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

Area of Science:

• Computational Chemistry
• Bioinformatics
• Scientific Computing

Background:

• High-performance computing (HPC) environments are essential for computational research like quantum chemistry (QC).
• Non-expert users face challenges utilizing specialized QC software and performing in silico mass spectra prediction for annotation.
• Limited computational expertise hinders researchers from leveraging advanced computational tools.

Purpose of the Study:

• To develop a robust and interoperable workflow for computational chemistry tasks, specifically mass spectra prediction and molecular geometry optimization.
• To enable researchers with limited computational expertise to utilize HPC resources for advanced molecular analysis.
• To integrate quantum chemistry tools into a user-friendly platform for automated fragmentation mechanism analysis.

Main Methods:

• Leveraged interoperable file formats for molecular structures to ensure seamless integration across QC tools.
• Integrated a quantum chemistry package for mass spectral predictions (electron ionization, collision-induced dissociation) into the Galaxy platform.
• Utilized an extended tight binding quantum chemistry package for accurate and efficient molecular geometry optimization.
• Encapsulated the software stack within a Docker image for simplified deployment and accessibility.
• Demonstrated the workflow's scalability and efficiency through runtime performance analysis on four molecules.

Main Results:

• Successfully integrated mass spectra prediction and molecular geometry optimization into an automated workflow.
• The workflow demonstrated scalability and efficiency, validated by runtime performance analysis.
• Non-HPC users can now perform complex computational chemistry predictions with ease.
• Automated analysis of fragmentation mechanisms is now achievable through the integrated Galaxy platform.

Conclusions:

• The developed workflow significantly lowers the barrier for non-expert users to access and utilize HPC resources for quantum chemistry applications.
• This solution empowers researchers to conduct in silico mass spectra prediction and molecular optimization without requiring deep computational expertise.
• The interoperable design and Docker encapsulation ensure broad applicability and ease of use across diverse research settings.