Advanced Overview of Biological Targets
1. Proteins
Proteins are the most common drug targets due to their functional diversity.
- Allosteric Sites: Enable selective modulation beyond active sites.
- Conformational Dynamics: Ligand binding stabilizes specific conformers.
- Post-translational Modifications: Influence druggability and function.
- Advanced Strategies: PROTACs, molecular glues, and fragment-based discovery.
2. Genes
Genes encode proteins and regulate cellular behavior. Targeting genes allows for long-term correction.
- Base Editing: Precise single-nucleotide changes.
- Prime Editing: Versatile edits without double-strand breaks.
- Epigenetic Modulation: Reversible control via histone/DNA modifiers.
- CRISPR-dCas9: Transcriptional regulation using epigenetic fusions.
3. RNA Molecules
RNA is a versatile target for modulating protein expression and regulatory functions.
- Structural Targeting: Small molecules bind RNA motifs (e.g., hairpins).
- Splice-switching Oligos: Redirect splicing to restore function.
- Circular RNAs: Emerging stable regulators and therapeutic agents.
4. Signaling Pathways
Pathways control cell fate and are modulated at multiple levels.
| Pathway | Role | Disease Association |
|---|---|---|
| MAPK/ERK | Cell growth & survival | Cancer |
| PI3K/AKT | Metabolism & apoptosis | Diabetes, cancer |
| Wnt/β-catenin | Development & differentiation | Colorectal cancer |
| NF-κB | Inflammation & immunity | Autoimmune diseases |
- Systems Biology: Models predict compensatory mechanisms and feedback loops.
- Multi-Omics: Phosphoproteomics and single-cell transcriptomics refine targeting.
Target Validation Pipeline
| Stage | Techniques | Insights |
|---|---|---|
| Target Identification | GWAS, transcriptomics, proteomics | Disease association, expression profiles |
| Target Validation | RNAi, CRISPR screens, chemical probes | Functional relevance, druggability |
| Structural Characterization | X-ray, cryo-EM, NMR | Binding site mapping, conformational states |
| Computational Modeling | Molecular dynamics, AI-based docking | Predict ligand interactions, off-target effects |
AI in Target Discovery
- Deep Learning: Predicts protein–ligand interactions and novel pockets.
- Graph Neural Networks: Model signaling pathways as dynamic networks.
- Multi-modal Integration: Combines omics and imaging for prioritization.