In Silico Enzymatic Synthesis of a 400鈥?00 Compound Biochemical Database for Nontargeted Metabolomics

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• 作者：Lochana C. Menikarachchi ; Dennis W. Hill ; Mai A. Hamdalla ; Ion I. Mandoiu ; David F. Grant
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：September 23, 2013
• 年：2013
• 卷：53
• 期：9
• 页码：2483-2492
• 全文大小：549K
• 年卷期：v.53,no.9(September 23, 2013)
• ISSN：1549-960X

文摘

Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. Here we present the In Vivo/In Silico Metabolites Database (IIMDB), a database of in silico enzymatically synthesized metabolites, to partially address this problem. The database, which is available at f="http://metabolomics.pharm.uconn.edu/iimdb/" class="extLink">http://metabolomics.pharm.uconn.edu/iimdb/, includes 23鈥?00 known compounds (mammalian metabolites, drugs, secondary plant metabolites, and glycerophospholipids) collected from existing biochemical databases plus more than 400鈥?00 computationally generated human phase-I and phase-II metabolites of these known compounds. IIMDB features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21鈥?40 were identical to compounds already listed in PubChem, HMDB, KEGG, or HumanCyc. Furthermore, the vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for nontargeted metabolomics applications.