Understanding and Classifying Metabolite Space and Metabolite-Likeness

Abstract

While the entirety of ‘Chemical Space’ is huge (and assumed to contain between 1063 and 10200 ‘small molecules’), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as ‘naturally occurring’ products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a ‘metabolite-likeness’ score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more ‘synthetic’ and the other being more ‘metabolite-like’. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.

Illuminating the dark matter in metabolomics

“…For CSI:FingerID, Dührkop et al (2) enrich the molecular property information with molecules that have known biological activity (11) and weight these signatures with higher scores in their identifications. This is crucial to avoid convoluting the search with synthetic compounds (12), as strategies to differentiate signatures of metabolites and synthetic compounds improve the quality of results from search tools (13,14). Databases with biologically relevant chemical information are becoming available, including the ChEBI database, Kyoto Encyclopedia of Genes and Genomes, and others (15,16).…”

NMD Server: Natural Medicines Database for Drug Discovery

“…Federator data warehouse approach has been highly envisaged in inters operating ability of the medical care and its research (Dobson, et al 2009;Peironcely, et al 2011).…”

Solving CASMI 2013 with MetFrag, MetFusion and MOLGEN-MS/MS

“…While some research has been done into indexes of metabolite likeness (e.g. Hamdalla et al, 22) Peironcely et al 23) ) or using references to rank candidates (Little et al 24) ), these features were not yet implemented into MetFrag or MetFusion at the time of CASMI 2013.…”