J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space.

Tópicos

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Resumo

The structural identification of unknown biochemical compounds in complex biofluids continues to be a major challenge in metabolomics research. Using LC/MS, there are currently two major options for solving this problem: searching small biochemical databases, which often do not contain the unknown of interest or searching large chemical databases which include large numbers of nonbiochemical compounds. Searching larger chemical databases (larger chemical space) increases the odds of identifying an unknown biochemical compound, but only if nonbiochemical structures can be eliminated from consideration. In this paper we present BioSM; a cheminformatics tool that uses known endogenous mammalian biochemical compounds (as scaffolds) and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSM identifies endogenous mammalian biochemical structures with high accuracy. In a leave-one-out cross validation experiment, BioSM correctly predicted 95% of 1388 Kyoto Encyclopedia of Genes and Genomes (KEGG) compounds as endogenous mammalian biochemicals using 1565 scaffolds. Analysis of two additional biological data sets containing 2330 human metabolites (HMDB) and 2416 plant secondary metabolites (KEGG) resulted in biochemical annotations of 89% and 72% of the compounds, respectively. When a data set of 3895 drugs (DrugBank and USAN) was tested, 48% of these structures were predicted to be biochemical. However, when a set of synthetic chemical compounds (Chembridge and Chemsynthesis databases) were examined, only 29% of the 458,207 structures were predicted to be biochemical. Moreover, BioSM predicted that 34% of 883,199 randomly selected compounds from PubChem were biochemical. We then expanded the scaffold list to 3927 biochemical compounds and reevaluated the above data sets to determine whether scaffold number influenced model performance. Although there were significant improvements in model sensitivity and specificity using the larger scaffold list, the data set comparison results were very similar. These results suggest that additional biochemical scaffolds will not further improve our representation of biochemical structure space and that the model is reasonably robust. BioSM provides a qualitative (yes/no) and quantitative (ranking) method for endogenous mammalian biochemical annotation of chemical space and, thus, will be useful in the identification of unknown biochemical structures in metabolomics. BioSM is freely available at http://metabolomics.pharm.uconn.edu.

Resumo Limpo

structur identif unknown biochem compound complex biofluid continu major challeng metabolom research use lcms current two major option solv problem search small biochem databas often contain unknown interest search larg chemic databas includ larg number nonbiochem compound search larger chemic databas larger chemic space increas odd identifi unknown biochem compound nonbiochem structur can elimin consider paper present biosm cheminformat tool use known endogen mammalian biochem compound scaffold graph match method identifi endogen mammalian biochem structur chemic structur space result comprehens set empir experi suggest biosm identifi endogen mammalian biochem structur high accuraci leaveoneout cross valid experi biosm correct predict kyoto encyclopedia gene genom kegg compound endogen mammalian biochem use scaffold analysi two addit biolog data set contain human metabolit hmdb plant secondari metabolit kegg result biochem annot compound respect data set drug drugbank usan test structur predict biochem howev set synthet chemic compound chembridg chemsynthesi databas examin structur predict biochem moreov biosm predict random select compound pubchem biochem expand scaffold list biochem compound reevalu data set determin whether scaffold number influenc model perform although signific improv model sensit specif use larger scaffold list data set comparison result similar result suggest addit biochem scaffold will improv represent biochem structur space model reason robust biosm provid qualit yesno quantit rank method endogen mammalian biochem annot chemic space thus will use identif unknown biochem structur metabolom biosm freeli avail httpmetabolomicspharmuconnedu

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