Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification

Abstract

Motivation: Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve the MS based identifications. Results: We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining tandem mass spectrometry data (MS2) and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features have MS2 measurements available besides MS1.

Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

“…The MS 2 matching scores and predicted ROs were used to compute max-marginal ranking scores using the framework by Bach et al [34]. We used the author’s implementation in version 0.2.3 [69]. The hyper-parameters β and k of the model were optimized for each evaluation LC-MS 2 experiment separately using the respective training data.…”