Chemoinformatics on Metabolic Pathways: Attaching Biochemical Information on Putative Enzymatic Reactions

Chemoinformatics on Metabolic Pathways: Attaching Biochemical Information on Putative Enzymatic Reactions

Masahiro Hattori (Tokyo University of Technology, Japan) and Masaaki Kotera (Kyoto University, Japan)
Copyright: © 2013 |Pages: 24
DOI: 10.4018/978-1-4666-3604-0.ch053
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Abstract

Chemical genomics is one of the cutting-edge research areas in the post-genomic era, which requires a sophisticated integration of heterogeneous information, i.e., genomic and chemical information. Enzymes play key roles for dynamic behavior of living organisms, linking information in the chemical space and genomic space. In this chapter, the authors report our recent efforts in this area, including the development of a similarity measure between two chemical compounds, a prediction system of a plausible enzyme for a given substrate and product pair, and two different approaches to predict the fate of a given compound in a metabolic pathway. General problems and possible future directions are also discussed, in hope to attract more activities from many researchers in this research area.
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Introduction

A cellular system is composed of a considerable number of elements including genes, proteins or enzymes, and metabolites. They are dynamically connected and interdependent to construct a network diagram, which is referred to as a metabolic pathway. The significance of a systems approach has been recognized for understanding the activities of living organisms (Eisenberg et al., 2000; Kanehisa, 2001; Kanehisa & Bork, 2003).

Among the elements mentioned above, genetic information has been studied from a holistic viewpoint since the late 1990s. Whole genome sequencing projects have uncovered the genomic aspects of biological substances, i.e., the repertoire of genes and their products encoded on each genome, generating an exhaustive encyclopedia containing macromolecules such as DNA, RNA and proteins. The information of molecular interactions like metabolic pathways and regulatory systems has also been compiled from text-based literature and stored in several computer databases including KEGG (Kanehisa & Goto, 2000; Kanehisa et al., 2006 & 2008), EcoCyc (Keseler et al., 2009), MetaCyc (Caspi et al., 2008), and BioCarta (BioCarta LLC). Motivation of the early genome analyses was mainly to obtain the whole set of fundamental molecules in biology, hoping that the interaction network of them would be depicted by collecting the text information on pathways. Initially, pathway information was utilized to improve genome annotation in order to add in-depth information for macromolecules (Kanehisa, 1997).

As we gain more omic-scale information, e.g., proteome (Wilkins et al., 1996), transcriptome (Velculescu et al., 1997), metabolome (Tweeddale et al., 1998), and interactome (Sanchez et al., 1999), studies based on those holistic viewpoints has become applicable to the other substances in living organisms. As a result, the emerging main stream has become what is referred to as chemical genomics, integrating heterogeneous information including genomic, chemical and interaction data (Yamanishi et al., 2004). It has been shown that a sophisticated method combined with both genomic and chemical knowledge is also applicable to computationally predict missing enzymes in particular species in a given metabolic pathway (Yamanishi et al., 2007).

Attaching biological information on a given genome sequence is referred to as genome annotation, which is mostly based on the assumption that if the sequences of genes are similar enough, then their functions would be identical. In the case of annotating enzyme genes, the function is generally inferred by a previously known enzyme reaction. The missing-enyzme finding problem mentioned above suggests putative genes on pre-defined enzyme reactions, which can also be described as one of the genome annotation approaches.

On the contrary, chemoinformatics has the potential to provide a reverse method of annotation, which we refer to as a “chemical annotation” approach. This is based on the assumption that when an enzyme reaction is found to be similar to a previously known reaction, then we can gain a clue what the enzyme or enzyme gene could be. The topic can be divided into two parts: how to find pairs of metabolites that are connected in metabolic pathways, and what kind of intermediate enzymes would catalyze those reactions. These are the main themes of this chapter.

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