Ms Ms Fragment Assignment And Annotation

MS/MS Fragment Assignment and Annotation

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

This skill assigns m/z values and relative intensity annotations to predicted MS/MS fragments from N-Me derivatized unsaturated sterol lipids, then compiles them into a structured record table with lipid identifiers and fragmentation metrics. It bridges quantum chemistry fragmentation predictions and downstream CCS prediction or LC-IM-MS/MS matching workflows.

When to use

When you have predicted MS/MS fragments from quantum chemistry calculations on N-Me derived unsaturated sterol structures and need to map each fragment to its precursor lipid, calculate exact m/z values, estimate relative intensities, and produce a machine-readable reference table for spectral matching or model training.

When NOT to use

• Input is already a validated MS/MS spectral library with empirical m/z and intensities — use direct spectral matching instead.
• Lipids lack derivatization or do not contain C=C bonds — the RDkit-based N-Me fragmentation pattern recognition will not apply.
• Input is raw LC-IM-MS/MS instrument data — use data processing and sterol identification workflows instead.

Inputs

• Predicted fragmentation pathways and collision energies (from quantum chemistry module)
• N-Me derivatized unsaturated sterol lipid structures (SMILES or molecular formula)
• Lipid identifiers (e.g., sterol names, database IDs)
• Double bond position data for each lipid structure

Outputs

• Structured fragment assignment table (CSV or JSON)
• Columns: lipid identifier, fragment assignment, m/z values, relative intensity annotations, fragmentation metrics
• Reference file compatible with CCS prediction and LC-IM-MS/MS matching workflows

How to apply

After quantum chemistry calculations predict fragmentation pathways and collision energies for each N-Me derivatized sterol lipid, use RDkit-based functions to recognize double bond positions and generate fragment structures according to N-Me fragmentation patterns. Calculate the m/z value and predict relative intensity for each fragment based on the fragmentation mechanism and collision energy. Assign each fragment to its parent lipid identifier and compile all results into a structured table (CSV or JSON) with columns for lipid identifier, fragment structure/formula, m/z, relative intensity, and fragmentation metric annotations. Export in a format compatible with downstream CCS prediction or LC-IM-MS/MS matching modules.

Related tools

• RDkit (Recognizes double bond positions and generates MS/MS fragments based on N-Me fragmentation patterns; computes molecular properties and m/z calculations) — https://www.rdkit.org/
• Python (Primary scripting language for fragment assignment logic, table compilation, and I/O operations)
• Jupyter Notebook (Interactive development and execution environment for fragment assignment workflows)

Evaluation signals

• All fragments in the output table have valid m/z values within the expected mass range for N-Me derivatized sterols.
• Relative intensities sum to 100% or are normalized consistently across all lipids.
• Each fragment is linked to exactly one parent lipid identifier with no missing or orphaned assignments.
• Fragment structures obey N-Me derivatization rules (e.g., fragmentation occurs at predicted double bond positions or N-Me functional groups).
• Output CSV/JSON validates against a predefined schema with required columns (lipid_id, fragment_formula, m_z, relative_intensity, fragmentation_metric).

Limitations

• Method has been tested primarily on sterol lipids; applicability to other molecule classes with C=C bonds is theoretical.
• Relative intensity predictions depend on the accuracy of the upstream quantum chemistry collision energy calculations.
• RDkit-based pattern matching may not capture rare or unexpected fragmentation pathways not represented in the N-Me derivatization ruleset.
• No empirical validation of predicted m/z or intensities against real LC-IM-MS/MS data is performed within this step; validation occurs downstream in the matching workflow.

Evidence

• [other] Generate predicted MS/MS fragments with corresponding m/z values and relative intensity annotations.: "Generate predicted MS/MS fragments with corresponding m/z values and relative intensity annotations."
• [other] Compile fragment predictions into a structured table with lipid identifiers, fragment assignments, and fragmentation metrics.: "Compile fragment predictions into a structured table with lipid identifiers, fragment assignments, and fragmentation metrics."
• [other] Export results as a CSV or JSON record file compatible with downstream CCS prediction and LC-IM-MS/MS matching workflows.: "Export results as a CSV or JSON record file compatible with downstream CCS prediction and LC-IM-MS/MS matching workflows."
• [readme] The script recognises double bond positions and generates MS/MS based on N-Me fragmentation patterns.: "The script recognises double bond positions and generates MS/MS based on N-Me fragmentation patterns."