PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes.

BACKGROUND: Enzymes play an irreplaceable and important role in maintaining the lives of living organisms. The Enzyme Commission (EC) number of an enzyme indicates its essential functions. Correct identification of the first digit (family class) of the EC number for a given enzyme is a hot topic in the past twenty years. Several previous methods adopted functional domain composition to represent enzymes. However, it would lead to dimension disaster, thereby reducing the efficiency of the methods. On the other hand, most previous methods can only deal with enzymes belonging to one family class. In fact, several enzymes belong to two or more family classes. RESULTS: In this study, a fast and efficient multi-label classifier, named PredictEFC, was designed. To construct this classifier, a novel feature extraction scheme was designed for processing functional domain information of enzymes, which counting the distribution of each functional domain entry across seven family classes in the training dataset. Based on this scheme, each training or test enzyme was encoded into a 7-dimenion vector by fusing its functional domain information and above statistical results. Random k-labelsets (RAKEL) was adopted to build the classifier, where random forest was selected as the base classification algorithm. The two tenfold cross-validation results on the training dataset shown that the accuracy of PredictEFC can reach 0.8493 and 0.8370. The independent test on two datasets indicated the accuracy values of 0.9118 and 0.8777. CONCLUSION: The performance of PredictEFC was slightly lower than the classifier directly using functional domain composition. However, its efficiency was sharply improved. The running time was less than one-tenth of the time of the classifier directly using functional domain composition. In additional, the utility of PredictEFC was superior to the classifiers using traditional dimensionality reduction methods and some previous methods, and this classifier can be transplanted for predicting enzyme family classes of other species. Finally, a web-server available at http://124.221.158.221/ was set up for easy usage.

The carbohydrate-active enzyme database: functions and literature.

Thirty years have elapsed since the emergence of the classification of carbohydrate-active enzymes in sequence-based families that became the CAZy database over 20 years ago, freely available for browsing and download at www.cazy.org. In the era of large scale sequencing and high-throughput Biology, it is important to examine the position of this specialist database that is deeply rooted in human curation. The three primary tasks of the CAZy curators are (i) to maintain and update the family classification of this class of enzymes, (ii) to classify sequences newly released by GenBank and the Protein Data Bank and (iii) to capture and present functional information for each family. The CAZy website is updated once a month. Here we briefly summarize the increase in novel families and the annotations conducted during the last 8 years. We present several important changes that facilitate taxonomic navigation, and allow to download the entirety of the annotations. Most importantly we highlight the considerable amount of work that accompanies the analysis and report of biochemical data from the literature.

Engineering functional thermostable proteins using ancestral sequence reconstruction.

Natural proteins are often only slightly more stable in the native state than the denatured state, and an increase in environmental temperature can easily shift the balance toward unfolding. Therefore, the engineering of proteins to improve protein stability is an area of intensive research. Thermostable proteins are required to withstand industrial process conditions, for increased shelf-life of protein therapeutics, for developing robust 'biobricks' for synthetic biology applications, and for research purposes (e.g., structure determination). In addition, thermostability buffers the often destabilizing effects of mutations introduced to improve other properties. Rational design approaches to engineering thermostability require structural information, but even with advanced computational methods, it is challenging to predict or parameterize all the relevant structural factors with sufficient precision to anticipate the results of a given mutation. Directed evolution is an alternative when structures are unavailable but requires extensive screening of mutant libraries. Recently, however, bioinspired approaches based on phylogenetic analyses have shown great promise. Leveraging the rapid expansion in sequence data and bioinformatic tools, ancestral sequence reconstruction can generate highly stable folds for novel applications in industrial chemistry, medicine, and synthetic biology. This review provides an overview of the factors important for successful inference of thermostable proteins by ancestral sequence reconstruction and what it can reveal about the determinants of stability in proteins.

The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes.

Microorganisms produce natural products that are frequently used in the development of antibacterial, antiviral, and anticancer drugs, pesticides, herbicides, or fungicides. In recent years, genome mining has evolved into a prominent method to access this potential. antiSMASH is one of the most popular tools for this task. Here, we present version 3 of the antiSMASH database, providing a means to access and query precomputed antiSMASH-5.2-detected biosynthetic gene clusters from representative, publicly available, high-quality microbial genomes via an interactive graphical user interface. In version 3, the database contains 147 517 high quality BGC regions from 388 archaeal, 25 236 bacterial and 177 fungal genomes and is available at https://antismash-db.secondarymetabolites.org/.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

INTEDE: interactome of drug-metabolizing enzymes.

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

Short and simple sequences favored the emergence of N-helix phospho-ligand binding sites in the first enzymes.

The ubiquity of phospho-ligands suggests that phosphate binding emerged at the earliest stage of protein evolution. To evaluate this hypothesis and unravel its details, we identified all phosphate-binding protein lineages in the Evolutionary Classification of Protein Domains database. We found at least 250 independent evolutionary lineages that bind small molecule cofactors and metabolites with phosphate moieties. For many lineages, phosphate binding emerged later as a niche functionality, but for the oldest protein lineages, phosphate binding was the founding function. Across some 4 billion y of protein evolution, side-chain binding, in which the phosphate moiety does not interact with the backbone at all, emerged most frequently. However, in the oldest lineages, and most characteristically in αßα sandwich enzyme domains, N-helix binding sites dominate, where the phosphate moiety sits atop the N terminus of an α-helix. This discrepancy is explained by the observation that N-helix binding is uniquely realized by short, contiguous sequences with reduced amino acid diversity, foremost Gly, Ser, and Thr. The latter two amino acids preferentially interact with both the backbone amide and the side-chain hydroxyl (bidentate interaction) to promote binding by short sequences. We conclude that the first αßα sandwich domains emerged from shorter and simpler polypeptides that bound phospho-ligands via N-helix sites.

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class.

Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

Conserved unique peptide patterns (CUPP) online platform: peptide-based functional annotation of carbohydrate active enzymes.

The CUPP platform includes a web server for functional annotation and sub-grouping of carbohydrate active enzymes (CAZymes) based on a novel peptide-based similarity assessment algorithm, i.e. protein grouping according to Conserved Unique Peptide Patterns (CUPP). This online platform is open to all users and there is no login requirement. The web server allows the user to perform genome-based annotation of carbohydrate active enzymes to CAZy families, CAZy subfamilies, CUPP groups and EC numbers (function) via assessment of peptide-motifs by CUPP. The web server is intended for functional annotation assessment of the CAZy inventory of prokaryotic and eukaryotic organisms from genomic DNA (up to 30MB compressed) or directly from amino acid sequences (up to 10MB compressed). The custom query sequences are assessed using the CUPP annotation algorithm, and the outcome is displayed in interactive summary result pages of CAZymes. The results displayed allow for inspection of members of the individual CUPP groups and include information about experimentally characterized members. The web server and the other resources on the CUPP platform can be accessed from https://cupp.info.

MNAzyme-Catalyzed Amplification Assay with Lanthanide Tags for the Simultaneous Detection of Multiple microRNAs by Inductively Coupled Plasma-Mass Spectrometry.

Quantification of multiple disease-related microRNAs (miRNAs) is of great significance for clinical diagnosis. Based on the simultaneous multiple element detection ability of inductively coupled plasma-mass spectrometry (ICP-MS) and good specificity of multicomponent nucleic acid enzymes (MNAzymes), a novel and simple method based on the MNAzyme amplification strategy and lanthanide labeling coupled with ICP-MS detection was proposed for the sensitive and simultaneous detection of three miRNAs (miRNA-21, miRNA-155, and miRNA-10b). Specifically, a probe consisting of streptavidin-modified magnetic beads (SA-MBs) and three DNA substrates labeled with lanthanide tags (159Tb/165Ho/175Lu) was constructed. In the presence of target miRNAs, three pairs of MNAzymes were assembled where each pair was hybridized with the corresponding miRNA, and then the substrates on the SA-MBs were cleaved by the activated MNAzymes, continuously releasing the fragment with lanthanide tags. The released lanthanide tags in the supernatant were collected after magnetic separation and analyzed by ICP-MS, realizing the simultaneous quantification of multiple miRNAs. The correlation of the lanthanide tag signal with the miRNA concentration fitted well in a linear model in the range of 50-1000 pmol L-1 (miRNA-21) and 50-2000 pmol L-1 (miRNA-155 and miRNA-10b). The limits of detection for three miRNAs were 11-20 pmol L-1, with the relative standard deviations of 2.2-2.7%. The recoveries of target miRNAs in the human serum and HepG-2 cells were in the range of 87.2-111% and 93.3-111%, respectively. Overall, the method is ideal for the simultaneous quantification of multiple miRNAs with advantages of low spectral interference, high sensitivity, good selectivity, and strong resistance to the complex matrix.

Enzyme classification and the entanglement of values and epistemic standards.

This paper investigates the case of enzyme classification to evaluate different ideals for regulating values in science. I show that epistemic and non-epistemic considerations are inevitably and untraceably entangled in enzyme classification, and argue that this has significant implications for the two main kinds of views on values in science, namely, Epistemic Priority Views and Joint Satisfaction Views. More precisely, I argue that the case of enzyme classification poses a problem for the usability and descriptive accuracy of these two views. The paper ends by suggesting that these two views provide different but complementary perspectives, and that both are useful for evaluating values in science.

PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms.

Predicting enzyme function and enzyme subclasses is always a key objective in fields such as biotechnology, biochemistry, medicinal chemistry, physiology, and so on. The Protein Data Bank (PDB) is the largest information archive of biological macromolecular structures, with more than 150 000 entries for proteins, nucleic acids, and complex assemblies. Among these entries, there are more than 4000 proteins whose functions remain unknown because no detectable homology to proteins whose functions are known has been found. The problem is that our ability to isolate proteins and identify their sequences far exceeds our ability to assign them a defined function. As a result, there is a growing interest in this topic, and several methods have been developed to identify protein function based on these innovative approaches. In this work, we have applied perturbation theory to an original data set consisting of 19 187 enzymes representing all 59 subclasses present in the protein data bank. In addition, we developed a series of artificial neural network models able to predict enzyme-enzyme pairs of query-template sequences with accuracy, specificity, and sensitivity greater than 90% in both training and validation series. As a likely application of this methodology and to further validate our approach, we used our novel model to predict a set of enzymes belonging to the yeast Pichia stipites. This yeast has been widely studied because it is commonly present in nature and produces a high ethanol yield by converting lignocellulosic biomass into bioethanol through the xylose reductase enzyme. Using this premise, we tested our model on 222 enzymes including xylose reductase, that is, the enzyme responsible for the conversion of biomass into bioethanol.

Systems Biology Meets Enzymology: Recent Insights into Communal Metabolism of Methane and the Role of Lanthanides.

In this review article, we cover the recent developments in understanding the principles and the mechanisms by which microbial communities participating in methane consumption in natural environmental niches are assembled, and the physiological and biochemical mechanisms and regulators that allow efficient carbon transfer within the communities. We first give a brief overview of methanotrophy. We then describe the recent evidence on non-random assembly of bacterial communities that utilize carbon from methane, based on stable isotope probing experiments as well as on results from natural community manipulations followed by metagenomic analysis. We follow up by highlighting results from synthetic methanotophic community manipulations identifying the importance of a lanthanide switch that regulates alternative methanol dehydrogenase enzymes in these communities. We further expand on the recently uncovered significance of lanthanides in methylotrophy and review data on the biochemical properties of representatives of two different clades of lanthanide-dependent enzymes. We also provide an overview of the occurrence and the distribution of the lanthanide-dependent alcohol dehydrogenases in the bacterial domain, these data strongly suggesting significance of these metals beyond methylotrophy.

The effect of algal turbidity on larval performance and the ontogeny of digestive enzymes in the grey mullet (Mugil cephalus).

A study comprised of two trials determined the effects of water turbidity produced by live microalgae and inert clay particles on the larval rearing of grey mullet (Mugil cephalus). Trial 1 evaluated the effect of microalgae produced water turbidity on grey mullet larval performance and digestive tract (DT) enzyme ontogeny. Two microalgae (Nannochloropsis oculata and Isochrysis galbana) water turbidity levels (0.76 and 1.20 NTU, respectively) and a non-microalgae control (0.26 NTU) were investigated on 2 to 23 dph grey mullet larvae. The higher turbidity (1.2 NTU) larvae (5 dph) consumed markedly (P < .05) more rotifers than other treatment fish, independently of the microalgae type. There was no clear effect of the turbidity treatments on DT enzyme ontogeny. However, in all treatments lipase and alkaline proteases appeared to be modulated by the diet. Alkaline phosphatase activity was ca. 8 times higher and α-amylase activity increased 5.3 times in 79 dph fish compared to 40 dph individuals. The ratio of alkaline phosphatase and leucine-alanine aminopeptidase indicated gut maturation occurred around 61 dph. Trial 2 compared the most effective N.occulata produced turbidity level (1.2 NTU) with the identical water turbidity produced by inert clay on larval performance. M. cephalus larvae exposed to high algal turbidity demonstrated superior performance (P < .05), in terms of rotifer ingestion, dry weight gain and survival, compared to cohorts reared under the clay treatment and the lower microalgae produced turbidity. These findings suggested that water algal turbidity is not the dominant factor determining improved grey mullet larval performance.

Alignment-Free Method to Predict Enzyme Classes and Subclasses.

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 across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.

BACKGROUND: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers. RESULTS: In ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study. CONCLUSIONS: ECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred . ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html .

Extracellular hydrolytic enzymes produced by halophilic bacteria and archaea isolated from hypersaline lake.

The screening of bacteria and archaea from Chott El Jerid, a hypersaline lake in the south of Tunisia, led to the isolation of 68 extremely halophilic prokaryotes growing in media with 15-25% of salt. Assessment of 68 partial 16S rRNA analyzed by amplified rDNA restriction analysis (ARDRA) revealed 15 different bacterial and archaeal taxonomic groups. Based on ARDRA results, phenotypic and hydrolytic activity tests, 20 archaeal and 6 bacterial isolates were selected for sequencing. The halophilic isolates were identified as members of the genera: Salicola, Bacillus, Halorubrum, Natrinema and Haloterrigena. Most of these isolates are able to produce hydrolytic enzymes such as amylase, protease, lipase, cellulase, xylanase, pectinase and some of them showed combined activities. Natrinema genus is an excellent candidate for lipase production. These results indicated that the extremely halophilic archaea and bacteria from Chott El Jerid are a potential source of hydrolytic enzymes and may possess commercial value.

Biotechnological applications of archaeal enzymes from extreme environments.

To date, many industrial processes are performed using chemical compounds, which are harmful to nature. An alternative to overcome this problem is biocatalysis, which uses whole cells or enzymes to carry out chemical reactions in an environmentally friendly manner. Enzymes can be used as biocatalyst in food and feed, pharmaceutical, textile, detergent and beverage industries, among others. Since industrial processes require harsh reaction conditions to be performed, these enzymes must possess several characteristics that make them suitable for this purpose. Currently the best option is to use enzymes from extremophilic microorganisms, particularly archaea because of their special characteristics, such as stability to elevated temperatures, extremes of pH, organic solvents, and high ionic strength. Extremozymes, are being used in biotechnological industry and improved through modern technologies, such as protein engineering for best performance. Despite the wide distribution of archaea, exist only few reports about these microorganisms isolated from Antarctica and very little is known about thermophilic or hyperthermophilic archaeal enzymes particularly from Antarctica. This review summarizes current knowledge of archaeal enzymes with biotechnological applications, including two extremozymes from Antarctic archaea with potential industrial use, which are being studied in our laboratory. Both enzymes have been discovered through conventional screening and genome sequencing, respectively.

EC-BLAST: a tool to automatically search and compare enzyme reactions.

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering.

Study on the morphology, histology and enzymatic activity of the digestive tract of Gymnocypris eckloni Herzenstein.

The present research was conducted to study the morphology, histology and enzymatic activities of the digestive tract of Gymnocypris eckloni by light and transmission electron microscopes as well as by enzyme assays. The digestive tract of G. eckloni consisted of the oropharyngeal cavity, oesophagus and intestine. The wall of the digestive tract was composed of mucosa, submucosa, muscularis and serosa but lacked muscularis mucosa and glands. The stratified epithelium of the oropharyngeal cavity and oesophagus contained numerous mucous cells. Taste buds were found in the epithelium of the oropharyngeal cavity. A large number of isolated longitudinal striated muscular bundles were present in the submucosa of the oesophagus. The mucosal epithelium of the intestine was composed of simple columnar cells containing absorptive, goblet and endocrine cells. Numerous mitochondria and endoplasmic reticulum were observed in the absorptive cells, especially in the anterior intestine. From the anterior to the posterior intestine, the number and length of mucosal folds and microvilli decreased, but the number of goblet cells increased. The intestinal coefficient was 2.38. Maximum trypsin activity was measured in the anterior intestine, while the lowest lipase and amylase activities were tested in the middle and posterior intestines, respectively. The results provided experimental evidence for evaluating physiological condition of G. eckloni digestive tract, which will be useful for improving current rearing practices and diagnoses of digestive tract diseases.