MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites

[Background]In contrast to the increasing number of the successful genome projects, there still remain many orphan metabolites for which their synthesis processes are unknown. Metabolites, including these orphan metabolites, can be classified into groups that share the same core substructures, originated from the same biosynthetic pathways. It is known that many metabolites are synthesized by adding up building blocks to existing metabolites. Therefore, it is proposed that, for any given group of metabolites, finding the core substructure and the branched substructures can help predict their biosynthetic pathway. There already have been many reports on the multiple graph alignment techniques to find the conserved chemical substructures in relatively small molecules. However, they are optimized for ligand binding and are not suitable for metabolomic studies. [Results]We developed an efficient multiple graph alignment method named as MUCHA (Multiple Chemical Alignment), specialized for finding metabolic building blocks. This method showed the strength in finding metabolic building blocks with preserving the relative positions among the substructures, which is not achieved by simply applying the frequent graph mining techniques. Compared with the combined pairwise alignments, this proposed MUCHA method generally reduced computational costs with improving the quality of the alignment. [Conclusions]MUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns

Functional Group and Substructure Searching as a Tool in Metabolomics

BACKGROUND: A direct link between the names and structures of compounds and the functional groups contained within them is important, not only because biochemists frequently rely on literature that uses a free-text format to describe functional groups, but also because metabolic models depend upon the connections between enzymes and substrates being known and appropriately stored in databases. METHODOLOGY: We have developed a database named "Biochemical Substructure Search Catalogue" (BiSSCat), which contains 489 functional groups, >200,000 compounds and >1,000,000 different computationally constructed substructures, to allow identification of chemical compounds of biological interest. CONCLUSIONS: This database and its associated web-based search program (http://bisscat.org/) can be used to find compounds containing selected combinations of substructures and functional groups. It can be used to determine possible additional substrates for known enzymes and for putative enzymes found in genome projects. Its applications to enzyme inhibitor design are also discussed

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed

The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals.

In this chapter, we demonstrate the usability of the KEGG (Kyoto encyclopedia of genes and genomes) databases and tools, especially focusing on the visualization of the omics data. The desktop application KegArray and many Web-based tools are tightly integrated with the KEGG knowledgebase, which helps visualize and interpret large amount of data derived from high-throughput measurement techniques including microarray, metagenome, and metabolome analyses. Recently developed resources for human disease, drug, and plant research are also mentioned

Predictive genomic and metabolomic analysis for the standardization of enzyme data

The IUBMB׳s Enzyme List gives a valuable library of the individual experimental facts on enzyme activities, providing the standard classification and nomenclature of enzymes. Empirical knowledge about the relationships between the enzyme protein sequences (or structures) and their functions (the capability of catalyzing chemical reactions) has been accumulating in public literatures and databases. This provides a complementary approach to standardize and organize enzyme data, i.e., predicting the possible enzymes, reactions and metabolites that remain to be identified experimentally. Thus, we suggest the necessity of classifying enzymes based on the evidence and different perspectives obtained from various experimental works. The KEGG (Kyoto Encyclopedia of Genes and Genomes) database describes enzymes from many different viewpoints including; the IUBMB׳s enzyme nomenclature/classification (EC numbers), the similarity group of enzyme reactions (KEGG Reaction Class; RCLASS) based solely on the chemical structure transformation patterns, and the similarity groups of enzyme genes (KEGG Orthology; KO) based on the orthologous groups that can be mapped to the KEGG PATHWAY and BRITE functional hierarchy. Some unique identifiers were additionally introduced to the KEGG database other than the EC numbers established by IUBMB. R, RP and RC numbers are given to distinguish reactions, reactant pairs and RCLASS, respectively. Genes, including enzyme genes, have their own ID numbers in specific organisms, and they are classified into ortholog groups that are identified by K numbers. In this review, we explain the concept and methodology of this formulation with some concrete example cases. We propose it beneficial to create a standard classification scheme that deals with both experimentally identified and theoretically predicted enzymes