ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High-throughput,
comprehensive, and confident identifications of
metabolites and other chemicals in biological and environmental samples
will revolutionize our understanding of the role these chemically
diverse molecules play in biological systems. Despite recent technological
advances, metabolomics studies still result in the detection of a
disproportionate number of features that cannot be confidently assigned
to a chemical structure. This inadequacy is driven by the single most
significant limitation in metabolomics, the reliance on reference
libraries constructed by analysis of authentic reference materials
with limited commercial availability. To this end, we have developed
the in silico chemical library engine (ISiCLE), a high-performance
computing-friendly cheminformatics workflow for generating libraries
of chemical properties. In the instantiation described here, we predict
probable three-dimensional molecular conformers (i.e., conformational
isomers) using chemical identifiers as input, from which collision
cross sections (CCS) are derived. The approach employs first-principles
simulation, distinguished by the use of molecular dynamics, quantum
chemistry, and ion mobility calculations, to generate structures and
chemical property libraries, all without training data. Importantly,
optimization of ISiCLE included a refactoring of the popular MOBCAL
code for trajectory-based mobility calculations, improving its computational
efficiency by over 2 orders of magnitude. Calculated CCS values were
validated against 1983 experimentally measured CCS values and compared
to previously reported CCS calculation approaches. Average calculated
CCS error for the validation set is 3.2% using standard parameters,
outperforming other density functional theory (DFT)-based methods
and machine learning methods (e.g., MetCCS). An online database is
introduced for sharing both calculated and experimental CCS values
(metabolomics.pnnl.gov), initially
including a CCS library with over 1 million entries. Finally, three
successful applications of molecule characterization using calculated
CCS are described, including providing evidence for the presence of
an environmental degradation product, the separation of molecular
isomers, and an initial characterization of complex blinded mixtures
of exposure chemicals. This work represents a method to address the
limitations of small molecule identification and offers an alternative
to generating chemical identification libraries experimentally by
analyzing authentic reference materials. All code is available at github.com/pnnl.