Enzyme Commission Research Articles

Electrostatic fingerprints of catalytically active amino acids in enzymes.

The computed electrostatic and proton transfer properties are studied for 20 enzymes that represent all six major enzyme commission classes and a variety of different folds. The properties of aspartate, glutamate, and lysine residues that have been previously experimentally determined to be catalytically active are reported. The catalytic aspartate and glutamate residues studied here are strongly coupled to at least one other aspartate or glutamate residue and often to multiple other carboxylate residues with intrinsic pKa differences less than 1 pH unit. Sometimes these catalytic acidic residues are also coupled to a histidine residue, such that the intrinsic pKa of the acidic residue is higher than that of the histidine. All catalytic lysine residues studied here are strongly coupled to tyrosine or cysteine residues, wherein the intrinsic pKa of the anion-forming residue is higher than that of the lysine. Some catalytic lysines are also coupled to other lysines with intrinsic pKa differences within 1 pH unit. Some evidence of the possible types of interactions that facilitate nucleophilicity is discussed. The interactions reported here provide important clues about how side chain functional groups that are weak Brønsted acids or bases for the free amino acid in solution can achieve catalytic potency and become strong acids, bases or nucleophiles in the enzymatic environment.

An Interpretable Double-Scale Attention Model for Enzyme Protein Class Prediction Based on Transformer Encoders and Multi-Scale Convolutions.

Background Classification and annotation of enzyme proteins are fundamental for enzyme research on biological metabolism. Enzyme Commission (EC) numbers provide a standard for hierarchical enzyme class prediction, on which several computational methods have been proposed. However, most of these methods are dependent on prior distribution information and none explicitly quantifies amino-acid-level relations and possible contribution of sub-sequences. Methods In this study, we propose a double-scale attention enzyme class prediction model named DAttProt with high reusability and interpretability. DAttProt encodes sequence by self-supervised Transformer encoders in pre-training and gathers local features by multi-scale convolutions in fine-tuning. Specially, a probabilistic double-scale attention weight matrix is designed to aggregate multi-scale features and positional prediction scores. Finally, a full connection linear classifier conducts a final inference through the aggregated features and prediction scores. Results On DEEPre and ECPred datasets, DAttProt performs as competitive with the compared methods on level 0 and outperforms them on deeper task levels, reaching 0.788 accuracy on level 2 of DEEPre and 0.967 macro-F 1 on level 1 of ECPred. Moreover, through case study, we demonstrate that the double-scale attention matrix learns to discover and focus on the positions and scales of bio-functional sub-sequences in the protein. Conclusion Our DAttProt provides an effective and interpretable method for enzyme class prediction. It can predict enzyme protein classes accurately and furthermore discover enzymatic functional sub-sequences such as protein motifs from both positional and spatial scales.

DeepNEC: a novel alignment-free tool for the identification and classification of nitrogen biochemical network-related enzymes using deep learning.

Nitrogen is essential for life and its transformations are an important part of the global biogeochemical cycle. Being an essential nutrient, nitrogen exists in a range of oxidation states from +5 (nitrate) to -3 (ammonium and amino-nitrogen), and its oxidation and reduction reactions catalyzed by microbial enzymes determine its environmental fate. The functional annotation of the genes encoding the core nitrogen network enzymes has a broad range of applications in metagenomics, agriculture, wastewater treatment and industrial biotechnology. This study developed an alignment-free computational approach to determine the predicted nitrogen biochemical network-related enzymes from the sequence itself. We propose deepNEC, a novel end-to-end feature selection and classification model training approach for nitrogen biochemical network-related enzyme prediction. The algorithm was developed using Deep Learning, a class of machine learning algorithms that uses multiple layers to extract higher-level features from the raw input data. The derived protein sequence is used as an input, extracting sequential and convolutional features from raw encoded protein sequences based on classification rather than traditional alignment-based methods for enzyme prediction. Two large datasets of protein sequences, enzymes and non-enzymes were used to train the models with protein sequence features like amino acid composition, dipeptide composition (DPC), conformation transition and distribution, normalized Moreau-Broto (NMBroto), conjoint and quasi order, etc. The k-fold cross-validation and independent testing were performed to validate our model training. deepNEC uses a four-tier approach for prediction; in the first phase, it will predict a query sequence as enzyme or non-enzyme; in the second phase, it will further predict and classify enzymes into nitrogen biochemical network-related enzymes or non-nitrogen metabolism enzymes; in the third phase, it classifies predicted enzymes into nine nitrogen metabolism classes; and in the fourth phase, it predicts the enzyme commission number out of 20 classes for nitrogen metabolism. Among all, the DPC + NMBroto hybrid feature gave the best prediction performance (accuracy of 96.15% in k-fold training and 93.43% in independent testing) with an Matthews correlation coefficient (0.92 training and 0.87 independent testing) in phase I; phase II (accuracy of 99.71% in k-fold training and 98.30% in independent testing); phase III (overall accuracy of 99.03% in k-fold training and 98.98% in independent testing); phase IV (overall accuracy of 99.05% in k-fold training and 98.18% in independent testing), the DPC feature gave the best prediction performance. We have also implemented a homology-based method to remove false negatives. All the models have been implemented on a web server (prediction tool), which is freely available at http://bioinfo.usu.edu/deepNEC/.

The c.323 G>C mutation in LORICRIN causes new-found late-onset autosomal dominant loricrin keratoderma in a Chinese Han Pedigree

BackgroundLoricrin keratoderma is a rare early-onset autosomal dominant skin disorder. At present, no clinical reports have been published on characteristics of progressive aggravation and late-onset. ObjectivesTo identified a new-found pedigree with c.323 G>C mutation leading to progressive aggravation and late-onset loricrin keratoderma. MethodsTargeted next-generation sequencing of 267 genes associated with all skin abnormalities, sanger sequencing, and bioinformatics tools were used to identify the mutation in this new-found pedigree. Palm skin biopsy was used to observe the clinicopathological features of patient. Further, we constructed pcDNA3.1/V5-His-wild-LORICRIN, pcDNA3.1/V5-His-c.323G>C-LORICRIN, and pcDNA3.1/V5-His-730insG-LORICRIN vectors, nucleofected into HaCaT strain to observe the subcellular localization of loricrin by using the laser scanning confocal microscopy. ResultsThe proband and his affected father carried a heterozygous c.323 G>C missense mutation (p.Gly108Ala) on LORICRIN. Bioinformatics analysis hinted that it had potential pathogenicity; the types of ligands, enzyme commission active sites, and the spatial structure of protein changed enormously. Laser scanning confocal microscopy showed that the signals from cells transfected with the pcDNA3.1/V5-His-730insG-LORICRIN vector were distributed mainly in the nucleus, whereas those from cells transfected with the pcDNA3.1/V5-His-c.323G>C-LORICRIN vector were mainly located in the cytoplasm. Wild type loricrin was distributed in the nucleus and cytoplasm homogeneously ConclusionThe heterozygous c.323G>C missense mutation on LORICRIN caused late-onset and progressive loricrin keratoderma in this large Chinese family. Our study revealed that a large number of loricrin gathered in the cytoplasm may disturb the normal proliferation and terminal differentiation of keratinocytes and lead to the late-onset loricrin keratoderma disease.

Transcriptome characterization of Larrea tridentata and identification of genes associated with phenylpropanoid metabolic pathways.

Larrea tridentata (Sesse and Moc. ex DC.) Coville (family: Zygophyllaceae) is an aromatic evergreen shrub with resin-covered leaves, known to use in traditional medicine for diverse ailments. It also has immense pharmacological significance due to presence of powerful phenylpropanoids antioxidant, nordihydroguaiaretic acid (NDGA). The RNA sequence/transcriptome analyses connect the genomic information into the discovery of gene function. Hence, the acquaint analysis of L. tridentata is in lieu to characterize the transcriptome, and to identify the candidate genes involved in the phenylpropanoid biosynthetic pathway. To gain molecular insight, the bioinformatics analysis of transcriptome was performed. The total bases covered 48,630 contigs of length greater than 200 bp and above came out to 21,590,549 with an average GC content of 45% and an abundance of mononucleotide, SSR, including C3H, FAR1, and MADS transcription gene families. The best enzyme commission (EC) classification obtained from the assembled sequences represented major abundant enzyme classes e.g., RING-type E3 ubiquitin transferase and non-specific serine/threonine protein kinase. The KEGG pathway analysis mapped into 377 KEGG different metabolic pathways. The enrichment of phenylpropanoid biosynthesis pathways (22 genes i.e., phenylalanine ammonia-lyase, trans-cinnamate 4-monooxygenase, 4-coumarate-CoA ligase, cinnamoyl-CoA reductase, beta-glucosidase, shikimate O-hydroxycinnamoyl transferase, 5-O-(4-coumaroyl)-D-quinate 3’-monooxygenase, cinnamyl-alcohol dehydrogenase, peroxidase, coniferyl-alcohol glucosyltransferase, caffeoyl shikimate esterase, caffeoyl-CoA O-methyltransferase, caffeate O-methyltransferase, coniferyl-aldehyde dehydrogenase, feruloyl-CoA 6-hydroxylase, and ferulate-5-hydroxylase), and expression profile indicated antioxidant, anti-arthritic, and anticancer properties of L. tridentata. The present results could provide an important resource for squeezing biotechnological applications of L. tridentata.

Biocatalysed synthesis planning using data-driven learning

Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis. However, the use of biocatalysed reactions in retrosynthetic planning clashes with the difficulties in predicting the enzymatic activity on unreported substrates and enzyme-specific stereo- and regioselectivity. As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, we extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular Transformer architecture to biocatalysis. The enzymatic knowledge is learned from an extensive data set of publicly available biochemical reactions with the aid of a new class token scheme based on the enzyme commission classification number, which captures catalysis patterns among different enzymes belonging to the same hierarchy. The forward reaction prediction model (top-1 accuracy of 49.6%), the retrosynthetic pathway (top-1 single-step round-trip accuracy of 39.6%) and the curated data set are made publicly available to facilitate the adoption of enzymatic catalysis in the design of greener chemistry processes.

In vitro and in silico interactions of antiulcer, glucocorticoids and urological drugs on human carbonic anhydrase I and II isozymes.

Carbonic anhydrases (CAs, Enzyme Commission 4.2.1.1) convert carbon dioxide to bicarbonate in metabolism and use Zn2+ ions as a cofactor for their catalytic activity. The activators or inhibitors of CA-I and CA-II, which are the most abundant CA isozymes in erythrocytes, have pharmacological applications in medicine. So, investigation of drug-protein interaction of these isozymes is significant. On this basis, the objective of this study was to clarify the primer effects of widely used drugs on the activity of human CA-I and CA-II enzymes and elucidate the inhibition mechanism through molecular docking studies. For this aim isozymes were purified from human erythrocytes by affinity chromatography technique. Then inhibition profiles of antiulcer, glucocorticoids, and urological drugs were investigated. As a result, while budesonide had the highest inhibitory potency on hydratase activity of hCA-I with the IC50 of 0.08mM, levofloxacin showed the highest inhibition effect on hCA-II with the IC50 of 0.886mM. The most effective inhibitor on the esterase activity of isozymes was found as fluticasone propionate with the Ki values of 0.0365±0.016mM and 0.054±0.018mM respectively. However, by molecular docking study, it was estimated that budesonide showed maximum inhibition potency for both isozymes with the free binding energy of -7.58 and -6.97kcal/mol, respectively. Consequently, it was observed that some of the drugs studied did not show any inhibitory effect. Drug-enzyme interactions were also estimated by molecular docking. This study could contribute to the discovery of new drug candidates and as well as target proteins.

Comparative Genomic Analysis of Pseudoxanthomonas sp. X-1, a Bromoxynil Octanoate-Degrading Bacterium, and Its Related Type Strains.

Most Pseudoxanthomonas species described have been derived from water, plants, or contaminated soils. Here, a strain Pseudoxanthomonas sp. X-1 isolated from bromoxynil octanoate (BO)-contaminated soil is presented. Strain X-1 could degrade BO and produce bromoxynil. The optimal conditions for degradation of BO by strain X-1 were an initial BO concentration of 0.1mM, 30°C, pH 7, and Mn2+ concentration of 1.0mM. The bacterial morphological, physiological, and biochemical characteristics of strain X-1 were described, which showed differences comparing with other related type strains. The genome of strain X-1 was sequenced, and a comparative genomic analysis of X-1 and other Pseudoxanthomonas species was conducted to explore the mechanisms underlying the differences among these strains. The genome of strain X-1 encodes 4160 genes, 4078 of which are protein-coding genes and 68 are RNA coding genes. Specifically, strain X-1 encodes enzymes belonging to 778 Enzyme Commission (EC) numbers, much more than those of other related strains, and 62 of them are unique. Eight genes coding esterase are detected in strain X-1 which leads to the ability of BO degradation. This study provides strain, enzyme, and genome resources for the microbial remediation of environments polluted by herbicide BO.

Unraveling Synergism between Various GH Family Xylanases and Debranching Enzymes during Hetero-Xylan Degradation.

Enzymes classified with the same Enzyme Commission (EC) that are allotted in different glycoside hydrolase (GH) families can display different mechanisms of action and substrate specificities. Therefore, the combination of different enzyme classes may not yield synergism during biomass hydrolysis, as the GH family allocation of the enzymes influences their behavior. As a result, it is important to understand which GH family combinations are compatible to gain knowledge on how to efficiently depolymerize biomass into fermentable sugars. We evaluated GH10 (Xyn10D and XT6) and GH11 (XynA and Xyn2A) β-xylanase performance alone and in combination with various GH family α-l-arabinofuranosidases (GH43 AXH-d and GH51 Abf51A) and α-d-glucuronidases (GH4 Agu4B and GH67 AguA) during xylan depolymerization. No synergistic enhancement in reducing sugar, xylose and glucuronic acid released from beechwood xylan was observed when xylanases were supplemented with either one of the glucuronidases, except between Xyn2A and AguA (1.1-fold reducing sugar increase). However, overall sugar release was significantly improved (≥1.1-fold reducing sugar increase) when xylanases were supplemented with either one of the arabinofuranosidases during wheat arabinoxylan degradation. Synergism appeared to result from the xylanases liberating xylo-oligomers, which are the preferred substrates of the terminal arabinofuranosyl-substituent debranching enzyme, Abf51A, allowing the exolytic β-xylosidase, SXA, to have access to the generated unbranched xylo-oligomers. Here, it was shown that arabinofuranosidases are key enzymes in the efficient saccharification of hetero-xylan into xylose. This study demonstrated that consideration of GH family affiliations of the carbohydrate-active enzymes (CAZymes) used to formulate synergistic enzyme cocktails is crucial for achieving efficient biomass saccharification.

ProPythia: A Python package for protein classification based on machine and deep learning

The field of protein data mining has been growing rapidly in the last years. To characterize proteins and determine their function from their amino acid sequences are challenging and long-standing problems, where Bioinformatics and Machine Learning have an emergent role. A myriad of machine and deep learning algorithms have been applied in these tasks with exciting results. However, tools and platforms to calculate protein features and perform both Machine Learning (ML) and Deep Learning (DL) pipelines, taking as inputs protein sequences, are still lacking and have their limitations in terms of performance, user-friendliness and restricted domains of application.Here, to address these limitations, we propose ProPythia, a generic and modular Python package that allows to easily deploy ML and DL approaches for a plethora of problems in protein sequence analysis and classification. It facilitates the implementation, comparison and validation of the major tasks in ML or DL pipelines including modules to read and alter sequences, calculate protein features, preprocess datasets, execute feature selection and dimensionality reduction, perform clustering and manifold analysis, as well as to train and optimize ML/DL models and use them to make predictions.ProPythia has an adaptable modular architecture being a versatile and easy-to-use tool, which will be useful to transform protein data in valuable knowledge even for people not familiarized with ML code. This platform was tested in several applications comparing with results from literature. Here, we illustrate its applicability in two cases studies: the prediction of antimicrobial peptides and the prediction of enzymes Enzyme commission (EC) numbers. Furthermore, we assess the performance of the different descriptors on four different protein classification challenges. Its source code and documentation, including an user guide and case studies are freely available at https://github.com/BioSystemsUM/propythia.

Global Transcriptome Characterization and Assembly of the Thermophilic Ascomycete Chaetomium thermophilum.

A correct genome annotation is fundamental for research in the field of molecular and structural biology. The annotation of the reference genome of Chaetomium thermophilum has been reported previously, but it is essentially limited to open reading frames (ORFs) of protein coding genes and contains only a few noncoding transcripts. In this study, we identified and annotated full-length transcripts of C. thermophilum by deep RNA sequencing. We annotated 7044 coding genes and 4567 noncoding genes. Astonishingly, 23% of the coding genes are alternatively spliced. We identified 679 novel coding genes as well as 2878 novel noncoding genes and corrected the structural organization of more than 50% of the previously annotated genes. Furthermore, we substantially extended the Gene Ontology (GO) and Enzyme Commission (EC) lists, which provide comprehensive search tools for potential industrial applications and basic research. The identified novel transcripts and improved annotation will help to understand the gene regulatory landscape in C. thermophilum. The analysis pipeline developed here can be used to build transcriptome assemblies and identify coding and noncoding RNAs of other species.

Cyclophilins and Their Functions in Abiotic Stress and Plant-Microbe Interactions.

Plants have developed a variety of mechanisms and regulatory pathways to change their gene expression profiles in response to abiotic stress conditions and plant–microbe interactions. The plant–microbe interaction can be pathogenic or beneficial. Stress conditions, both abiotic and pathogenic, negatively affect the growth, development, yield and quality of plants, which is very important for crops. In contrast, the plant–microbe interaction could be growth-promoting. One of the proteins involved in plant response to stress conditions and plant–microbe interactions is cyclophilin. Cyclophilins (CyPs), together with FK506-binding proteins (FKBPs) and parvulins, belong to a big family of proteins with peptidyl-prolyl cis-trans isomerase activity (Enzyme Commission (EC) number 5.2.1.8). Genes coding for proteins with the CyP domain are widely expressed in all organisms examined, including bacteria, fungi, animals, and plants. Their different forms can be found in the cytoplasm, endoplasmic reticulum, nucleus, chloroplast, mitochondrion and in the phloem space. They are involved in numerous processes, such as protein folding, cellular signaling, mRNA processing, protein degradation and apoptosis. In the past few years, many new functions, and molecular mechanisms for cyclophilins have been discovered. In this review, we aim to summarize recent advances in cyclophilin research to improve our understanding of their biological functions in plant defense and symbiotic plant–microbe interactions.

The transferred translocases: An old wine in a new bottle.

The role of translocases was underappreciated and was not included as a separate class in the enzyme commission until August 2018. The recent research interests in proteomics of orphan enzymes, ionomics, and metallomics along with high-throughput sequencing technologies generated overwhelming data and revamped this enzyme into a separate class. This offers a great opportunity to understand the role of new or orphan enzymes in general and specifically translocases. The enzymes belonging to translocases regulate/permeate the transfer of ions or molecules across the membranes. These enzyme entries were previously associated with other enzyme classes, which are now transferred to a new enzyme class 7 (EC 7). The entries that are reclassified are important to extend the enzyme list, and it is the need of the hour. Accordingly, there is an upgradation of entries of this class of enzymes in several databases. This review is a concise compilation of translocases with reference to the number of entries currently available in the databases. This review also focuses on function as well as dysfunction of translocases during normal and disordered states, respectively.

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases.

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

A hierarchical deep learning based approach for multi-functional enzyme classification.

Enzymes are critical proteins in every organism. They speed up essential chemical reactions, help fight diseases, and have a wide use in the pharmaceutical and manufacturing industries. Wet lab experiments to figure out an enzyme's function are time consuming and expensive. Therefore, the need for computational approaches to address this problem are becoming necessary. Usually, an enzyme is extremely specific in performing its function. However, there exist enzymes that can perform multiple functions. A multi-functional enzyme has vast potential as it reduces the need to discover/use different enzymes for different functions. We propose an approach to predict a multi-functional enzyme's function up to the most specific fourth level of the hierarchy of the Enzyme Commission (EC) number. Previous studies can only predict the function of the enzyme till level 1. Using a dataset of 2,583 multi-functional enzymes, we achieved a hierarchical subset accuracy of 71.4% and a Macro F1 Score of 96.1% at the fourth level. The robustness of the network was further tested on a multi-functional isoforms dataset. Our method is broadly applicable and may be used to discover better enzymes. The web-server can be freely accessed at http://hecnet.cbrlab.org/.

BENZ WS: the Bologna ENZyme Web Server for four-level EC number annotation.

The Bologna ENZyme Web Server (BENZ WS) annotates four-level Enzyme Commission numbers (EC numbers) as defined by the International Union of Biochemistry and Molecular Biology (IUBMB). BENZ WS filters a target sequence with a combined system of Hidden Markov Models, modelling protein sequences annotated with the same molecular function, and Pfams, carrying along conserved protein domains. BENZ returns, when successful, for any enzyme target sequence an associated four-level EC number. Our system can annotate both monofunctional and polyfunctional enzymes, and it can be a valuable resource for sequence functional annotation.

Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines.

Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases.

Photophore Distribution and Enzymatic Diversity Within the Photogenic Integument of the Cookie-Cutter Shark Isistius brasiliensis (Chondrichthyes: Dalatiidae)

The cookie-cutter shark Isistius brasiliensis (Squaliformes: Dalatiidae) is a deep-sea species that emits a blue luminescence ventrally, except at the level of a black band located beneath the jaw. This study aims to (i) investigate the distribution and histology of the photophores (i.e., light-emitting organs) along the shark body, (ii) describe the tissue-specific transcriptomes of the black band integument region (i.e., non-photogenic) and the ventral integument region (i.e., photogenic), (iii) describe the repertoire of enzyme-coding transcripts expressed the two integument regions, and (iv) analyze the potential expression of transcripts coding for luciferase-like enzymes (i.e., close homologs of known luciferases involved in the bioluminescence of other organisms). Our analyses confirm the black band’s non-photogenic status and photophore absence within this region. The sub-rostral area is the region where the photophore density is the highest. In parallel, paired-end Illumina sequencing has been used to generate two pilot transcriptomes, from the black band and the ventral integument tissues of one individual. In total, 68,943 predicted unigenes have been obtained (i.e., 64,606 for the black band transcriptome, 43,996 for the ventral integument transcriptome) with 43,473 unigenes showing significant similarities to known sequences from public databases. BLAST search analyses of known luciferases, coupled with comparative predicted gene expression (i.e., photogenic versus non-photogenic), support the hypothesis that the species uses an unknown luciferase system. An enzymatic repertoire was predicted based on the PRIAM database, and Enzyme Commission numbers were assigned for all detected enzyme-coding unigenes. These pilot transcriptomes based on a single specimen, and the predicted enzyme repertoire, constitute a valuable resource for future investigations on the biology of this enigmatic luminous shark.

Contemporary therapeutics and new drug developments for treatment of Fabry disease: a narrative review.

Fabry disease (OMIM 301500) is an X-linked (Xq22.1) lysosomal storage disorder leading to a progressive multisystem disease with high variability in both genotype and phenotype expression. The pathophysiological origin is found in an enzyme deficiency of the α-galactosidase A (enzyme commission no. 3.2.1.22) leading to accumulation of globotriaosylceramides in all lysosome carrying tissue. Especially organ manifestations of the heart, kidneys and nervous system are of significant prognostic value and might complicate with Fabry-associated pain, young aged cryptogenic stroke, proteinuria, kidney failure, hypertrophic cardiomyopathy, heart failure, malign cardiac rhythm disturbances and eventually sudden cardiac death. Up to the introduction of the first enzyme replacement agent in 2001, patients faced the disease's natural course with no disease-specific therapies available. Today, two recombinant enzyme replacement agents (Fabrazyme®, Sanofi Genzyme, Cambridge, MA, USA; Replagal®, Takeda Pharmaceutical, Tokio, Japan) and one oral chaperone therapy (Migalastat®, Amicus Therapeutics, USA) are available and well-established in daily clinical practice. Substrate reduction therapy, second-generation enzyme replacement agents and different gene therapy approaches are currently undergoing preclinical and clinical trial phases and aim to improve therapeutic success and long-term outcome of patients with Fabry disease. This narrative review summarizes the currently available therapeutic options and future perspectives in Fabry disease.

QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs

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.