To derive disease information from human protein decorations (post-translational modifications, PTMs) and mutations, follow this structured approach:
### 1. **Data Annotation and Analysis**
- **Mutations**:
- Use tools like **PolyPhen-2**, **SIFT**, or **MutationTaster** to predict pathogenicity.
- Check databases like **ClinVar**, **COSMIC**, or **OMIM** for known disease associations.
- **PTMs**:
- Use **dbPTM**, **PhosphoSitePlus**, or **UniProt** to identify known modification sites.
- Predict functional impacts with tools like **NetPhos** (phosphorylation) or **GPS-Uber** (ubiquitination).
### 2. **Pathway and Network Analysis**
- Map proteins to pathways using **KEGG**, **Reactome**, or **WikiPathways** to identify disease-related pathways (e.g., apoptosis in cancer).
- Analyze protein-protein interactions via **STRING** or **BioGRID** to find connections to disease-associated proteins.
### 3. **Gene Ontology (GO) and Functional Enrichment**
- Perform GO term enrichment (**DAVID**, **Enrichr**) to highlight disrupted biological processes (e.g., "cell cycle regulation" in cancer).
### 4. **Literature and Database Mining**
- Cross-reference findings with **PubMed**, **Google Scholar**, or **DisGeNET** for existing studies linking your protein/mutation to diseases.
- Explore cancer-specific resources like **TCGA** or **cBioPortal** for mutations in tumor genomes.
### 5. **Experimental Validation**
- Validate predictions using wet-lab techniques (e.g., CRISPR for mutation knock-in/out, mass spectrometry for PTMs).
- Check **GWAS Catalog** or patient cohort studies (e.g., UK Biobank) for clinical correlations.
### 6. **Integration and Hypothesis Building**
- Combine computational and experimental evidence to propose disease mechanisms.
- Use tools like **Cytoscape** to visualize networks linking proteins, pathways, and diseases.
### Example Workflow:
- **Mutation in BRCA1**:
- Annotate via ClinVar (known for breast cancer).
- Pathway analysis (DNA repair pathways).
- Validate with TCGA data on patient mutations.
- **Hyperphosphorylation of Tau**:
- Check PhosphoSitePlus (linked to Alzheimer’s).
- Pathway analysis (neurodegeneration pathways).
### Key Tools/Databases:
- **Mutations**: COSMIC, ClinVar, OMIM, cBioPortal.
- **PTMs**: dbPTM, PhosphoSitePlus, UniProt.
- **Pathways**: KEGG, Reactome, DAVID.
- **Interactions**: STRING, BioGRID.
- **Validation**: PubMed, TCGA, GWAS Catalog.
### Challenges:
- **Novel variants**: Require functional studies (e.g., knock-in models).
- **False positives**: Cross-validate predictions with multiple tools/databases.
This approach bridges molecular changes to disease contexts, aiding in biomarker discovery or therapeutic targeting.
