Lipid fragmentation pattern application

lipid-fragmentation-pattern-application

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Summary

This skill applies peer-reviewed lipid class- and adduct-specific fragmentation patterns to generate theoretical mass spectra for enumerated lipid species. It is used to create in silico spectral libraries for lipid identification via data-dependent acquisition (DDA) on high-resolution instruments.

When to use

Apply this skill when you have an enumerated list of lipid species (identified by class, fatty acid composition, and chain length) and need to generate theoretical precursor m/z values, fragment ion masses, and relative intensities for targeted or untargeted lipidomics experiments. This is essential when preparing a spectral library for DDA analysis on Orbitrap instruments or when you need to predict fragmentation patterns for lipid–adduct combinations not yet measured empirically.

When NOT to use

• Input lipid species lack defined class or fatty acid composition metadata; fragmentation cannot be assigned without this structural information.
• Empirical spectra from authentic standards are already available; in silico generation is unnecessary when ground truth data exists.
• Target instrument does not support the export format (MSP, CSV precursor lists, or Skyline transition lists); LSG output formats may not align with non-Orbitrap or non-DDA workflows.

Inputs

• lipid species list (with lipid class, fatty acid chain composition, and chain length)
• adduct type specification ([M+H]+, [M+Na]+, [M−H]−, etc.)
• peer-reviewed fragmentation pattern rules for target lipid classes

Outputs

• theoretical mass spectra (precursor m/z, fragment m/z values, relative intensities)
• spectral library in MSP format
• Excalibur-compatible precursor list (CSV) for DDA analysis

How to apply

Begin by loading a validated lipid species list that specifies lipid class (e.g., PC, PE, TAG) and fatty acid composition (chain lengths and saturation). For each lipid species, enumerate all relevant adduct forms (e.g., [M+H]+, [M+Na]+, [M−H]−) based on your ionization mode. Apply the corresponding peer-reviewed fragmentation pattern for that lipid class and adduct type to calculate theoretical fragment m/z values and assign relative intensities based on known fragmentation rules (e.g., neutral losses, characteristic fragment ions). Construct complete mass spectra as m/z–intensity pairs for each lipid–adduct combination. Finally, export the spectral library in MSP format for spectral matching or as an Excalibur-compatible precursor list (CSV) for DDA scheduling on Orbitrap instruments.

Related tools

• LSG (Lipid Spectrum Generator) (Primary tool for enumerating lipid species and applying peer-reviewed fragmentation patterns to generate theoretical spectra; handles adduct enumeration and exports in MSP or Excalibur-compatible CSV format.) — https://github.com/98104781/LSG/releases/tag/v1.3.0
• Excalibur (Target mass spectrometry control and data acquisition software on Orbitrap instruments; receives DDA precursor lists and transition lists exported by LSG for scheduling targeted lipid analysis.)

Evaluation signals

• Verify that all enumerated lipid species have corresponding entries in the output spectral library with no missing m/z values or intensities.
• Confirm that precursor m/z values match theoretical calculations for each lipid–adduct combination (e.g., verify [M+H]+ = monoisotopic mass + 1.00783 Da within <5 ppm error on Orbitrap).
• Check that fragment m/z values conform to expected neutral losses and characteristic fragments for the lipid class (e.g., PC loss of 59 for trimethylamine, PE loss of 141 for phosphatidylethanolamine head group).
• Validate that relative intensities are ranked logically (precursor typically 10–100%, base peak = 100%, minor fragments <10%).
• Confirm export format validity: MSP files parse without error in spectral matching software; CSV precursor lists load successfully into Excalibur with correct column structure and m/z precision.

Limitations

• Fragmentation patterns are derived from peer-reviewed studies, which may not cover all lipid classes, rare saturation patterns, or uncommon adducts; coverage depends on literature availability.
• In silico spectra assume idealized fragmentation and may not capture instrument-specific variation in relative intensities or unexpected fragment ions observed in real mass spectra.
• Precursor intensity predictions are approximate and do not account for chromatographic or ionization efficiency differences across lipid species.
• Export to Skyline-compatible transition lists is mentioned but not detailed in this context; validation of Skyline compatibility is beyond the scope of this documentation.

Evidence

• [readme] Lipids are generated with respect to class and fatty acid composition, spectra are then generated with respect to their adducts.: "Lipids are generated with respect to class and fatty acid composition, spectra are then generated with respect to their adducts."
• [readme] The fragmentation patterns used to generate the spectra are based on peer-reviewed studies.: "The fragmentation patterns used to generate the spectra are based on peer-reviewed studies."
• [other] Spectra are generated with respect to lipid adducts using fragmentation patterns based on peer-reviewed studies.: "Spectra are generated with respect to lipid adducts using fragmentation patterns based on peer-reviewed studies."
• [other] For each lipid species, enumerate all relevant adduct forms (e.g., [M+H]+, [M+Na]+, [M−H]−).: "For each lipid species, enumerate all relevant adduct forms (e.g., [M+H]+, [M+Na]+, [M−H]−)."
• [readme] Spectral libraries can be exported with the file extension '.MSP' selected. Otherwise, an Excalibur compatible precursor list (for DDA analysis via orbitrap) or Skylike compatible transition list may be exported: "Spectral libraries can be exported with the file extension '.MSP' selected. Otherwise, an Excalibur compatible precursor list (for DDA analysis via orbitrap) or Skylike compatible transition list may"