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Journal articles on the topic 'Enzyme function'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Dunaway-Mariano, Debra. "Enzyme Function Discovery." Structure 16, no. 11 (2008): 1599–600. http://dx.doi.org/10.1016/j.str.2008.10.001.

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2

Tan, Yong Quan, Bo Xue, and Wen Shan Yew. "Genetically Encodable Scaffolds for Optimizing Enzyme Function." Molecules 26, no. 5 (2021): 1389. http://dx.doi.org/10.3390/molecules26051389.

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Enzyme engineering is an indispensable tool in the field of synthetic biology, where enzymes are challenged to carry out novel or improved functions. Achieving these goals sometimes goes beyond modifying the primary sequence of the enzyme itself. The use of protein or nucleic acid scaffolds to enhance enzyme properties has been reported for applications such as microbial production of chemicals, biosensor development and bioremediation. Key advantages of using these assemblies include optimizing reaction conditions, improving metabolic flux and increasing enzyme stability. This review summariz

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3

Gerlt, John A., Karen N. Allen, Steven C. Almo, et al. "The Enzyme Function Initiative." Biochemistry 50, no. 46 (2011): 9950–62. http://dx.doi.org/10.1021/bi201312u.

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4

Romero, Philip A., Tuan M. Tran, and Adam R. Abate. "Dissecting enzyme function with microfluidic-based deep mutational scanning." Proceedings of the National Academy of Sciences 112, no. 23 (2015): 7159–64. http://dx.doi.org/10.1073/pnas.1422285112.

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Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme’s sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence–function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape.

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5

Page, Michael J., and Enrico Di Cera. "Role of Na+and K+in Enzyme Function." Physiological Reviews 86, no. 4 (2006): 1049–92. http://dx.doi.org/10.1152/physrev.00008.2006.

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Metal complexation is a key mediator or modifier of enzyme structure and function. In addition to divalent and polyvalent metals, group IA metals Na+and K+play important and specific roles that assist function of biological macromolecules. We examine the diversity of monovalent cation (M+)-activated enzymes by first comparing coordination in small molecules followed by a discussion of theoretical and practical aspects. Select examples of enzymes that utilize M+as a cofactor (type I) or allosteric effector (type II) illustrate the structural basis of activation by Na+and K+, along with unexpect

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6

Duskey, Jason Thomas, Federica da Ros, Ilaria Ottonelli, et al. "Enzyme Stability in Nanoparticle Preparations Part 1: Bovine Serum Albumin Improves Enzyme Function." Molecules 25, no. 20 (2020): 4593. http://dx.doi.org/10.3390/molecules25204593.

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Enzymes have gained attention for their role in numerous disease states, calling for research for their efficient delivery. Loading enzymes into polymeric nanoparticles to improve biodistribution, stability, and targeting in vivo has led the field with promising results, but these enzymes still suffer from a degradation effect during the formulation process that leads to lower kinetics and specific activity leading to a loss of therapeutic potential. Stabilizers, such as bovine serum albumin (BSA), can be beneficial, but the knowledge and understanding of their interaction with enzymes are not

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7

BORMAN, STU. "PROBING ENZYME FUNCTION IN CELLS." Chemical & Engineering News 84, no. 44 (2006): 12. http://dx.doi.org/10.1021/cen-v084n044.p012a.

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8

Mitchell, John BO. "Enzyme function and its evolution." Current Opinion in Structural Biology 47 (December 2017): 151–56. http://dx.doi.org/10.1016/j.sbi.2017.10.004.

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9

Crunkhorn, Sarah. "Enzyme inhibitor improves cognitive function." Nature Reviews Drug Discovery 13, no. 10 (2014): 726. http://dx.doi.org/10.1038/nrd4452.

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10

Poulos, Thomas L. "Heme Enzyme Structure and Function." Chemical Reviews 114, no. 7 (2014): 3919–62. http://dx.doi.org/10.1021/cr400415k.

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11

Zhang, Wen, and Tao Pan. "A dual function PUS enzyme." Nature Chemical Biology 16, no. 2 (2020): 107–8. http://dx.doi.org/10.1038/s41589-019-0450-z.

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12

GUPTA, Munishwar N. "Enzyme function in organic solvents." European Journal of Biochemistry 203, no. 1-2 (1992): 25–32. http://dx.doi.org/10.1111/j.1432-1033.1992.tb19823.x.

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13

Latip, Wahhida, Victor Feizal Knight, Norhana Abdul Halim, et al. "Microbial Phosphotriesterase: Structure, Function, and Biotechnological Applications." Catalysts 9, no. 8 (2019): 671. http://dx.doi.org/10.3390/catal9080671.

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The role of phosphotriesterase as an enzyme which is able to hydrolyze organophosphate compounds cannot be disputed. Contamination by organophosphate (OP) compounds in the environment is alarming, and even more worrying is the toxicity of this compound, which affects the nervous system. Thus, it is important to find a safer way to detoxify, detect and recuperate from the toxicity effects of this compound. Phosphotriesterases (PTEs) are mostly isolated from soil bacteria and are classified as metalloenzymes or metal-dependent enzymes that contain bimetals at the active site. There are three sep

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14

Sher, Hassan, Hazrat Ali, Muhammad H. Rashid, et al. "Enzyme Immobilization on Metal-Organic Framework (MOF): Effects on Thermostability and Function." Protein & Peptide Letters 26, no. 9 (2019): 636–47. http://dx.doi.org/10.2174/0929866526666190430120046.

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MOFs are porous materials with adjustable porosity ensuing a tenable surface area and stability. MOFs consist of metal containing joint where organic ligands are linked with coordination bonding rendering a unique architecture favouring the diverse applications in attachment of enzymes, Chemical catalysis, Gases storage and separation, biomedicals. In the past few years immobilization of soluble enzymes on/in MOF has been the topic of interest for scientists working in diverse field. The activity of enzyme, reusability, storage, chemical and thermal stability, affinity with substrate can be gr

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15

Zarzycki, Jan, Onur Erbilgin, and Cheryl A. Kerfeld. "Bioinformatic Characterization of Glycyl Radical Enzyme-Associated Bacterial Microcompartments." Applied and Environmental Microbiology 81, no. 24 (2015): 8315–29. http://dx.doi.org/10.1128/aem.02587-15.

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ABSTRACTBacterial microcompartments (BMCs) are proteinaceous organelles encapsulating enzymes that catalyze sequential reactions of metabolic pathways. BMCs are phylogenetically widespread; however, only a few BMCs have been experimentally characterized. Among them are the carboxysomes and the propanediol- and ethanolamine-utilizing microcompartments, which play diverse metabolic and ecological roles. The substrate of a BMC is defined by its signature enzyme. In catabolic BMCs, this enzyme typically generates an aldehyde. Recently, it was shown that the most prevalent signature enzymes encoded

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16

Fiehn, Oliver, Dinesh K. Barupal, and Tobias Kind. "Extending Biochemical Databases by Metabolomic Surveys." Journal of Biological Chemistry 286, no. 27 (2011): 23637–43. http://dx.doi.org/10.1074/jbc.r110.173617.

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Metabolomics can map the large metabolic diversity in species, organs, or cell types. In addition to gains in enzyme specificity, many enzymes have retained substrate and reaction promiscuity. Enzyme promiscuity and the large number of enzymes with unknown enzyme function may explain the presence of a plethora of unidentified compounds in metabolomic studies. Cataloguing the identity and differential abundance of all detectable metabolites in metabolomic repositories may detail which compounds and pathways contribute to vital biological functions. The current status in metabolic databases is r

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17

IKEDA, Tokuji. "Enzyme-modified electrodes with bioelectrocatalytic function." Bunseki kagaku 44, no. 5 (1995): 333–54. http://dx.doi.org/10.2116/bunsekikagaku.44.333.

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18

Dalby, Paul A. "Optimising enzyme function by directed evolution." Current Opinion in Structural Biology 13, no. 4 (2003): 500–505. http://dx.doi.org/10.1016/s0959-440x(03)00101-5.

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19

Brokaw, C. J. "Mechanical components of motor enzyme function." Biophysical Journal 73, no. 2 (1997): 938–51. http://dx.doi.org/10.1016/s0006-3495(97)78126-8.

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20

Rost, Burkhard. "Enzyme Function Less Conserved than Anticipated." Journal of Molecular Biology 318, no. 2 (2002): 595–608. http://dx.doi.org/10.1016/s0022-2836(02)00016-5.

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21

Sakiyama, Fumio. "Modification of enzyme structure and function." Kobunshi 35, no. 10 (1986): 950–53. http://dx.doi.org/10.1295/kobunshi.35.950.

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22

Sharma, Krishna Kant, and Ramesh Chander Kuhad. "Laccase: enzyme revisited and function redefined." Indian Journal of Microbiology 48, no. 3 (2008): 309–16. http://dx.doi.org/10.1007/s12088-008-0028-z.

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23

Yoshikuni, Yasuo, Thomas E. Ferrin, and Jay D. Keasling. "Designed divergent evolution of enzyme function." Nature 440, no. 7087 (2006): 1078–82. http://dx.doi.org/10.1038/nature04607.

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24

Taivonen, Hannu J., Nevine Makari, and John D. Catravas. "Monitoring of Pulmonary Endothelial Enzyme Function." Anesthesiology 68, no. 1 (1988): 44–52. http://dx.doi.org/10.1097/00000542-198801000-00008.

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25

Baly, Deborah L. "Manganese in Metabolism and Enzyme Function." Journal of Nutrition 119, no. 2 (1989): 327. http://dx.doi.org/10.1093/jn/119.2.327.

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26

Thiele, Alexandra, Gabriele I. Stangl, and Mike Schutkowski. "Deciphering Enzyme Function Using Peptide Arrays." Molecular Biotechnology 49, no. 3 (2011): 283–305. http://dx.doi.org/10.1007/s12033-011-9402-x.

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27

Memon, Safyan Aman, Kinaan Aamir Khan, and Hammad Naveed. "Enzyme Function Prediction using Deep Learning." Biophysical Journal 118, no. 3 (2020): 533a. http://dx.doi.org/10.1016/j.bpj.2019.11.2926.

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28

Minshull, Jeremy, Jon E. Ness, Claes Gustafsson, and Sridhar Govindarajan. "Predicting enzyme function from protein sequence." Current Opinion in Chemical Biology 9, no. 2 (2005): 202–9. http://dx.doi.org/10.1016/j.cbpa.2005.02.003.

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29

Dean, Antony M., Daniel E. Dykhuizen та Daniel L. Hartl. "Fitness as a function of β-galactosidase activity inEscherichia coli". Genetical Research 48, № 1 (1986): 1–8. http://dx.doi.org/10.1017/s0016672300024587.

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SummaryChemostat cultures in which the limiting nutrient was lactose have been used to study the relative growth rate ofEscherichia coliin relation to the enzyme activity of β-galactosidase. A novel genetic procedure was employed in order to obtain amino acid substitutions within thelacZ-encoded β-galactosidase that result in differences in enzyme activity too small to be detected by ordinary mutant screens. The cryptic substitutions were obtained as spontaneous revertants of nonsense mutations within thelacZgene, and the enzymes differing from wild type were identified by means of polyacrylam

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30

Busk, Peter Kamp, and Lene Lange. "Function-Based Classification of Carbohydrate-Active Enzymes by Recognition of Short, Conserved Peptide Motifs." Applied and Environmental Microbiology 79, no. 11 (2013): 3380–91. http://dx.doi.org/10.1128/aem.03803-12.

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ABSTRACTFunctional prediction of carbohydrate-active enzymes is difficult due to low sequence identity. However, similar enzymes often share a few short motifs, e.g., around the active site, even when the overall sequences are very different. To exploit this notion for functional prediction of carbohydrate-active enzymes, we developed a simple algorithm, peptide pattern recognition (PPR), that can divide proteins into groups of sequences that share a set of short conserved sequences. When this method was used on 118 glycoside hydrolase 5 proteins with 9% average pairwise identity and represent

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31

Tan, Jiu-Xin, Hao Lv, Fang Wang, Fu-Ying Dao, Wei Chen, and Hui Ding. "A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods." Current Drug Targets 20, no. 5 (2019): 540–50. http://dx.doi.org/10.2174/1389450119666181002143355.

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Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databank

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32

Cieśla, Joanna. "Metabolic enzymes that bind RNA: yet another level of cellular regulatory network?" Acta Biochimica Polonica 53, no. 1 (2006): 11–32. http://dx.doi.org/10.18388/abp.2006_3360.

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Several enzymes that were originally characterized to have one defined function in intermediatory metabolism are now shown to participate in a number of other cellular processes. Multifunctional proteins may be crucial for building of the highly complex networks that maintain the function and structure in the eukaryotic cell possessing a relatively low number of protein-encoding genes. One facet of this phenomenon, on which I will focus in this review, is the interaction of metabolic enzymes with RNA. The list of such enzymes known to be associated with RNA is constantly expanding, but the mos

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33

Arnold, Frances H. "Enzymes by Evolution: Bringing New Chemistry to Life." Molecular Frontiers Journal 02, no. 01 (2018): 9–18. http://dx.doi.org/10.1142/s2529732518400023.

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Not satisfied with nature’s vast enzyme repertoire, we want to create new ones and expand the space of genetically encoded enzyme functions. We use the most powerful biological design process, evolution, to optimize existing enzymes and invent new ones, thereby circumventing our profound ignorance of how sequence encodes function. Mimicking nature’s evolutionary tricks and using a little chemical intuition, we can generate whole new enzyme families that catalyze important reactions, including ones not known in biology. These new capabilities increase the scope of molecules and materials we can

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34

Maria-Solano, Miguel A., Eila Serrano-Hervás, Adrian Romero-Rivera, Javier Iglesias-Fernández, and Sílvia Osuna. "Role of conformational dynamics in the evolution of novel enzyme function." Chemical Communications 54, no. 50 (2018): 6622–34. http://dx.doi.org/10.1039/c8cc02426j.

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Enzymes exist as a dynamic ensemble of conformations, each potentially playing a key role in substrate binding, the chemical transformation, or product release. We discuss recent advances in the evaluation of the enzyme conformational dynamics and its evolution towards new functions or substrate preferences.

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35

Ptak, Christopher, Chantelle Gwozd, J. Torin Huzil, Todd J. Gwozd, Grace Garen, and Michael J. Ellison. "Creation of a Pluripotent Ubiquitin-Conjugating Enzyme." Molecular and Cellular Biology 21, no. 19 (2001): 6537–48. http://dx.doi.org/10.1128/mcb.21.19.6537-6548.2001.

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ABSTRACT We describe the creation of a pluripotent ubiquitin-conjugating enzyme (E2) generated through a single amino acid substitution within the catalytic domain of RAD6 (UBC2). This RAD6 derivative carries out the stress-related function of UBC4 and the cell cycle function of CDC34 while maintaining its own DNA repair function. Furthermore, it carries out CDC34's function in the absence of the CDC34 carboxy-terminal extension. By using sequence and structural comparisons, the residues that define the unique functions of these three E2s were found on the E2 catalytic face partitioned to eith

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36

Holliday, Michael Joseph, Carlo Camilloni, Geoffrey Stuart Armstrong, Michele Vendruscolo, and Elan Zohar Eisenmesser. "Networks of Dynamic Allostery Regulate Enzyme Function." Structure 25, no. 2 (2017): 276–86. http://dx.doi.org/10.1016/j.str.2016.12.003.

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37

Hatzimanikatis, Vassily, Chunhui Li, Justin A. Ionita, and Linda J. Broadbelt. "Metabolic networks: enzyme function and metabolite structure." Current Opinion in Structural Biology 14, no. 3 (2004): 300–306. http://dx.doi.org/10.1016/j.sbi.2004.04.004.

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38

Frankish, Helen. "Researchers uncover function of key Alzheimer's enzyme." Lancet Neurology 5, no. 11 (2006): 904. http://dx.doi.org/10.1016/s1474-4422(06)70589-0.

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39

Thornton, Janet. "THE EVOLUTION OF ENZYME STRUCTURE AND FUNCTION." Biochemical Society Transactions 28, no. 3 (2000): A53. http://dx.doi.org/10.1042/bst028a053.

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40

Mason, Nancy A. "Angiotensin-Converting Enzyme Inhibitors and Renal Function." DICP 24, no. 5 (1990): 496–505. http://dx.doi.org/10.1177/106002809002400511.

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41

Keller, F., C. Emde, and A. Schwarz. "Exponential function for calculating saturable enzyme kinetics." Clinical Chemistry 34, no. 12 (1988): 2486–89. http://dx.doi.org/10.1093/clinchem/34.12.2486.

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Abstract Enzyme kinetics are usually described by the Michaelis-Menten equation, where the time-dependent decrease of substrate (-dS/dt) is a hyperbolic function of maximal velocity (Vmax), Michaelis constant (Km), and amount of substrate (S). Because the Michaelis-Menten function in its most general meaning requires an assumption of steady-state, it is less curvilinear than true enzyme kinetics. A saturation-type exponential function is more curvilinear than the hyperbolic function and more closely approximates enzyme kinetics: -dS/dt = Vmax [1 - exp(-S/Km)]. The mathematical representation o

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42

LEE, JINAH. "Function of Deubiquitinating Enzyme USP1 in Adipogenesis." Diabetes 67, Supplement 1 (2018): 284—LB. http://dx.doi.org/10.2337/db18-284-lb.

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43

Kuriki, Takashi, Han-Ping Guan, and Jack Preiss. "Structure and Function of Starch Branching Enzyme." Journal of the agricultural chemical society of Japan 68, no. 11 (1994): 1581–84. http://dx.doi.org/10.1271/nogeikagaku1924.68.1581.

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44

Brokaw, Charles J. "Weakly-coupled models for motor enzyme function." Journal of Muscle Research and Cell Motility 16, no. 3 (1995): 197–211. http://dx.doi.org/10.1007/bf00121129.

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45

Kim, Jungbae, and Jonathan S. Dordick. "Pressure affects enzyme function in organic media." Biotechnology and Bioengineering 42, no. 6 (1993): 772–76. http://dx.doi.org/10.1002/bit.260420613.

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46

White, Robert H. "The Twists and Turns of Enzyme Function." Journal of Bacteriology 192, no. 8 (2010): 2023–25. http://dx.doi.org/10.1128/jb.00087-10.

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47

Astikainen, Katja, Liisa Holm, Esa Pitkänen, Sandor Szedmak, and Juho Rousu. "Towards structured output prediction of enzyme function." BMC Proceedings 2, Suppl 4 (2008): S2. http://dx.doi.org/10.1186/1753-6561-2-s4-s2.

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48

von Grotthuss, M., D. Plewczynski, G. Vriend, and L. Rychlewski. "3D-Fun: predicting enzyme function from structure." Nucleic Acids Research 36, Web Server (2008): W303—W307. http://dx.doi.org/10.1093/nar/gkn308.

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49

Gifford, Stacey M., and Pablo Meyer. "Enzyme function is regulated by its localization." Computational Biology and Chemistry 59 (December 2015): 113–22. http://dx.doi.org/10.1016/j.compbiolchem.2015.08.004.

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50

Duckworth, Benjamin P., and Courtney C. Aldrich. "Assigning Enzyme Function from the Metabolic Milieu." Chemistry & Biology 17, no. 4 (2010): 313–14. http://dx.doi.org/10.1016/j.chembiol.2010.04.001.

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