Abstract
Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.
Highlights
Identification of small molecules is a critical task in various areas of life science
In the past we introduced Dereplicator+25, a database search method for identification of small molecules from their mass spectra, that follows a similar series of steps to search mass spectra against chemical structures
We evaluated the performance of molDiscovery for lipid identification in searching spectra from the Pacific Northwest National Laboratory (PNNL) lipid library against 39,126 unique compounds from LIPID MAPS Structure Database (LMSD)
Summary
Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods. The majority of in silico database search methods are rulebased models that incorporate domain knowledge to score small molecule-spectrum pairs. MAGMa+ further scores for both matched and missed peaks[16]
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