tcr-pmhc-prediction-agent

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