Structure Hunter: Prediction of novel chemical structures in a mixture.
Date Issued
2011
Date
2011
Author(s)
Harn, Yeu-Chern
Abstract
Identification of individual chemical constituents in a mixture solution especially the medicinal plants is a time-consuming task. In addition, the identification results are limited with available reference mass spectra. Here we present a novel approach to efficiently and accurately predict individual components in a mixture by a relational database of natural products and a set of known reference compounds as “seeds”. For a natural products structural elucidation experiment from LC-MS analysis, Structure Hunter takes the peak table of the mixture sample from LC-MS spectra and seeds information, such as chemical structure scaffolds of the potential components in the mixture as input. Aside from the user inputs, Structure Hunter includes a natural products scaffold relationship database containing scaffolds, the major chemical scaffold classifications and the parent and sibling relationship between each scaffold of the natural products. A children scaffold contains chemical structure of its parent. The sibling scaffolds are scaffolds with same numbers of ring, similar size and same parent. The parent and sibling scaffolds of the seed along with seeds itself form the list of scaffolds for further comparison. Structure Hunter computationally formulates possible chemical structures by extending the list of scaffolds with a weighted list of side chains from analyzing the collections of natural products. Therefore, the multiple components in a mixture spectrum will be proposed by matching to the most probably chemical structures of the natural products and its analogous. Compared to the previous developed method with heuristics rules or chemical structural search to generate structure, Structure Hunter can generate structures relatively to their frequency in nature. Structure Hunter was proven to identify novel compounds and retrieve high-precision results with multiple natural products mixture samples.
Subjects
Liquid Chromatography-Mass Spectrometry
natural products structures elucidation
peak identification
cheminformatics
chemometrics
Type
thesis
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