Sterol structure representation and curation

sterol-structure-representation-and-curation

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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"