ISiCLE: A molecular collision cross section calculation pipeline for establishing large in silico reference libraries for compound identification

TL;DR

ISiCLE is a high-performance computational pipeline that generates large in silico chemical libraries with predicted collision cross sections, enhancing metabolite identification without relying on experimental reference standards.

Contribution

The paper introduces ISiCLE, a novel in silico library generation method using advanced simulations and optimized code, significantly expanding reference libraries for metabolomics.

Findings

Validated CCS predictions against nearly 2,000 experimental values.

Achieved over 100-fold improvement in computational efficiency.

Created an online database with over 1 million CCS entries.

Abstract

Comprehensive and confident identifications of metabolites and other chemicals in complex samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent advances, metabolomics studies still result in the detection of a disproportionate number of features than 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 chemicals. 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 using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate structures and 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 two orders of magnitude. Calculated CCS values were validated against 1,983 experimentally-measured CCS values and compared to previously reported CCS calculation approaches. 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. This work represents a promising method to address the limitations of small molecule identification.