🏗️ Structural Proteomics

Cross-linking mass spectrometry (XL-MS) analysis. Identifies protein-protein interaction interfaces and distance constraints.

CLI Reference

python omicsclaw.py run struct-proteomics --demo
python omicsclaw.py run struct-proteomics --input <data.csv> --output <dir>

Parameters

--method

xlinkx

Why This Exists

• Without it: Identifying inter-linked peptides is a computational nightmare computationally mapping combinatorial massive search spaces
• With it: Efficiently deconvolutes cross-linker mass shifts to prove physical protein-protein interactions
• Why OmicsClaw: Standardizes structural XL-MS parsing which is traditionally highly vendor-locked

Workflow

1. Calculate: Generate combinatorial databases based on cross-linker specificity.
2. Execute: Score intra- and inter-peptide linkages.
3. Assess: Estimate coordinate FDR constraints.
4. Generate: Output structural distance restraints.
5. Report: Synthesize 2D interaction network topologies.

Example Queries

• "Analyze cross-linking MS data with XlinkX"
• "Find protein interactions from this pLink output"

Output Structure

output_directory/
├── report.md
├── result.json
├── crosslinks.csv
├── figures/
│   └── interaction_network.png
├── tables/
│   └── specific_linkages.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• data-import

  — Upstream format parsing

Citations

• XlinkX
• pLink