tcr-pmhc-prediction-agent

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---

name: 'tcr-pmhc-prediction-agent'

description: 'AI-powered TCR-peptide-MHC interaction prediction using AlphaFold3 and deep learning for therapeutic TCR discovery, neoantigen validation, and T cell immunogenicity assessment.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

TCR-pMHC Prediction Agent

The TCR-pMHC Prediction Agent predicts T-cell receptor interactions with peptide-MHC complexes using AlphaFold3-based structural modeling and deep learning. Accurate TCR-pMHC prediction enables therapeutic TCR discovery, neoantigen vaccine validation, and identification of immunogenic epitopes for cancer and infectious disease applications.

When to Use This Skill

• When predicting which peptides a TCR will recognize.
• For validating neoantigen immunogenicity computationally.
• To screen therapeutic TCR candidates against target antigens.
• When assessing cross-reactivity of TCRs with self-peptides.
• For understanding TCR specificity determinants.

Core Capabilities

1. Binding Prediction: Predict TCR-pMHC binding affinity/probability.

1. Structural Modeling: Generate TCR-pMHC complex structures with AlphaFold3.

1. Epitope Specificity: Determine which epitopes a TCR recognizes.

1. Cross-Reactivity Assessment: Predict off-target self-peptide binding.

1. Immunogenicity Scoring: Rank peptide immunogenicity.

1. Therapeutic TCR Screening: Screen TCRs for desired specificity.

Prediction Approaches

| Approach | Method | Strengths |

|----------|--------|-----------|

| AlphaFold3 | Structure prediction | High accuracy, interpretable |

| TCR-BERT | Sequence transformer | Fast, large-scale |

| ERGO-II | RNN-based | Established benchmark |

| pMTnet | Multi-task learning | Generalizable |

| NetTCR | CNN-based | HLA-specific |

| TITAN | Attention-based | State-of-art sequence |

Workflow

1. Input: TCR sequence (alpha/beta CDR3), peptide, HLA allele.

1. Structure Prediction: Generate pMHC and TCR structures.

1. Docking: Model TCR-pMHC complex.

1. Scoring: Calculate binding probability/affinity.

1. Cross-Reactivity: Screen against self-peptide database.

1. Validation Features: Extract structural determinants.

1. Output: Binding predictions, structures, safety assessment.

Example Usage

User: "Predict whether this tumor-reactive TCR binds the identified neoantigen and check for cross-reactivity with self-peptides."

Agent Action:

python3 Skills/Immunology_Vaccines/TCR_pMHC_Prediction_Agent/tcr_pmhc_predict.py \
    --tcr_alpha_cdr3 CAVSDRGSTLGRLYF \
    --tcr_beta_cdr3 CASSLGQAYEQYF \
    --tcr_v_genes TRAV12-1,TRBV7-9 \
    --peptide KRAS_G12D_VVGADGVGK \
    --hla HLA-A*11:01 \
    --check_cross_reactivity true \
    --self_peptide_db human_proteome_9mers.fasta \
    --method alphafold3 \
    --output tcr_pmhc_results/

Input Requirements

| Input | Format | Required |

|-------|--------|----------|

| TCR CDR3 alpha | Amino acid sequence | Yes |

| TCR CDR3 beta | Amino acid sequence | Yes |

| V gene usage | IMGT notation | Recommended |

| Peptide | 8-11mer amino acids | Yes |

| HLA allele | 4-digit resolution | Yes |

Output Components

| Output | Description | Format |

|--------|-------------|--------|

| Binding Score | Probability of binding | .json |

| Complex Structure | TCR-pMHC model | .pdb |

| Contact Map | Residue interactions | .csv, .png |

| Cross-Reactivity | Self-peptide hits | .csv |

| Confidence Score | Prediction reliability | .json |

| Binding Determinants | Key residues | .csv |

AlphaFold3 Integration

| Component | Application | Output |

|-----------|-------------|--------|

| pMHC Modeling | Peptide-MHC structure | Complex structure |

| TCR Modeling | Variable region structure | TCR structure |

| Complex Prediction | Full ternary complex | Docked model |

| pLDDT Scores | Confidence per residue | Quality metric |

| PAE | Positional error | Interface confidence |

Binding Prediction Thresholds

| Score Range | Interpretation | Action |

|-------------|----------------|--------|

| >0.9 | Strong predicted binder | High confidence |

| 0.7-0.9 | Moderate predicted binder | Likely positive |

| 0.5-0.7 | Weak/uncertain | Experimental validation needed |

| <0.5 | Predicted non-binder | Low priority |

AI/ML Components

Structural Prediction:

• AlphaFold3 for complex modeling
• Molecular dynamics refinement
• Interface scoring functions

Sequence Models:

• TCR-specific language models
• Cross-attention for TCR-peptide
• Transfer learning from pMHC binding

Cross-Reactivity:

• Embedding similarity search
• Structural hotspot analysis
• Self-tolerance modeling

Performance Benchmarks

| Method | Dataset | AUC | Notes |

|--------|---------|-----|-------|

| AlphaFold3 | VDJdb benchmark | 0.85 | Structural |

| TCR-BERT | IEDB | 0.82 | Fast screening |

| ERGO-II | McPAS-TCR | 0.78 | Established |

| Ensemble | Combined | 0.88 | Best overall |

Clinical Applications

| Application | Use Case | TCR-pMHC Role |

|-------------|----------|---------------|

| Neoantigen Vaccines | Validate immunogenicity | Predict T cell response |

| TCR-T Therapy | Select therapeutic TCRs | Screen candidates |

| Safety Assessment | Check cross-reactivity | Avoid autoimmunity |

| Epitope Discovery | Find immunogenic peptides | Prioritize targets |

Prerequisites

• Python 3.10+
• AlphaFold3 installation
• PyTorch, transformers
• BioPython, MDAnalysis
• GPU with 16GB+ VRAM
• Self-peptide reference database

Related Skills

• TCR_Repertoire_Analysis_Agent - Repertoire analysis
• Neoantigen_Prediction_Agent - Neoantigen identification
• HLA_Typing_Agent - HLA determination
• CART_Design_Optimizer_Agent - TCR-based therapy

Cross-Reactivity Safety Analysis

| Database | Content | Purpose |

|----------|---------|---------|

| Human Proteome | All self-peptides | Primary safety |

| Tissue-Specific | Expression-weighted | Toxicity prediction |

| Viral Mimicry | Viral homologs | Infection mimics |

| Cancer-Testis | CT antigens | On-target activity |

Structural Determinants

| Feature | Location | Significance |

|---------|----------|--------------|

| CDR3 beta apex | Peptide contact | Specificity |

| CDR3 alpha | MHC/peptide | Fine-tuning |

| CDR1/2 | MHC helices | HLA restriction |

| Germline-encoded | Framework | Base recognition |

Special Considerations

1. HLA Restriction: Predictions are HLA-specific
2. CDR3 Dominance: CDR3 beta often most predictive
3. Paired Chains: Alpha-beta pairing crucial
4. Structural Validation: Validate with known structures
5. Experimental Follow-up: Tetramer/functional validation

Limitations

| Limitation | Impact | Mitigation |

|------------|--------|------------|

| Training Data Bias | Common HLA over-represented | Use diverse training |

| Novel TCRs | Out-of-distribution | Lower confidence |

| Post-translational | PTM peptides not modeled | Experimental validation |

| Dynamics | Static structures | MD simulation |

Author

AI Group - Biomedical AI Platform

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