Search

The glycoside hydrolase family contains enzymes that break the glycosidic bonds of carbohydrates by hydrolysis. Inulinase is one of the most important industrial enzymes in the family of Glycoside Hydrolases 32 (GH32). In this study, to identify and classify bacterial inulinases initially, 16,002 protein sequences belonging to the GH32 family were obtained using various databases. The inulin‐effective enzymes (endoinulinase and exoinulinase) were identified. Eight endoinulinases (EC 3.2.1.7) and 4318 exoinulinases (EC 3.2.1.80) were found. Then, the localization of endoinulinase and exoinulinase enzymes in the cell was predicted. Among them, two extracellular endoinulinases and 1232 extracellular exoinulinases were found. The biochemical properties of 363 enzymes of the genus Arthrobacter, Bacillus, and Streptomyces (most abundant) showed that exoinulinases have an acid isoelectric point up to the neutral range due to their amino acid length. That is, the smaller the protein (336 aa), the more acidic the pI (4.39), and the larger the protein (1207 aa), the pI is in the neutral range (8.84). Also, a negative gravitational index indicates the hydrophilicity of exoinulinases. Finally, considering the biochemical properties affecting protein stability and post‐translational changes studies, one enzyme for endoinulinase and 40 enzymes with desirable characteristics were selected to identify their enzyme production sources. To screen and isolate enzyme‐containing strains, now with the expansion of databases and the development of bioinformatics tools, it is possible to classify, review and analyze a lot of data related to different enzyme‐producing strains. Although, in laboratory studies, a maximum of 20 to 30 strains can be examined. Therefore, when more strains are examined, finally, strains with more stable and efficient enzymes were selected and introduced for laboratory activities. The findings of this study can help researchers to select the appropriate gene source from introduced strains for cloning and expression heterologous inulinase, or to extract native inulinase from introduced strains. [ABSTRACT FROM AUTHOR]

The IUBMB enzyme classification system, available at the IUBMB ExplorEnz website, uses a four‐component number (the EC number) that identifies an enzyme in terms of reaction catalysed. There were originally six recognized groups of enzymes: Oxidoreductases (EC 1), Transferases (EC 2), Hydrolases (EC 3), Lyases (EC 4), Isomerases (EC 5) and Ligases (EC 6). Of these, the lyases, which are defined as 'enzymes that cleave C‐C, C‐O, C‐N and other bonds by means other than by hydrolysis or oxidation', present particular recognition and classification problems. Recently, a new class, the Translocases (EC 7), has been added, which incorporates enzymes that catalyse the movement of ions or molecules across membranes or their separation within membranes. A new subclass of the isomerases has also been included for those enzymes that alter the conformations of proteins and nucleic acids. Newly reported enzymes are being regularly added to the list after validation and where new information affects the classification of an existing entry, a new EC number is created, but the old one is not reused. [ABSTRACT FROM AUTHOR]

Enzymes play essential roles in all life processes and are used extensively in the biomedical and biotechnological fields. However, enzyme‐related information is spread across multiple resources making its retrieval time‐consuming. In response to this challenge, the Enzyme Portal has been established to facilitate enzyme research, by providing a freely available hub where researchers can easily find and explore enzyme‐related information. It integrates relevant enzyme data for a wide range of species from various resources such as UniProtKB, PDBe and ChEMBL. Here, we describe what type of enzyme‐related data the Enzyme Portal provides, how the information is organized and, by show‐casing two potential use cases, how to access and retrieve it. [ABSTRACT FROM AUTHOR]

As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention.

As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention. [ABSTRACT FROM AUTHOR]

Visualizing large-scale data produced by the high throughput experiments as a biological graph leads to better understanding and analysis. This study describes a customized force-directed layout algorithm, EClerize, for biological graphs that represent pathways in which the nodes are associated with Enzyme Commission (EC) attributes. The nodes with the same EC class numbers are treated as members of the same cluster. Positions of nodes are then determined based on both the biological similarity and the connection structure. EClerize minimizes the intra-cluster distance, that is the distance between the nodes of the same EC cluster and maximizes the inter-cluster distance, that is the distance between two distinct EC clusters. EClerize is tested on a number of biological pathways and the improvement brought in is presented with respect to the original algorithm. EClerize is available as a plug-in to Cytoscape (http://apps.cytoscape.org/apps/eclerize). [ABSTRACT FROM AUTHOR]

The digestive tract of triatomines (DTT) is an ecological niche favored by microbiota whose enzymatic profile is adapted to the specific substrate availability in this medium. This report describes the molecular enzymatic properties that promote bacterial prominence in the DTT. The microbiota composition was assessed previously based on 16S ribosomal DNA, and whole sequenced genomes of bacteria from the same genera were used to calculate the GC level of rare and prominent bacterial species in the DTT. The enzymatic reactions encoded by coding sequences of both rare and common bacterial species were then compared and revealed key functions explaining why some genera outcompete others in the DTT. Representativeness of DTT microbiota was investigated by shotgun sequencing of DNA extracted from bacteria grown in liquid Luria-Bertani broth (LB) medium. Results showed that GC-rich bacteria outcompete GC-poor bacteria and are the dominant components of the DTT microbiota. In addition, oxidoreductases are the main enzymatic components of these bacteria. In particular, nitrate reductases (anaerobic respiration), oxygenases (catabolism of complex substrates), acetate-CoA ligase (tricarboxylic acid cycle and energy metabolism), and kinase (signaling pathway) were the major enzymatic determinants present together with a large group of minor enzymes including hydrogenases involved in energy and amino acid metabolism. In conclusion, despite their slower growth in liquid LB medium, bacteria from GC-rich genera outcompete the GC-poor bacteria because their specific enzymatic abilities impart a selective advantage in the DTT. [ABSTRACT FROM AUTHOR]

A brief overview of the work in the research group of the present author on extracting knowledge from chemical reaction data is presented. Methods have been developed to calculate physicochemical effects at the reaction site. It is shown that these physicochemical effects can quite favourably be used to derive equations for the calculation of data on gas phase reactions and on reactions in solution such as aqueous acidity of alcohols or carboxylic acids or the hydrolysis of amides. Furthermore, it is shown that these physicochemical effects are quite effective for assigning reactions into reaction classes that correspond to chemical knowledge. Biochemical reactions constitute a particularly interesting and challenging task for increasing our understanding of living species. The BioPath.Database is a rich source of information on biochemical reactions and has been used for a variety of applications of chemical, biological, or medicinal interests. Thus, it was shown that biochemical reactions can be assigned by the physicochemical effects into classes that correspond to the classification of enzymes by the EC numbers. Furthermore, 3D models of reaction intermediates can be used for searching for novel enzyme inhibitors. It was shown in a combined application of chemoinformatics and bioinformatics that essential pathways of diseases can be uncovered. Furthermore, a study showed that bacterial flavor-forming pathways can be discovered. [ABSTRACT FROM AUTHOR]

Since the publication of a list of enzymes classified according to the reactions that they catalysed, by Dixon and Webb in 1958, its content and presentation have undergone a number of significant changes. These have been necessitated by new information, as well as the need to improve clarity. The move from printed versions to the online environment, through the Explor Enz website, has allowed the process of adding newly reported enzymes to be automated and the information content to be enriched. Search and output facilities have also been enhanced. These and the problems attendant on the use of the Enzyme Commission classification system for some groups of enzymes are the subject of this review. [ABSTRACT FROM AUTHOR]

Due to the rising number of solved protein structures, computer-based techniques for automatic protein functional annotation and classification into families are of high scientific interest. DoGSiteScorer automatically calculates global descriptors for self-predicted pockets based on the 3D structure of a protein. Protein function predictors on three levels with increasing granularity are built by use of a support vector machine (SVM), based on descriptors of 26632 pockets from enzymes with known structure and enzyme classification. The SVM models represent a generalization of the available descriptor space for each enzyme class, subclass, and substrate-specific sub-subclass. Cross-validation studies show accuracies of 68.2% for predicting the correct main class and accuracies between 62.8% and 80.9% for the six subclasses. Substrate-specific recall rates for a kinase subset are 53.8%. Furthermore, application studies show the ability of the method for predicting the function of unknown proteins and gaining valuable information for the function prediction field. Proteins 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Expressed sequence tag (EST) analyses were performed with the aim of identifying enzyme genes in the liver of the Bleeker’s squid Loligo bleekeri. Of the 768 ESTs identified and sequenced, 669 were grouped into 324 clusters. Of these clusters, 123 comprising 245 ESTs were found to be homologous to genes reported to date. Among these, 43 clusters were annotated as enzymes according to the Enzyme Commission (EC) numbering system. Two EC groups, oxidoreductases and hydrolases, possessed a large number of ESTs. A cluster homologous to the glutathione peroxidase, an enzyme in the oxidoreductase group, contained 16 ESTs, which accounted for 2.4% of the total ESTs sequenced. There are three serine proteases, three cathepsins, two triacylgricerol lipases, and two chitinases among the clusters homologous to the enzymes in the hydrolase group. Since the squid liver functions in the digestive process, these enzymes would be involved in food digestion. Our data provide information on the various types of enzymes expressed in the squid liver and may provide a useful basis for further characterization of these enzymes. [ABSTRACT FROM AUTHOR]

Despite the current availability of several hundreds of thousands of amino acid sequences, more than 39% of the well-defined enzyme activities (EC numbers) are not associated with any sequence in major public databases. This wide gap separating knowledge of biochemical function and sequence information is found in nearly all classes of enzymes. Thus, there is an urgent need to explore the 1525 orphan enzymes (EC numbers without associated sequences), in order to progressively bridge this unwanted gap. Improving genome annotation could unveil a significant proportion of sequenceless enzymes. Peptide mass mapping and further genome mining would be useful to identify proper sequence for enzymes found in species for which genetic tools are missing. Finally, the whole community must help major public databases to begin addressing the problem of missing or incomplete information. [ABSTRACT FROM AUTHOR]

Methods for predicting protein function from structure are becoming more important as the rate at which structures are solved increases more rapidly than experimental knowledge. As a result, protein structures now frequently lack functional annotations. The majority of methods for predicting protein function are reliant upon identifying a similar protein and transferring its annotations to the query protein. This method fails when a similar protein cannot be identified, or when any similar proteins identified also lack reliable annotations. Here, we describe a method that can assign function from structure without the use of algorithms reliant upon alignments. Using simple attributes that can be calculated from any crystal structure, such as secondary structure content, amino acid propensities, surface properties and ligands, we describe each enzyme in a non-redundant set. The set is split according to Enzyme Classification (EC) number. We combine the predictions of one-class versus one-class support vector machine models to make overall assignments of EC number to an accuracy of 35% with the top-ranked prediction, rising to 60% accuracy with the top two ranks. In doing so we demonstrate the utility of simple structural attributes in protein function prediction and shed light on the link between structure and function. We apply our methods to predict the function of every currently unclassified protein in the Protein Data Bank. [Copyright &y& Elsevier]

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein-protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

KEGG is an integrated database resource for linking sequences to biological functions from molecular to higher levels. Knowledge on molecular functions is stored in the KO (KEGG Orthology) database, while cellular- and organism-level functions are represented in the PATHWAY and MODULE databases. Genes in the complete genomes, which are stored in the GENES database, are given KO identifiers by the internal annotation procedure, enabling reconstruction of KEGG pathways and modules for interpretation of higher-level functions. This is possible because all the KEGG pathways and modules are represented as networks of KO nodes. Here we present knowledge-based prediction methods for functional characterization of amino acid sequences using the KEGG resource. Specifically we show how the tools available at the KEGG website including BlastKOALA and KEGG Mapper can be utilized for enzyme annotation and metabolic reconstruction.

Relationships between genes are best represented using networks constructed from information of different types, with metabolic information being the most valuable and widely used for genetic network reconstruction. Other types of information are usually also available, and it would be desirable to systematically include them in algorithms for network reconstruction. Here, we present an algorithm to construct a global metabolic network that uses all available enzymatic and metabolic information about the organism. We construct a global enzymatic network (GEN) with a total of 4226 nodes (EC numbers) and 42723 edges representing all known metabolic reactions. As an example we use microarray data for Arabidopsis thaliana and combine it with the metabolic network constructing a final gene interaction network for this organism with 8212 nodes (genes) and 4606,901 edges. All scripts are available to be used for any organism for which genomic data is available.