Hierarchical Harmonization of Atom-Resolved Metabolic Re-actions Across Metabolic Databases

Metabolic models have been proven to be useful tools in system biology and have been suc-cessfully 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 con-structing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big chal-lenge. Incomplete and inconsistent atomistic details in compound representations across data-bases 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 re-lationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmoniza-tion across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclo-pedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we estab-lished hierarchical relationships between metabolic reactions, enabling the harmonization of 3,856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the con-sistency of atom mappings within and between harmonized reactions, detecting some con-sistency issues between the reaction and compound descriptions in these metabolic 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.

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

Transcriptor: a comprehensive platform for annotation of the enzymatic functions of transcripts

Motivation Rapid advances in sequencing technology have resulted huge increases in the accessibility of sequencing data. Moreover, researchers are focusing more on organisms that lack a reference genome. However, few easy-to-use web servers focusing on annotations of enzymatic functions are available. Accordingly, in this study, we describe Transcriptor, a novel platform for annotating transcripts encoding enzymes. Results The transcripts were evaluated using more than 300 000 in-house enzymatic reactions through bridges of Enzyme Commission numbers. Transcriptor also enabled ontology term identification and along with associated enzymes, visualization and prediction of domains and annotation of regulatory structure, such as long noncoding RNAs, which could facilitate the discovery of new functions in model or nonmodel species. Transcriptor may have applications in elucidation of the roles of organs transcriptomes and secondary metabolite biosynthesis in organisms lacking a reference genome. Availability and implementation Transcriptor is available at http://design.rxnfinder.org/transcriptor/. Supplementary information Supplementary data are available at Bioinformatics online.

Application of Enzymes in Regioselective and Stereoselective Organic Reactions

Nowadays, biocatalysts have received much more attention in chemistry regarding their potential to enable high efficiency, high yield, and eco-friendly processes for a myriad of applications. Nature’s vast repository of catalysts has inspired synthetic chemists. Furthermore, the revolutionary technologies in bioengineering have provided the fast discovery and evolution of enzymes that empower chemical synthesis. This article attempts to deliver a comprehensive overview of the last two decades of investigation into enzymatic reactions and highlights the effective performance progress of bio-enzymes exploited in organic synthesis. Based on the types of enzymatic reactions and enzyme commission (E.C.) numbers, the enzymes discussed in the article are classified into oxidoreductases, transferases, hydrolases, and lyases. These applications should provide us with some insight into enzyme design strategies and molecular mechanisms.

Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases

Metabolic flux analysis requires both a reliable metabolic model and metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

Effect of Sequence Padding on the Performance of Protein-Based Deep Learning Models

Background The use of raw amino acid sequences as input for protein-based deep learning models has gained popularity in recent years. This scheme obliges to manage proteins with different lengths, while deep learning models require same-shape input. To accomplish this, zeros are usually added to each sequence up to a established common length in a process called zero-padding. However, the effect of different padding strategies on model performance and data structure is yet unknown. Results We analysed the impact of different ways of padding the amino acid sequences in a hierarchical Enzyme Commission number prediction problem. Our results show that padding has an effect on model performance even when there are convolutional layers implied. We propose and implement four novel types of padding the amino acid sequences. Conclusions The present study highlights the relevance of the step of padding the one-hot encoded amino acid sequences when building deep learning-based models for Enzyme Commission number prediction. The fact that this has an effect on model performance should raise awareness on the need of justifying the details of this step on future works. The code of this analysis is available at https://github.com/b2slab/padding_benchmark.

eCAMI: simultaneous classification and motif identification for enzyme annotation

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 annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction. Results This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes. Availability and implementation https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI. Supplementary information Supplementary data are available at Bioinformatics online.

A Binary Classifier for the Prediction of EC Numbers of Enzymes

Background: Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number. Objective: In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes. Methods: Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers. The classic machining learning algorithm, Support Vector Machine (SVM), was adopted as the prediction engine. Results: The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier. In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms. Conclusion: The proposed classifier was quite effective for prediction of EC number of enzymes and was specially designed for dealing with the problem addressed in this study by testing it on five datasets containing randomly produced samples.