Biopolym. Cell. 2011; 27(6):432-435 .
http://dx.doi.org/10.7124/bc.000113
Reviews
Current scenario on computer-aided metalloenzymes designing
1Chellapandi P.
  1. Department of Bioinformatics, School of Life Sciences,
    Bharathidasan University
    Tiruchirappalli-620024, Tamil Nadu, India

Abstract

The metalloenzymes are proteins with enzymatic activity which contain metals tightly bound in their active sites to display a chemical action. This review describes the recent developments and success of using computational methods and algorithms for designing industrial enzymes. A recent approach based on functional amino acids or peptides as characteristic molecular moieties and their conservations, has led to a significant expansion of the field of enzyme designing or enzyme mimics. Evolutionary conservation is accounted to consider designing enzymes while the metalloenzymes are a major concern due to their extensive role in catalytic activity and stability. The enzymes from methanogens may provide useful biocatalysts and may be even more valuable for biotransformation reactions, but their biotechnological applications are restricted. Therefore, a method based on the evolutionary hypothesis of conserved domain of sequences obtained from methanogens would make a significant interest in synthetic enzyme biotechnology.
Keywords: metalloenzymes designing, methanogens, evolutionary conservation
Full text: (PDF, in English)

References

[1] Hibbert E. G., Dalby P. A. Directed evolution strategies for improved enzymatic performance Microb. Cell Fact 2005 4 P. 29.
[2] Luetz S., Giver L., Lalonde J. Engineered enzymes for chemical production Biotechnol. Bioeng 2008 101, N 4 P. 647–653.
[3] Monti D., Riva S. Natural and artificial microenzymes: is it possible to have small and efficient biocatalyst? Biocatalysis and Biotransformation 2001 19, N 4 P. 251–266.
[4] Ilyina A. D., Ibarra-Coronado D., Gurumurthy K., Cerda-Ramarez F. Evidence of catalytic activity of polypeptides artificially synthesized from conservative aminoacids. Moscow Uni. Chem. Bullet. 2006 47, N 2 P. 134–142.
[5] Lu Y. Metalloprotein and metallo-DNA/RNAzyme design: current approaches, success measures, and future challenges Inorg. Chem 2006 45, N 25 P. 9930–9940.
[6] Lu Y., Valentine J. S. Engineering metal-binding sites in proteins Curr. Opin. Struct. Biol 1997 7, N 4 P. 495–500.
[7] Benson D. E., Wisz M. S., Hellinga H. W. Rational design of nascent metalloenzymes Proc. Natl Acad. Sci. USA 2000 97, N 12 P. 6292–6297.
[8] Marshall S. A., Mayo S. L. Achieving stability and conformational specificity in designed proteins via binary patterning J. Mol. Biol 2001 305, N 3 P. 619–631.
[9] Harbury P. B., Plecs J. J., Tidor B., Alber T., Kim P. S. High-resolution protein design with backbone freedom Science 1998 282, N 5393 P. 1462–1467.
[10] Looger L. L., Dwyer M. A., Smith J. J., Hellinga H. W. Computational design of receptor and sensor proteins with novel functions Nature 2003 423, N 6936 P. 185–190.
[11] Chellapandi P., Balachandramohan J. Molecular evolution-directed approach for designing of -methylaspartate mutase from the sequences of Haloarchaea Int. J. Chem. Mod 2011 3, N 3 P. 143–154.
[12] Chellapandi P., Balachandramohan J. Molecular evolution-directed approach for designing archaeal formyltetrahydrofolate ligase Turk. J. Biochem 2011 36, N 2 P. 122–136.
[13] Bolon D. N., Mayo S. L. Enzyme-like proteins by computational design Proc. Natl Acad. Sci. USA 2001 98, N 25 P. 14274– 14279.
[14] Zanghellini A., Jiang L., Wollacott A. M., Cheng G., Meiler J., Althoff E. A., Ruthlisberger D., Baker D. New algorithms and an in silico benchmark for computational enzyme design Protein Sci 2006 15, N 12 P. 2785–2794.
[15] Chowdry A. B., Reynolds K. A., Hanes M. S., Voorhies M., Pokala N., Handel T. M. An object-oriented library for computational protein design J. Comput. Chem 2007 28, N 14 P. 2378– 2388.
[16] Hellinga H. W., Richards F. M. Construction of new ligand binding sites in proteins of known structure. I. Computer-aided modeling of sites with pre-defined geometry J. Mol. Biol 1991 222, N 3 P. 763–785.
[17] Hellinga H. W. Metalloprotein design Curr. Opin. Biotechnol 1996 7, N 4 P. 437–441.
[18] Dahiyat B. I., Mayo S. L. Protein design automation Protein Sci 1996 5, N 5 P. 895–903.
[19] Clarke N. D., Yuan S. M. Metal search: a computer program that helps design tetrahedral metal-binding sites Proteins 1995 23, N 2 P. 256–263.
[20] Klemba M., Gardner K. H., Marino S., Clarke N. D., Regan L. Novel metal-binding proteins by design Nat. Struct. Biol 1995 2, N 5 P. 368–373.
[21] Voigt C. A., Gordon D. B., Mayo S. L. Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design J. Mol. Biol 2000 299, N 3 P. 789–803.
[22] Reetz M. T. Application of directed evolution in the development of enantioselective enzymes Pure Appl. Chem 2000 72, N 9 P. 1615–1622.
[23] Reetz M. T., Carballeira J. D. Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes Nat. Protoc 2007 2, N 4 P. 891–903.
[24] Fischer A., Enkler N., Neudert G., Bocola M., Sterner R., Merkl R. TransCent: computational enzyme design by transferring active sites and considering constraints relevant for catalysis BMC Bioinformatics 2009 10 P. 54.
[25] Riley D. P. Functional mimics of superoxide dismutase enzymes as therapeutic agents Chem. Rev 1999 99, N 9 P. 2573– 2588.
[26] Riley D. P., Henke S. L., Lennon P. J., Aston K. Computer-Aided Design (CAD) of Synzymes: use of molecular mechanics (MM) for the rational design of superoxide dismutase mimics Inorg. Chem 1999 38, N 8 P. 1908–1917.
[27] Tann C. M., Qi D., Distefano M. D. Enzyme design by chemical modification of protein scaffolds Curr. Opin. Chem. Biol 2001 5, N 6 P. 696–704.
[28] Zanghellini A., Jiang L., Wollacott A. M., Cheng G., Meiler J., Althoff E. A., Rothlisberger D., Baker D. New algorithms and an in silico benchmark for computational enzyme design Protein Sci 2006 15, N 12 P. 2785–2794.
[29] Marti S., Andres J., Moliner V., Silla E., Tunon I., Bertran J. Predicting an improvement of secondary catalytic activity of promiscuous isochorismate pyruvate lyase by computational design J. Am. Chem. Soc 2008 130, N 10 P. 2894–2895.
[30] Vardi-Kilshtain A., Roca M., Warshel A. The empirical valence bond as an effective strategy for computer-aided enzyme design Biotechnol. J 2009 4, N 4 P. 495–500.
[31] Toscano M. D., Muller M. M., Hilvert D. Enhancing activity and controlling stereoselectivity in a designed PLP-dependent aldolase Angew. Chem. Int. Ed. Engl 2007 46, N 24 P. 4468– 4470.
[32] Liao J., Warmuth M. K., Govindarajan S., Ness J. E., Wang R. P., Gustafsson C., Minshull J. Engineering proteinase K using machine learning and synthetic genes BMC Biotechnol 2007 7 P. 16.
[33] Leon M., Isorna P., Menendez M., Sanz-Aparicio J., Polaina J. Comparative study and mutational analysis of distinctive structural elements of hyperthermophilic enzymes Protein J 2007 26, N 6 P. 435–444.
[34] Nishioka M., Tanimoto K., Higashi N., Fukada H., Ishikawa K., Taya M. Alteration of metal ions improves the activity and thermostability of aminoacylase from hyperthermophilic archaeon Pyrococcus horikoshii Biotechnol. Lett 2008 30, N 9 P. 1639–1643.
[35] Chellapandi P. Molecular evolution of methanogens based on their metabolic facets Front. Biol 2011 6, N 6 P. 490–503.
[36] Chellapandi P. A molecular conception for protein engineering algorithms Adv. Biotech 2011 10, N 7 P. 41–46.