MSAC

MSAC calculates mass-to-charge (m/z) ratios of potential adducts in mass spectrometry data to support identification and analysis of molecular species.

Key Features:

• m/z prediction: Calculates expected m/z values for charged ions formed by addition or loss of atoms and electrons, including common adducts such as [M + H]+.
• Adduct database: Contains 147 potential adducts and combinations of adducts with neutral losses for comprehensive coverage of observed species.
• Curated sources: Adduct entries are curated from the NIST/EPA/NIH Mass Spectral Library (NIST17), Global Natural Products Social Molecular Networking Public Spectral Libraries (GNPS), and MassBank of North America (MoNA).
• Subset selection: Allows selection of specific subsets from the combined database to restrict calculations to relevant adducts.
• Custom inputs: Accepts user-provided lists of adducts and charge states to tailor m/z calculations.
• Statistical analysis: Incorporates statistical analyses derived from spectra in the referenced libraries to report adduct occurrence patterns.
• Implementation: Implemented as an automated Python application for programmatic m/z calculation.

Scientific Applications:

• LC–MS data interpretation: Supports liquid chromatography–mass spectrometry analyses by predicting adduct m/z values encountered in experimental datasets.
• Identification library generation: Assists creation of identification libraries that aggregate evidence for the presence of molecules in mass spectrometry datasets.
• Adduct occurrence analysis: Enables analysis of adduct occurrence patterns and neutral losses across spectral libraries.
• Analytical and molecular research: Facilitates mass spectrometry-based studies in analytical chemistry, biochemistry, and molecular biology.

Methodology:

MSAC, implemented in Python, computes m/z values for charged adduct ions from supplied molecular formulas using a database of 147 adducts and adduct+neutral-loss combinations curated from NIST17, GNPS, and MoNA, supports selection of database subsets and user-defined adducts/charge states, and performs statistical analyses derived from spectra in those libraries.