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Journal articles on the topic 'Enzymes – Classification'

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Published: 4 June 2021

Last updated: 6 February 2022

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1

Memon, Safyan Aman, Kinaan Aamir Khan, and Hammad Naveed. "HECNet: a hierarchical approach to enzyme function classification using a Siamese Triplet Network." Bioinformatics 36, no. 17 (2020): 4583–89. http://dx.doi.org/10.1093/bioinformatics/btaa536.

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Abstract Motivation Understanding an enzyme’s function is one of the most crucial problem domains in computational biology. Enzymes are a key component in all organisms and many industrial processes as they help in fighting diseases and speed up essential chemical reactions. They have wide applications and therefore, the discovery of new enzymatic proteins can accelerate biological research and commercial productivity. Biological experiments, to determine an enzyme’s function, are time-consuming and resource expensive. Results In this study, we propose a novel computational approach to predict

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ARPIGNY, Jean Louis, and Karl-Erich JAEGER. "Bacterial lipolytic enzymes: classification and properties." Biochemical Journal 343, no. 1 (1999): 177–83. http://dx.doi.org/10.1042/bj3430177.

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Knowledge of bacterial lipolytic enzymes is increasing at a rapid and exciting rate. To obtain an overview of this industrially very important class of enzymes and their characteristics, we have collected and classified the information available from protein and nucleotide databases. Here we propose an updated and extensive classification of bacterial esterases and lipases based mainly on a comparison of their amino acid sequences and some fundamental biological properties. These new insights result in the identification of eight different families with the largest being further divided into s

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Chehili, Hamza, Salah Eddine Aliouane, Abdelhafedh Bendahmane, and Mohamed Abdelhafid Hamidechi. "Deepenz: prediction of enzyme classification by deep learning." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1108. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1108-1115.

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<span>Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests show the effectiveness

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ARPIGNY, Jean Louis, and Karl-Erich JAEGER. "Bacterial lipolytic enzymes: classification and properties." Biochemical Journal 343, no. 1 (1999): 177. http://dx.doi.org/10.1042/0264-6021:3430177.

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Lombard, Vincent, Thomas Bernard, Corinne Rancurel, Harry Brumer, Pedro M. Coutinho, and Bernard Henrissat. "A hierarchical classification of polysaccharide lyases for glycogenomics." Biochemical Journal 432, no. 3 (2010): 437–44. http://dx.doi.org/10.1042/bj20101185.

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Carbohydrate-active enzymes face huge substrate diversity in a highly selective manner using only a limited number of available folds. They are therefore subjected to multiple divergent and convergent evolutionary events. This and their frequent modularity render their functional annotation in genomes difficult in a number of cases. In the present paper, a classification of polysaccharide lyases (the enzymes that cleave polysaccharides using an elimination instead of a hydrolytic mechanism) is shown thoroughly for the first time. Based on the analysis of a large panel of experimentally charact

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Concu, Riccardo, and M. Natália D. S. Cordeiro. "Alignment-Free Method to Predict Enzyme Classes and Subclasses." International Journal of Molecular Sciences 20, no. 21 (2019): 5389. http://dx.doi.org/10.3390/ijms20215389.

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The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules acro

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Xu, Jing, Han Zhang, Jinfang Zheng, Philippe Dovoedo, and Yanbin Yin. "eCAMI: simultaneous classification and motif identification for enzyme annotation." Bioinformatics 36, no. 7 (2019): 2068–75. http://dx.doi.org/10.1093/bioinformatics/btz908.

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Abstract Motivation Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to a

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Bush, Karen, та George A. Jacoby. "Updated Functional Classification of β-Lactamases". Antimicrobial Agents and Chemotherapy 54, № 3 (2009): 969–76. http://dx.doi.org/10.1128/aac.01009-09.

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ABSTRACT Two classification schemes for β-lactamases are currently in use. The molecular classification is based on the amino acid sequence and divides β-lactamases into class A, C, and D enzymes which utilize serine for β-lactam hydrolysis and class B metalloenzymes which require divalent zinc ions for substrate hydrolysis. The functional classification scheme updated herein is based on the 1995 proposal by Bush et al. (K. Bush, G. A. Jacoby, and A. A. Medeiros, Antimicrob. Agents Chemother. 39:1211-1233, 1995). It takes into account substrate and inhibitor profiles in an attempt to group the

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SENGUPTA, RAJIB, DHUNDY R. BASTOLA, and HESHAM H. ALI. "CLASSIFICATION AND IDENTIFICATION OF FUNGAL SEQUENCES USING CHARACTERISTIC RESTRICTION ENDONUCLEASE CUT ORDER." Journal of Bioinformatics and Computational Biology 08, no. 02 (2010): 181–98. http://dx.doi.org/10.1142/s0219720010004616.

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Restriction Fragment Length Polymorphism (RFLP) is a powerful molecular tool that is extensively used in the molecular fingerprinting and epidemiological studies of microorganisms. In a wet-lab setting, the DNA is cut with one or more restriction enzymes and subjected to gel electrophoresis to obtain signature fragment patterns, which is utilized in the classification and identification of organisms. This wet-lab approach may not be practical when the experimental data set includes a large number of genetic sequences and a wide pool of restriction enzymes to choose from. In this study, we intr

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Tao, Zhiyu, Benzhi Dong, Zhixia Teng, and Yuming Zhao. "The Classification of Enzymes by Deep Learning." IEEE Access 8 (2020): 89802–11. http://dx.doi.org/10.1109/access.2020.2992468.

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11

毕, 鹏丽. "Classification Technology and Application of Multifunctional Enzymes." Computer Science and Application 11, no. 03 (2021): 476–88. http://dx.doi.org/10.12677/csa.2021.113048.

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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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Qiu, Jingwen, Casper Wilkens, Kristian Barrett, and Anne S. Meyer. "Microbial enzymes catalyzing keratin degradation: Classification, structure, function." Biotechnology Advances 44 (November 2020): 107607. http://dx.doi.org/10.1016/j.biotechadv.2020.107607.

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Henrissat, B. "A classification of glycosyl hydrolases based on amino acid sequence similarities." Biochemical Journal 280, no. 2 (1991): 309–16. http://dx.doi.org/10.1042/bj2800309.

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The amino acid sequences of 301 glycosyl hydrolases and related enzymes have been compared. A total of 291 sequences corresponding to 39 EC entries could be classified into 35 families. Only ten sequences (less than 5% of the sample) could not be assigned to any family. With the sequences available for this analysis, 18 families were found to be monospecific (containing only one EC number) and 17 were found to be polyspecific (containing at least two EC numbers). Implications on the folding characteristics and mechanism of action of these enzymes and on the evolution of carbohydrate metabolism

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Munteanu, Cristian Robert, Humberto González-Díaz, and Alexandre L. Magalhães. "Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices." Journal of Theoretical Biology 254, no. 2 (2008): 476–82. http://dx.doi.org/10.1016/j.jtbi.2008.06.003.

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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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Worsfold, P. J. "Classification and chemical characteristics of immobilized enzymes (Technical Report)." Pure and Applied Chemistry 67, no. 4 (1995): 597–600. http://dx.doi.org/10.1351/pac199567040597.

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Amidi, Shervine, Afshine Amidi, Dimitrios Vlachakis, Nikos Paragios, and Evangelia I. Zacharaki. "Automatic single- and multi-label enzymatic function prediction by machine learning." PeerJ 5 (March 29, 2017): e3095. http://dx.doi.org/10.7717/peerj.3095.

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The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing diff

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Cheng, Jingping, Wanxin Liu, Siyang Chen, et al. "Abnormal myocardial enzymes are important indicators of poor prognosis in COVID-19 patients." Future Virology 16, no. 4 (2021): 265–76. http://dx.doi.org/10.2217/fvl-2020-0304.

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Objective: Researching the prognostic value of myocardial enzymes in COVID-19 patients. Materials & methods: We collected 113 confirmed COVID-19 patients. The dynamic changes of CK, LDH and α-HBDH in patients were studied retrospectively, the correlation between myocardial enzyme index, clinical classification and outcome of patients and its significance to prognosis. Results: There are significant statistical differences between LDH, α-HBDH, CK and the clinical classification, and patient’s outcome. In the receiver operating characteristic curve analysis, LDH, α-HBDH and CK have a good di

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Henrissat, B., and A. Bairoch. "New families in the classification of glycosyl hydrolases based on amino acid sequence similarities." Biochemical Journal 293, no. 3 (1993): 781–88. http://dx.doi.org/10.1042/bj2930781.

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301 glycosyl hydrolases and related enzymes corresponding to 39 EC entries of the I.U.B. classification system have been classified into 35 families on the basis of amino-acid-sequence similarities [Henrissat (1991) Biochem. J. 280, 309-316]. Approximately half of the families were found to be monospecific (containing only one EC number), whereas the other half were found to be polyspecific (containing at least two EC numbers). A > 60% increase in sequence data for glycosyl hydrolases (181 additional enzymes or enzyme domains sequences have since become available) allowed us to update the c

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Amidi, Afshine, Shervine Amidi, Dimitrios Vlachakis, Vasileios Megalooikonomou, Nikos Paragios, and Evangelia I. Zacharaki. "EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation." PeerJ 6 (May 4, 2018): e4750. http://dx.doi.org/10.7717/peerj.4750.

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During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank (PDB) has increased more than 15-fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence, however, is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper pr

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Robinson, Peter K. "Enzymes: principles and biotechnological applications." Essays in Biochemistry 59 (October 26, 2015): 1–41. http://dx.doi.org/10.1042/bse0590001.

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Enzymes are biological catalysts (also known as biocatalysts) that speed up biochemical reactions in living organisms, and which can be extracted from cells and then used to catalyse a wide range of commercially important processes. This chapter covers the basic principles of enzymology, such as classification, structure, kinetics and inhibition, and also provides an overview of industrial applications. In addition, techniques for the purification of enzymes are discussed.

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Hughes, R. K., E. J. Belfield, and R. Casey. "CYP74C3 and CYP74A1, plant cytochrome P450 enzymes whose activity is regulated by detergent micelle association, and proposed new rules for the classification of CYP74 enzymes." Biochemical Society Transactions 34, no. 6 (2006): 1223–27. http://dx.doi.org/10.1042/bst0341223.

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CYP74C3 (cytochrome P450 subfamily 74C3), an HPL (hydroperoxide lyase) from Medicago truncatula (barrel medic), and CYP74A1, an AOS (allene oxide synthase) from Arabidopsis thaliana, are key membrane-associated P450 enzymes in plant oxylipin metabolism. Both recombinant detergent-free enzymes are monomeric proteins with dual specificity and very low enzyme activity that can be massively activated with detergent. This effect is a result of the formation of a complex between the protein monomer and a single detergent micelle and, in the case of CYP74A1, has a major effect on the substrate specif

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Six, David A., and Edward A. Dennis. "The expanding superfamily of phospholipase A2 enzymes: classification and characterization." Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 1488, no. 1-2 (2000): 1–19. http://dx.doi.org/10.1016/s1388-1981(00)00105-0.

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Rawlings, Neil D. "Twenty-five years of nomenclature and classification of proteolytic enzymes." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1868, no. 2 (2020): 140345. http://dx.doi.org/10.1016/j.bbapap.2019.140345.

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Yadav, Sanjeev Kumar, and Arvind Kumar Tiwari. "Classification of Enzymes Using Machine Learning Based Approaches: A Review." Machine Learning and Applications: An International Journal 2, no. 3/4 (2015): 30–49. http://dx.doi.org/10.5121/mlaij.2015.2404.

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Ribeiro, António J. M., Sayoni Das, Natalie Dawson, et al. "Emerging concepts in pseudoenzyme classification, evolution, and signaling." Science Signaling 12, no. 594 (2019): eaat9797. http://dx.doi.org/10.1126/scisignal.aat9797.

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The 21st century is witnessing an explosive surge in our understanding of pseudoenzyme-driven regulatory mechanisms in biology. Pseudoenzymes are proteins that have sequence homology with enzyme families but that are proven or predicted to lack enzyme activity due to mutations in otherwise conserved catalytic amino acids. The best-studied pseudoenzymes are pseudokinases, although examples from other families are emerging at a rapid rate as experimental approaches catch up with an avalanche of freely available informatics data. Kingdom-wide analysis in prokaryotes, archaea and eukaryotes reveal

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Srivastava, Neha, Rishabh Rathour, Sonam Jha, et al. "Microbial Beta Glucosidase Enzymes: Recent Advances in Biomass Conversation for Biofuels Application." Biomolecules 9, no. 6 (2019): 220. http://dx.doi.org/10.3390/biom9060220.

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The biomass to biofuels production process is green, sustainable, and an advanced technique to resolve the current environmental issues generated from fossil fuels. The production of biofuels from biomass is an enzyme mediated process, wherein β-glucosidase (BGL) enzymes play a key role in biomass hydrolysis by producing monomeric sugars from cellulose-based oligosaccharides. However, the production and availability of these enzymes realize their major role to increase the overall production cost of biomass to biofuels production technology. Therefore, the present review is focused on evaluati

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Hou, Li, Gaoyan Jiang, Ying Sun, et al. "Progress and Trend on the Regulation Methods for Nanozyme Activity and Its Application." Catalysts 9, no. 12 (2019): 1057. http://dx.doi.org/10.3390/catal9121057.

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Natural enzymes, such as biocatalysts, are widely used in biosensors, medicine and health, the environmental field, and other fields. However, it is easy for natural enzymes to lose catalytic activity due to their intrinsic shortcomings including a high purification cost, insufficient stability, and difficulties of recycling, which limit their practical applications. The unexpected discovery of the Fe3O4 nanozyme in 2007 has given rise to tremendous efforts for developing natural enzyme substitutes. Nanozymes, which are nanomaterials with enzyme-mimetic catalytic activity, can serve as ideal c

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Khan, Mohammad Rafiq. "Current and future role of immobilized enzymes in medical field." CURRENT MEDICAL AND DRUG RESEARCH 5, no. 01 (2021): 1–9. http://dx.doi.org/10.53517/cmdr.2581-5008.512021213.

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Although the history of immobilized enzymes and their applications in different fields are traced back to the second half of the twentieth century, their importance in bioreactors and biosensors highlighted at the turn of the current century is under active consideration in these days for broad-spectrum applications in different medical fields. Thus, this article presents a review of the literature concerning the current and future role of the immobilized enzymes in different medical fields. As the author and his supervised research groups have also been actively involved in research on immobi

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Rodrigues, Heb C. S. R., Andrea L. Carvalho, Carolina O. Souza, and Marcelo A. Umsza-Guez. "Evolution of World and Brazilian Markets for Enzymes Produced by Solid-state Fermentation: A Patent Analysis." Recent Patents on Biotechnology 14, no. 2 (2020): 112–20. http://dx.doi.org/10.2174/1872208313666191017143845.

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Background: The use of enzymes in various industrial processes has become increasingly frequent. When added to productive processes, it can accelerate reactions and generate a number of new products. The solid state fermentation (SSF), among other applications, has been employed also to obtain enzymes. Objective: The purpose of this prospection was to map registered patent documents about enzymes production by this type of fermentation in the world, identify the most obtained enzymes with patent documents and compilate information about the world and Brazilian enzyme markets. Methods: The expe

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Khalikova, Elvira, Petri Susi, and Timo Korpela. "Microbial Dextran-Hydrolyzing Enzymes: Fundamentals and Applications." Microbiology and Molecular Biology Reviews 69, no. 2 (2005): 306–25. http://dx.doi.org/10.1128/mmbr.69.2.306-325.2005.

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SUMMARY Dextran is a chemically and physically complex polymer, breakdown of which is carried out by a variety of endo- and exodextranases. Enzymes in many groups can be classified as dextranases according to function: such enzymes include dextranhydrolases, glucodextranases, exoisomaltohydrolases, exoisomaltotriohydrases, and branched-dextran exo-1,2-α-glucosidases. Cycloisomalto-oligosaccharide glucanotransferase does not formally belong to the dextranases even though its side reaction produces hydrolyzed dextrans. A new classification system for glycosylhydrolases and glycosyltransferases,

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Reddy, BP Niranjan, B. Prasad Rao, GBKS Prasad, and K. Raghavendra. "Identification and classification of detoxification enzymes from Culex quinquefasciatus (Diptera: Culicidae)." Bioinformation 8, no. 9 (2012): 430–36. http://dx.doi.org/10.6026/97320630008430.

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Jormakka, Mika, David Richardson, Bernadette Byrne, and So Iwata. "Architecture of NarGH Reveals a Structural Classification of Mo-bisMGD Enzymes." Structure 12, no. 1 (2004): 95–104. http://dx.doi.org/10.1016/j.str.2003.11.020.

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Matyugina, E. S., A. P. Khandazhinskaya, and Sergei N. Kochetkov. "Carbocyclic nucleoside analogues: classification, target enzymes, mechanisms of action and synthesis." Russian Chemical Reviews 81, no. 8 (2012): 729–46. http://dx.doi.org/10.1070/rc2012v081n08abeh004314.

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Bais, Vaibhav Singh, Pooja Aggarwal, Prashant Bharadwaj, and Balaji Prakash. "Classification, characterization and structural analysis of sugar nucleotidylyltransferase family of enzymes." Biochemical and Biophysical Research Communications 525, no. 3 (2020): 780–85. http://dx.doi.org/10.1016/j.bbrc.2020.02.148.

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Hitch, Thomas C. A., and Thomas Clavel. "A proposed update for the classification and description of bacterial lipolytic enzymes." PeerJ 7 (July 8, 2019): e7249. http://dx.doi.org/10.7717/peerj.7249.

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Bacterial lipolytic enzymes represent an important class of proteins: they provide their host species with access to additional resources and have multiple applications within the biotechnology sector. Since the formalisation of lipolytic enzymes into families and subfamilies, advances in molecular biology have led to the discovery of lipolytic enzymes unable to be classified via the existing system. Utilising sequence-based comparison methods, we have integrated these novel families within the classification system so that it now consists of 35 families and 11 true lipase subfamilies. Represe

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Stasyuk, Nataliya, Oleh Smutok, Olha Demkiv, et al. "Synthesis, Catalytic Properties and Application in Biosensorics of Nanozymes and Electronanocatalysts: A Review." Sensors 20, no. 16 (2020): 4509. http://dx.doi.org/10.3390/s20164509.

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The current review is devoted to nanozymes, i.e., nanostructured artificial enzymes which mimic the catalytic properties of natural enzymes. Use of the term “nanozyme” in the literature as indicating an enzyme is not always justified. For example, it is used inappropriately for nanomaterials bound with electrodes that possess catalytic activity only when applying an electric potential. If the enzyme-like activity of such a material is not proven in solution (without applying the potential), such a catalyst should be named an “electronanocatalyst”, not a nanozyme. This paper presents a review o

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Krtenic, Bojan, Adrian Drazic, Thomas Arnesen, and Nathalie Reuter. "Classification and phylogeny for the annotation of novel eukaryotic GNAT acetyltransferases." PLOS Computational Biology 16, no. 12 (2020): e1007988. http://dx.doi.org/10.1371/journal.pcbi.1007988.

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The enzymes of the GCN5-related N-acetyltransferase (GNAT) superfamily count more than 870 000 members through all kingdoms of life and share the same structural fold. GNAT enzymes transfer an acyl moiety from acyl coenzyme A to a wide range of substrates including aminoglycosides, serotonin, glucosamine-6-phosphate, protein N-termini and lysine residues of histones and other proteins. The GNAT subtype of protein N-terminal acetyltransferases (NATs) alone targets a majority of all eukaryotic proteins stressing the omnipresence of the GNAT enzymes. Despite the highly conserved GNAT fold, sequen

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De Meester, F., J. M. Frère, S. G. Waley, S. J. Cartwright, R. Virden та F. Lindberg. "6-β-Iodopenicillanate as a probe for the classification of β-lactamases". Biochemical Journal 239, № 3 (1986): 575–80. http://dx.doi.org/10.1042/bj2390575.

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An inactivator of serine beta-lactamases, 6 beta-iodopenicillanate, can be utilized as a probe in the classification of beta-lactamases. It is a substrate for class-B Zn2+-containing beta-lactamase II. Although it inactivates enzymes from both classes A and C, it is much more efficient for the former group, with which it sometimes interacts following a branched pathway. On the basis of these observations, predictions are made concerning the class to which several enzymes belong.

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Borek, D., and M. Jaskólski. "Sequence analysis of enzymes with asparaginase activity." Acta Biochimica Polonica 48, no. 4 (2001): 893–902. http://dx.doi.org/10.18388/abp.2001_3855.

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Asparaginases catalyze the hydrolysis of asparagine to aspartic acid and ammonia. Enzymes with asparaginase activity play an important role both in the metabolism of all living organisms as well as in pharmacology. The main goal of this paper is to attempt a classification of all known enzymes with asparaginase activity, based on their amino acid sequences. Some possible phylogenetic consequences are also discussed using dendrograms and structural information derived from crystallographic studies.

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Klesmith, Justin R., John-Paul Bacik, Emily E. Wrenbeck, Ryszard Michalczyk, and Timothy A. Whitehead. "Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning." Proceedings of the National Academy of Sciences 114, no. 9 (2017): 2265–70. http://dx.doi.org/10.1073/pnas.1614437114.

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Proteins are marginally stable, and an understanding of the sequence determinants for improved protein solubility is highly desired. For enzymes, it is well known that many mutations that increase protein solubility decrease catalytic activity. These competing effects frustrate efforts to design and engineer stable, active enzymes without laborious high-throughput activity screens. To address the trade-off between enzyme solubility and activity, we performed deep mutational scanning using two different screens/selections that purport to gauge protein solubility for two full-length enzymes. We

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Zhang, Lifu, Benzhi Dong, Zhixia Teng, Ying Zhang, and Liran Juan. "Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs." BioMed Research International 2020 (May 27, 2020): 1–11. http://dx.doi.org/10.1155/2020/9235920.

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Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (C

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Egorov, A. M., M. M. Ulyashova, and M. Yu Rubtsova. "Bacterial Enzymes and Antibiotic Resistance." Acta Naturae 10, no. 4 (2018): 33–48. http://dx.doi.org/10.32607/20758251-2018-10-4-33-48.

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The resistance of microorganisms to antibiotics has been developing for more than 2 billion years and is widely distributed among various representatives of the microbiological world. Bacterial enzymes play a key role in the emergence of resistance. Classification of these enzymes is based on their participation in various biochemical mechanisms: modification of the enzymes that act as antibiotic targets, enzymatic modification of intracellular targets, enzymatic transformation of antibiotics, and the implementation of cellular metabolism reactions. The main mechanisms of resistance developmen

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Robinson, Serina L., Barbara R. Terlouw, Megan D. Smith та ін. "Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic Nocardia". Journal of Biological Chemistry 295, № 44 (2020): 14826–39. http://dx.doi.org/10.1074/jbc.ra120.013528.

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Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming en

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Genot, Anthony J., Teruo Fujii, and Yannick Rondelez. "Scaling down DNA circuits with competitive neural networks." Journal of The Royal Society Interface 10, no. 85 (2013): 20130212. http://dx.doi.org/10.1098/rsif.2013.0212.

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DNA has proved to be an exquisite substrate to compute at the molecular scale. However, nonlinear computations (such as amplification, comparison or restoration of signals) remain costly in term of strands and are prone to leak. Kim et al. showed how competition for an enzymatic resource could be exploited in hybrid DNA/enzyme circuits to compute a powerful nonlinear primitive: the winner-take-all (WTA) effect. Here, we first show theoretically how the nonlinearity of the WTA effect allows the robust and compact classification of four patterns with only 16 strands and three enzymes. We then ge

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Stevenson, Donald D., Mario Sanchez-Borges, and Andrew Szczeklik. "Classification of allergic and pseudoallergic reactions to drugs that inhibit cyclooxygenase enzymes." Annals of Allergy, Asthma & Immunology 87, no. 3 (2001): 177–80. http://dx.doi.org/10.1016/s1081-1206(10)62221-1.

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Ramnath, L., B. Sithole, and R. Govinden. "Classification of lipolytic enzymes and their biotechnological applications in the pulping industry." Canadian Journal of Microbiology 63, no. 3 (2017): 179–92. http://dx.doi.org/10.1139/cjm-2016-0447.

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In the pulp and paper industry, during the manufacturing process, the agglomeration of pitch particles (composed of triglycerides, fatty acids, and esters) leads to the formation of black pitch deposits in the pulp and on machinery, which impacts on the process and pulp quality. Traditional methods of pitch prevention and treatment are no longer feasible due to environmental impact and cost. Consequently, there is a need for more efficient and environmentally friendly approaches. The application of lipolytic enzymes, such as lipases and esterases, could be the sustainable solution to this prob

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AOKI, Hiroyoshi, and Yoshiyuki SAKANO. "A classification of dextran-hydrolysing enzymes based on amino-acid-sequence similarities." Biochemical Journal 323, no. 3 (1997): 859–61. http://dx.doi.org/10.1042/bj3230859.

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Philippon, Alain, Patrick Slama, Paul Dény та Roger Labia. "A Structure-Based Classification of Class A β-Lactamases, a Broadly Diverse Family of Enzymes". Clinical Microbiology Reviews 29, № 1 (2015): 29–57. http://dx.doi.org/10.1128/cmr.00019-15.

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SUMMARYFor medical biologists, sequencing has become a commonplace technique to support diagnosis. Rapid changes in this field have led to the generation of large amounts of data, which are not always correctly listed in databases. This is particularly true for data concerning class A β-lactamases, a group of key antibiotic resistance enzymes produced by bacteria. Many genomes have been reported to contain putative β-lactamase genes, which can be compared with representative types. We analyzed several hundred amino acid sequences of class A β-lactamase enzymes for phylogenic relationships, the

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