Therapeutic Protein Designer

AI-guided de novo protein design using RFdiffusion backbone generation, ProteinMPNN sequence optimization, and structure validation for therapeutic protein development.

KEY PRINCIPLES:

• Structure-first design - Generate backbone geometry before sequence

• Target-guided - Design binders with target structure in mind

• Iterative validation - Predict structure to validate designs

• Developability-aware - Consider aggregation, immunogenicity, expression

• Evidence-graded - Grade designs by confidence metrics

• Actionable output - Provide sequences ready for experimental testing

• English-first queries - Always use English terms in tool calls (protein names, target names), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language

When to Use

Apply when user asks:

• "Design a protein binder for [target]"

• "Create a therapeutic protein against [protein/epitope]"

• "Design a protein scaffold with [property]"

• "Optimize this protein sequence for [function]"

• "Design a de novo enzyme for [reaction]"

• "Generate protein variants for [target binding]"

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

Create the report file FIRST:

• File name: [TARGET]_protein_design_report.md

• Initialize with section headers

• Add placeholder: [Designing...]

Progressively update as designs are generated

Output separate files:

• [TARGET]_designed_sequences.fasta

• All designed sequences

• [TARGET]_top_candidates.csv

• Ranked candidates with metrics

2. Design Documentation (MANDATORY)

Every design MUST include:

Design: Binder_001

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

Quality Metrics:

Source: NVIDIA NIM via NvidiaNIM_rfdiffusion, NvidiaNIM_proteinmpnn, NvidiaNIM_esmfold

Phase 0: Tool Verification

NVIDIA NIM Tools Required

Tool Purpose API Key Required

NvidiaNIM_rfdiffusion

Backbone generation Yes

NvidiaNIM_proteinmpnn

Sequence design Yes

NvidiaNIM_esmfold

Fast structure validation Yes

NvidiaNIM_alphafold2

High-accuracy validation Yes

NvidiaNIM_esm2_650m

Sequence embeddings Yes

Parameter Verification

Tool WRONG Parameter CORRECT Parameter

NvidiaNIM_rfdiffusion

num_steps

diffusion_steps

NvidiaNIM_proteinmpnn

pdb

pdb_string

NvidiaNIM_esmfold

seq

sequence

Workflow Overview

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

Phase 1: Target Characterization

1.1 Get Target Structure

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}

1.1b EMDB for Membrane Proteins (NEW)

When to prioritize EMDB: Membrane proteins, large complexes, and targets where conformational states matter.

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

Output for Report:

1.1b Cryo-EM Structures (EMDB)

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

Source: EMDB

1.2 Identify Binding Epitope

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)

1.3 Output for Report

1. Target Characterization

1.1 Target Information

1.2 Epitope Analysis

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

Source: PDB 4ZQK, surface analysis

Phase 2: Backbone Generation

2.1 RFdiffusion Design

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

2.2 Design Modes

Mode Use Case Key Parameters

Unconditional De novo scaffold diffusion_steps only

Binder design Target-guided binder target_structure , hotspot_residues

Motif scaffolding Functional motif embedding motif_sequence , motif_structure

2.3 Output for Report

2. Backbone Generation

2.1 Design Parameters

2.2 Generated Backbones

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

Source: NVIDIA NIM via NvidiaNIM_rfdiffusion

Phase 3: Sequence Design

3.1 ProteinMPNN Design

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

3.2 Sampling Parameters

Parameter Conservative Moderate Diverse

Temperature 0.1 0.2 0.5

Sequences per backbone 4 8 16

Use case Validated scaffold Exploration Diversity

3.3 Output for Report

3. Sequence Design

3.1 Design Parameters

3.2 Designed Sequences (Top 10 by Score)

3.3 Top Sequence: Seq_001_A

Seq_001_A (85 aa, MPNN score: -1.89) MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKL

Source: NVIDIA NIM via NvidiaNIM_proteinmpnn

Phase 4: Structure Validation

4.1 ESMFold Validation

def validate_structure(tu, sequence): """Validate designed sequence by structure prediction."""

# Fast validation with ESMFold
predicted = tu.tools.NvidiaNIM_esmfold(sequence=sequence)

# Extract quality metrics
plddt = extract_plddt(predicted)
ptm = extract_ptm(predicted)

return {
    'structure': predicted,
    'mean_plddt': np.mean(plddt),
    'ptm': ptm,
    'passes': np.mean(plddt) > 70 and ptm > 0.7
}

4.2 Validation Criteria

Metric Threshold Interpretation

Mean pLDDT

70 Confident fold

pTM

0.7 Good global topology

RMSD to backbone <2 Å Design recapitulated

4.3 Output for Report

4. Structure Validation

4.1 Validation Results

4.2 Top Validated Design: Seq_001_A

Overall: Well-folded 3-helix bundle with high confidence core

Source: NVIDIA NIM via NvidiaNIM_esmfold

Phase 5: Developability Assessment

5.1 Aggregation Propensity

def assess_aggregation(sequence): """Assess aggregation propensity."""

# Calculate hydrophobic patches
# Calculate isoelectric point
# Identify aggregation-prone motifs

return {
    'aggregation_score': score,
    'hydrophobic_patches': patches,
    'risk_level': 'Low' if score < 0.5 else 'Medium' if score < 0.7 else 'High'
}

5.2 Developability Metrics

Metric Favorable Marginal Unfavorable

Aggregation score <0.5 0.5-0.7

0.7

Isoelectric point 5-9 4-5 or 9-10 <4 or >10

Hydrophobic patches <3 3-5

5

Cysteine count 0 or even Odd Multiple unpaired

5.3 Output for Report

5. Developability Assessment

5.1 Developability Scores

5.2 Recommendations

Best candidate for expression: Seq_001_A

• Low aggregation propensity
• Neutral pI (easy purification)
• No cysteines (no misfolding risk)
• Predicted high E. coli expression

Source: Sequence analysis

Report Template

Therapeutic Protein Design Report: [TARGET]

Generated: [Date] | Query: [Original query] | Status: In Progress

Executive Summary

[Designing...]

1. Target Characterization

1.1 Target Information

[Designing...]

1.2 Binding Epitope

[Designing...]

2. Backbone Generation

2.1 Design Parameters

[Designing...]

2.2 Generated Backbones

[Designing...]

3. Sequence Design

3.1 ProteinMPNN Results

[Designing...]

3.2 Top Sequences

[Designing...]

4. Structure Validation

4.1 ESMFold Validation

[Designing...]

4.2 Quality Metrics

[Designing...]

5. Developability Assessment

5.1 Scores

[Designing...]

5.2 Recommendations

[Designing...]

6. Final Candidates

6.1 Ranked List

[Designing...]

6.2 Sequences for Testing

[Designing...]

7. Experimental Recommendations

[Designing...]

8. Data Sources

[Will be populated...]

Evidence Grading

Tier Symbol Criteria

T1 ★★★ pLDDT >85, pTM >0.8, low aggregation, neutral pI

T2 ★★☆ pLDDT >75, pTM >0.7, acceptable developability

T3 ★☆☆ pLDDT >70, pTM >0.65, developability concerns

T4 ☆☆☆ Failed validation or major developability issues

Completeness Checklist

Phase 1: Target

• Target structure obtained (PDB or predicted)

• Binding epitope identified

• Existing binders noted

Phase 2: Backbones

• ≥5 backbones generated

• Top 3-5 selected for sequence design

• Selection criteria documented

Phase 3: Sequences

• ≥8 sequences per backbone designed

• MPNN scores reported

• Top 10 sequences listed

Phase 4: Validation

• All sequences validated by ESMFold

• pLDDT and pTM reported

• Pass/fail criteria applied

• ≥3 passing designs

Phase 5: Developability

• Aggregation assessed

• pI calculated

• Expression prediction

• Final ranking

Phase 6: Deliverables

• Ranked candidate list

• FASTA file with sequences

• Experimental recommendations

Fallback Chains

Primary Tool Fallback 1 Fallback 2

NvidiaNIM_rfdiffusion

Manual backbone design Scaffold from PDB

NvidiaNIM_proteinmpnn

Rosetta ProteinMPNN Manual sequence design

NvidiaNIM_esmfold

NvidiaNIM_alphafold2

AlphaFold DB

PDB structure NvidiaNIM_alphafold2

AlphaFold DB

Tool Reference

See TOOLS_REFERENCE.md for complete tool documentation.