Metabolome

Metabolome

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Proteins and nucleic acids make up only part of the cell, and the multitude of small-molecules that contribute to homeostasis and metabolism are studied with increasingly sophisticated approaches in metabolomics, a discipline in its own right. Chemoinformatics is a related but distinct field: storing, searching and retrieving structural and chemical information about molecules, primarily motivated by the needs of drug-design.

 

Introductory reading

Wishart (2010) Computational approaches to metabolomics. Methods Mol Biol 593:283-313. (pmid: 19957155)

[ PubMed ] [ DOI ] This chapter is intended to familiarize readers with the field of metabolomics and some of the algorithms, data analysis strategies, and computer programs used to analyze or interpret metabolomic data. Specifically, this chapter provides a brief overview of the experimental approaches and applications of metabolomics followed by a description of the spectral and statistical analysis tools for metabolomics. The chapter concludes with a discussion of the resources that can be used to interpret and analyze metabolomic data at a biological or clinical level. Emerging needs, challenges, and recent progress being made in these areas are also discussed.

 

Contents

   

Further reading and resources

Go (2010) Database resources in metabolomics: an overview. J Neuroimmune Pharmacol 5:18-30. (pmid: 19418229)

[ PubMed ] [ DOI ] Metabolomics is the characterization, identification, and quantitation of metabolites resulting from a wide range of biochemical processes in living systems. Its rapid development over the past few years has increased the demands for bioinformatics and cheminformatics resources that span from data processing tools, comprehensive databases, statistical tools, and computational tools for modeling metabolic networks. With the wealth of information that is being amassed, new types of metabolomic databases are emerging that are not only designed to store, manage, and analyze metabolomic data but are also designed to serve as gateways to the vast information space of metabolism in living systems. At present, metabolomics is underpinned by a number of freely and commercially available databases that provide information on the chemical structures, physicochemical and pharmacological properties, spectral profiles, experimental workflows, and biological functions of metabolites. This review provides an overview of the recent progress in databases employed in metabolomics.

Warr (2011) Some Trends in Chem(o)informatics. Methods Mol Biol 672:1-37. (pmid: 20838963)

[ PubMed ] [ DOI ] This introductory chapter gives a brief overview of the history of cheminformatics, and then summarizes some recent trends in computing, cultures, open systems, chemical structure representation, docking, de novo design, fragment-based drug design, molecular similarity, quantitative structure-activity relationships (QSAR), metabolite prediction, the use of phamacophores in drug discovery, data reduction and visualization, and text mining. The aim is to set the scene for the more detailed exposition of these topics in the later chapters.

Maggiora & Shanmugasundaram (2011) Molecular similarity measures. Methods Mol Biol 672:39-100. (pmid: 20838964)

[ PubMed ] [ DOI ] Molecular similarity is a pervasive concept in chemistry. It is essential to many aspects of chemical reasoning and analysis and is perhaps the fundamental assumption underlying medicinal chemistry. Dissimilarity, the complement of similarity, also plays a major role in a growing number of applications of molecular diversity in combinatorial chemistry, high-throughput screening, and related fields. How molecular information is represented, called the representation problem, is important to the type of molecular similarity analysis (MSA) that can be carried out in any given situation. In this work, four types of mathematical structure are used to represent molecular information: sets, graphs, vectors, and functions. Molecular similarity is a pairwise relationship that induces structure into sets of molecules, giving rise to the concept of chemical space. Although all three concepts - molecular similarity, molecular representation, and chemical space - are treated in this chapter, the emphasis is on molecular similarity measures. Similarity measures, also called similarity coefficients or indices, are functions that map pairs of compatible molecular representations that are of the same mathematical form into real numbers usually, but not always, lying on the unit interval. This chapter presents a somewhat pedagogical discussion of many types of molecular similarity measures, their strengths and limitations, and their relationship to one another. An expanded account of the material on chemical spaces presented in the first edition of this book is also provided. It includes a discussion of the topography of activity landscapes and the role that activity cliffs in these landscapes play in structure-activity studies.

Oprea et al. (2011) Computational systems chemical biology. Methods Mol Biol 672:459-88. (pmid: 20838980)

[ PubMed ] [ DOI ] There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Li et al. (2010) PubChem as a public resource for drug discovery. Drug Discov Today 15:1052-7. (pmid: 20970519)

[ PubMed ] [ DOI ] PubChem is a public repository of small molecules and their biological properties. Currently, it contains more than 25 million unique chemical structures and 90 million bioactivity outcomes associated with several thousand macromolecular targets. To address the potential utility of this public resource for drug discovery, we systematically summarized the protein targets in PubChem by function, 3D structure and biological pathway. Moreover, we analyzed the potency, selectivity and promiscuity of the bioactive compounds identified for these biological targets, including the chemical probes generated by the NIH Molecular Libraries Program. As a public resource, PubChem lowers the barrier for researchers to advance the development of chemical tools for modulating biological processes and drug candidates for disease treatments.