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

• Published in: Analytical chemistry (Washington) 2012-11, Vol.84 (21), p.9388-9394
• Main Authors: Menikarachchi, Lochana C, Cawley, Shannon, Hill, Dennis W, Hall, L. Mark, Hall, Lowell, Lai, Steven, Wilder, Janine, Grant, David F
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Chemical compounds; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography, High Pressure Liquid - methods; Exact sciences and technology; Mass spectrometry; Mass Spectrometry - methods; Molecular structure; Molecular weight; Other chromatographic methods; Software; Spectrometric and optical methods
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac302048x

In this paper, we present MolFind, a highly multithreaded 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 of 35 example cases, filtering PubChem bins with RI and ECOM50 predictive models resulted in improved ranking of the unknown compounds compared to previous studies using CID spectra matching alone. In 19 of 35 examples, the correct candidate was ranked within the top 20 compounds in bins containing an average of 1635 compounds.