Protein Therapeutic Design

### Design: Binder_001

**Sequence**: MVLSPADKTN...
**Length**: 85 amino acids
**Target**: PD-L1 (UniProt: Q9NZQ7)
**Method**: RFdiffusion → ProteinMPNN → ESMFold validation

**Quality Metrics**:
| Metric | Value | Interpretation |
|--------|-------|----------------|
| pLDDT | 88.5 | High confidence |
| pTM | 0.82 | Good fold |
| ProteinMPNN score | -2.3 | Favorable |
| Predicted binding | Strong | Based on interface pLDDT |

*Source: NVIDIA NIM via `NvidiaNIM_rfdiffusion`, `NvidiaNIM_proteinmpnn`, `NvidiaNIM_esmfold`*

Phase 1: Target Characterization
├── Get target structure (PDB, EMDB cryo-EM, or AlphaFold)
├── Identify binding epitope
├── Analyze existing binders
├── Check EMDB for membrane protein structures (NEW)
└── OUTPUT: Target profile
    ↓
Phase 2: Backbone Generation (RFdiffusion)
├── Define design constraints
├── Generate multiple backbones
├── Filter by geometry quality
└── OUTPUT: Candidate backbones
    ↓
Phase 3: Sequence Design (ProteinMPNN)
├── Design sequences for each backbone
├── Sample multiple sequences per backbone
├── Score by ProteinMPNN likelihood
└── OUTPUT: Designed sequences
    ↓
Phase 4: Structure Validation
├── Predict structure (ESMFold/AlphaFold2)
├── Compare to designed backbone
├── Assess fold quality (pLDDT, pTM)
└── OUTPUT: Validated designs
    ↓
Phase 5: Developability Assessment
├── Aggregation propensity
├── Expression likelihood
├── Immunogenicity prediction
└── OUTPUT: Developability scores
    ↓
Phase 6: Report Synthesis
├── Ranked candidate list
├── Experimental recommendations
├── Next steps
└── OUTPUT: Final report

def get_target_structure(tu, target_id):
    """Get target structure from PDB, EMDB, or predict."""
    
    # Try PDB first (X-ray/NMR)
    pdb_results = tu.tools.PDB_search_by_uniprot(uniprot_id=target_id)
    
    if pdb_results:
        # Get highest resolution structure
        best_pdb = sorted(pdb_results, key=lambda x: x['resolution'])[0]
        structure = tu.tools.PDB_get_structure(pdb_id=best_pdb['pdb_id'])
        return {'source': 'PDB', 'pdb_id': best_pdb['pdb_id'], 
                'resolution': best_pdb['resolution'], 'structure': structure}
    
    # Try EMDB for cryo-EM structures (valuable for membrane proteins)
    protein_info = tu.tools.UniProt_get_protein_by_accession(accession=target_id)
    emdb_results = tu.tools.emdb_search(
        query=protein_info['proteinDescription']['recommendedName']['fullName']['value']
    )
    
    if emdb_results and len(emdb_results) > 0:
        # Get highest resolution cryo-EM entry
        best_emdb = sorted(emdb_results, key=lambda x: x.get('resolution', 99))[0]
        # Get associated PDB model if available
        emdb_details = tu.tools.emdb_get_entry(entry_id=best_emdb['emdb_id'])
        if emdb_details.get('pdb_ids'):
            structure = tu.tools.PDB_get_structure(pdb_id=emdb_details['pdb_ids'][0])
            return {'source': 'EMDB cryo-EM', 'emdb_id': best_emdb['emdb_id'],
                    'pdb_id': emdb_details['pdb_ids'][0], 
                    'resolution': best_emdb.get('resolution'), 'structure': structure}
    
    # Fallback to AlphaFold prediction
    sequence = tu.tools.UniProt_get_protein_sequence(accession=target_id)
    structure = tu.tools.NvidiaNIM_alphafold2(
        sequence=sequence['sequence'],
        algorithm="mmseqs2"
    )
    return {'source': 'AlphaFold2 (predicted)', 'structure': structure}

def get_cryoem_structures(tu, target_name):
    """Get cryo-EM structures for membrane proteins/complexes."""
    
    # Search EMDB
    emdb_results = tu.tools.emdb_search(
        query=f"{target_name} membrane OR receptor"
    )
    
    structures = []
    for entry in emdb_results[:5]:
        details = tu.tools.emdb_get_entry(entry_id=entry['emdb_id'])
        structures.append({
            'emdb_id': entry['emdb_id'],
            'resolution': entry.get('resolution', 'N/A'),
            'title': entry.get('title', 'N/A'),
            'conformational_state': details.get('state', 'Unknown'),
            'pdb_models': details.get('pdb_ids', [])
        })
    
    return structures

### 1.1b Cryo-EM Structures (EMDB)

| EMDB ID | Resolution | PDB Model | Conformation |
|---------|------------|-----------|--------------|
| EMD-12345 | 2.8 Å | 7ABC | Active state |
| EMD-23456 | 3.1 Å | 8DEF | Inactive state |

**Note**: Cryo-EM structures capture physiologically relevant conformations for membrane protein targets.

*Source: EMDB*

def identify_epitope(tu, target_structure, epitope_residues=None):
    """Identify or validate binding epitope."""
    
    if epitope_residues:
        # User-specified epitope
        return {'residues': epitope_residues, 'source': 'user-defined'}
    
    # Find surface-exposed regions
    # Use structural analysis to identify potential epitopes
    return analyze_surface(target_structure)

## 1. Target Characterization

### 1.1 Target Information

| Property | Value |
|----------|-------|
| **Target** | PD-L1 (Programmed death-ligand 1) |
| **UniProt** | Q9NZQ7 |
| **Structure source** | PDB: 4ZQK (2.0 Å resolution) |
| **Binding epitope** | IgV domain, residues 19-127 |
| **Known binders** | Atezolizumab, durvalumab, avelumab |

### 1.2 Epitope Analysis

| Residue Range | Type | Surface Area | Druggability |
|---------------|------|--------------|--------------|
| 54-68 | Loop | 850 Ų | High |
| 115-125 | Beta strand | 420 Ų | Medium |
| 19-30 | N-terminus | 380 Ų | Medium |

**Selected Epitope**: Residues 54-68 (PD-1 binding interface)

*Source: PDB 4ZQK, surface analysis*

def generate_backbones(tu, design_params):
    """Generate de novo backbones using RFdiffusion."""
    
    backbones = tu.tools.NvidiaNIM_rfdiffusion(
        diffusion_steps=design_params.get('steps', 50),
        # Additional parameters depending on design type
    )
    
    return backbones

## 2. Backbone Generation

### 2.1 Design Parameters

| Parameter | Value |
|-----------|-------|
| **Method** | RFdiffusion via NVIDIA NIM |
| **Design mode** | Unconditional scaffold generation |
| **Diffusion steps** | 50 |
| **Number generated** | 10 backbones |

### 2.2 Generated Backbones

| Backbone | Length | Topology | Quality |
|----------|--------|----------|---------|
| BB_001 | 85 aa | 3-helix bundle | Good |
| BB_002 | 92 aa | Beta sandwich | Good |
| BB_003 | 78 aa | Alpha-beta | Good |
| BB_004 | 88 aa | All-alpha | Moderate |
| BB_005 | 95 aa | Mixed | Good |

**Selected for sequence design**: BB_001, BB_002, BB_003, BB_005 (top 4)

*Source: NVIDIA NIM via `NvidiaNIM_rfdiffusion`*

def design_sequences(tu, backbone_pdb, num_sequences=8):
    """Design sequences for backbone using ProteinMPNN."""
    
    sequences = tu.tools.NvidiaNIM_proteinmpnn(
        pdb_string=backbone_pdb,
        num_sequences=num_sequences,
        temperature=0.1  # Lower = more conservative
    )
    
    return sequences