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.

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

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

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

5. Immunogenicity Scoring: Rank peptide immunogenicity.

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

Prediction Approaches

Workflow

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

2. Structure Prediction: Generate pMHC and TCR structures.

3. Docking: Model TCR-pMHC complex.

4. Scoring: Calculate binding probability/affinity.

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

6. Validation Features: Extract structural determinants.

7. 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

Output Components

AlphaFold3 Integration

Binding Prediction Thresholds

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

Clinical Applications

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

Structural Determinants

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

Author

AI Group - Biomedical AI Platform