MolFind: A Software Package Enabling HPLC/MS-Based Identification of Unknown Chemical Structures

Abstract

In this paper, we present MolFind, a highly multi-threaded pipeline type software package for use as an aid in identifying chemical structures in complex biofluids and mixtures. MolFind is specifically designed for high performance liquid chromatography/mass spectrometry (HPLC/MS) data inputs typical of metabolomics studies where structure identification is the ultimate goal. MolFind enables compound identification by matching HPLC/MS based experimental data obtained for an unknown compound with computationally derived HPLC/MS values for candidate compounds downloaded from chemical databases such as PubChem. The downloaded “bins” consist of all compounds matching the monoisotopic molecular weight of the unknown. The computational HPLC/MS values predicted include retention index (RI), ECOM50 (energy required to fragment 50% of a selected precursor ion), drift time and collision induced dissociation (CID) spectrum. RI, ECOM50, and drift time models are used for filtering compounds downloaded from PubChem. The remaining candidates are then ranked based on CID spectra matching. Current RI and ECOM50 models allow for the removal of about 28% of compounds from PubChem bins. Our estimates suggest that this could be improved to as much as 87% with additional chemical structures included in the computational models. Quantitative structure property relationship based modeling of drift times showed a better correlation with experimentally determined drift times than did Mobcal cross sectional areas. In 23/35 example cases, filtering PubChem bins with RI and ECOM50 predictive models resulted in improved ranking of the unknown compound compared to previous studies using CID spectra matching alone. In 19/35 examples, the correct candidate was ranked within the top 20 compounds in bins containing an average of 1635 compounds.

Collision cross section prediction of deprotonated phenolics in a travelling-wave ion mobility spectrometer using molecular descriptors and chemometrics

“…Also, most of the chemometric predictive models in literature use positive ionization, for instance a chemometric tool for prediction of drift times in positive mode has been previously described [20], and relatively few CCS measurements have been done for negatively charged ions [21]. This is unfortunate especially because many small metabolites such as phenolics ionize better in negative ionization mode.…”

Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

“…For a more detailed discussion of relevance and the Boruta algorithm see Kohavi and John (1997) and Rudnicki et al (2015). This algorithm has been used in different fields, including bioinformatics, remote sensing, bacteriology and medicine (Aagaard et al, 2012;Ackerman et al, 2013;Buday et al, 2013;Duro et al, 2012;Herrera and Bazaga, 2013;Leutner et al, 2012;Ma et al, 2014;Menikarachchi et al, 2012;Saulnier et al, 2011;Strempel et al, 2013).…”

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

“…Limited metabolite coverage is obtained when using data-dependent modes since only intense ions are fragmented and many artifact ions will be detected and fragmented due to the inherent presence of isotopes, in-source fragments or high abundance ions from contamination or chemical noise [53]. Resources to annotate MS/MS spectra by comparison with experimental [53] or in-silico spectral libraries have been proposed, including MolFind [54], MetFrag [60], MetFusion [55], MyCompoundID [56, 57], CFM-ID [58] and MS-FINDER [59]. Alternatively, data-independent acquisition (DIA) modes such as MS E [62] or SWATH [63], in which all fragment ions for all precursors are simultaneously acquired, allows an increased MS/MS spectral coverage and reinforces the annotation confidence [64].…”