Galaxy QCxMS for straightforward semi-empirical quantum mechanical EI-MS prediction

TL;DR

This paper introduces a user-friendly workflow for predicting mass spectra using quantum chemistry, making advanced computational tools accessible to non-experts.

Contribution

A Galaxy-integrated workflow using Docker and semi-empirical quantum chemistry for accessible EI-MS prediction.

Findings

The workflow enables automated analysis of fragmentation mechanisms using interoperable molecular structure formats.

Runtime performance analysis shows the solution is scalable and efficient for non-HPC users.

Four molecules were used to demonstrate the workflow's effectiveness.

Abstract

High-performance computing (HPC) environments are crucial for computational research, including quantum chemistry (QC), but pose challenges for non-expert users. Researchers with limited computational knowledge struggle to utilise domain-specific software and access mass spectra prediction for in silico annotation.

Here, we provide a robust workflow that leverages interoperable file formats for molecular structures to ensure integration across various QC tools. The quantum chemistry package for mass spectral predictions after electron ionization or collision-induced dissociation has been integrated into the Galaxy platform, enabling automated analysis of fragmentation mechanisms. The extended tight binding quantum chemistry package, chosen for its balance between accuracy and computational efficiency, provides molecular geometry optimisation. A Docker image encapsulates the necessary software stack.

We demonstrated the workflow for four molecules, highlighting the scalability and efficiency of our solution via runtime performance analysis. This work shows how non-HPC users can make these predictions effortlessly, using advanced computational tools without needing in-depth expertise.

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