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.

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.

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been veri¯ed, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been veri¯ed. 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 identi¯ers of the § § § Corresponding author. ¶ ¶ ¶ First and second author contributed equally to the paper.Journal of Bioinformatics and Computational Biology Vol. 12, No. 6 (2014) 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.

TheBiophysicalSociety of Japan General IncorporatedAssociation A huge amoullt molecuLar interaction data are accumu]ated to elucidate entire tandscape ef cellu]ar activities. 3D comp]ex structures of interacting pToteins can add their physical mechanisms, such as interacting residues and dtiving binding forces. The goal of this study is to gather all the reported molecutar interactions, and to predict their 3D structures using hornology modeling as many as poss{b]e, Another goa] is to propose biologically important interacting protein sets to be so]ved their 3D complex structures in the future. However, current experimental molecular interaction data arc not comprehensive and re]iab]e. To overcome these prob]ems, wc eempare interactions of severa] different organisms, and censider conserved interaetions to be highly reliable, As the preiiminary study, we analyzed yeast and human interactromes. The INTACT database stored 70,OOO and 40.000 two-body protein-protein interaetions foT yeast and human, respectively. Ameng these interactions, 3D complex structures for l.5 % yeast and 6 % human pairs can be mode]ed using horno]ogjes for known 3D eomplexes. To reduce false positive data, we extracted conserved interactions (interoiogs) between the two species, Number of the interologs are about 5,OOO, they may be more re]iable and shou]d be biological important. 3D complex struetures can be assigned for 1 1 9S and 24 % of the interlogs for yeast and human, respectively. The remaining 4,OOO protein pairs are good candidates protein pairs to be solved their 3D complex structures. regfim ta77(lbeta=reEptregffmzz{gdimtaes(esem KvlYVtaopHza Deyelopment of re-docking method by obtaining native interaction pattern i, Yuri Matsuzaki2, Masahito Ohue?, Yutaka Akiyamai, (iDept. Ph.vs., Sbh. Sci. & Eng., Chuo Univ, 2Grad. Sck. Sbi., Tbb,o inst. Tbch., ]CIBRC ALS7). it is important to predict which inteTact in living cel] and how these bind each other. We these problems using rigid-body docking algorithrn, generating and ligand 2D structure weTe atso calculated and conslsteney among these three similarlty measures was evaluated.As a resu]t, basieally, pairs with similar protein folds had sjmilar ligands and interactions. However, theTe were many exceptjons, i.e,, dissirnilar inteTactions with similar fotds and ligands, and similar interactions with dissimilar folds and ligands. Novel relationships umong proteins and ligands were mined by our study. 2PTI04Nobuyuki Uchikega Takatsugu Hirokawa3 Comput. &In a study ofprotein-protein Interaction network pairs ef proteins approach to many decoys including false positives. A]though this docking method is popuTar and usefu1, there are some cases with no near-native decoys. In previous work, we developed a method of generated Profile ofInteraction FingerPrints (P-IFP) rather than generating near-native decoys. Then, we applied a profile method to obtaining region including native intcracting residue pairs for re-docking process.We have been developed Interaction FingerPrints (IFP) for the post-...

No abstract