Abstract
Enzymatic reactions are very basic processes in biological systems, and parameters related to enzymatic reactions always provide good indicators for understanding of mechanisms underlined in enzymatic reactions, for better controlling of enzymatic reactions, and for comparison of different enzymes. In this mini-review: first, parameters in enzymatic reactions were briefly reviewed from three different standpoints; second, predictions of parameters in enzymatic reactions without information on enzyme structure were shortly reviewed from viewpoints of geometric approach, graphic approach and compartmental approach; third, predictions of parameters in enzymatic reaction with information on enzyme structure were reviewed from the points of view of modeling, with 19 currently available databases, and 17 software packages and web servers; fourth, the current state of prediction on parameters in enzymatic reaction in biofuel industry with respect to cellulolytic enzymes were reviewed; fifth, the pros and cons for future development were discussed; and finally, a worked example was given in the Appendix to describe the whole procedures of prediction of enzymatic parameters in reactions.
Similar content being viewed by others
References
Bairoch, A. (2000). Nucleic Acids Research, 28, 304–5.
McDonald, A. G., Boyce, S., Moss, G. P., Dixon, H. B., & Tipton, K. F. (2007). BMC Biochemistry, 8, 14.
McDonald, A. G., Boyce, S., & Tipton, K. F. (2009). Nucleic Acids Research, 37, D593–7.
Bannert, C., Welfle, A., Aus-dem Spring, C., & Schomburg, D. (2010). BMC Bioinformatics, 11, 589.
Chen, L. H., Kenyon, G. L., Curtin, F., Harayama, S., Bembenek, M. E., Hajipour, G., & Whitman, C. P. (1992). Journal of Biological Chemistry, 267, 17716–21.
Smith, S. (1994). FASEB Journal, 8, 1248–59.
Jeong, G. T., & Park, D. H. (2010). Applied Biochemistry and Biotechnology, 161, 195–208.
Samhan-Arias, A. K., Tyurina, Y. Y., & Kagan, V. E. (2011). Journal of Clinical Biochemistry and Nutrition, 48, 91–5.
Ishikawa, Y., & Fujii, S. (2011). Bioinformation, 6, 221–5.
Smith, R. H., Jr., Jorgensen, W. L., Tirado-Rives, J., Lamb, M. L., Janssen, P. A., Michejda, C. J., & Kroeger Smith, M. B. (1998). Journal of Medical Chemistry, 41, 5272–86.
Kumar, V., & Madan, A. K. (2005). European Journal of Pharmaceutical Sciences, 24, 213–8.
Sárváry, E., Nemes, B., Járay, J., Dinya, E., Borka, P., Varga, M., Sulyok, B., Remport, A., Tóth, A., & Perner, F. (2000). Transplantation, 69, 1397–402.
Wang, J. S., Araki, T., Ogawa, T., Sakai, M., Matsuoka, M., & Fukuda, H. (1999). Journal of Theoretical Biology, 196, 9–17.
Kirchmair, J., Williamson, M. J., Tyzack, J. D., Tan, L., Bond, P. J., Bender, A., & Glen, R. C. (2012). Journal of Chemical Information and Modeling, 52, 617–48.
Burgard, P., Rupp, A., Konecki, D. S., Trefz, F. K., Schmidt, H., & Lichter-Konecki, U. (1996). European Journal of Pediatrics, 155(Suppl 1), S11–5.
Berrondo, M., & Gray, J. J. (2011). Proteins, 79, 2844–60.
Chen, W., Feng, P., & Lin, H. (2012). Journal of Industrial Microbiology & Biotechnology, 39, 579–84.
Saremy, S., Boroujeni, M. E., Bhattacharjee, B., Mittal, V., & Chatterjee, J. (2011). Bioinformation, 7, 379–83.
Wu, X. Q., Wang, J., Lü, Z. R., Tang, H. M., Park, D., Oh, S. H., Bhak, J., Shi, L., Park, Y. D., & Zou, F. (2010). Applied Biochemistry and Biotechnology, 160, 1341–55.
Li, J., Schneebeli, S. T., Bylund, J., Farid, R., & Friesner, R. A. (2011). Journal of Chemical Theory and Computation, 7, 3829–45.
Wang, P., Wang, Y., & Su, Z. (2012). Applied Biochemistry and Biotechnology, 167, 62–72.
Moors, S. L., Vos, A. M., Cummings, M. D., Van Vlijmen, H., & Ceulemans, A. (2011). Journal of Medical Chemistry, 54, 6098–105.
Faust, K., & van Helden, J. (2012). Methods in Molecular Biology, 804, 107–30.
Varga, E. G., Titchener-Hooker, N. J., & Dunnill, P. (2001). Biotechnology and Bioengineering, 74, 96–107.
Nicholson, J. K., & Lindon, J. C. (2008). Nature, 455, 1054–6.
Zhang, A., Sun, H., & Wang, X. (2012). Applied Biochemistry and Biotechnology, 168, 1718–27.
Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., Hau, D. D., Psychogios, N., Dong, E., Bouatra, S., Mandal, R., Sinelnikov, I., Xia, J., Jia, L., Cruz, J. A., Lim, E., Sobsey, C. A., Shrivastava, S., Huang, P., Liu, P., Fang, L., Peng, J., Fradette, R., Cheng, D., Tzur, D., Clements, M., Lewis, A., De Souza, A., Zuniga, A., Dawe, M., Xiong, Y., Clive, D., Greiner, R., Nazyrova, A., Shaykhutdinov, R., Li, L., Vogel, H. J., & Forsythe, I. (2009). Nucleic Acids Research, 37, D603–10.
OuYang, D., Xu, J., Huang, H., & Chen, Z. (2011). Applied Biochemistry and Biotechnology, 165, 148–54.
Wiggins, J. (2011). Nephron Experimental Nephrology, 119(Suppl 1), e1–5.
Gramer, G., Garbade, S. F., Blau, N., & Lindner, M. (2009). Journal of Inherited Metabolic Disease, 32, 52–7.
Albaek, M. O., Gernaey, K. V., Hansen, M. S., & Stocks, S. M. (2012). Biotechnology and Bioengineering, 109, 950–61.
Armstrong, F. A., & Hirst, J. (2011). Proceedings of National Academy of Sciences of United States of American, 108, 14049–54.
Rothlisberger, U., Carloni, P., Doclo, K., & Parrinello, M. (2000). Journal of Biological Inorganic Chemistry, 5, 236–50.
Michaelis, L., & Menten, M. L. (1913). Biochemische Zeitschrift, 49, 333–69.
Price, N. C. (1979). Trends in Biochemical Sciences, 4, pN272.
Cheng, Y., & Prusoff, W. H. (1973). Biochemical Pharmacology, 22, 3099–108.
Ebrahimi, M., Lakizadeh, A., Agha-Golzadeh, P., Ebrahimie, E., & Ebrahimi, M. (2011). PLoS One, 6, e23146.
Barthelmes, J., Ebeling, C., Chang, A., Schomburg, I., & Schomburg, D. (2007). Nucleic Acids Research, 35, D511–4.
Chang, A., Scheer, M., Grote, A., Schomburg, I., & Schomburg, D. (2009). Nucleic Acids Research, 37, D588–92.
Scheer, M., Grote, A., Chang, A., Schomburg, I., Munaretto, C., Rother, M., Söhngen, C., Stelzer, M., Thiele, J., & Schomburg, D. (2011). Nucleic Acids Research, 39, D670–6.
Eadie, G. S. (1942). Journal of Biological Chemistry, 146, 85–93.
Hofstee, B. H. J. (1959). Nature, 184, 1296–8.
Hanes, C. S. (1932). Biochemical Journal, 26, 1406–21.
Lineweaver, H., & Burk, D. (1934). Journal of the American Chemical Society, 56, 658–66.
Bravo, I. G., Busto, F., De Arriaga, D., Ferrero, M. A., Rodríguez-Aparicio, L. B., Martínez-Blanco, H., & Reglero, A. (2001). Biochemical Journal, 358(Pt 3), 573–83.
Biggs, N., Lloyd, E., & Wilson, R. (1986) Graph Theory. Oxford University Press
King, E. L., & Altman, C. (1956). Journal of Physical Chemistry, 60, 1375–8.
Weber, H., & Richterich, R. (1963). Klinische Wochenschrift, 41, 665–7.
Jenkins, W. T. (1970). Analytical Biochemistry, 38, 409–16.
Katsumata, M. (1972). Journal of Theoretical Biology, 36, 327–38.
Diachina, E. G. (1973). Laboratornoe Delo, 2, 120–1.
Ermakov, N. V., & Tokarev, I. N. (1982). Laboratornoe Delo, 10, 17–21.
Chou, K. C. (1983). Biophysical Chemistry, 17, 51–5.
Zhou, G. P., & Deng, M. H. (1984). Biochemical Journal, 222, 169–76.
Zhao, M. (1992). Biochemical Journal, 287(Pt 2), 391–3.
Chou, K. C. (1989). Journal of Biological Chemistry, 264, 12074–9.
Lather, V., & Madan, A. K. (2005). Journal of Molecular Graphics and Modelling, 23, 339–45.
Comte, P., Vassiliev, S., Houghten, S., & Bruce, D. (2011). Biosystems, 105, 263–70.
Yuki, H., Honma, T., Hata, M., & Hoshino, T. (2012). Bioorganic and Medicinal Chemistry, 20, 775–83.
Chou, K. C. (2010). Current Drug Metabolism, 11, 369–78.
Chou, K. C., Jiang, S. P., Liu, W. M., & Fee, C. H. (1979). Scientia Sinica, 22, 341–58.
Chou, K. C. (1990). Biophysical Chemistry, 35, 1–24.
Wu, G. (1996). Pharmacological Research, 33, 379–83.
el Kebbaj, M. S., & Latruffe, N. (1986). Archives of Biochemistry and Biophysics, 244, 662–70.
Musch, W., Thimpont, J., Vandervelde, D., Verhaeverbeke, I., Berghmans, T., & Decaux, G. (1995). American Journal of Medicine, 99, 348–55.
Prigent, A. (2008). Seminars in Nuclear Medicine, 38, 32–46.
Salgado, J. V., Neves, F. A., Bastos, M. G., França, A. K., Brito, D. J., Santos, E. M., & Salgado Filho, N. (2010). Brazilian Journal of Medical and Biological Research, 43, 528–36.
Gotch, F. A. (1998). Nephrology, Dialysis, Transplantation, 13(Suppl 6), 10–4.
Gotch, F. A., Sargent, J. A., & Keen, M. L. (2000). Kidney International, Suppl 76, S3–S18.
Sigel, R., Sigel, A., & Sigel, H. (2007). The ubiquitous roles of cytochrome P450 proteins: metal ions in life sciences. New York: Wiley.
Mishra, N. K. (2011). Expert Opinion on Drug Metabolism and Toxicology, 7, 1211–31.
Long, W. F., & Labute, P. (2010). Journal of Computer-Aided Molecular Design, 24, 907–16.
Chen, X., Wang, X., Li, Z., Kong, L., Liu, G., Fu, J., & Wang, A. (2012). Gene, 495, 170–7.
Duverle, D. A., & Mamitsuka, H. (2012). Briefings in Bioinformatics, 13, 337–49.
VandenBrink, B. M., Foti, R. S., Rock, D. A., Wienkers, L. C., & Wahlstrom, J. L. (2012). Drug Metabolism and Disposition, 40, 47–53.
Choi, K., & Kim, S. (2011). Journal of Bioinformatics and Computational Biology, 9, 597–611.
Chen, L., Feng, K. Y., Cai, Y. D., Chou, K. C., & Li, H. P. (2010). BMC Bioinformatics, 11, 293.
Basit, N., & Wechsler, H. (2011). Advances in Bioinformatics, 958129.
Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2011). Journal of Visualized Experiments, 57, e3259.
Needleman, S. B., & Wunsch, C. D. (1970). Journal of Molecular Biology, 48, 443–53.
Wu, G. (2000). Medical Hypotheses, 54, 748–9.
Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2007). Nucleic Acids Research, 35, D198–201.
Porter, C. T., Bartlett, G. J., & Thornton, J. M. (2004). Nucleic Acids Research, 32, D129–33.
Bartlett, G. J., Porter, C. T., Borkakoti, N., & Thornton, J. M. (2002). Journal of Molecular Biology, 324, 105–21.
Torrance, J. W., Bartlett, G. J., Porter, C. T., & Thornton, J. M. (2005). Journal of Molecular Biology, 347, 565–81.
Quester, S., & Schomburg, D. (2011). BMC Bioinformatics, 12, 376.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012). Nucleic Acids Research, 40, D109–14.
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010). Nucleic Acids Research, 38, D355–60.
Holliday, G. L., Andreini, C., Fischer, J. D., Rahman, S. A., Almonacid, D. E., Williams, S. T., & Pearson, W. R. (2012). Nucleic Acids Research, 40, D783–9.
Caspi1, R., Altman, T., Dale, J. M., Dreher, K., Fulcher, C. A., Gilham, F., Kaipa, P., Karthikeyan, A. S., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., Paley, S., Popescu, L., Pujar, A., Shearer, A. G., Zhang, P., & Karp, P. D. (2010) Nucleic Acids Research, 38 (suppl 1), D473–D479
Zhang, P., Foerster, H., Tissier, C. P., Mueller, L., Paley, S., Karp, P. D., & Rhee, S. Y. (2005). Plant Physiology, 138, 27–37.
Caspi, R., Foerster, H., Fulcher, C. A., Hopkinson, R., Ingraham, J., Kaipa, P., Krummenacker, M., Paley, S., Pick, J., Rhee, S. Y., Tissier, C., Zhang, P., & Karp, P. D. (2006). Nucleic Acids Research, 34, D511–6.
Caspi, R., Foerster, H., Fulcher, C. A., Kaipa, P., Krummenacker, M., Latendresse, M., Paley, S., Rhee, S. Y., Shearer, A. G., Tissier, C., Walk, T. C., Zhang, P., & Karp, P. D. (2008). Nucleic Acids Research, 36, D623–31.
Caspi, R., Altman, T., Dreher, K., Fulcher, C. A., Subhraveti, P., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., Ong, Q., Paley, S., Pujar, A., Shearer, A. G., Travers, M., Weerasinghe, D., Zhang, P., & Karp, P. D. (2012). Nucleic Acids Research, 40, D742–53.
Lespinet, O., & Labedan, B. (2006). BMC Bioinformatics, 7, 436.
Von Grotthuss, M., Plewczynski, D., Ginalski, K., Rychlewski, L., & Shakhnovich, E. I. (2006). BMC Bioinformatics, 7, 53.
Arcuri, H. A., Zafalon, G. F., Marucci, E. A., Bonalumi, C. E., da Silveira, N. J., Machado, J. M., de Azevedo, W. F., & Jr Palma, M. S. (2010). BMC Bioinformatics, 11, 12.
Hoppe, A., Hoffmann, S., Gerasch, A., Gille, C., & Holzhütter, H. G. (2011). BMC Bioinformatics, 12, 28.
Schremmer, S. D., Waser, M. R., Kohn, M. C., & Garfinkel, D. (1984). Computers and Biomedical Research, 17, 289–301.
Kohn, M. C., Menten, L. E., & Garfinkel, D. (1979). Computers and Biomedical Research, 12, 461–9.
Kohn, M. C., Menten, L. E., & Garfinkel, D. (1981). Computers and Biomedical Research, 14, 91–102.
Damboský, J., Prokop, M., & Koca, J. (2001). Trends in Biochemical Sciences, 26, 71–3.
Prokop, M., Damborský, J., & Koca, J. (2000). Bioinformatics, 16, 845–6.
Prokop, M., Adam, J., Kríz, Z., Wimmerová, M., & Koca, J. (2008). Bioinformatics, 24, 1955–6.
Schwarz, R., Musch, P., von Kamp, A., Engels, B., Schirmer, H., Schuster, S., & Dandekar, T. (2005). BMC Bioinformatics, 6, 135.
Otto, T. D., Guimarães, A. C., Degrave, W. M., & de Miranda, A. B. (2008). BMC Bioinformatics, 9, 544.
Latendresse, M., & Karp, P. D. (2011). BMC Bioinformatics, 12, 176.
Li, G. H., & Huang, J. F. (2010). BMC Bioinformatics, 11, 439.
Audit, B., Levy, E. D., Gilks, W. R., Goldovsky, L., & Ouzounis, C. A. (2007). BMC Bioinformatics, 8(Suppl 4), S3.
Hung, S. S., Wasmuth, J., Sanford, C., & Parkinson, J. (2010). Bioinformatics, 26, 1690–8.
Arakaki, A. K., Huang, Y., & Skolnick, J. (2009). BMC Bioinformatics, 10, 107.
Bevc, S., Konc, J., Stojan, J., Hodošček, M., Penca, M., Praprotnik, M., & Janežič, D. (2011). PLoS One, 6, e22265.
Yamanishi, Y., Hattori, M., Kotera, M., Goto, S., & Kanehisa, M. (2009). Bioinformatics, 25, i179–86.
Shen, H. B., & Chou, K. C. (2007). Biochemical and Biophysical Research Communications, 364, 53–9.
Li, C., Li, Y., Zhang, X., Stafford, P., & Dinu, V. (2009). BMC Bioinformatics, 10, 286.
Moriya, Y., Shigemizu, D., Hattori, M., Tokimatsu, T., Kotera, M., Goto, S., & Kanehisa, M. (2010). Nucleic Acids Research, 38, W138–43.
Zhang, K., Xu, Y., & Chen, G. (2008). International Journal of Bioinformatics Research and Applications, 4, 295–305.
Yan, S., Shi, D., Nong, H., & Wu, G. (2011). Guangxi Sciences, 18, 253–60.
Yan, S., & Wu, G. (2011). Applied Biochemistry and Biotechnology, 165, 856–69.
Yan, S., & Wu, G. (2012). Applied Biochemistry and Biotechnology, 166, 997–1107.
Yan, S. M., & Wu, G. (2011). Protein Peptide Letters, 18, 1053–7.
Yan, S., & Wu, G. (2012). Protein Peptide Letters, 19, 29–39.
Yan, S., Shi, D., Nong, H., & Wu, G. (2012). Interdisciplinary Science Computational Life Sciences, 4, 46–53.
Yan, S., & Wu, G. (2011). Enzyme Engineering, 1, 102.
Yan, S., & Wu, G. (2012). Protein Peptide Letters, 20, 255–64.
Carroll, A., & Somerville, C. (2009). Annual Review of Plant Biology, 60, 165–82.
Barnard, D., Casanueva, A., Tuffin, M., & Cowan, D. (2010). Environmental Technology, 31, 871–88.
van Zyl, W. H., Bloom, M., & Viktor, M. J. (2012). Applied Microbiology and Biotechnology, 95, 1377–88.
Fuciños, P., González, R., Atanes, E., Sestelo, A. B., Pérez-Guerra, N., Pastrana, L., & Rúa, M. L. (2012). Methods in Molecule Biology, 861, 239–66.
Duan, C. J., & Feng, J. X. (2010). Biotechnology Letters, 32, 1765–75.
Yu, E. H., Prodanovic, R., Güven, G., Ostafe, R., & Schwaneberg, U. (2011). Applied Biochemistry and Biotechnology, 165, 1448–57.
Nghiem, N. P., Taylor, F., Johnston, D. B., Shetty, J. K., & Hicks, K. B. (2011). Applied Biochemistry and Biotechnology, 165, 870–82.
Ribeiro, B. D., de Castro, A. M., Coelho, M. A., & Freire, D. M. (2011). Enzyme Research, 2011, 615803.
Struck, A. W., Thompson, M. L., Wong, L. S., & Micklefield, J. (2012). Chembiochem, 13, 2642–55.
Jeong, Y. S., Na, H. B., Kim, S. K., Kim, Y. H., Kwon, E. J., Kim, J., Yun, H. D., Lee, J. K., & Kim, H. (2012). Applied Biochemistry and Biotechnology, 166, 1328–39.
Sheldon, R. A. (2011). Applied Microbiology and Biotechnology, 92, 467–77.
Skoko, N., Baralle, M., Tisminetzky, S., & Buratti, E. (2011). Molecular Biotechnology, 48, 290–7.
Li, Q., Zhao, Q., Hu, Y., & Wang, H. (2006). Biotechnological Journal, 1, 1205–14.
Ibrahim, C. O. (2008). Bioresource Technology, 99, 4572–82.
de Castro, A. M., Carvalho, D. F., Freire, D. M., & Dos Reis Castilho, L. (2010). Enzyme Research, 2010, 576872.
Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., & Kanehisa, M. (2008). Nucleic Acids Research, 36, D202–5.
Wu, G., & Yan, S. (2008). Lecture notes on computational mutation. New York: Nova Science.
Yang, X. Y., Shi, X. H., Meng, X., Li, X. L., Lin, K., Qian, Z. L., Feng, K. Y., Kong, X. Y., & Cai, Y. D. (2010). Protein Peptide Letters, 17, 899–908.
Atchley, W. R., Zhao, J., Fernandes, A. D., & Druke, T. (2005). Proceedings of National Academy of Sciences of United States of American, 102, 6395–400.
Zamyatin, A. A. (1972). Progress in Biophysics and Molecular Biology, 24, 107–23.
Darby, N. J., & Creighton, T. E. (1993). Journal of Molecular Biology, 232, 873–96.
Kyte, J., & Doolittle, R. F. (1982). Journal of Molecular Biology, 157, 105–32.
Trinquier, G., Sanejouand, Y. H., & Hausman, R. E. (1998). Protein Engineering, 11, 153–69.
Cooper, G. M. (2004). The cell: a molecular approach (p. 51). Washington, D.C: ASM.
Dwyer, D. S. (2005). BMC Chemical Biology, 5, 2.
Chou, P. Y., & Fasman, G. D. (1978). Advances in Enzymology and Related Subjects of Biochemistry, 47, 45–148.
Burlingame, A. L., & Carr, S. A. (1996). Mass spectrometry in the biological sciences. Totowa, NJ: Humana.
Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design. Boston, MA: PWS.
Demuth, H., & Beale, M. (2001) Neural Network Toolbox for Use with MatLab. User’s guide, version 4.
MathWorks Inc. (2001) MatLab—the language of technical computing (version 6.1.0.450, release 12.1), 1984–2001.
Chou, K. C. (2011). Journal of Theoretical Biology, 273, 236–47.
Chou, K. C., & Shen, H. B. (2007). Analytical Biochemistry, 370, 1–16.
Chou, K. C., & Shen, H. B. (2010). PLoS One, 5, e9931.
Wu, G., Baraldo, M., & Furlanut, M. (1995). Pharmacological Research, 32, 241–8.
Wu, G. (1995). Pharmacological Research, 31, 393–9.
Zhou, W., Irwin, D. C., Escovar-Kousen, J., & Wilson, D. B. (2004). Biochemistry, 43, 9655–63.
Kang, H. J., & Ishikawa, K. (2007). Journal of Microbiology and Biotechnology, 17, 1249–53.
Kim, H. W., Takagi, Y., Hagihara, Y., & Ishikawa, K. (2007). Bioscience, Biotechnology and Biochemistry, 71, 2585–7.
The UniProt Consortium. (2010) Nucleic Acids Research, 38, D142–D148.
Akaike, H. (1974). IEEE Transactions on Automatic Control, 19, 716–23.
Feller, W. (1968). An introduction to probability theory and its applications. 3rd ed. Vol, I. New York: Wiley.
http://www.dreamscitech.com/Web-Based-Computation/ADP.htm, (2013).
Acknowledgments
This study was partly supported by Guangxi Science Foundation (12237022, 13-051-08, 13-051-50, 1347704-1 and 2013GXNSDA019007) and by BaGui Scholars Program Foundation.
Author information
Authors and Affiliations
Corresponding author
Appendix—a worked example
Appendix—a worked example
In order to fully explain the prediction of parameters in enzymatic reaction, here we present a worked example to demonstrate the prediction of K m in cellulases (EC 3.2.1.4) with all the procedures discussed in the “Current State of Predicting Parameters in Enzymatic Reactions in Biofuel Industry” section.
In BRENDA [38–40], there were four cellulases with their sequence information under the category of K m as functional parameters, of which two cellulases were documented with their mutants [162]. There were three reduced-size mutants and 17 site-directed mutagenesis from the cellulose O58925 isolated from Pyrococcus horikoshii [163, 164]. Also, two K m values were documented in each of the cellulose O58925, Q9REW0, and Q9X273. In total, BRENDA provided 29 matched sequences and K m values of cellulases (Table 6), and their corresponding sequences were found in Universal Protein Resource [165]. The sequences of those 29 enzymes were coded each time with an amino acid property or the amino acid distribution probability, which is a property combining amino acid property and property of whole protein, and coded property served as predictors and their K m as predicted value in neural network model.
In this worked example, we used not only the 20-1 feedforward backpropagation neural network to model the relationship between coded enzyme sequences and their K m values (Fig. 1), but also different combinations of layers and neurons of neural network model, including the models of 20-5-1, 20-10-1, 20-10-5-1, 20-30-10-5-1, and 20-30-20-12-5-1 (Fig. 2).
The issue of convergence can be seen in Fig. 3, which in fact plays a role to screen 25 predictors, that is, the predictors that do not converge should be eliminated from predictions as shown in Table 5 that summarized all our previous studies including this worked example.
As discussed in the “Current State of Predicting Parameters in Enzymatic Reactions in Biofuel Industry” section, linear regression was used to evaluate the predicted K m with their recorded ones. The Chi-square test was used to compare the predicted K m, which falls into different ranges of recorded K m with respect to various predictors. Figure 2 summarizes the predictive performances of six different neural network models with eleven different predictors. This is a process to select the best predictive model among various models, which is important because the Akaike information criterion [166] suggested how to select a model statistically. In Figure 2, R and P values were used to evaluate the predictive performance. As can be seen from top to bottom, the more layers a neural network model has, the better the predictive performance is. However, this improvement is mainly related to the fitting. For the validation, the best performances were obtained from 20-10-1 model, beyond which the increase of neuron layers could not improve the predictions. On the other hand, the problem with multi-layer is that the model parameters increase unnecessarily, which compromise the model identification although the predictions are better. Thus, the 20-10-1 neural network model should be the best choice for predictions. With respect to predictors, the amino acid properties seem to work in a similar way because their predictions are similar while the prediction using the amino acid distribution probability, which combines the properties of both amino acids and whole protein, is better, except the R value for validation. Another characteristic is that amino acid distribution probability can get good predictive performance using 2-layer models, which can dramatically reduce model parameters compared to the use of multi-layer models.
Based on the predicted K m of cellulases listed in Table 7, we need to furthermore look at the predictive performance in much greater details as showed in Table 6, where we stratified the predicted K m according to its mean value of 100 predictions with respect to different ranges of recorded K m. For example, the recorded K m of cellulase O58925 H155A (row 3 Table 6) was 6.69, so the values less than or equal to ±10 % of 6.69 ranged from 6.021 to 7.359, and we can see that all the predicted K m using various predictors did not fall into this range (row 3, Table 7). A similar manner was applied to all the ranges listed in Table 7 for all 29 cellulases. As seen in Table 7, the prediction using the amino acid distribution probability as predictor (last column) is statistically better than the predictions using other predictors. Figure 5 displays the results of training and validation groups using the predictor f(i + 1).
Rights and permissions
About this article
Cite this article
Yan, S., Wu, G. Predictions of Enzymatic Parameters: A Mini-Review with Focus on Enzymes for Biofuel. Appl Biochem Biotechnol 171, 590–615 (2013). https://doi.org/10.1007/s12010-013-0328-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12010-013-0328-6