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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.

  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

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.

  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

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