Skip to main content

Abstract

A fundamental principle of proteins is to act as enzymes-biocatalysts working as highly efficient machines at the molecular level by accelerating the conversion of substrates into products. Although RNAs are also capable of catalyzing some biochemical reactions, but most are catalyzed by proteins. A variety of experimental and computational techniques continue to reveal that proteins are dynamically active machines. Due to the growing complexity and inconsistency in the naming of enzymes, the nomenclature committee of the International Union of Biochemistry and Molecular Biology (IUBMB) has assigned an EC number a four level hierarchical description to enzyme proteins. In the past, enzymes function has been explained on the basis of direct structural interactions between the enzyme and the substrate. The structural characterization of enzymes can be elucidating by various techniques such as spectroscopic methods, x-ray crystallography and more recently, multidimensional NMR methods. This chapter covers the basic principles of enzymes such as proteinaceous nature and substrate binding, classification, and structural characterization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Agarwal, P. K. (2006). Enzymes: An integrated view of structure, dynamics and function. Microbial Cell Factories, 5, 2. https://doi.org/10.1186/1475-2859-5-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ashburner, M., Ball, C. A., & Sherlock, G. (2000). The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics, 25, 25–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Audit, B., Levy, E. D., Gilks, W. R., Goldovsky, L., & Ouzounis, C. A. (2007). CORRIE: Enzyme sequence annotation with confidence estimates. BMC Bioinformatics, 8(Suppl 4), S3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Bachovchin, W. W. (2001). Contributions of NMR spectroscopy to the study of hydrogen bonds in serine protease active sites. Magnetic Resonance in Chemistry, 39, 199–213.

    Article  Google Scholar 

  • Benkovic, S. J., & Hammes-Schiffer, S. (2003). A perspective on enzyme catalysis. Sci, 301(5637), 1196–1202.

    Article  CAS  Google Scholar 

  • Berliner, L. J. (1989). Laser chemically induced dynamic nuclear polarization studies in proteins: α-lactalbumin. Archivos de Biología y Medicina Experimentales, 22, 123–128.

    CAS  PubMed  Google Scholar 

  • Bhatia, S. (2018). Enzymes, proteins and bioinformatics. In Introduction to pharmaceutical biotechnology (2nd ed.). Bristol, UK: IOP Publishing. https://doi.org/10.1088/978-0-7503-1302-5ch1

    Chapter  Google Scholar 

  • Borro, L. C., Oliveira, S. R. M., Yamagishi, M. E. B., Mancini, A. L., Jardine, J. G., Mazoni, I., … Neshich, G. (2006). Predicting enzyme class from protein structure using Bayesian classification. Genetics and Molecular Research, 5(1), 193–202.

    CAS  PubMed  Google Scholar 

  • Bugg, T. D. H. (2004). Introduction to enzyme and coenzyme chemistry (2nd ed.). Oxford, UK: Blackwell Publishing Ltd.

    Book  Google Scholar 

  • Cai, Y. D., Zhou, G. P., & Chou, K. C. (2005). Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition. Journal of Theoretical Biology, 234(1), 145–149.

    Article  CAS  PubMed  Google Scholar 

  • Creighton, T. E. (1993). Proteins (2nd ed.). New York, NY: W. H. Freeman and Company, 507pp.

    Google Scholar 

  • Cuesta, S. M., Rahman, S. A., Furnham, N., & Thornton, J. M. (2015). The classification and evolution of enzyme function. Biophysical Journal, 109, 1082–1086.

    Article  CAS  Google Scholar 

  • Danielson, M. A., & Falke, J. J. (1996). Use of 19F NMR to probe protein structure and conformational changes. Annual Review of Biophysics and Biomolecular Structure, 25, 163–195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dixon M, Webb EC (1979) Enzymes 3rd ed. Academic Press, New York, NY, 1116pp. Drauz K, Waldmann H (2002) Enzyme catalysis in organic synthesis, vol II. Weinhein, Germany: Wiley VCH, 998pp.

    Google Scholar 

  • Dobson, P. D., & Doig, A. J. (2005). Predicting enzyme class from protein structure without alignments. Journal of Molecular Biology, 345(1), 187–199.

    Article  CAS  PubMed  Google Scholar 

  • Feiten, M. C., Luccio, M. D., Santos, K. F., de Oliveira, D., & Oliveira, J. V. (2017). X-ray crystallography as a tool to determine three-dimensional structures of commercial enzymes subjected to treatment in pressurized fluids. Applied Biochemistry and Biotechnology, 182(2), 429–451.

    Article  CAS  PubMed  Google Scholar 

  • Geric, J. T. (1981). Fluorine magnetic resonance in biochemistry. In L. J. Berliner & J. Reuben (Eds.), Biological magnetic resonance (Vol. 1, pp. 139–203). New York, NY: Plenum Press.

    Chapter  Google Scholar 

  • Helliwell, J. R. (2017). New developments in crystallography: exploring its technology, methods and scope in the molecular biosciences. Bioscience Reports, 37, BSR20170204. https://doi.org/10.1042/BSR20170204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang, C., & Kalodimos, C. G. (2017). Structures of large protein complexes determined by nuclear magnetic resonance spectroscopy. Annual Review of Biophysics, 46, 317–336.

    Article  CAS  PubMed  Google Scholar 

  • Ilari, A., & Savino, C. (2008). Protein structure determination by X-ray crystallography. In J. M. Keith (Ed.), Bioinformatics, volume I: Data, sequence analysis, and evolution. Totowa, NJ: Humana Press. https://doi.org/10.1007/978-1-60327-159-2

    Chapter  Google Scholar 

  • Illanes, A. (2008). Enzyme biocatalysis: Principles and applications. Dordrecht, Netherlands: Springer.

    Book  Google Scholar 

  • Juncker, A. S., Jensen, L. J., Pierleoni, A., Bernsel, A., Tress, M. L., Bork, P., … Brunak, S. (2009). Sequence-based feature prediction and annotation of proteins. Genome Biology, 10(2), 206.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kaptein, R. (1982). Photo-CIDNP studies. In L. J. Berliner & J. Reuben (Eds.), Proteins in biological magnetic resonance (Vol. 4, pp. 145–191). New York, NY: Plenum Press.

    Chapter  Google Scholar 

  • Kersey, P. J., Allen, J. E., & Staines, D. M. (2014). Ensembl Genomes 2013: scaling up access to genome-wide data. Nucleic Acids Research, 42, D546–D552.

    Article  CAS  PubMed  Google Scholar 

  • Kevin, H., Lewis, G., & Kay, E. (1998). The use of 2H, 13C, 15N multidimensional NMR to study the structure and dynamics of proteins. Annual Review of Biophysics and Biomolecular Structure, 27, 357–406.

    Article  Google Scholar 

  • Kleywegt, G., Hoier, H., & Jones, T. (1996). A re-evaluation of the crystal structure of chloromuconate cycloisomerase. Acta Crystallographica. Section D, Biological Crystallography, 52, 858–863.

    Article  CAS  PubMed  Google Scholar 

  • Kleywegt, G. J., Harris, M. R., Zou, J. Y., Taylor, T. C., Wahlby, A., & Jones, T. A. (2004). The Uppsala Electron-Density server. Acta Crystallographica. Section D, Biological Crystallography, 60, 2240–2249.

    Article  PubMed  CAS  Google Scholar 

  • Kristensen, D. M., Ward, R. M., Lisewski, A. M., Erdin, S., Chen, B. Y., Fofanov, V. Y., … Lichtarge, O. (2008). Prediction of enzyme function based on 3D templates of evolutionarily important amino acids. BMC Bioinformatics, 9, 17.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kumar, S., Bhola, A., & Tiwari, A. K. (2015). Classification of enzyme functional classes and subclasses using support vector machine. In: 2015 1st International Conference on Futuristic trend in Computational Analysis and Knowledge Management (ABLAZE-2015).

    Google Scholar 

  • Latino, D. A. R. S., Zhang, Q. Y., & Aires-De-Sousa, J. (2008). Genome-scale classification of metabolic reactions and assignment of EC numbers with self-organizing maps. Bioinformatics, 24(19), 2236–2244.

    Article  CAS  PubMed  Google Scholar 

  • Lee, H. C. (2006). Structure and enzymatic functions of human CD38. Molecular Medicine, 12, 317–323.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Longo, M., & Combes, D. (1999). Thermostability of modified enzymes: A detailed study. Journal of Chemical Technology and Biotechnology, 74, 25–32.

    Article  CAS  Google Scholar 

  • Markley, J. L. (1975). Observation of histidine residues in proteins by means of nuclear magnetic resonance spectroscopy. Accounts of Chemical Research, 8, 70–80.

    Article  CAS  Google Scholar 

  • Martınez Cuesta, S., Furnham, N., & Thornton, J. M. (2014). The evolution of enzyme function in the isomerases. Current Opinion in Structural Biology, 26, 121–130.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Meadows, D. H., & Jardetzky, O. (1986). Nuclear magnetic resonance studies of the structure and binding sites of enzymes IV. Cytidine 30-monophosphate binding to ribonuclease. Proceedings of the National Academy of Sciences, 61, 406–413.

    Article  Google Scholar 

  • Meshitsuka, S., Smith, G. M., & Mildvan, A. S. (1981). Proton NMR studies of the histidine residues of rabbit muscle pyruvate kinase and of its phosphoenol pyruvate complex. The Journal of Biological Chemistry, 256, 4460–4465.

    Article  CAS  PubMed  Google Scholar 

  • Monasterio, O. (2014). Nomenclature for the applications of nuclear magnetic resonance to the study of enzymes. Perspectives on Science, 1, 88–97.

    Article  Google Scholar 

  • Monasterio, O., Nova, E., Lopez-Brauet, A., & Lagos, R. (1995). Tubulin–tyrosine ligase catalyzes covalent binding of mfluorotyrosine to tubulin. Kinetic and 19F-NMR Studies. FEBS Letters, 374, 165–168.

    Article  CAS  PubMed  Google Scholar 

  • Nasibov, E., & Kandemir-Cavas, C. (2009). Efficiency analysis of KNN and minimum distance-based classifiers in enzyme family prediction. Computational Biology and Chemistry, 33(6), 461–464.

    Article  CAS  PubMed  Google Scholar 

  • Ong, S. A., Lin, H. H., Chen, Y. Z., Li, Z. R., & Cao, Z. (2007). Efficacy of different protein descriptors in predicting protein functional families. BMC Bioinformatics, 8, 300.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Petsko, G. A., & Ringe, D. (2004). Protein structure and function. London, UK: New Science Press. 195pp.

    Google Scholar 

  • Rahman, S. A., Cuesta, S. M., & Thornton, J. M. (2014). EC-BLAST: A tool to automatically search and compare enzyme reactions. Nature Methods, 11, 171–174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rausch, C., Weber, T., Kohlbacher, O., Wohlleben, W., & Huson, D. H. (2005). Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs). Nucleic Acids Research, 33(18), 5799–5808.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Redfield, A. G., Mclntosh, L. P., & Dahlquist, F. W. (1989). Use of 13C and 15N isotope labels for proton nuclear magnetic resonance and nuclear Overhauser effect. Structural and dynamic studies of larger proteins and nucleic acids. Archivos de Biología y Medicina Experimentales, 22, 129–138.

    CAS  PubMed  Google Scholar 

  • Rhodes, G. (2000). Crystallography made crystal clear. San Diego, CA: Academic Press, 269pp.

    Google Scholar 

  • Rhodes, G. (2006). Crystallography made crystal clear–A guide for users of macromolecular models (3rd ed.). London, UK: Academic Press Publications.

    Google Scholar 

  • Robinson, P. K. (2015). Enzymes: Principles and biotechnological applications. Essays in Biochemistry, 59, 1–41.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rost, B. (2002). Enzyme function less conserved than anticipated. Journal of Molecular Biology, 318(2), 595–608.

    Article  CAS  PubMed  Google Scholar 

  • Schumacher, G., Sizmann, D., & Haug, H. (1986). Penicillin acylase from E. coli: Unique gene–protein relation. Nucleic Acids Research, 14(14), 5713–5727.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shah, I., & Hunter, L. (1997). Predicting enzyme function from sequence: A systematic appraisal. Proceedings of the International Conference on Intelligent Systems for Molecular Biology, 5, 276–283.

    CAS  Google Scholar 

  • Soding, J., Biegert, A., & Lupas, A. (2005). The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Research, 33, 244–248.

    Article  CAS  Google Scholar 

  • Sonkaria, S., Boucher, G., & Fl’orez-Alvarez, J. (2004). Evidence for ‘lock and key character in an anti-phosphonate hydrolytic antibody catalytic site augmented by non-reaction centre recognition: Variation in substrate selectivity between an anti-phosphonate antibody, an anti-phosphate antibody and two hydrolytic enzymes. The Biochemical Journal, 381, 125–130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sykes, E. D., & Weiner, J. H. (1980). Biosynthesis and 19F NMR characterization of fluoro amino acid containing proteins. In J. S. Cohen (Ed.), Magnetic resonance in biology (Vol. 1, pp. 1–196). New York, NY: Wiley.

    Google Scholar 

  • Union of Pure, I. & Applied Chemistry. (2005–2009). IUPAC compendium of chemical terminology - The gold book. http://goldbook.iupac.org/.

  • UniProt Consortium. (2013). Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acids Research, 41, D43–D47.

    Article  CAS  Google Scholar 

  • Wang, Y. C., Wang, Y., Yang, Z. X., & Deng, N. Y. (2011). Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context. BMC Systems Biology, 5, S6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Whitehurst, R. J., & van Oort, M. (2009). Enzymes in Food Technology (2nd ed.). Chichester, UK: Wiley-Blackwell.

    Book  Google Scholar 

  • Yadav, S. K., & Tiwari, A. K. (2015). Classification of enzymes using machine learning based approaches: A review. Machine Learning and Applications: An International Journal, 2, 30–49.

    Article  Google Scholar 

  • Yousef, M. S., Clark, S. A., Pruett, P. K., Somasundaram, T., Ellington, W. R., & Chapman, M. S. (2003). Induced fit in guanidino kinases – Comparison of substrate-free and transition state analog structures of arginine kinase. Protein Science, 12, 103–111.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rather, S.A., Masoodi, F.A., Rather, J.A., Ganaie, T.A., Akhter, R., Wani, S.M. (2021). Proteins as Enzymes. In: Gani, A., Ashwar, B.A. (eds) Food biopolymers: Structural, functional and nutraceutical properties. Springer, Cham. https://doi.org/10.1007/978-3-030-27061-2_13

Download citation

Publish with us

Policies and ethics