ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries
Abstract
High throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and envi-ronmental samples will revolutionize our understanding of the role of these intriguing and chemically diverse molecules in biological systems. Despite recent technological advances, a metabolomics study still results in the detection of a dispropor-tionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the sin-gle most significant limitation in metabolomics: the reliance on reference libraries constructed through the analysis of au-thentic reference chemicals and the limited commercial availability of these compounds. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating 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 ap-proach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate predictions, all without training data. Importantly, optimization of ISiCLE includ-ed a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational effi-ciency by over two…
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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