Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs

Although there are several databases that contain data on many metabolites and reactions in biochemical pathways, there is still a big gap in the numbers between experimentally identified enzymes and metabolites. It is supposed that many catalytic enzyme genes are still unknown. Although there are previous studies that estimate the number of candidate enzyme genes, these studies required some additional information aside from the structures of metabolites such as gene expression and order in the genome. In this study, we developed a novel method to identify a candidate enzyme gene of a reaction using the chemical structures of the substrate-product pair (reactant pair). The proposed method is based on a search for similar reactant pairs in a reference database and offers ortholog groups that possibly mediate the given reaction. We applied the proposed method to two experimentally validated reactions. As a result, we confirmed that the histidine transaminase was correctly identified. Although our method could not directly identify the asparagine oxo-acid transaminase, we successfully found the paralog gene most similar to the correct enzyme gene. We also applied our method to infer candidate enzyme genes in the mesaconate pathway. The advantage of our method lies in the prediction of possible genes for orphan enzyme reactions where any associated gene sequences are not determined yet. We believe that this approach will facilitate experimental identification of genes for orphan enzymes.

PIERO ontology for analysis of biochemical transformations: effective implementation of reaction information in the IUBMB enzyme list

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

Modular architecture of metabolic pathways revealed by conserved sequences of reactions

The metabolic network is both a network of chemical reactions and a network of enzymes that catalyze reactions. Toward better understanding of this duality in the evolution of the metabolic network, we developed a method to extract conserved sequences of reactions called reaction modules from the analysis of chemical compound structure transformation patterns in all known metabolic pathways stored in the KEGG PATHWAY database. The extracted reaction modules are repeatedly used as if they are building blocks of the metabolic network and contain chemical logic of organic reactions. Furthermore, the reaction modules often correspond to traditional pathway modules defined as sets of enzymes in the KEGG MODULE database and sometimes to operon-like gene clusters in prokaryotic genomes. We identified well-conserved, possibly ancient, reaction modules involving 2-oxocarboxylic acids. The chain extension module that appears as the tricarboxylic acid (TCA) reaction sequence in the TCA cycle is now shown to be used in other pathways together with different types of modification modules. We also identified reaction modules and their connection patterns for aromatic ring cleavages in microbial biodegradation pathways, which are most characteristic in terms of both distinct reaction sequences and distinct gene clusters. The modular architecture of biodegradation modules will have a potential for predicting degradation pathways of xenobiotic compounds. The collection of these and many other reaction modules is made available as part of the KEGG database.