University of Louisville

Poster Title

Developing Computational Tools for Metabolite Molecular Comparison and Search

Institution

University of Louisville

Abstract

Metabolism is the set of chemical reactions that occur in living systems that make life possible [1]. The majority of these chemical reactions are catalyzed by protein enzymes which interconvert a vast network of organic molecules into forms needed for cells to live and grow. To better understand the underlying complexity of living systems, we must understand the complex network of chemical reactions that comprise cellular metabolism. Studying these complex metabolic networks requires databases that can represent this level of complexity and computational tools that can analyze the thousands of metabolites present in these networks. For human metabolism, the Human Metabolome Database (HMDB) contains entries for over 7900 small molecule metabolites found in human cells [2]. Included in the database entries are the structure of each metabolite and 2-D coordinates for each atom of each molecule. We are developing an algorithm that can compare a query molecule to the HMDB to detect similar molecules via a comparison of molecular substructure. Such a tool can generate hypotheses for where newly discovered metabolites fit within the complicated human metabolic network, aiding in filling out our incomplete picture of human metabolism.

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Developing Computational Tools for Metabolite Molecular Comparison and Search

Metabolism is the set of chemical reactions that occur in living systems that make life possible [1]. The majority of these chemical reactions are catalyzed by protein enzymes which interconvert a vast network of organic molecules into forms needed for cells to live and grow. To better understand the underlying complexity of living systems, we must understand the complex network of chemical reactions that comprise cellular metabolism. Studying these complex metabolic networks requires databases that can represent this level of complexity and computational tools that can analyze the thousands of metabolites present in these networks. For human metabolism, the Human Metabolome Database (HMDB) contains entries for over 7900 small molecule metabolites found in human cells [2]. Included in the database entries are the structure of each metabolite and 2-D coordinates for each atom of each molecule. We are developing an algorithm that can compare a query molecule to the HMDB to detect similar molecules via a comparison of molecular substructure. Such a tool can generate hypotheses for where newly discovered metabolites fit within the complicated human metabolic network, aiding in filling out our incomplete picture of human metabolism.