1016/j.jtbi.2006.06.014] [PMID: 16890961]
[76]
Shen, H.B.; Yang, J.; Chou, K.C. 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]
[77]
Wang, S.Q.; Yang, J.; Chou, K.C. Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition. J. Theor. Biol., 2006, 242(4), 941-946.
[http://dx.doi.org/10.1016/j.jtbi.2006.05.006] [PMID: 16806277]
[78]
Xiao, X.; Shao, S.; Ding, Y.; Huang, Z.; Chou, K.C. 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]
[79]
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]
[80]
Zhang, S.W.; Pan, Q.; Zhang, H.C.; Shao, Z.C.; Shi, J.Y. 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), 461-468.
[http://dx.doi.org/10.1007/s00726-006-0263-8] [PMID: 16773245]
[81]
Zhou, G.P.; Cai, Y.D. Predicting protease types by hybridizing gene ontology and pseudo amino acid composition. Proteins, 2006, 63(3), 681-684.
[http://dx.doi.org/10.1002/prot.20898] [PMID: 16456852]
[82]
Chen, Y.L.; Li, Q.Z. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J. Theor. Biol., 2007, 248(2), 377-381.
[http://dx.doi.org/10.1016/j.jtbi.2007.05.019] [PMID: 17572445]
[83]
Ding, Y.S.; Zhang, T.L.; Chou, K.C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept. Lett., 2007, 14(8), 811-815.
[http://dx.doi.org/10.2174/092986607781483778] [PMID: 17979824]
[84]
Lin, H.; Li, Q.Z. Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochem. Biophys. Res. Commun., 2007, 354(2), 548-551.
[http://dx.doi.org/10.1016/j.bbrc.2007.01.011] [PMID: 17239817]
[85]
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]
[86]
Mundra, P.; Kumar, M.; Kumar, K.K.; Jayaraman, V.K.; Kulkarni, B.D. Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM. Pattern Recognit. Lett., 2007, 28, 1610-1615.
[http://dx.doi.org/10.1016/j.patrec.2007.04.001]
[87]
Shi, J.Y.; Zhang, S.W.; 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]
[88]
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]
[89]
Zhou, X.B.; Chen, C.; Li, Z.C.; Zou, X.Y. Using Chou’s amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J. Theor. Biol., 2007, 248(3), 546-551.
[http://dx.doi.org/10.1016/j.jtbi.2007.06.001] [PMID: 17628605]
[90]
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), 111-117.
[http://dx.doi.org/10.1007/s00726-007-0550-z] [PMID: 17520325]
[91]
Ding, Y.S.; Zhang, T.L. Using Chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: An approach with immune genetic algorithm-based ensemble classifier. Pattern Recognit. Lett., 2008, 29, 1887-1892.
[http://dx.doi.org/10.1016/j.patrec.2008.06.007]
[92]
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), 103-109.
[http://dx.doi.org/10.1007/s00726-007-0568-2] [PMID: 17624492]
[93]
Gu, Q.; Ding, Y.; Zhang, T. Prediction of G-protein-coupled receptor classes with pseudo amino acid composition In: IEEE Xplore; (iCBBE, Shanghai, China), 2008.
[94]
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), 392-396.
[http://dx.doi.org/10.2174/092986608784246443] [PMID: 18473953]
[95]
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), 669-675.
[http://dx.doi.org/10.1007/s00726-008-0034-9] [PMID: 18256886]
[96]
Li, F.M.; Li, Q.Z. Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids, 2008, 34(1), 119-125.
[http://dx.doi.org/10.1007/s00726-007-0545-9] [PMID: 17514493]
[97]
Li, F.M.; Li, Q.Z. Predicting protein subcellular location using Chou’s pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett., 2008, 15(6), 612-616.
[http://dx.doi.org/10.2174/092986608784966930] [PMID: 18680458]
[98]
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), 350-356.
[http://dx.doi.org/10.1016/j.jtbi.2008.02.004] [PMID: 18355838]
[99]
Lin, H.; Ding, H.; Guo, F-B.; Zhang, A.Y.; Huang, J. Predicting subcellular localization of mycobacterial proteins by using Chou’s pseudo amino acid composition. Protein Pept. Lett., 2008, 15(7), 739-744.
[http://dx.doi.org/10.2174/092986608785133681] [PMID: 18782071]
[100]
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]
[101]
Shi, J.Y.; Zhang, S.W.; Pan, Q.; Zhou, G.P. Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution. Amino Acids, 2008, 35(2), 321-327.
[http://dx.doi.org/10.1007/s00726-007-0623-z] [PMID: 18209947]
[102]
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]
[103]
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]
[104]
Zhang, G.Y.; Fang, B.S. 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), 310-315.
[http://dx.doi.org/10.1016/j.jtbi.2008.03.015] [PMID: 18471832]
[105]
Zhang, G.Y.; Li, H.C.; Gao, J.Q.; Fang, B.S. Predicting lipase types by improved Chou’s pseudo-amino acid composition. Protein Pept. Lett., 2008, 15(10), 1132-1137.
[http://dx.doi.org/10.2174/092986608786071184] [PMID: 19075826]
[106]
Zhang, S.W.; 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), 591-598.
[http://dx.doi.org/10.1007/s00726-008-0086-x] [PMID: 18427713]
[107]
Zhang, S.W.; Zhang, Y.L.; Yang, H.F.; Zhao, C.H.; 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), 565-572.
[http://dx.doi.org/10.1007/s00726-007-0010-9] [PMID: 18074191]
[108]
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]
[109]
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]
[110]
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]
[111]
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), 351-355.
[http://dx.doi.org/10.2174/092986609787848045] [PMID: 19356130]
[112]
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), 330-335.
[http://dx.doi.org/10.1016/j.jtbi.2009.08.004] [PMID: 19679138]
[113]
Gao, Q.B.; Jin, Z.C.; Ye, X.F.; Wu, C.; He, J. Prediction of nuclear receptors with optimal pseudo amino acid composition. Anal. Biochem., 2009, 387(1), 54-59.
[http://dx.doi.org/10.1016/j.ab.2009.01.018] [PMID: 19454254]
[114]
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]
[115]
Lin, H.; Wang, H.; Ding, H.; Chen, Y.L.; Li, Q.Z. Prediction of subcellular localization of apoptosis protein using Chou’s pseudo amino acid composition. Acta Biotheor., 2009, 57(3), 321-330.
[http://dx.doi.org/10.1007/s10441-008-9067-4] [PMID: 19169652]
[116]
Qiu, J.D.; Huang, J.H.; Liang, R.P.; Lu, X.Q. 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]
[117]
Xiao, X.; Wang, P.; Chou, K.C. Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. J. Appl. Cryst., 2009, 42, 169-173.
[http://dx.doi.org/10.1107/S0021889809002751]
[118]
Zeng, Y.H.; Guo, Y.Z.; Xiao, R.Q.; Yang, L.; Yu, L.Z.; Li, M.L. 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), 366-372.
[http://dx.doi.org/10.1016/j.jtbi.2009.03.028] [PMID: 19341746]
[119]
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), 203-209.
[http://dx.doi.org/10.1016/j.jtbi.2009.11.016] [PMID: 19961864]
[120]
Gao, Q.B.; Ye, X.F.; Jin, Z.C.; He, J. Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition. Anal. Biochem., 2010, 398(1), 52-59.
[http://dx.doi.org/10.1016/j.ab.2009.10.040] [PMID: 19874797]
[121]
Gu, Q.; Ding, Y.; Zhang, T.; Shen, Y. [Prediction of G-protein-coupled receptor classes with pseudo amino acid composition]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2010, 27, 500-504.
[122]
Gu, Q.; Ding, Y.S.; Zhang, T.L. 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), 559-567.
[http://dx.doi.org/10.2174/092986610791112693] [PMID: 19594431]
[123]
Kandaswamy, K.K.; Pugalenthi, G.; Möller, S.; Hartmann, E.; Kalies, K.U.; Suganthan, P.N.; Martinetz, T. 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), 1473-1479.
[http://dx.doi.org/10.2174/0929866511009011473] [PMID: 20666727]
[124]
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), 1263-1269.
[http://dx.doi.org/10.2174/092986610792231528] [PMID: 20670213]
[125]
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]
[126]
Xiaohui, N.; Nana, L.; Feng, S.; Xuehai, H.; Jingbo, X.; Huijuan, X. Predicting protein solubility with a hybrid approach by pseudo amino acid composition. Protein Pept. Lett., 2010, 17(12), 1466-1472.
[http://dx.doi.org/10.2174/0929866511009011466] [PMID: 20937038]
[127]
Qiu, J.D.; Huang, J.H.; Shi, S.P.; Liang, R.P. 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), 715-722.
[http://dx.doi.org/10.2174/092986610791190372] [PMID: 19961429]
[128]
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]
[129]
Wang, Y.C.; Wang, X.B.; Yang, Z.X.; Deng, N.Y. Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature. Protein Pept. Lett., 2010, 17(11), 1441-1449.
[http://dx.doi.org/10.2174/0929866511009011441] [PMID: 20666729]
[130]
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]
[131]
Yu, L.; Guo, Y.; Li, Y.; Li, G.; Li, M.; Luo, J.; Xiong, W.; Qin, W. 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]
[132]
Ding, H.; Liu, L.; Guo, F.B.; Huang, J.; Lin, H. Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept. Lett., 2011, 18(1), 58-63.
[http://dx.doi.org/10.2174/092986611794328708] [PMID: 20955168]
[133]
Guo, J.; Rao, N.; Liu, G.; Yang, Y.; Wang, G. Predicting protein folding rates using the concept of Chou’s pseudo amino acid composition. J. Comput. Chem., 2011, 32(8), 1612-1617.
[http://dx.doi.org/10.1002/jcc.21740] [PMID: 21328402]
[134]
Hayat, M.; Khan, A. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. J. Theor. Biol., 2011, 271(1), 10-17.
[http://dx.doi.org/10.1016/j.jtbi.2010.11.017] [PMID: 21110985]
[135]
Hu, L.; Zheng, L.; Wang, Z.; Li, B.; Liu, L. Using pseudo amino acid composition to predict protease families by incorporating a series of protein biological features. Protein Pept. Lett., 2011, 18(6), 552-558.
[http://dx.doi.org/10.2174/092986611795222795] [PMID: 21271978]
[136]
Huang, Y.; Yang, L.; Wang, T. Phylogenetic analysis of DNA sequences based on the generalized pseudo-amino acid composition. J. Theor. Biol., 2011, 269(1), 217-223.
[http://dx.doi.org/10.1016/j.jtbi.2010.10.027] [PMID: 21040733]
[137]
Jingbo, X.; Silan, Z.; Feng, S.; Huijuan, X.; Xuehai, H.; Xiaohui, N.; Zhi, L. Using the concept of pseudo amino acid composition to predict resistance gene against Xanthomonas oryzae pv. oryzae in rice: an approach from chaos games representation. J. Theor. Biol., 2011, 284(1), 16-23.
[http://dx.doi.org/10.1016/j.jtbi.2011.06.003] [PMID: 21703279]
[138]
Lin, H.; Ding, H. Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J. Theor. Biol., 2011, 269(1), 64-69.
[http://dx.doi.org/10.1016/j.jtbi.2010.10.019] [PMID: 20969879]
[139]
Lin, J.; Wang, Y. Using a novel AdaBoost algorithm and Chou’s Pseudo amino acid composition for predicting protein subcellular localization. Protein Pept. Lett., 2011, 18(12), 1219-1225.
[http://dx.doi.org/10.2174/092986611797642797] [PMID: 21728988]
[140]
Lin, J.; Wang, Y.; Xu, X. A novel ensemble and composite approach for classifying proteins based on Chou’s pseudo amino acid composition. Afr. J. Biotechnol., 2011, 10, 16963-16968.
[141]
Liu, X.L.; Lu, J.L.; Hu, X.H. Predicting thermophilic proteins with pseudo amino acid composition:approached from chaos game representation and principal component analysis. Protein Pept. Lett., 2011, 18(12), 1244-1250.
[http://dx.doi.org/10.2174/092986611797642661] [PMID: 21787282]
[142]
Mahdavi, A.; Jahandideh, S. Application of density similarities to predict membrane protein types based on pseudo-amino acid composition. J. Theor. Biol., 2011, 276(1), 132-137.
[http://dx.doi.org/10.1016/j.jtbi.2011.01.048] [PMID: 21296088]
[143]
Mohabatkar, H.; Mohammad Beigi, M.; Esmaeili, A. Prediction of GABAA receptor proteins using the concept of Chou’s pseudo-amino acid composition and support vector machine. J. Theor. Biol., 2011, 281(1), 18-23.
[http://dx.doi.org/10.1016/j.jtbi.2011.04.017] [PMID: 21536049]
[144]
Mohammad Beigi, M.; Behjati, M.; Mohabatkar, H. Prediction of metalloproteinase family based on the concept of Chou’s pseudo amino acid composition using a machine learning approach. J. Struct. Funct. Genomics, 2011, 12(4), 191-197.
[http://dx.doi.org/10.1007/s10969-011-9120-4] [PMID: 22143437]
[145]
Qiu, J.D.; Sun, X.Y.; Suo, S.B.; Shi, S.P.; Huang, S.Y.; Liang, R.P.; Zhang, L. Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: an approach from discrete wavelet transformation. Biochimie, 2011, 93(7), 1132-1138.
[http://dx.doi.org/10.1016/j.biochi.2011.03.010] [PMID: 21466835]
[146]
Qiu, J.D.; Suo, S.B.; Sun, X.Y.; Shi, S.P.; Liang, R.P. OligoPred: a web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou’s pseudo amino acid composition. J. Mol. Graph. Model., 2011, 30, 129-134.
[http://dx.doi.org/10.1016/j.jmgm.2011.06.014] [PMID: 21802968]
[147]
Shi, R.; Xu, C. Prediction of rat protein subcellular localization with pseudo amino acid composition based on multiple sequential features. Protein Pept. Lett., 2011, 18(6), 625-633.
[http://dx.doi.org/10.2174/092986611795222768] [PMID: 21309740]
[148]
Shu, M.; Cheng, X.; Zhang, Y.; Wang, Y.; Lin, Y.; Wang, L.; Lin, Z. Predicting the activity of ACE inhibitory peptides with a novel mode of pseudo amino acid composition. Protein Pept. Lett., 2011, 18(12), 1233-1243.
[http://dx.doi.org/10.2174/092986611797642706] [PMID: 21728992]
[149]
Wang, D.; Yang, L.; Fu, Z.; Xia, J. Prediction of thermophilic protein with pseudo amino Acid composition: an approach from combined feature selection and reduction. Protein Pept. Lett., 2011, 18(7), 684-689.
[http://dx.doi.org/10.2174/092986611795446085] [PMID: 21413920]
[150]
Wang, W.; Geng, X.; Dou, Y.; Liu, T.; Zheng, X. Predicting protein subcellular localization by pseudo amino acid composition with a segment-weighted and features-combined approach. Protein Pept. Lett., 2011, 18(5), 480-487.
[http://dx.doi.org/10.2174/092986611794927947] [PMID: 21235488]
[151]
Xiao, X.; Chou, K.C. Using pseudo amino acid composition to predict protein attributes via cellular automata and other approaches. Curr. Bioinform., 2011, 6, 251-260.
[http://dx.doi.org/10.2174/1574893611106020251]
[152]
Xiao, X.; Wang, P.; Chou, K.C. 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), 911-919.
[http://dx.doi.org/10.1039/C0MB00170H] [PMID: 21180772]
[153]
Rehman, ZU.; Khan, A. Prediction of GPCRs with pseudo amino acid composition: employing composite features and grey incidence degree based classification. Protein Pept. Lett., 2011, 18(9), 872-878.
[http://dx.doi.org/10.2174/092986611796011491] [PMID: 21443502]
[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), 271-278.
[http://dx.doi.org/10.1002/jcc.21616] [PMID: 20652881]
[155]
Cao, J.Z.; Liu, W.Q.; Gu, H. Predicting viral protein subcellular localization with Chou’s pseudo amino acid composition and imbalance-weighted multi-label K-nearest neighbor algorithm. Protein Pept. Lett., 2012, 19(11), 1163-1169.
[http://dx.doi.org/10.2174/092986612803216999] [PMID: 22185509]
[156]
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]
[157]
Chen, Y.L.; Li, Q.Z.; Zhang, L.Q. Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet. Amino Acids, 2012, 42(4), 1309-1316.
[http://dx.doi.org/10.1007/s00726-010-0825-7] [PMID: 21191803]
[158]
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]
[159]
Fan, G.L.; Li, Q.Z. Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol., 2012, 304, 88-95.
[http://dx.doi.org/10.1016/j.jtbi.2012.03.017] [PMID: 22459701]
[160]
Fan, G.L.; Li, Q.Z. Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition. Amino Acids, 2012, 43(2), 545-555.
[http://dx.doi.org/10.1007/s00726-011-1143-4] [PMID: 22102053]
[161]
Gao, Q.B.; Zhao, H.; Ye, X.; He, J. Prediction of pattern recognition receptor family using pseudo-amino acid composition. Biochem. Biophys. Res. Commun., 2012, 417(1), 73-77.
[http://dx.doi.org/10.1016/j.bbrc.2011.11.057] [PMID: 22138239]
[162]
Li, L.Q.; Zhang, Y.; Zou, L.Y.; Zhou, Y.; Zheng, X.Q. Prediction of protein subcellular multi-localization based on the general form of Chou’s pseudo amino acid composition. Protein Pept. Lett., 2012, 19(4), 375-387.
[http://dx.doi.org/10.2174/092986612799789369] [PMID: 22185507]
[163]
Lin, W.Z.; Fang, J.A.; Xiao, X.; Chou, K.C. Predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model. PLoS One, 2012, 7(11) e49040
[http://dx.doi.org/10.1371/journal.pone.0049040] [PMID: 23189138]
[164]
Liu, L.; Hu, X.Z.; Liu, X.X.; Wang, Y.; Li, S.B. Predicting protein fold types by the general form of Chou’s pseudo amino acid composition: approached from optimal feature extractions. Protein Pept. Lett., 2012, 19(4), 439-449.
[http://dx.doi.org/10.2174/092986612799789378] [PMID: 22185500]
[165]
Nanni, L.; Brahnam, S.; Lumini, A. Wavelet images and Chou’s pseudo amino acid composition for protein classification. Amino Acids, 2012, 43(2), 657-665.
[http://dx.doi.org/10.1007/s00726-011-1114-9] [PMID: 21993538]
[166]
Nanni, L.; Lumini, A.; Gupta, D.; Garg, A. Identifying bacterial virulent proteins by fusing a set of classifiers based on variants of Chou’s pseudo amino acid composition and on evolutionary information. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2012, 9(2), 467-475.
[http://dx.doi.org/10.1109/TCBB.2011.117] [PMID: 21860064]
[167]
Niu, X.H.; Hu, X.H.; Shi, F.; Xia, J.B. Predicting protein solubility by the general form of Chou’s pseudo amino acid composition: approached from chaos game representation and fractal dimension. Protein Pept. Lett., 2012, 19(9), 940-948.
[http://dx.doi.org/10.2174/092986612802084492] [PMID: 22486614]
[168]
Ren, L.Y.; Zhang, Y.S.; Gutman, I. Predicting the classification of transcription factors by incorporating their binding site properties into a novel mode of Chou’s pseudo amino acid composition. Protein Pept. Lett., 2012, 19(11), 1170-1176.
[http://dx.doi.org/10.2174/092986612803217088] [PMID: 22185505]
[169]
Wang, J.; Li, Y.; Wang, Q.; You, X.; Man, J.; Wang, C.; Gao, X. ProClusEnsem: predicting membrane protein types by fusing different modes of pseudo amino acid composition. Comput. Biol. Med., 2012, 42(5), 564-574.
[http://dx.doi.org/10.1016/j.compbiomed.2012.01.012] [PMID: 22386149]
[170]
Yu, X.; Zheng, X.; Liu, T.; Dou, Y.; Wang, J. Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids, 2012, 42(5), 1619-1625.
[http://dx.doi.org/10.1007/s00726-011-0848-8] [PMID: 21344173]
[171]
Zhao, X.W.; Ma, Z.Q.; Yin, M.H. Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou’s Pseudo amino acid composition. Protein Pept. Lett., 2012, 19(5), 492-500.
[http://dx.doi.org/10.2174/092986612800191080] [PMID: 22486644]
[172]
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]
[173]
Chen, Y.K.; Li, K.B. Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition. J. Theor. Biol., 2013, 318, 1-12.
[http://dx.doi.org/10.1016/j.jtbi.2012.10.033] [PMID: 23137835]
[174]
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]
[175]
Georgiou, D.N.; Karakasidis, T.E.; Megaritis, A.C. A short survey on genetic sequences, Chou’s pseudo amino acid composition and its combination with fuzzy set theory. Open Bioinform. J., 2013, 7, 41-48.
[http://dx.doi.org/10.2174/1875036201307010041]
[176]
Gupta, M.K.; Niyogi, R.; Misra, M. An alignment-free method to find similarity among protein sequences via the general form of Chou’s pseudo amino acid composition. SAR QSAR Environ. Res., 2013, 24(7), 597-609.
[http://dx.doi.org/10.1080/1062936X.2013.773378] [PMID: 23710804]
[177]
Huang, C.; Yuan, J. Using radial basis function on the general form of Chou’s pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites. Biosystems, 2013, 113(1), 50-57.
[http://dx.doi.org/10.1016/j.biosystems.2013.04.005] [PMID: 23669601]
[178]
Huang, C.; Yuan, J.Q. A multilabel model based on Chou’s pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types. J. Membr. Biol., 2013, 246(4), 327-334.
[http://dx.doi.org/10.1007/s00232-013-9536-9] [PMID: 23546013]
[179]
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]
[180]
Khosravian, M.; Faramarzi, F.K.; Beigi, M.M.; Behbahani, M.; Mohabatkar, H. Predicting antibacterial peptides by the concept of Chou’s pseudo-amino acid composition and machine learning methods. Protein Pept. Lett., 2013, 20(2), 180-186.
[http://dx.doi.org/10.2174/092986613804725307] [PMID: 22894156]
[181]
Lin, H.; Ding, C.; Yuan, L.F.; Chen, W.; Ding, H.; Li, Z.Q.; Guo, F.B.; Hung, J.; Rao, N.N. Predicting subchloroplast locations of proteins based on the general form of Chou’s pseudo amino acid composition: Approached from optimal tripeptide composition. Int. J. Biomath., 2013, 61350003
[http://dx.doi.org/10.1142/S1793524513500034]
[182]
Liu, B.; Wang, X.; Zou, Q.; Dong, Q.; Chen, Q. Protein remote homology detection by combining Chou’s pseudo amino acid composition and profile-based protein representation. Mol. Inform., 2013, 32(9-10), 775-782.
[http://dx.doi.org/10.1002/minf.201300084] [PMID: 27480230]
[183]
Mohabatkar, H.; Beigi, M.M.; Abdolahi, K.; Mohsenzadeh, S. Prediction of allergenic proteins by means of the concept of Chou’s pseudo amino acid composition and a machine learning approach. Med. Chem., 2013, 9(1), 133-137.
[http://dx.doi.org/10.2174/157340613804488341] [PMID: 22931491]
[184]
Qin, Y.F.; Zheng, L.; Huang, J. Locating apoptosis proteins by incorporating the signal peptide cleavage sites into the general form of Chou’s Pseudo amino acid composition. Int. J. Quantum Chem., 2013, 113, 1660-1667.
[http://dx.doi.org/10.1002/qua.24383]
[185]
Sarangi, A.N.; Lohani, M.; Aggarwal, R. Prediction of essential proteins in prokaryotes by incorporating various physico-chemical features into the general form of Chou’s pseudo amino acid composition. Protein Pept. Lett., 2013, 20(7), 781-795.
[http://dx.doi.org/10.2174/0929866511320070008] [PMID: 23276224]
[186]
Wan, S.; Mak, M.W.; Kung, S.Y. GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou’s pseudo-amino acid composition. J. Theor. Biol., 2013, 323, 40-48.
[http://dx.doi.org/10.1016/j.jtbi.2013.01.012] [PMID: 23376577]
[187]
Wang, X.; Li, G.Z.; Lu, W.C. Virus-ECC-mPLoc: a multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou’s pseudo amino acid composition. Protein Pept. Lett., 2013, 20(3), 309-317.
[PMID: 22591474]
[188]
Xiaohui, N.; Nana, L.; Jingbo, X.; Dingyan, C.; Yuehua, P.; Yang, X.; Weiquan, W.; Dongming, W.; Zengzhen, W. 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, 211-217.
[http://dx.doi.org/10.1016/j.jtbi.2013.03.010] [PMID: 23524162]
[189]
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]
[190]
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]
[191]
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]
[192]
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]
[193]
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]
[194]
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]
[195]
Mondal, S.; Pai, P.P. Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction. J. Theor. Biol., 2014, 356, 30-35.
[http://dx.doi.org/10.1016/j.jtbi.2014.04.006] [PMID: 24732262]
[196]
Nanni, L.; Brahnam, S.; Lumini, A. Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition. J. Theor. Biol., 2014, 360, 109-116.
[http://dx.doi.org/10.1016/j.jtbi.2014.07.003] [PMID: 25026218]
[197]
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.[BMRI], 2014, 2014 947416
[http://dx.doi.org/10.1155/2014/947416] [PMID: 24977164]
[198]
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]
[199]
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]
[200]
Zhang, J.; Sun, P.; Zhao, X.; Ma, Z. PECM: prediction of extracellular matrix proteins using the concept of Chou’s pseudo amino acid composition. J. Theor. Biol., 2014, 363, 412-418.
[http://dx.doi.org/10.1016/j.jtbi.2014.08.002] [PMID: 25123433]
[201]
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]
[202]
Ali, F.; Hayat, M. Classification of membrane protein types using Voting Feature Interval in combination with Chou’s Pseudo Amino Acid Composition. J. Theor. Biol., 2015, 384, 78-83.
[http://dx.doi.org/10.1016/j.jtbi.2015.07.034] [PMID: 26297889]
[203]
Chen, L.; Chu, C.; Huang, T.; Kong, X.; Cai, Y.D. Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models. Amino Acids, 2015, 47(7), 1485-1493.
[http://dx.doi.org/10.1007/s00726-015-1974-5] [PMID: 25894890]
[204]
Huang, C.; Yuan, J.Q. Simultaneously identify three different attributes of proteins by fusing their three different modes of Chou’s pseudo amino acid compositions. Protein Pept. Lett., 2015, 22(6), 547-556.
[http://dx.doi.org/10.2174/0929866522666150209151344] [PMID: 25666038]
[205]
Khan, Z.U.; Hayat, M.; Khan, M.A. Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model. J. Theor. Biol., 2015, 365, 197-203.
[http://dx.doi.org/10.1016/j.jtbi.2014.10.014] [PMID: 25452135]
[206]
Kumar, R.; Srivastava, A.; Kumari, B.; Kumar, M. Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine. J. Theor. Biol., 2015, 365, 96-103.
[http://dx.doi.org/10.1016/j.jtbi.2014.10.008] [PMID: 25454009]
[207]
Liu, B.; Chen, J.; Wang, X. Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis. Mol. Genet. Genomics, 2015, 290(5), 1919-1931.
[http://dx.doi.org/10.1007/s00438-015-1044-4] [PMID: 25896721]
[208]
Wang, X.; Zhang, W.; Zhang, Q.; Li, G.Z. MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou’s pseudo amino acid composition and a novel multi-label classifier. Bioinformatics, 2015, 31(16), 2639-2645.
[http://dx.doi.org/10.1093/bioinformatics/btv212] [PMID: 25900916]
[209]
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. [JBSD], 2015, 33(8), 1720-1730.
[http://dx.doi.org/10.1080/07391102.2014.968624] [PMID: 25252709]
[210]
Zhu, P.P.; Li, W.C.; Zhong, Z.J.; Deng, E.Z.; Ding, H.; Chen, W.; Lin, H. Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition. Mol. Biosyst., 2015, 11(2), 558-563.
[http://dx.doi.org/10.1039/C4MB00645C] [PMID: 25437899]
[211]
Ahmad, K.; Waris, M.; Hayat, M. Prediction of protein submitochondrial locations by incorporating dipeptide composition into Chou’s general pseudo amino acid composition. J. Membr. Biol., 2016, 249(3), 293-304.
[http://dx.doi.org/10.1007/s00232-015-9868-8] [PMID: 26746980]
[212]
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]
[213]
Fan, G.L.; Liu, Y.L.; Wang, H. Identification of thermophilic proteins by incorporating evolutionary and acid dissociation information into Chou’s general pseudo amino acid composition. J. Theor. Biol., 2016, 407, 138-142.
[http://dx.doi.org/10.1016/j.jtbi.2016.07.010] [PMID: 27396359]
[214]
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.[JBSD], 2016, 34(9), 1946-1961.
[http://dx.doi.org/10.1080/07391102.2015.1095116] [PMID: 26375780]
[215]
Jiao, Y.S.; Du, P.F. Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection. J. Theor. Biol., 2016, 402, 38-44.
[http://dx.doi.org/10.1016/j.jtbi.2016.04.032] [PMID: 27155042]
[216]
Tang, H.; Chen, W.; Lin, H. Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol. Biosyst., 2016, 12(4), 1269-1275.
[http://dx.doi.org/10.1039/C5MB00883B] [PMID: 26883492]
[217]
Xu, C.; Sun, D.; Liu, S.; Zhang, Y. Protein sequence analysis by incorporating modified chaos game and physicochemical properties into Chou’s general pseudo amino acid composition. J. Theor. Biol., 2016, 406, 105-115.
[http://dx.doi.org/10.1016/j.jtbi.2016.06.034] [PMID: 27375218]
[218]
Zou, H.L.; Xiao, X. Predicting the functional types of singleplex and multiplex eukaryotic membrane proteins via different models of Chou’s pseudo amino acid compositions. J. Membr. Biol., 2016, 249(1-2), 23-29.
[http://dx.doi.org/10.1007/s00232-015-9830-9] [PMID: 26458844]
[219]
Zou, H.L.; Xiao, X. Classifying multifunctional enzymes by incorporating three different models into Chou's general pseudo amino acid composition J. Membr. Biol., 2016, 249, 561-567.
[http://dx.doi.org/10.1007/s00232-016-9904-3]
[220]
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]
[221]
Rahimi, M.; Bakhtiarizadeh, M.R.; Mohammadi-Sangcheshmeh, A. OOgenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou’s pseudo amino acid composition. J. Theor. Biol., 2017, 414, 128-136.
[http://dx.doi.org/10.1016/j.jtbi.2016.11.028] [PMID: 27916703]
[222]
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]
[223]
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]
[224]
Arif, M.; Hayat, M.; Jan, Z. iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou’s pseudo amino acid composition. J. Theor. Biol., 2018, 442, 11-21.
[http://dx.doi.org/10.1016/j.jtbi.2018.01.008] [PMID: 29337263]
[225]
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]
[226]
Mei, J.; Fu, Y.; Zhao, J. Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition. J. Theor. Biol., 2018, 456, 41-48.
[http://dx.doi.org/10.1016/j.jtbi.2018.07.040] [PMID: 30075172]
[227]
Mei, J.; Zhao, J. Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers. Sci. Rep., 2018, 8(1), 2359.
[http://dx.doi.org/10.1038/s41598-018-20819-x] [PMID: 29402983]
[228]
Mei, J.; Zhao, J. Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features. J. Theor. Biol., 2018, 447, 147-153.
[http://dx.doi.org/10.1016/j.jtbi.2018.03.034] [PMID: 29596863]
[229]
Awais, M.; Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. 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.
[http://dx.doi.org/10.1109/TCBB.2019.2919025]
[230]
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]
[231]
Butt, A.H.; Khan, Y.D. Prediction of S-sulfenylation sites using statistical moments based features via Chou's 5-step rule. Int. J. Pept. Res. Therap., 2019. [Epub ahead of print]
[http://dx.doi.org/10.1007/s10989-019-09931-2]
[232]
Barukab, O.; Khan, Y.D.; Khan, S.A.; Chou, K.C. iSulfoTyr-PseAAC: Identify tyrosine sulfation sites by incorporating statistical moments via Chou’s 5-steps rule and pseudo components. Curr. Genomics, 2019, 20(4), 306-320.
[http://dx.doi.org/10.2174/1389202920666190819091609]
[233]
Butt, A.H.; Khan, Y.D. Prediction of S-sulfenylation sites using statistical moments based features via Chou’s 5-step rule. Int. J. Pept. Res. Ther.[IJPRT], 2019. [Epub ahead of print]
[http://dx.doi.org/10.1007/s10989-019-09931-2]
[234]
Du, X.; Diao, Y.; Liu, H.; Li, S. MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule. J. Proteome Res., 2019, 18(8), 3119-3132.
[http://dx.doi.org/10.1021/acs.jproteome.9b00226] [PMID: 31267738]
[235]
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]
[236]
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]
[237]
Ju, Z.; Wang, S.Y. Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou’s 5-steps rule and general pseudo components. Genomics, 2019, S0888-7543(19), 30219-8.
[http://dx.doi.org/10.1016/j.ygeno.2019.05.027] [PMID: 31175975]
[238]
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, S0888-7543(18), 30694-3.
[http://dx.doi.org/10.1016/j.ygeno.2019.02.006] [PMID: 30779939]
[239]
Khan, Z.U.; Ali, F.; Khan, I.A.; Hussain, Y.; Pi, D. iRSpot-SPI: Deep learning-based recombination spots prediction byincorporating secondary sequence information coupled withphysio-chemical properties via Chou’s 5-step rule and pseudo components. Chemom. Intell. Lab. Syst.[CHEMOLAB], 2019, 189, 169-180.
[http://dx.doi.org/10.1016/j.chemolab.2019.05.003]
[240]
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. Genet. Genomics, 2019, 294(5), 1173-1182.
[http://dx.doi.org/10.1007/s00438-019-01570-y]
[241]
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]
[242]
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]
[243]
Liang, Y.; Zhang, S. Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou’s 5-steps rule. Biophys. Chem., 2019, 253 106227
[http://dx.doi.org/10.1016/j.bpc.2019.106227] [PMID: 31325710]
[244]
Nazari, I.; Tahir, M.; Tayari, H.; Chong, K.T. iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou’s 5-step rules and Chou’s general PseKNC. Chemom. Intell. Lab. Syst.[CHEMOLAB], 2019.
[http://dx.doi.org/10.1016/j.chemolab.2019.103811]
[245]
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]
[246]
Salman; Khan, M.; Iqbal, N.; Hussain, T.; Afzal, S.; Chou, K.C. 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.[IJPRT], 2019.
[http://dx.doi.org/10.1007/s10989-019-09887-3]
[247]
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]
[248]
Vishnoi, S.; Garg, P.; Arora, P. Physicochemical n-Grams Tool: A tool for protein physicochemical descriptor generation via Chou’s 5-step rule. Chem. Biol. Drug Des., 2019.
[http://dx.doi.org/10.1111/cbdd.13617] [PMID: 31483930]
[249]
Yang, L.; Lv, Y.; Wang, S.; Zhang, Q.; Pan, Y.; Su, D.; Lu, Q.; Zuo, Y. Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou’s 5-steps rule. Genomics, 2019, S0888-7543(19), 30391-X.
[http://dx.doi.org/10.1016/j.ygeno.2019.08.021] [PMID: 31472243]
[250]
Chou, K.C. Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem., 2004, 11(16), 2105-2134.
[http://dx.doi.org/10.2174/0929867043364667] [PMID: 15279552]
[251]
Kuo-chen, C.; Shou-ping, J. Studies on the rate of diffusion-controlled reactions of enzymes. Spatial factor and force field factor. Sci. Sin., 1974, 27(5), 664-680.
[PMID: 4219062]
[252]
Chou, K.C.; Kuo, C.K.; Li, T.T. The quantitative relations between diffusion-controlled reaction rate and characteristic parameters in enzyme-substrate reaction system: 2. Charged substrates. Sci. Sin., 1975, 18, 366-380.
[253]
Li, T.T.; Chou, K.C. The quantitative relations between diffusion-controlled reaction rate and characteristic parameters in enzyme-substrate reaction systems. I. Neutral substrates. Sci. Sin., 1976, 19(1), 117-136.
[PMID: 1273571]
[254]
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]
[255]
Chou, K.C.; Zhou, G.P. Role of the protein outside active site on the diffusion-controlled reaction of enzyme. J. Am. Chem. Soc., 1982, 104, 1409-1413.
[http://dx.doi.org/10.1021/ja00369a043]
[256]
Chou, K.C.; Chen, N.Y.; Forsen, S. The biological functions of low-frequency phonons: 2. Cooperative effects. Chem. Scr., 1981, 18, 126-132.
[257]
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]
[258]
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]
[259]
Chou, K.C. Biological functions of low-frequency vibrations (phonons). III. Helical structures and microenvironment. Biophys. J., 1984, 45(5), 881-889.
[http://dx.doi.org/10.1016/S0006-3495(84)84234-4] [PMID: 6428481]
[260]
Chou, K.C. 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]
[261]
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]
[262]
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]
[263]
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]
[264]
Chou, K.C.; Kiang, Y.S. The biological functions of low-frequency vibrations (phonons) 5. A phenomenological theory. Biophys. Chem., 1985, 22(3), 219-235.
[http://dx.doi.org/10.1016/0301-4622(85)80045-4] [PMID: 4052576]
[265]
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]
[266]
Chou, K.C. The biological functions of low-frequency vibrations (phonons). VI. 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]
[267]
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]
[268]
Chou, K.C.; Maggiora, G.M. The biological functions of low-frequency phonons: 7. The impetus for DNA to accommodate intercalators. Br. Polym. J., 1988, 20, 143-148.
[http://dx.doi.org/10.1002/pi.4980200209]
[269]
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]
[270]
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]
[271]
Liu, H.; Wang, M.; Chou, K.C. Low-frequency Fourier spectrum for predicting membrane protein types. Biochem. Biophys. Res. Commun.[BBRC], 2005, 336(3), 737-739.
[http://dx.doi.org/10.1016/j.bbrc.2005.08.160] [PMID: 16140260]
[272]
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.
[273]
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]
[274]
Chou, K.C. A new graphical rule for rate laws of enzyme reactions with branched pathways. Can. J. Biochem., 1981, 59(9), 757-761.
[http://dx.doi.org/10.1139/o81-105] [PMID: 7317822]
[275]
Chou, K.C.; Carter, R.E.; Forsen, S. A new graphical method for deriving rate equations for complicated mechanisms. Chem. Scr., 1981, 18, 82-86.
[276]
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]
[277]
Chou, K.C. Advances in graphic methods of enzyme kinetics. Biophys. Chem., 1983, 17(1), 51-55.
[http://dx.doi.org/10.1016/0301-4622(83)87013-6] [PMID: 6824763]
[278]
Chou, K.C. Graphic rules in steady and non-steady state enzyme kinetics. J. Biol. Chem., 1989, 264(20), 12074-12079.
[PMID: 2745429]
[279]
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]
[280]
Chou, K.C. Graphic rule for non-steady-state enzyme kinetics and protein folding kinetics. J. Math. Chem., 1993, 12, 97-108.
[http://dx.doi.org/10.1007/BF01164628]
[281]
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]
[282]
Wu, Z.C.; Xiao, X.; Chou, K.C. 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids. J. Theor. Biol., 2010, 267(1), 29-34.
[http://dx.doi.org/10.1016/j.jtbi.2010.08.007] [PMID: 20696175]
[283]
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]
[284]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene (Erratum: ibid., 2018, Vol.644, 156-156), 2017, 628, 315-321.
[285]
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]
[286]
Xiao, X.; Cheng, X.; Su, S.; Nao, Q.; Chou, K.C. 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]
[287]
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]
[288]
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]
[289]
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]
[290]
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]
[291]
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]
[292]
Chou, K.C.; Cheng, X.; Xiao, X. pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics, 2018, S0888-7543(18), 30276-3.
[http://dx.doi.org/10.1016/j.ygeno.2018.08.007] [PMID: 30179658]
[293]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K.C. 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]
[294]
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]
[295]
Chou, K.C.; 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), 472-485.
[http://dx.doi.org/10.2174/1573406415666181218102517] [PMID: 30569871]
[296]
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]
[297]
Chou, K.C. Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis. Curr. Top. Med. Chem., 2019, 19(25), 2283-2300.
[http://dx.doi.org/10.2174/1568026619666191018100141]

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