--- name: sterol-structure-representation-and-curation description: Use when when you have a collection of N-Me derivatized unsaturated sterol structures from tissue samples or standards that must be fed into MS/MS fragmentation prediction or collision cross section (CCS) prediction workflows. license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3280 edam_topics: - http://edamontology.org/topic_3375 - http://edamontology.org/topic_0154 tools: - Python - Jupyter Notebook - RDKit techniques: - LC-MS - ion-mobility-MS derived_from: - doi: 10.1002/anie.202507483 title: NA evidence_spans: - collection of Python scripts - All functions are implemented in jupyter notebook claims: [] provenance: collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2 assembled_by: scripts/collect_metabolomics_collection.py sources: - build: coll_na_cq doi: 10.1002/anie.202507483 title: NA dedup_kept_from: coll_na_cq schema_version: 0.2.0 attribution: generator: AgenticScienceBuilder original_doi: 10.1002/anie.202507483 all_source_dois: - 10.1002/anie.202507483 zenodo_doi: 10.5281/zenodo.20794027 curators: [] promoter: Louis-Félix Nothias sponsor: CNRS & Université Côte d'Azur --- # sterol-structure-representation-and-curation ## Summary Represent N-Me derived unsaturated sterol lipid structures as SMILES strings or molecular formulas, and curate them for downstream quantum chemistry and MS/MS fragmentation prediction. This skill ensures consistent, machine-readable encoding of sterol isomers and their double-bond positions for automated analysis pipelines. ## When to use When you have a collection of N-Me derivatized unsaturated sterol structures from tissue samples or standards that must be fed into MS/MS fragmentation prediction or collision cross section (CCS) prediction workflows. Use this skill if your input is a mixture of structural formats (chemical names, drawings, molecular formulas) and you need normalized, queryable representations that preserve double-bond regiochemistry. ## When NOT to use - Structures are already in validated SMILES format with confirmed double-bond regiochemistry and you are ready to apply MS/MS calculations immediately. - Input lipids lack C=C bonds or are saturated sterols (the method is documented as theoretically applicable to all molecules with C=C, but testing is limited to unsaturated sterol lipids). - You are working with non-N-Me derivatized sterol lipids; the fragmentation patterns and workflow are specifically designed for N-Me derivatives. ## Inputs - Sterol lipid structure identifiers (chemical names, PubChem IDs, or manual drawings) - N-Me derivatization state annotation - Double-bond position assignments (e.g., Δ5, Δ7, Δ24) - Molecular formula or partial structure data ## Outputs - Curated SMILES string table indexed by lipid identifier - Molecular formula registry with N-Me and double-bond annotations - Structured input CSV/JSON file compatible with RDKit-based MS/MS calculation scripts - Quality report documenting double-bond position recognition by RDKit ## How to apply Convert input sterol structures to SMILES or molecular formula format, ensuring that N-methyl derivatization and all C=C double-bond positions are explicitly encoded. Load structures into RDKit-compatible data structures within a Python/Jupyter environment. Validate that double-bond positions are recognized and preserved by RDKit's built-in functions, since downstream MS/MS fragmentation relies on accurate position annotation. Organize curated structures into a structured input table (CSV or JSON) with lipid identifiers, SMILES/formula strings, and metadata (tissue source, derivatization state). Export this table for downstream MS/MS calculation and CCS prediction steps. ## Related tools - **RDKit** (Structure parsing, double-bond position recognition, and SMILES validation for N-Me sterol lipids) - **Python** (Scripting environment for structure curation and SMILES generation) - **Jupyter Notebook** (Interactive development and documentation of sterol structure curation workflows) — github.com/Chen-micslab/QCCAssisted4DSterol ## Evaluation signals - All curated SMILES strings parse without error in RDKit and preserve N-methyl and double-bond annotations. - Double-bond positions inferred by RDKit match manual annotations or reference standards (100% agreement or documented exceptions). - Curated structure table has no missing identifiers, SMILES, or derivatization metadata; all rows are complete and non-redundant. - Exported CSV/JSON can be successfully loaded by downstream MS/MS calculation functions without conversion errors. - Visual inspection or structural similarity checks confirm that SMILES-encoded isomers are distinct (e.g., Δ5 vs. Δ7 sterols produce different RDKit substructure hashes). ## Limitations - The method relies on RDKit's built-in double-bond recognition; manually drawn or ambiguous structural inputs may require manual curation before automated processing. - Testing has been limited to sterol lipids; applicability to other unsaturated lipid classes (e.g., polyunsaturated fatty acyl chains) is theoretical and untested. - SMILES and molecular formula representations do not encode 3D stereochemistry or ring geometry; structures are treated as 2D graphs, which may lose spatial information relevant to some fragmentation pathways. - N-Me derivatization must be explicitly represented in the SMILES string; failure to do so will cause downstream MS/MS fragmentation predictions to be incorrect. ## Evidence - [other] Load input lipid structure data (SMILES or molecular formula) for N-Me derivatized unsaturated sterols.: "Load input lipid structure data (SMILES or molecular formula) for N-Me derivatized unsaturated sterols" - [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" - [readme] Theoretically applicable to all molecules including C=C bond (only test sterol lipids).: "Theoretically applicable to all molecules including C=C bond (only test sterol lipids)" - [readme] All functions are implemented in jupyter notebook: "All functions are implemented in jupyter notebook" - [readme] The script is written on the basis of RDkit's built-in functions.: "The script is written on the basis of RDkit's built-in functions"