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."
1---2name: ms-ms-fragment-assignment-and-annotation-23description: Use when 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.4license: CC-BY-4.05---67# MS/MS Fragment Assignment and Annotation89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112This 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.1314## When to use1516When 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.1718## When NOT to use1920- Input is already a validated MS/MS spectral library with empirical m/z and intensities — use direct spectral matching instead.21- Lipids lack derivatization or do not contain C=C bonds — the RDkit-based N-Me fragmentation pattern recognition will not apply.22- Input is raw LC-IM-MS/MS instrument data — use data processing and sterol identification workflows instead.2324## Inputs2526- Predicted fragmentation pathways and collision energies (from quantum chemistry module)27- N-Me derivatized unsaturated sterol lipid structures (SMILES or molecular formula)28- Lipid identifiers (e.g., sterol names, database IDs)29- Double bond position data for each lipid structure3031## Outputs3233- Structured fragment assignment table (CSV or JSON)34- Columns: lipid identifier, fragment assignment, m/z values, relative intensity annotations, fragmentation metrics35- Reference file compatible with CCS prediction and LC-IM-MS/MS matching workflows3637## How to apply3839After 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.4041## Related tools4243- **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/44- **Python** (Primary scripting language for fragment assignment logic, table compilation, and I/O operations)45- **Jupyter Notebook** (Interactive development and execution environment for fragment assignment workflows)4647## Evaluation signals4849- All fragments in the output table have valid m/z values within the expected mass range for N-Me derivatized sterols.50- Relative intensities sum to 100% or are normalized consistently across all lipids.51- Each fragment is linked to exactly one parent lipid identifier with no missing or orphaned assignments.52- Fragment structures obey N-Me derivatization rules (e.g., fragmentation occurs at predicted double bond positions or N-Me functional groups).53- Output CSV/JSON validates against a predefined schema with required columns (lipid_id, fragment_formula, m_z, relative_intensity, fragmentation_metric).5455## Limitations5657- Method has been tested primarily on sterol lipids; applicability to other molecule classes with C=C bonds is theoretical.58- Relative intensity predictions depend on the accuracy of the upstream quantum chemistry collision energy calculations.59- RDkit-based pattern matching may not capture rare or unexpected fragmentation pathways not represented in the N-Me derivatization ruleset.60- 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.6162## Evidence6364- [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."65- [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."66- [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."67- [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."
HolobiomicsLab