NS  Vol.1 No.2 , September 2009
REVIEW : Recent advances in developing web-servers for predicting protein attributes
ABSTRACT
Recent advance in large-scale genome se-quencing has generated a huge volume of pro-tein sequences. In order to timely utilize the in-formation hidden in these newly discovered sequences, it is highly desired to develop com- putational methods for efficiently identifying their various attributes because the information thus obtained will be very useful for both basic research and drug development. Particularly, it would be even more useful and welcome if a user-friendly web-server could be provided for each of these methods. In this minireview, a sy- stematic introduction is presented to highlight the development of these web-servers by our group during the last three years.

Cite this paper
Chou, K. and Shen, H. (2009) REVIEW : Recent advances in developing web-servers for predicting protein attributes. Natural Science, 1, 63-92. doi: 10.4236/ns.2009.12011.
References
[1]   Chou, K.C. (2002) A new branch of proteomics: predic-tion of protein cellular attributes. In Weinrer, P. W. and Lu, Q. (eds.), Gene Cloning & Expression Technologies, Chapter 4. Eaton Publishing, Westborough, MA, pp. 57-70.

[2]   Chou, K.C. (2004) Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry, 11, 2105-2134.

[3]   Chou, K.C. (2006) Structural bioinformatics and its im-pact to biomedical science and drug discovery. Frontiers in Medicinal Chemistry, 3, 455-502.

[4]   Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K. and Watson, J.D. (1994) Molecular Biology of the Cell, chap.1. 3rd ed. Garland Publishing, New York & Lon-don.

[5]   Lodish, H., Baltimore, D., Berk, A., Zipursky, S.L., Ma-tsudaira, P. and Darnell, J. (1995) Molecular Cell Biol-ogy , Chap.3. 3rd ed. Scientific American Books, New York.

[6]   Nakai, K. and Kanehisa, M. (1991) Expert system for predicting protein localization sites in Gram-negative bacteria. Proteins: Structure, Function and Genetics, 11, 95-110.

[7]   Nakashima, H. and Nishikawa, K. (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol, 238, 54-61.

[8]   Cedano, J., Aloy, P., P'erez-Pons, J.A. and Querol, E. (1997) Relation between amino acid composition and cellular location of proteins. J Mol Biol, 266, 594-600.

[9]   Nakai, K. and Horton, P. (1999) PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends in Biochemical Science, 24, 34-36.

[10]   Chou, K.C. and Elrod, D.W. (1998) Using discriminant function for prediction of subcellular location of pro-karyotic proteins. BBRC, 252, 63-68.

[11]   Reinhardt, A. and Hubbard, T. (1998) Using neural net-works for prediction of the subcellular location of pro-teins. Nucleic Acids Research, 26, 2230-2236.

[12]   Chou, K.C. and Elrod, D.W. (1999) Protein subcellular location prediction. Protein Engineering, 12, 107-118.

[13]   Yuan, Z. (1999) Prediction of protein subcellular loca-tions using Markov chain models. FEBS Letters, 451, 23-26.

[14]   Nakai, K. (2000) Protein sorting signals and prediction of subcellular localization. Advances in Protein Chemistry, 54, 277-344.

[15]   Murphy, R.F., Boland, M.V. and Velliste, M. (2000) To-wards a systematics for protein subcellular location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope im-ages. Proc Int Conf Intell Syst Mol Biol, 8, 251-259.

[16]   Chou, K.C. (2000) Review: Prediction of protein struc-tural classes and subcellular locations. Current Protein and Peptide Science, 1, 171-208.

[17]   Emanuelsson, O., Nielsen, H., Brunak, S. and von Heijne, G. (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. Journal of Molecular Biology, 300, 1005-1016.

[18]   Chou, K.C. (2001) Prediction of protein cellular attrib-utes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60), 43, 246-255.

[19]   Feng, Z.P. (2001) Prediction of the subcellular location of prokaryotic proteins based on a new representation of the amino acid composition. Biopolymers, 58, 491-499.

[20]   Hua, S. and Sun, Z. (2001) Support vector machine ap-proach for protein subcellular localization prediction. Bioinformatics, 17, 721-728.

[21]   Feng, Z.P. and Zhang, C.T. (2001) Prediction of the sub-cellular location of prokaryotic proteins based on the hy-drophobicity index of amino acids. Int J Biol Macromol, 28, 255-261.

[22]   Feng, Z.P. (2002) An overview on predicting the subcel-lular location of a protein. In Silico Biol, 2, 291-303.

[23]   Chou, K.C. and Cai, Y.D. (2002) Using functional do-main composition and support vector machines for pre-diction of protein subcellular location. J Biol Chem, 277, 45765-45769.

[24]   Zhou, G.P. and Doctor, K. (2003) Subcellular location prediction of apoptosis proteins. PROTEINS: Structure, Function, and Genetics, 50, 44-48.

[25]   Pan, Y.X., Zhang, Z.Z., Guo, Z.M., Feng, G.Y., Huang, Z.D. and He, L. (2003) Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. Journal of Protein Chemistry, 22, 395-402.

[26]   Park, K.J. and Kanehisa, M. (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acid and amino acid pairs. Bioin-formatics, 19, 1656-1663.

[27]   Gardy, J.L., Spencer, C., Wang, K., Ester, M., Tusnady, G.E., Simon, I., Hua, S., deFays, K., Lambert, C., Nakai, K. et al. (2003) PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nu-cleic Acids Research, 31, 3613-3617.

[28]   Huang, Y. and Li, Y. (2004) Prediction of protein subcel-lular locations using fuzzy k-NN method. Bioinformatics, 20, 21-28.

[29]   Xiao, X., Shao, S., Ding, Y., Huang, Z., Huang, Y. and Chou, K.C. (2005) Using complexity measure factor to predict protein subcellular location. Amino Acids, 28, 57-61.

[30]   Gao, Y., Shao, S.H., Xiao, X., Ding, Y.S., Huang, Y.S., Huang, Z.D. and Chou, K.C. (2005) Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids, 28, 373-376.

[31]   Lei, Z. and Dai, Y. (2005) An SVM-based system for predicting protein subnuclear localizations. BMC Bioin-formatics, 6, 291.

[32]   Shen, H.B. and Chou, K.C. (2005) Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem Biophys Res Comm, 337, 752-756.

[33]   Garg, A., Bhasin, M. and Raghava, G.P. (2005) Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. J Biol Chem, 280, 14427-14432.

[34]   Matsuda, S., Vert, J.P., Saigo, H., Ueda, N., Toh, H. and Akutsu, T. (2005) A novel representation of protein se-quences for prediction of subcellular location using sup-port vector machines. Protein Sci, 14, 2804-2813.

[35]   Gao, Q.B., Wang, Z.Z., Yan, C. and Du, Y.H. (2005) Prediction of protein subcellular location using a com-bined feature of sequence. FEBS Lett, 579, 3444-3448.

[36]   Chou, K.C. and Shen, H.B. (2006) Predicting protein subcellular location by fusing multiple classifiers. Jour-nal of Cellular Biochemistry, 99, 517-527.

[37]   Guo, J., Lin, Y. and Liu, X. (2006) GNBSL: A new inte-grative system to predict the subcellular location for Gram-negative bacteria proteins. Proteomics, 6, 5099-5105.

[38]   Xiao, X., Shao, S.H., Ding, Y.S., Huang, Z.D. and Chou, K.C. (2006) Using cellular automata images and pseudo amino acid composition to predict protein subcellular lo-cation. Amino Acids, 30, 49-54.

[39]   Hoglund, A., Donnes, P., Blum, T., Adolph, H.W. and Kohlbacher, O. (2006) MultiLoc: prediction of protein subcellular localization using N-terminal targeting se-quences, sequence motifs and amino acid composition. Bioinformatics, 22, 1158-1165.

[40]   Lee, K., Kim, D.W., Na, D., Lee, K.H. and Lee, D. (2006) PLPD: reliable protein localization prediction from im-balanced and overlapped datasets. Nucleic Acids Res, 34, 4655-4666.

[41]   Zhang, Z.H., Wang, Z.H., Zhang, Z.R. and Wang, Y.X. (2006) A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine. FEBS Lett, 580, 6169-6174.

[42]   Shi, J.Y., Zhang, S.W., Pan, Q., Cheng, Y.-M. and Xie, J. (2007) Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids, 33, 69-74.

[43]   Chou, K.C. and Shen, H.B. (2007) Large-scale plant protein subcellular location prediction. Journal of Cellu-lar Biochemistry, 100, 665-678.

[44]   Shen, H.B. and Chou, K.C. (2007) Hum-mPLoc: An ensemble classifier for large-scale human protein sub-cellular location prediction by incorporating samples with multiple sites. Biochem Biophys Res Commun, 355, 1006-1011.

[45]   Shen, H.B., Yang, J. and Chou, K.C. (2007) Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids, 33, 57-67.

[46]   Chen, Y.L. and Li, Q.Z. (2007) Prediction of apoptosis protein subcellular location using improved hybrid ap-proach and pseudo amino acid composition. Journal of Theoretical Biology, 248, 377–381.

[47]   Chen, Y.L. and Li, Q.Z. (2007) Prediction of the subcel-lular location of apoptosis proteins. Journal of Theoreti-cal Biology, 245, 775-783.

[48]   Mundra, P., Kumar, M., Kumar, K.K., Jayaraman, V.K. and Kulkarni, B.D. (2007) Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM. Pattern Recognition Letters, 28, 1610-1615.

[49]   Chou, K.C. and Shen, H.B. (2007) Review: Recent pro-gresses in protein subcellular location prediction. Ana-lytical Biochemistry, 370, 1-16.

[50]   Chou, K.C. and Shen, H.B. (2008) Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 3, 153-162.

[51]   Chou, K.C. and Shen, H.B. (2007) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research, 6, 1728-1734.

[52]   Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T. et al. (2000) Gene ontology: tool for the unification of biology. Nature Genetics, 25, 25-29.

[53]   Chou, K.C. (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioin-formatics, 21, 10-19.

[54]   Chou, K.C. and Shen, H.B. (2007) MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem Biophys Res Comm, 360, 339-345.

[55]   Chou, K.C. and Shen, H.B. (2008) ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem Biophys Res Comm, 376, 321-325.

[56]   Shen, H.B. and Chou, K.C. (2009) A top-down approach to enhance the power of predicting human protein sub-cellular localization: Hum-mPLoc 2.0. Analytical Bio-chemistry, in press.

[57]   Shen, H.B. and Chou, K.c. (2009) Gpos-mPLoc: A top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial pro-teins. Protein & Peptide Letters, submitted.

[58]   Shen, H.B. and Chou, K.C. (2009) Gneg-mPLoc: A top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial pro-teins, to be submitted.

[59]   Chou, K.C. and Shen, H.B. (2009) Plant-mPLoc: A top-down strategy to augment the power for predicting plant protein subcellular localization, to be submitted.

[60]   Shen, H.B. and Chou, K.C. (2007) Gpos-PLoc: an en-semble classifier for predicting subcellular localization of Gram-positive bacterial proteins. Protein Engineering, Design, and Selection, 20, 39-46.

[61]   Chou, K.C. and Shen, H.B. (2006) Large-scale predic-tions of Gram-negative bacterial protein subcellular loca-tions. Journal of Proteome Research, 5, 3420-3428.

[62]   Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K. and Walter, P. (2002) Molecular biology of the cell, 4th edition. Garland Science, New York.

[63]   Shen, H.B. and Chou, K.C. (2007) Nuc-PLoc: A new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Engineering, Design & Selection, 20, 561-567.

[64]   Rapoport, T.A. (1992) Transport of proteins across the endoplasmic reticulum membrane. Science, 258, 931-936.

[65]   Zheng, N. and Gierasch, L.M. (1996) Signal sequences: the same yet different. Cell, 86, 849-852.

[66]   Chou, K.C. (2001) Prediction of signal peptides using scaled window. Peptides, 22, 1973-1979.

[67]   McGeoch, D.J. (1985) On the predictive recognition of signal peptide sequences. Virus Res, 3, 271-286.

[68]   von Heijne, G. (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Research, 14, 4683-4690.

[69]   Folz, R.J. and Gordon, J.I. (1987) Computer-assisted predictions of signal peptidase processing sites. Biochem Biophys Res Comm, 146, 870-877.

[70]   Ladunga, I., Czako, F., Csabai, I. and Geszti, T. (1991) Improving signal peptide prediction accuracy by simu-lated neural network. Comput Appl Biosci, 7, 485-487.

[71]   Arrigo, P., Giuliano, F., Scalia, F., Rapallo, A. and Damiani, G. (1991) Identification of a new motif on nu-cleic acid sequence data using Kohonen's self-organizing map. Comput Appl Biosci, 7, 353-357.

[72]   Schneider, G. and Wrede, P. (1993) Signal analysis of protein targeting sequences. Protein Seq Data Anal, 5, 227-236.

[73]   Nielsen, H., Engelbrecht, J., Brunak, S. and von Heijne, G. (1997) Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering, 10, 1-6.

[74]   Emanuelsson, O., Nielsen, H. and von Heijne, G. (1999) ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites. Pro-tein Science, 8, 978-984.

[75]   Bendtsen, J.D., Nielsen, H., von Heijne, G. and Brunak, S. (2004) Improved prediction of signal peptides: SignalP 3.0. J Mol Biol, 340, 783-795.

[76]   Hiller, K., Grote, A., Scheer, M., Munch, R. and Jahn, D. (2004) PrediSi: prediction of signal peptides and their cleavage positions. Nucleic Acids Res, 32, W375-379.

[77]   Chou, K.C. (2002) Review: Prediction of protein signal sequences. Current Protein and Peptide Science, 3, 615-622.

[78]   Chou, K.C. (2001) Using subsite coupling to predict signal peptides. Protein Engineering, 14, 75-79.

[79]   Chou, K.C. and Shen, H.B. (2007) Signal-CF: a sub-site-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Comm, 357, 633-640.

[80]   Hiss, J.A. and Schneider, G. (2009) Architecture, function and prediction of long signal peptides. Brief Bioinform, 10, 569-578.

[81]   Kall, L., Krogh, A. and Sonnhammer, E.L. (2007) Ad-vantages of combined transmembrane topology and sig-nal peptide prediction--the Phobius web server. Nucleic Acids Res, 35, W429-432.

[82]   Shen, H.B. and Chou, K.C. (2007) Signal-3L: a 3-layer approach for predicting signal peptide. Biochem Bio-phys Res Comm, 363, 297-303.

[83]   Reynolds, S.M., Kall, L., Riffle, M.E., Bilmes, J.A. and Noble, W.S. (2008) Transmembrane topology and signal peptide prediction using dynamic bayesian networks. PLoS Comput Biol, 4, e1000213.

[84]   Chou, K.C. (2004) Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5. Biochemical and Biophysical Research Communications, 316, 636-642.

[85]   Chou, K.C. (1993) Conformational change during photocycle of bacteriorhodopsin and its proton-pumping mechanism. Journal of Protein Chemistry, 12, 337-350.

[86]   Chou, K.C. (1994) Mini Review: A molecular piston mechanism of pumping protons by bacteriorhodopsin. Amino Acids, 7, 1-17.

[87]   Schnell, J.R. and Chou, J.J. (2008) Structure and mecha-nism of the M2 proton channel of influenza A virus. Na-ture, 451, 591-595.

[88]   Doyle, D.A., Morais, C.J., Pfuetzner, R.A., Kuo, A., Gulbis, J.M., Cohen, S.L., Chait, B.T. and MacKinnon, R. (1998) The structure of the potassium channel: molecular basis of K+ conduction and selectivity. Science, 280, 69-77.

[89]   Chou, K.C. (2004) Insights from modelling three-dimensional structures of the human potassium and sodium channels. Journal of Proteome Research, 3, 856-861.

[90]   Huang, R.B., Du, Q.S., Wang, C.H. and Chou, K.C. (2008) An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of in-fluenza A virus. Biochem Biophys Res Comm, 377, 1243-1247.

[91]   Du, Q.S., Huang, R.B., Wang, C.H., Li, X.M. and Chou, K.C. (2009) Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus. Journal of Theoretical Biology, 259, 159-164.

[92]   Pielak, R.M., Jason R. Schnell, J.R. and Chou, J.J. (2009) Mechanism of drug inhibition and drug resistance of in-fluenza A M2 channel. Proceedings of National Acad-emy of Science, USA, 106, 7379-7384.

[93]   Oxenoid, K. and Chou, J.J. (2005) The structure of phospholamban pentamer reveals a channel-like archi-tecture in membranes. Proc Natl Acad Sci U S A, 102, 10870-10875.

[94]   Douglas, S.M., Chou, J.J. and Shih, W.M. (2007) DNA-nanotube-induced alignment of membrane proteins for NMR structure determination. Proc Natl Acad Sci U S A, 104, 6644-6648.

[95]   Nakashima, H., Nishikawa, K. and Ooi, T. (1986) The folding type of a protein is relevant to the amino acid composition. J Biochem, 99, 152-162.

[96]   Klein, P. and Delisi, C. (1986) Prediction of protein structural class from amino acid sequence. Biopolymers, 25, 1659-1672.

[97]   Klein, P. (1986) Prediction of protein structural class by discriminant analysis. Biochim Biophys Acta, 874, 205-215.

[98]   Chou, K.C. and Zhang, C.T. (1994) Predicting protein folding types by distance functions that make allowances for amino acid interactions. J Biol Chem, 269, 22014-22020.

[99]   Chou, K.C. (1995) A novel approach to predicting pro-tein structural classes in a (20-1)-D amino acid composi-tion space. Proteins: Structure, Function & Genetics, 21, 319-344.

[100]   Liu, W. and Chou, K.C. (1998) Prediction of protein structural classes by modified Mahalanobis discriminant algorithm. Journal of Protein Chemistry, 17, 209-217.

[101]   Chou, K.C., Liu, W., Maggiora, G.M. and Zhang, C.T. (1998) Prediction and classification of domain structural classes. PROTEINS: Structure, Function, and Genetics, 31, 97-103.

[102]   Chou, K.C. and Maggiora, G.M. (1998) Domain struc-tural class prediction. Protein Engineering, 11, 523-538.

[103]   Chou, K.C. (1999) A key driving force in determination of protein structural classes. Biochemical and Biophysi-cal Research Communications, 264, 216-224.

[104]   Chou, K.C. and Elrod, D.W. (1999) Prediction of mem-brane protein types and subcellular locations. PROTEINS: Structure, Function, and Genetics, 34, 137-153.

[105]   Cai, Y.D., Liu, X.J. and Chou, K.C. (2001) Artificial neural network model for predicting membrane protein types. Journal of Biomolecular Structure and Dynamics, 18, 607-610.

[106]   Guo, Z.M. (2002) Prediction of Membrane protein types by using pattern recognition method based on pseudo amino acid composition. Master Thesis, Bio-X Life Sci-ence Research Center, Shanghai Jiaotong University.

[107]   Cai, Y.D., Zhou, G.P. and Chou, K.C. (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophysical Journal, 84, 3257-3263.

[108]   Cai, Y.D., Pong-Wong, R., Feng, K., Jen, J.C.H. and Chou, K.C. (2004) Application of SVM to predict mem-brane protein types. Journal of Theoretical Biology, 226, 373-376.

[109]   Wang, M., Yang, J., Liu, G.P., Xu, Z.J. and Chou, K.C. (2004) Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition. Protein Engineering, Design, and Selection, 17, 509-516.

[110]   Chou, K.C. and Cai, Y.D. (2005) Prediction of membrane protein types by incorporating amphipathic effects. Journal of Chemical Information and Modeling, 45, 407-413.

[111]   Liu, H., Wang, M. and Chou, K.C. (2005) Low-frequency Fourier spectrum for predicting mem-brane protein types. Biochem Biophys Res Commun, 336, 737-739.

[112]   Wang, M., Yang, J., Xu, Z.J. and Chou, K.C. (2005) SLLE for predicting membrane protein types. Journal of Theoretical Biology, 232, 7-15.

[113]   Shen, H.B. and Chou, K.C. (2005) Using optimized evi-dence-theoretic K-nearest neighbor classifier and pseudo amino acid composition to predict membrane protein types. Biochemical & Biophysical Research Communica-tions, 334, 288-292.

[114]   Shen, H.B., Yang, J. and Chou, K.C. (2006) Fuzzy KNN for predicting membrane protein types from pseudo amino acid composition. Journal of Theoretical Biology, 240, 9-13.

[115]   Wang, S.Q., Yang, J. and Chou, K.C. (2006) Using stacked generalization to predict membrane protein types based on pseudo amino acid composition. Journal of Theoretical Biology, 242, 941-946.

[116]   Shen, H.B. and Chou, K.C. (2007) Using ensemble clas-sifier to identify membrane protein types. Amino Acids, 32, 483-488.

[117]   Yang, X.G., Luo, R.Y. and Feng, Z.P. (2007) Using amino acid and peptide composition to predict membrane pro-tein types. Biochem Biophys Res Commun, 353, 164-169.

[118]   Pu, X., Guo, J., Leung, H. and Lin, Y. (2007) Prediction of membrane protein types from sequences and posi-tion-specific scoring matrices. J Theor Biol, 247, 259–265.

[119]   Afjehi-Sadat, L. and Lubec, G. (2007) Identification of enzymes and activity from two-dimensional gel electro-phoresis. Nature Protocols, 2, 2318-2324.

[120]   Chou, K.C. and Elrod, D.W. (2003) Prediction of enzyme family classes. Journal of Proteome Research, 2, 183-190.

[121]   Chou, K.C. and Cai, Y.D. (2004) Predicting enzyme fam-ily class in a hybridization space. Protein Science, 13, 2857-2863.

[122]   Cai, C.Z., Han, L.Y., Ji, Z.L. and Chen, Y.Z. (2004) En-zyme family classification by support vector machines. PROTEINS: Structure, Function, and Bioinformatics, 55, 66-76.

[123]   1Cai, Y.D. and Chou, K.C. (2005) Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. Journal of Proteome Research, 4, 967-971.

[124]   Huang, W.L., Chen, H.M., Hwang, S.F. and Ho, S.Y. (2006) Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method. Bio-systems, 90, 405-413.

[125]   Zhou, X.B., Chen, C., Li, Z.C. and Zou, X.Y. (2007) Using Chou's amphiphilic pseudo-amino acid composi-tion and support vector machine for prediction of enzyme subfamily classes. Journal of Theoretical Biology, 248, 546–551.

[126]   Shen, H.B. and Chou, K.C. (2007) EzyPred: A top-down approach for predicting enzyme functional classes and subclasses. Biochem Biophys Res Comm, 364, 53-59.

[127]   Bairoch, A. (2000) The ENZYME Database in 2000. Nucleic Acids Research, 28, 304-305.

[128]   Poorman, R.A., Tomasselli, A.G., Heinrikson, R.L. and Kezdy, F.J. (1991) A cumulative specificity model for proteases from human immunodeficiency virus types 1 and 2, inferred from statistical analysis of an extended substrate data base. J Biol Chem, 266, 14554-14561.

[129]   Qin, H., Srinvasula, S.M., Wu, G., Fernandes-Alnemri, T., Alnemri, E.S., and Shi, Y. (1999) Structural basis of pro-caspase-9 recruitment by the apoptotic prote-ase-activating factor 1. Nature, 399, 549-557.

[130]   Chou, J.J., Li, H., Salvessen, G.S., Yuan, J. and Wagner, G. (1999) Solution structure of BID, an intracellular am-plifier of apoptotic signalling. Cell, 96, 615-624.

[131]   Watt, W., Koeplinger, K.A., Mildner, A.M., Heinrikson, R.L., Tomasselli, A.G. and Watenpaugh, K.D. (1999) The atomic resolution structure of human caspase-8, a key activator of apoptosis. Structure, 7, 1135-1143.

[132]   Chou, K.C., Wei, D.Q. and Zhong, W.Z. (2003) Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. (Erra-tum: ibid., 2003, Vol.310, 675). Biochem Biophys Res Comm, 308, 148-151.

[133]   Puente, X.S., Sanchez, L.M., Overall, C.M. and Lo-pez-Otin, C. (2003) Human and mouse proteases: a comparative genomic approach. Nat Rev Genet, 4, 544-558.

[134]   Chou, K.C., Wei, D.Q., Du, Q.S., Sirois, S., Shen, H.B. and Zhong, W.Z. (2009) Study of inhibitors against SARS coronavirus by computational approaches. In Lendeckel, U. and Hooper, N. (eds.), Viral proteases and antiviral protease inhibitor therapy. Proteases in Biology and Disease, Springer Publishing, 8.

[135]   Chou, K.C. (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in pro-teins. J Biol Chem, 268, 16938-16948.

[136]   Chou, K.C. (1996) Review: Prediction of HIV protease cleavage sites in proteins. Analytical Biochemistry, 233, 1-14.

[137]   You, L., Garwicz, D. and Rognvaldsson, T. (2005) Com-prehensive bioinformatic analysis of the specificity of human immunodeficiency virus type 1 protease. J Virol, 79, 12477-12486.

[138]   Rognvaldsson, T., You, L. and Garwicz, D. (2007) Bio-informatic approaches for modeling the substrate speci-ficity of HIV-1 protease: an overview. Expert Rev Mol Diagn, 7, 435-451.

[139]   Liang, G.Z. and Li, S.Z. (2007) A new sequence repre-sentation as applied in better specificity elucidation for human immunodeficiency virus type 1 protease. Bio-polymers, 88, 401-412.

[140]   Rawlings, N.D., Tolle, D.P. and Barrett, A.J. (2004) MEROPS: the peptidase database. Nucleic Acids Re-search, 32, D160-D164.

[141]   Chou, K.C. and Cai, Y.D. (2006) Prediction of protease types in a hybridization space. Biochem Biophys Res Comm, 339, 1015-1020.

[142]   Zhou, G.P. and Cai, Y.D. (2006) Predicting protease types by hybridizing gene ontology and pseudo amino acid composition. PROTEINS: Structure, Function, and Bio-informatics, 63, 681-684.

[143]   Shen, H.B. and Chou, K.C. (2009) Identification of pro-teases and their types. Analytical Biochemistry, 385, 153-160.

[144]   Heuss, C. and Gerber, U. (2000) G-protein-independent signaling by G-protein-coupled receptors. Trends Neuro-sci, 23, 469-475.

[145]   Milligan, G. and White, J.H. (2001) Protein-protein in-teractions at G-protein-coupled receptors. Trends Phar-macol Sci, 22, 513-518.

[146]   Hall, R.A. and Lefkowitz, R.J. (2002) Regulation of G protein-coupled receptor signaling by scaffold proteins. Circ Res, 91, 672-680.

[147]   Chou, K.C. (2005) Coupling interaction between throm-boxane A2 receptor and alpha-13 subunit of guanine nu-cleotide-binding protein. Journal of Proteome Research, 4, 1681-1686.

[148]   Call, M.E., Schnell, J.R., Xu, C., Lutz, R.A., Chou, J.J. and Wucherpfennig, K.W. (2006) The structure of the zetazeta transmembrane dimer reveals features essential for its assembly with the T cell receptor. Cell, 127, 355-368.

[149]   Chou, K.C. (2004) Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochemical and Biophysical Re-search Communication, 319, 433-438.

[150]   Chou, K.C. (2004) Molecular therapeutic target for type-2 diabetes. Journal of Proteome Research, 3, 1284-1288.

[151]   Wei, D.Q., Du, Q.S., Sun, H. and Chou, K.C. (2006) Insights from modeling the 3D structure of H5N1 influ-enza virus neuraminidase and its binding interactions with ligands. Biochem Biophys Res Comm, 344, 1048-1055.

[152]   Wang, S.Q., Du, Q.S. and Chou, K.C. (2007) Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuramini-dases. Biochem Biophys Res Comm, 354, 634-640.

[153]   Wang, S.Q., Du, Q.S., Huang, R.B., Zhang, D.W. and Chou, K.C. (2009) Insights from investigating the inter-action of oseltamivir (Tamiflu) with neuraminidase of the 2009 H1N1 swine flu virus. Biochem Biophys Res Com-mun, 386, 432-436.

[154]   Elrod, D.W. and Chou, K.C. (2002) A study on the corre-lation of G-protein-coupled receptor types with amino acid composition. Protein Engineering, 15, 713-715.

[155]   Chou, K.C. and Elrod, D.W. (2002) Bioinformatical analysis of G-protein-coupled receptors. Journal of Pro-teome Research, 1, 429-433.

[156]   Bhasin, M. and Raghava, G.P. (2005) GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors. Nucleic Acids Research, 33, W143-147.

[157]   Chou, K.C. (2005) Prediction of G-protein-coupled re-ceptor classes. Journal of Proteome Research, 4, 1413-1418.

[158]   Wen, Z., Li, M., Li, Y., Guo, Y. and Wang, K. (2007) Delaunay triangulation with partial least squares projec-tion to latent structures: a model for G-protein coupled receptors classification and fast structure recognition. Amino Acids, 32, 277-283.

[159]   Gao, Q.B. and Wang, Z.Z. (2006) Classification of G-protein coupled receptors at four levels. Protein Eng Des Sel, 19, 511-516.

[160]   Xiao, X., Wang, P. and Chou, K.C. (2009) GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. Journal of Computational Chemistry, 30, 1414-1423.

[161]   Wolfram, S. (1984) Cellular automation as models of complexity. Nature, 311, 419-424.

[162]   Wolfram, S. (2002) A New Kind of Science. Wolfram Media Inc., Champaign, IL.

[163]   Xiao, X., Shao, S., Ding, Y., Huang, Z., Chen, X. and Chou, K.C. (2005) Using cellular automata to generate Image representation for biological sequences. Amino Acids, 28, 29-35.

[164]   Chou, K.C. (2000) Prediction of protein subcellular loca-tions by incorporating quasi-sequence-order effect. Bio-chemical & Biophysical Research Communications, 278, 477-483.

[165]   Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcrip-tase inhibitor U-87201E. J Biol Chem, 268, 6119-6124.

[166]   Althaus, I.W., Gonzales, A.J., Chou, J.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcrip-tase. J Biol Chem, 268, 14875-14880.

[167]   Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1993) Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry, 32, 6548-6554.

[168]   Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1994) Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia, 50, 23-28.

[169]   Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Thomas, R.C., Aristoff, P.A., Tarpley, W.G. et al. (1994) Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase in-hibitor U-90152E. Biochemical Pharmacology, 47, 2017-2028.

[170]   Althaus, I.W., Chou, K.C., Franks, K.M., Diebel, M.R., Kezdy, F.J., Romero, D.L., Thomas, R.C., Aristoff, P.A., Tarpley, W.G. and Reusser, F. (1996) The benzyl-thio-pyrididine U-31,355 is a potent inhibitor of HIV-1 reverse transcriptase. Biochemical Pharmacology, 51, 743-750.

[171]   Chou, K.C., Kezdy, F.J. and Reusser, F. (1994) Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Analytical Biochemistry, 221, 217-230.

[172]   McQuade, T.J., Tomasselli, A.G., Liu, L., Karacostas, V., Moss, B., Sawyer, T.K., Heinrikson, R.L. and Tarpley, W.G. (1990) A synthetic HIV-1 protease inhibitor with antiviral activity arrests HIV-like particle maturation. Science, 247, 454-456.

[173]   Meek, T.D., Lambert, D.M., Dreyer, G.B., Carr, T.J., Tomaszek, T.A., Jr., Moore, M.L., Strickler, J.E., De-bouck, C., Hyland, L.J., Matthews, T.J. et al. (1990) In-hibition of HIV-1 protease in infected T-lymphocytes by synthetic peptide analogues. Nature, 343, 90-92.

[174]   Wlodawer, A. and Erickson, J.W. (1993) Structure-based inhibitors of HIV-1 protease. Annu Rev Biochem, 62, 543-585.

[175]   Barre-Sinoussi, F., Chermann, J.C., Rey, F., Nugeyre, M.T., Chamaret, S., Gruest, J., Dauguet, C., Axler-Blin, C., Vezinet-Brun, F., Rouzioux, C. et al. (1983) Isolation of a T-lymphotropic retrovirus from a patient at risk for acquired immune deficiency syndrome (AIDS). Science, 220, 868-871.

[176]   Gallo, R.C., Salahuddin, S.Z., Popovic, M., Shearer, G.M., Kaplan, M., Haynes, B.F., Palker, T.J., Redfield, R., Oleske, J., Safai, B. et al. (1984) Frequent detection and isolation of cytopathic retroviruses (HTLV-III) from pa-tients with AIDS and at risk for AIDS. Science, 224, 500-503.

[177]   Miller, M., Schneider, J., Sathyanarayana, B.K., Toth, M.V., Marshall, G.R., Clawson, L., Selk, L., Kent, S.B. and Wlodawer, A. (1989) Structure of complex of syn-thetic HIV-1 protease with a substrate-based inhibitor at 2.3 A resolution. Science, 246, 1149-1152.

[178]   Schechter, I. and Berger, A. (1967) On the size of the active site in protease. I. Papain. Biochem Biophys Res Comm, 27, 157-162.

[179]   Chou, K.C., Chen, N.Y. and Forsen, S. (1981) The bio-logical functions of low-frequency phonons: 2. Coopera-tive effects. Chemica Scripta, 18, 126-132.

[180]   Chou, K.C., Zhang, C.T. and Kezdy, F.J. (1993) A vector approach to predicting HIV protease cleavage sites in proteins. Proteins: Structure, Function, and Genetics, 16, 195-204.

[181]   Chou, J.J. (1993) Predicting cleavability of peptide se-quences by HIV protease via correlation-angle approach. Journal of Protein Chemistry, 12, 291-302.

[182]   Chou, K.C. and Zhang, C.T. (1993) Studies on the speci-ficity of HIV protease: an application of Markov chain theory. Journal of Protein Chemistry, 12, 709-724.

[183]   Chou, J.J. (1993) A formulation for correlating properties of peptides and its application to predicting human im-munodeficiency virus protease-cleavable sites in proteins. Biopolymers, 33, 1405-1414.

[184]   Zhang, C.T. and Chou, K.C. (1993) An alter-nate-subsite-coupled model for predicting HIV protease cleavage sites in proteins. Protein Engineering, 7, 65-73.

[185]   Thompson, T.B., Chou, K.C. and Zheng, C. (1995) Neu-ral network prediction of the HIV-1 protease cleavage sites. Journal of Theoretical Biology 177, 369-379.

[186]   Chou, K.C., Tomasselli, A.L., Reardon, I.M. and Hein-rikson, R.L. (1996) Predicting HIV protease cleavage sites in proteins by a discriminant function method. PROTEINS: Structure, Function, and Genetics, 24, 51-72.

[187]   Shen, H.B. and Chou, K.C. (2008) HIVcleave: a web-server for predicting HIV protease cleavage sites in proteins. Analytical Biochemistry, 375, 388-390.

[188]   Klotz, I.M., Darnell, D.W. and Langerman, N.R. (1975) Quaternary structure of proteins. In Neurath, H. and Hill, R. L. (eds.), The Proteins (3rd ed). Academic Press, New York, 1, 226-241.

[189]   Chou, K.C. and Cai, Y.D. (2003) Predicting protein qua-ternary structure by pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics, 53, 282-289.

[190]   Goodsell, D.S. and Olson, A.J. (2000) Structural symme-try and protein function. Annu Rev Biophys Biomol Struct, 29, 105-153.

[191]   Levy, E.D., Boeri Erba, E., Robinson, C.V. and Teichmann, S.A. (2008) Assembly reflects evolution of protein complexes. Nature, 453, 1262-1265.

[192]   Chen, Z., Alcayaga, C., Suarez-Isla, B.A., O'Rourke, B., Tomaselli, G. and Marban, E. (2002) A "minimal" sodium channel construct consisting of ligated S5-P-S6 segments forms a toxin-activatable ionophore. J Biol Chem, 277, 24653-24658.

[193]   Oxenoid, K., Rice, A.J. and Chou, J.J. (2007) Comparing the structure and dynamics of phospholamban pentamer in its unphosphorylated and pseudo-phosphorylated states. Protein Sci, 16, 1977-1983.

[194]   Tretter, V., Ehya, N., Fuchs, K. and Sieghart, W. (1997) Stoichiometry and assembly of a recombinant GABAA receptor subtype. Journal of Neuroscience, 17, 2728-2737.

[195]   Perutz, M.F. (1964) The Hemoglobin Molecule. Scien-tific American, 211, 65-76.

[196]   Wei, H., Wang, C.H., Du, Q.S., Meng, J. and Chou, K.C. (2009) Investigation into adamantane-based M2 inhibi-tors with FB-QSAR. Medicinal Chemistry, 5, 305-317.

[197]   Shen, H.B. and Chou, K.C. (2009) QuatIdent: A web server for identifying protein quaternary structural attrib-ute by fusing functional domain and sequential evolution information. Journal of Proteome Research, 8, 1577–1584.

[198]   Xiao, X., Wang, P. and Chou, K.C. (2009) Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition. Journal of Applied Crystallography, 42, 169-173.

[199]   Garian, R. (2001) Prediction of quaternary structure from primary structure. Bioinformatics, 17, 551-556.

[200]   Zhang, S.W., Chen, W., Yang, F. and Pan, Q. (2008) Us-ing Chou's pseudo amino acid composition to predict protein quaternary structure: a sequence-segmented PseAAC approach. Amino Acids, 35, 591-598.

[201]   Anfinsen, C.B. and Scheraga, H.A. (1975) Experimental and theoretical aspects of protein folding. Adv Protein Chem, 29, 205-300.

[202]   Aguzzi, A. (2008) Unraveling prion strains with cell biology and organic chemistry. Proc Natl Acad Sci U S A, 105, 11-12.

[203]   Dobson, C.M. (2001) The structural basis of protein folding and its links with human disease. Philos Trans R Soc Lond B Biol Sci, 356, 133-145.

[204]   Prusiner, S.B. (1998) Prions. Proc Natl Acad Sci U S A, 95, 13363-13383.

[205]   Anfinsen, C.B. (1973) Principles that govern the folding of protein chains. Science, 181, 223-230.

[206]   Chou, K.C. and Scheraga, H.A. (1982) Origin of the right-handed twist of beta-sheets of poly-L-valine chains. Proceedings of National Academy of Sciences, USA, 79, 7047-7051.

[207]   Chou, K.C., Maggiora, G.M., Nemethy, G. and Scheraga, H.A. (1988) Energetics of the structure of the four-alpha-helix bundle in proteins. Proceedings of Na-tional Academy of Sciences, USA, 85, 4295-4299.

[208]   Chou, K.C., Nemethy, G. and Scheraga, H.A. (1990) Review: Energetics of interactions of regular structural elements in proteins. Accounts of Chemical Research, 23, 134-141.

[209]   Chou, K.C., Nemethy, G. and Scheraga, H.A. (1984) Energetic approach to packing of a-helices: 2. General treatment of nonequivalent and nonregular helices. Journal of American Chemical Society, 106, 3161-3170.

[210]   Chou, K.C., Nemethy, G., Pottle, M. and Scheraga, H.A. (1989) Energy of stabilization of the right-handed beta-alpha-beta crossover in proteins. Journal of Mo-lecular Biology, 205, 241-249.

[211]   Chou, K.C. and Carlacci, L. (1991) Energetic approach to the folding of alpha/beta barrels. Proteins: Structure, Function, and Genetics, 9, 280-295.

[212]   Chou, K.C. (1992) Energy-optimized structure of anti-freeze protein and its binding mechanism. Journal of Molecular Biology, 223, 509-517.

[213]   Carlacci, L., Chou, K.C. and Maggiora, G.M. (1991) A heuristic approach to predicting the tertiary structure of bovine somatotropin. Biochemistry, 30, 4389-4398.

[214]   Scheraga, H.A., Khalili, M. and Liwo, A. (2007) Pro-tein-folding dynamics: overview of molecular simulation techniques. Annu Rev Phys Chem, 58, 57-83.

[215]   Holm, L. and Sander, C. (1999) Protein folds and fami-lies: sequence and structure alignments. Nucleic Acids Research, 27, 244-247.

[216]   Chou, K.C. (1995) The convergence-divergence duality in lectin domains of the selectin family and its implica-tions. FEBS Letters, 363, 123-126.

[217]   Chou, K.C., Jones, D. and Heinrikson, R.L. (1997) Pre-diction of the tertiary structure and substrate binding site of caspase-8. FEBS Letters, 419, 49-54.

[218]   Chou, J.J., Matsuo, H., Duan, H. and Wagner, G. (1998) Solution structure of the RAIDD CARD and model for CARD/CARD interaction in caspase-2 and caspase-9 re-cruitment. Cell, 94, 171-180.

[219]   Chou, K.C., Tomasselli, A.G. and Heinrikson, R.L. (2000) Prediction of the Tertiary Structure of a Cas-pase-9/Inhibitor Complex. FEBS Letters, 470, 249-256.

[220]   Chou, K.C. and Howe, W.J. (2002) Prediction of the tertiary structure of the beta-secretase zymogen. BBRC, 292, 702-708.

[221]   Du, Q.S., Wang, S., Wei, D.Q., Sirois, S. and Chou, K.C. (2005) Molecular modelling and chemical modification for finding peptide inhibitor against SARS CoV Mpro. Analytical Biochemistry, 337, 262-270.

[222]   Zhang, R., Wei, D.Q., Du, Q.S. and Chou, K.C. (2006) Molecular modeling studies of peptide drug candidates against SARS. Medicinal Chemistry, 2, 309-314.

[223]   Wang, J.F., Wei, D.Q., Li, L., Zheng, S.Y., Li, Y.X. and Chou, K.C. (2007) 3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug de-sign. Biochem Biophys Res Commun (Corrigendum: ibid, 2007, Vol357, 330), 355, 513-519.

[224]   Wang, J.F., Wei, D.Q., Lin, Y., Wang, Y.H., Du, H.L., Li, Y.X. and Chou, K.C. (2007) Insights from modeling the 3D structure of NAD(P)H-dependent D-xylose reductase of Pichia stipitis and its binding interactions with NAD and NADP. Biochem Biophys Res Comm, 359, 323-329.

[225]   Wang, J.F., Wei, D.Q., Chen, C., Li, Y. and Chou, K.C. (2008) Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design. Protein & Peptide Letters, 15, 27-32.

[226]   Wang, J.F., Wei, D.Q., Du, H.L., Li, Y.X. and Chou, K.C. (2008) Molecular modeling studies on NADP-dependence of Candida tropicalis strain xylose reductase. The Open Bioinformatics Journal, 2, 72-79.

[227]   Ding, C.H. and Dubchak, I. (2001) Multi-class protein fold recognition using support vector machines and neu-ral networks. Bioinformatics, 17, 349-358.

[228]   Finkelstein, A.V. and Ptitsyn, O.B. (1987) Why do globular proteins fit the limited set of folding patterns? Prog Biophys Mol Biol, 50, 171-190.

[229]   Chou, K.C. and Zhang, C.T. (1995) Review: Prediction of protein structural classes. Critical Reviews in Bio-chemistry and Molecular Biology, 30, 275-349.

[230]   Dubchak, I., Muchnik, I., Mayor, C., Dralyuk, I. and Kim, S.H. (1999) Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) clas-sification. PROTEINS: Structure, Function, and Genetics, 35, 401-407.

[231]   Murzin, A.G., Brenner, S.E., Hubbard, T. and Chothia, C. (1995) SCOP: a structural classification of protein data-base for the investigation of sequence and structures. Journal of Molecular Biology, 247, 536-540.

[232]   Shen, H.B. and Chou, K.C. (2006) Ensemble classifier for protein fold pattern recognition. Bioinformatics, 22, 1717-1722.

[233]   Chou, K.C. and Shen, H.B. (2006) Predicting eukaryotic protein subcellular location by fusing optimized evi-dence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research, 5, 1888-1897.

[234]   Shen, H.B. and Chou, K.C. (2009) Predicting protein fold pattern with functional domain and sequential evo-lution information. Journal of Theoretical Biology, 256, 441-446.

[235]   Qiu, L.L., Pabit, S.A., Roitberg, A.E. and Hagen, S.J. (2002) Smaller and faster: The 20-residue Trp-cage pro-tein folds in 4 microseconds. Journal of American Chemical Society, 124, 12952-12953.

[236]   Goldberg, M.E., Semisotnov, G.V., Friguet, B., Kuwajima, K., Ptitsyn, O.B. and Sugai, S. (1990) An early immuno-reactive folding intermediate of the tryptophan synthease beta 2 subunit is a 'molten globule'. FEBS Lett, 263, 51-56.

[237]   Plaxco, K.W., Simons, K.T. and Baker, D. (1998) Contact order, transition state placement and the refolding rates of single domain proteins. J Mol Biol, 277, 985-994.

[238]   Ivankov, D.N., Garbuzynskiy, S.O., Alm, E., Plaxco, K.W., Baker, D. and Finkelstein, A.V. (2003) Contact or-der revisited: influence of protein size on the folding rate. Protein Science, 12, 2057-2062.

[239]   Zhou, H. and Zhou, Y. (2002) Folding rate prediction using total contact distance. Biophys Journal, 82, 458-463.

[240]   Gromiha, M.M. and Selvaraj, S. (2001) Comparison between long-range interactions and contact order in de-termining the folding rate of two-state proteins: applica-tion of long-range order to folding rate prediction. J Mol Biol, 310, 27-32.

[241]   Nolting, B., Schalike, W., Hampel, P., Grundig, F., Gantert, S., Sips, N., Bandlow, W. and Qi, P.X. (2003) Structural determinants of the rate of protein folding. J Theor Biol, 223, 299-307.

[242]   Ouyang, Z. and Liang, J. (2008) Predicting protein fold-ing rates from geometric contact and amino acid se-quence. Protein Science, 17, 1256-1263.

[243]   Ivankov, D.N. and Finkelstein, A.V. (2004) Prediction of protein folding rates from the amino acid se-quence-predicted secondary structure. Proc Natl Acad Sci USA, 101, 8942-8944.

[244]   Gromiha, M.M., Thangakani, A.M. and Selvaraj, S. (2006) FOLD-RATE: prediction of protein folding rates from amino acid sequence. Nucleic Acids Res, 34, W70-74.

[245]   Chou, K.C. (1990) Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophysical Chemistry, 35, 1-24.

[246]   Chou, K.C. (1989) Graphical rules in steady and non-steady enzyme kinetics. J Biol Chem, 264, 12074-12079.

[247]   Lin, S.X. and Neet, K.E. (1990) Demonstration of a slow conformational change in liver glucokinase by fluores-cence spectroscopy. J Biol Chem, 265, 9670-9675.

[248]   Beyer, W.H. (1988) CRC Handbook of Mathematical Science, (6th Edition), Chapter 10, page 544. CRC Press, Inc., Boca Raton, Florida.

[249]   Shen, H.B., Song, J.N. and Chou, K.C. (2009) Prediction of protein folding rates from primary sequence by fusing multiple sequential features. Journal of Biomedical Sci-ence and Engineering (JBiSE), 2, 136-143 (open acces-sible at http://www.srpublishing.org/journal/jbise/).

[250]   Chou, K.C. and Shen, H.B. (2009) FoldRate: A web-server for predicting protein folding rates from pri-mary sequence. The Open Bioinformatics Journal, 3, 31-50 (open accessible at http://www.bentham.org/open/tobioij/).

[251]   Zhang, Z. and Henzel, W.J. (2004) Signal peptide predic-tion based on analysis of experimentally verified cleav-age sites. Protein Sci, 13, 2819-2824.

[252]   Spector, D.L. (2001) Nuclear domains. J Cell Sci, 114, 2891-2893.

[253]   Spiess, M. (1995) Heads or tails - what determines the orientation of proteins in the membrane. FEBS Lett, 369, 76-79.

[254]   Schulz, G.E. and Schirmer, R.H. (1985) Principles of Protein Structure, Chapter 2, Springer-Verlag, New York.17-18.

 
 
Top