Researchers at Pacific Northwest National Laboratory are advancing standards-free metabolomics—the identification of small molecules without reliance on data from analysis of authentic chemical standards—using calculated chemical properties and associated matching with multiple experimental attributes. PNNL’s unique approach relies on multiple experimental data types, including accurate mass, isotopic distribution, collision cross section (a structural property derived from ion mobility spectrometry measurements), mass fragmentation patterns, and data from multiple adducts. These values are then compared to entries in in silico libraries, leveraging experimental, instrumental, and computational innovations.
The approach includes four key tools:
- Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS), a modular software tool that can extract features from data collected on multi-dimensional analytical platforms.
- The in silico chemical library engine (ISiCLE), a high-performance-computing-friendly approach for generating predicted chemical properties.
- The Multi Attribute Matching Example (MAME), which matches properties based on various chemical attributes.
- DarkChem, a variational autoencoder that learns a continuous numerical or latent representation of molecular structure, which can characterize and expand reference libraries.
Collision cross-section library for metabolites and other small molecules.