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AlphaFold Integration
Protein structure prediction for target validation and drug design
Overview
Omic's AlphaFold integration provides direct access to DeepMind's AlphaFold2 protein structure predictions, enabling structure-based drug design and target validation workflows.
Key Capabilities
- ✓Access 200M+ predicted protein structures from AlphaFold DB
- ✓On-demand structure prediction for novel sequences
- ✓Automated binding site identification
- ✓Structure-guided molecule generation
Data Flow
1. Input: Target protein sequence or UniProt ID
2. AlphaFold: Retrieve or predict 3D structure
3. Omic: Binding site analysis + druggability scoring
4. Output: Structure-guided compound designs
Technical Specifications
Input Formats
- • FASTA protein sequences
- • UniProt accession IDs
- • PDB structure files
- • Gene symbols (human, mouse, rat)
Output Formats
- • PDB structure coordinates
- • pLDDT confidence scores
- • Binding pocket coordinates
- • Druggability annotations
Example API Call
# Retrieve AlphaFold structure for a target
from omic import AlphaFold
# Get structure by UniProt ID
structure = AlphaFold.get_structure("P53_HUMAN")
# Analyze binding sites
sites = structure.find_binding_sites()
# Generate compounds for top site
compounds = omic.generate_compounds(
target=structure,
binding_site=sites[0],
num_molecules=100
)