Predicting EI+ mass spectra with QCxMS

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# Predicting EI+ mass spectra with QCxMS

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  <a href="/training-material/hall-of-fame/hechth/" class="contributor-badge contributor-hechth"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/hechth?s=36" alt="Helge Hecht avatar" width="36" class="avatar" />
    Helge Hecht</a>
  
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  <a href="/training-material/hall-of-fame/wverastegui/" class="contributor-badge contributor-wverastegui"><img src="https://avatars.githubusercontent.com/wverastegui?s=36" alt="Wudmir Rojas avatar" width="36" class="avatar" />
    Wudmir Rojas</a>
  
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  <a href="/training-material/hall-of-fame/zargham-ahmad/" class="contributor-badge contributor-zargham-ahmad"><img src="https://avatars.githubusercontent.com/zargham-ahmad?s=36" alt="Zargham Ahmad avatar" width="36" class="avatar" />
    Zargham Ahmad</a>
  
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  <a href="/training-material/hall-of-fame/wee-snufkin/" class="contributor-badge contributor-wee-snufkin"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/wee-snufkin?s=36" alt="Julia Jakiela avatar" width="36" class="avatar" />
    Julia Jakiela</a>
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## Requirements

Before diving into this slide deck, we recommend you to have a look at:

- [Introduction to Galaxy Analyses](/training-material/topics/introduction)

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name:qcxms-spectra-predictions

### Motivation

.pull-left[
- MS data annotation poses a universal bottleneck in research.
- *In silico* spectra prediction using machine learning or quantum chemistry is a promising technique for annotation of unknown compounds.
- QCxMS offers reasonably accurate in silico annotation, especially for organic molecules.
- The complexity of quantum chemistry predictions presents challenges for non-HPC experts.
- Integrating QCxMS into Galaxy provides valuable molecular insights.

Our Goal: Make semi-empirical Quantum Chemistry (QC)-based predictions accessible without advanced computational skills.
]

.pull-right[
![Proposed schema for assigning five levels of confidence in compound annotation using common evidence provided by GC-HRMS.](../../images/qcxms_motivation.png)
]

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### Method
![Overview of the QCxMS methodology. The tool starts with an equilibration and sampling step to create the base coordinates of the individual trajectories.](../../images/qcxms_method.png)

- Calculations can be performed using multiple quantum chemistry frameworks.
- This includes full *ab initio* molecular dynamics or parameterized *semi-empirical quantum mechanical* methods.
- Semi-empirical methods are significantly faster (hours vs. weeks) while yielding reasonable accuracy.

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### HPC Workflow
![Illustration of the QCxMS workflow executed on a high-performance computing (HPC) cluster](../../images/qcxms_hpc_workflow.jpeg)

- The HPC-based workflow is configured to work with a PBS job scheduler and is therefore somwhat limited in flexibility and user friendliness.
- Due to the difficulties of working with HPC clusters, we ported this workflow to Galaxy.

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### Galaxy Workflow
![Illustration of the QCxMS workflow executed on a high-performance computing (HPC) cluster](../../images/qcxms_galaxy_workflow.png)

- We use xTB to optimize the input geometry.
- The QCxMS neutral run tool performs the equilibration and sampling to create the trajectories.
- The QCxMS production run tool performs the simulation of each trajectory.
- The QCxMS getres tool collects the results into an MSP file.

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### Galaxy Tool Structure
![Architecture of the QCxMS Galaxy tools.](../../images/qcxms_galaxy_tool_architecture.png)

- Each trajectory is computed in a single job, using data-level parallelism.
- All QCxMS tools utilize the same docker container with an executable which was compiled with an optimized compiler.

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### Runtime Performance Metrics
![Performance of the QCxMS workflow for Ethylene (6 atoms), Mirex (22 atoms), Benzophenone (24 atoms) and Enilconazole (33 atoms). Benzophenone, despite being larger than Mirex, requires less time to compute due to its planar geometry and overall smaller complexity.](../../images/qcxms_runtime_statistics.png)

- Runtime depends primarily on size of the molecule.
- Structure also plays a crucial role - the more complex the geometry and structure, the longer the computations take.
- Memory requirements scale linear with size of the molecule.

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### References

- Grimme, S. (2013). Towards First Principles Calculation of Electron Impact Mass Spectra of Molecules. Angewandte Chemie International Edition, 52(24), 6306–6312. https://doi.org/10.1002/anie.201300158
- Bauer, C. A., & Grimme, S. (2016). How to Compute Electron Ionization Mass Spectra from First Principles. The Journal of Physical Chemistry A, 120(21), 3755–3766. https://doi.org/10.1021/acs.jpca.6b02907
- Bannwarth, C., Ehlert, S., & Grimme, S. (2019). GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions. Journal of Chemical Theory and Computation, 15(3), 1652–1671. https://doi.org/10.1021/acs.jctc.8b01176
- Koopman, J., & Grimme, S. (2021). From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics. Journal of the American Society for Mass Spectrometry, 32(7), 1735–1751. https://doi.org/10.1021/jasms.1c00098
- Hecht, H., Rojas, W. Y., Ahmad, Z., Křenek, A., Klánová, J., & Price, E. J. (2024). Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals. Analytical Chemistry, 96(33), 13652–13662. https://doi.org/10.1021/acs.analchem.4c02589

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## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

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		<td><abbr title="These people wrote the bulk of the tutorial, they may have done the analysis, built the workflow, and wrote the text themselves.">Author(s)</abbr></td>
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  <a href="/training-material/hall-of-fame/hechth/" class="contributor-badge contributor-hechth"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/hechth?s=36" alt="Helge Hecht avatar" width="36" class="avatar" />
    Helge Hecht</a>
  
  <a href="/training-material/hall-of-fame/wverastegui/" class="contributor-badge contributor-wverastegui"><img src="https://avatars.githubusercontent.com/wverastegui?s=36" alt="Wudmir Rojas avatar" width="36" class="avatar" />
    Wudmir Rojas</a>
  
  <a href="/training-material/hall-of-fame/zargham-ahmad/" class="contributor-badge contributor-zargham-ahmad"><img src="https://avatars.githubusercontent.com/zargham-ahmad?s=36" alt="Zargham Ahmad avatar" width="36" class="avatar" />
    Zargham Ahmad</a>
  
  <a href="/training-material/hall-of-fame/wee-snufkin/" class="contributor-badge contributor-wee-snufkin"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/wee-snufkin?s=36" alt="Julia Jakiela avatar" width="36" class="avatar" />
    Julia Jakiela</a>
  
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		<td><abbr title="These people reviewed this material for accuracy and correctness">Reviewers</abbr></td>
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  <a href="/training-material/hall-of-fame/shiltemann/" class="contributor-badge contributor-shiltemann">
    <img src="https://avatars.githubusercontent.com/shiltemann?s=36" alt="Saskia Hiltemann avatar" width="36" class="avatar" />
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    <img src="https://avatars.githubusercontent.com/foellmelanie?s=36" alt="Melanie Föll avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/wee-snufkin/" class="contributor-badge contributor-wee-snufkin">
    <img src="https://avatars.githubusercontent.com/wee-snufkin?s=36" alt="Julia Jakiela avatar" width="36" class="avatar" />
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Tutorial Content is licensed under <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.<br/>