MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

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. This is a challenging task as currently it is not clear how small molecules are fragmented in mass spectrometry. The existing approaches use the domain knowledge from chemistry to predict fragmentation of molecules. However, these rule-based methods fail to explain many of the peaks in mass spectra of small molecules. Recently, spectral libraries with tens of thousands of labelled mass spectra of small molecules have emerged, paving the path for learning more accurate fragmentation models for mass spectral database search. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) utilizing an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra. We show our database search is an order of magnitude more efficient than the state-of-the-art methods, which enables searching against databases with millions of molecules. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that our probabilistic model can correctly identify nearly six times more unique small molecules than previous methods. Moreover, by applying molDiscovery on microbial datasets with both mass spectral and genomics data we successfully discovered the novel biosynthetic gene clusters of three families of small molecules. Availability: The command-line version of molDiscovery and its online web service through the GNPS infrastructure are available at https://github.com/mohimanilab/molDiscovery.

New software tools, databases, and resources in metabolomics: updates from 2020

“…molDiscovery , is a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) using an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra (Mohimani et al 2020 ). A search of over 8 million spectra from the GNPS molecular networking infrastructure demonstrated that this probabilistic model can correctly identify nearly six times more unique compounds than other previously reported methods.…”

Genome-guided discovery of natural products through multiplexed low coverage whole-genome sequencing of soil Actinomycetes on Oxford Nanopore Flongle

“…We conducted a paired analysis by collecting MS/MS spectra for extracts from strain cultures grown in ISP1 and R2A media. MS/MS spectra were queried using molDiscovery against a database of PRISM predicted chemical structures from BGCs (12). The database comprised 1,177 structures generated from 180 BGCs sequenced in this study.…”

“…Similarity to characterized BGCs can also be used for strain dereplication. To assess this application on the current sequencing data, we screened MS/MS spectra for known natural products in the Natural Product Atlas database (3) (29,006 compounds) using molDiscovery (12). Subsequently, we analyzed genome sequences to confirm the presence of the corresponding BGCs.…”

“…Spectrum matching -known or unknown structures Spectrum matches for known metabolites using the Natural Product Atlas or unknown metabolites (PRISM predicted structures) were identified by using molDiscovery (3,12). molDiscovery computes theoretical MS/MS spectra of compounds in the database, identifies spectrum matches at user-defined mass-tolerance, and subsequently calculates statistical significance by matching the spectrum against a decoy database.…”