Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics

Abstract

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined. The process of identifying the structures of unknown compounds can be time intensive, costly and frequently requires the use of multiple orthogonal analytical techniques - this laborious procedure seems insurmountable for the long lists of unknowns that must be identified for each study. In addition, the limited sample volume and the extremely low concentration of most endogenous analytes frequently make purification and identification by other instrumentation nearly impossible. This review is intended to explore the problems and progress with current tools that are available for MS-based structure identification for both endogenous and exogenous metabolites.

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data

“…After the bitkey order was established, data were partitioned into train, test and validate sets using the 4 × 10 × 10 ensemble method published previously [2–3,7]. To create the data split, available data were divided into four fit/validate sets (A,B,C,D).…”

“…Since the requirement of MolFind/BioSM is a model with the smallest possible ±3 standard error filter range, an ANN solution was investigated to determine if IVal results could be improved. ANN was chosen because neural networks have been used to create a successful model [2–3,7] of similar data based on N -nitro alkane standards.…”

“…Likewise, improvements in chemometric tools have enabled the high-throughput extraction and comparison of raw data to determine which experimental features vary between sample groups. Unfortunately, the process of structure identification for observed features has remained time consuming, costly and frequently unsuccessful [2]. …”

“…MolFind/ BioSM uses the experimental exact mass of an unknown to search large databases and create a candidate list of possible matches. MolFind/BioSM applies five additional orthogonal filters to reduce the number of false positives returned from the exact mass search [2–6]. Filters are: HPLC retention index (RI) [3, 6–7], Ecom 50 [2,3] drift index (DI) [5], biological/nonbiological (BioSM) [8] and CID (MS/MS) spectra [5, 9].…”

Perspective: a systems approach to diabetes research

“…Metabonomic studies in T2DM can be classified in those based on (proton) nuclear magnetic resonance ( 1 H-NMR) spectroscopy (Rezzi et al, 2007; Lindon and Nicholson, 2008; Collino et al, 2009), and those deploying mass spectrometry (MS; Toyo’ oka, 2008; Kertesz et al, 2009; Mishur and Rea, 2012). NMR-enabled metabonomic profiling in the context of diabetes was typically done on human urine (Kussmann et al, 2006; Faber et al, 2007; Maher et al, 2009; Zhao et al, 2010).…”