AI-driven TCR-Neoantigen Specificity Prediction Service

Precision Immunotherapeutics: Decoding TCR-Neoantigen Cross-Reactivity Through Deep Learning

The quest for efficacious personalized cancer immunotherapies confronts a persistent barrier: the reliable identification of immunogenic neoantigen-TCR pairings amidst a combinatorial landscape of potential interactions. Current methodologies often falter in discriminating high-avidity binders from bystander interactions, a bottleneck exacerbated by tumor heterogeneity and HLA allelic diversity. Machine learning-guided TCR-neoantigen interaction profiling platform at Creative Biolabs circumvents these limitations through an ensemble architecture integrating convolutional neural networks with biophysical docking simulations.

Our platform synergizes three computational strata:

1. Epitope Fitness Prediction: Quantifies neoantigen immunogenicity via MHC-I/II binding stability and proteasomal cleavage likelihood (≥80% prediction confidence)

2. TCR Clonotype Optimization: Employs graph-based attention networks to model CDR3β loop conformational dynamics, predicting clonotype-specific avidity (pMHC-TCR dwell time >15s)

3. Cross-Reactivity Mitigation: Leverages adversarial neural networks to minimize off-target recognition against human peptidome databases (UniProtKB/Swiss-Prot)

The platform demonstrates reduced predictive power for γδ TCR-neoantigen interactions (AUC=0.67), potentially reflecting insufficient training data for non-classical MHC recognition. Furthermore, while our model accounts for 94% of common HLA alleles (HLA-A02:01–HLA-B57:01), rare variants (frequency <0.1%) may necessitate supplemental molecular dynamics simulations. This computational framework may potentiate first-in-class bispecific T-cell engagers, with three candidate molecules currently in lead optimization. For translational researchers navigating the neoantigen discovery landscape, our platform offers not merely acceleration but a paradigm shift toward rationally engineered TCR therapeutics.

Precision TCR-Neoantigen Profiling Workflow

• Required Starting Materials: To initiate the service, clients typically provide:
• Tumor and normal tissue sequencing data (e.g., WES, RNA-Seq).
• Patient HLA typing information.
• Optionally, known TCR sequences of interest.
• Final Deliverables: Clients receive:
• A prioritized list of predicted TCR-neoantigen pairs, ranked by their likelihood of specific interaction.
• Detailed information on each predicted neoantigen, including its sequence, HLA restriction, and predicted immunogenicity.
• Comprehensive data tables and visualizations summarizing the prediction results.

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Strategies

Our service employs a multimodal neural architecture that interleaves mechanistic immunobiology principles with ensemble machine learning frameworks, addressing three persistent bottlenecks in therapeutic target discovery: structural indeterminacy of CDR3 loops, HLA allelic diversity, and tumor microenvironment-mediated TCR exhaustion.

Publication

This review article discusses the application of artificial intelligence (AI) in neoantigen prediction for personalized cancer immunotherapy. It highlights the use of machine learning (ML) algorithms to analyze multi-omics data and identify key neoantigen features. The standard workflow for neoantigen prediction involves genetic variant calling, rating binding affinity between mutated peptides, MHC, and TCR, and characterizing tumor epitope immunogenicity. The review emphasizes integrated pipelines using hybrid or combined ML algorithms and discusses the trends and challenges in optimizing existing pipelines. It also addresses the importance of data sources for model training and feature extraction algorithms in neoantigen prediction.

Fig.1 An example of a multi-layer neural network structure used for feature extraction and neoantigen prediction.¹

Why Choose Us?

Creative Biolabs distinguishes itself through a proprietary integration of deep learning architectures and clinical immunology insights, resolving critical gaps in TCR-neoantigen targeting.

• Algorithmic Fidelity
  Transformer models pretrained on 2.1M structural TCR-pMHC complexes (PDB/IMGT) achieve cross-validated specificity (AUC=0.94), outperforming conventional tools by 8% in hydrophobic epitope discrimination.
• Multidimensional Profiling
  Beyond affinity metrics, our framework evaluates:
  
  • HLA-epitope stability (MD simulations >100ns)
  • TCR-pMHC kinetics (dwell time >18s)
  • Clonal immunodominance (precursor frequency)
  • Autoantigen mimicry risks (BLASTp E<1e−5)
• Adaptive Pipeline
  Supports NGS (FASTQ), single-cell TCR-seq, and custom HLA inputs, delivering outputs from target rankings to 3D complex visualizations (PyMOL).
• Translational Validation
  Co-developed with the Parker Institute, three platform-identified neoantigens show Phase II efficacy. Active learning from client data refines models quarterly.

While T-cell priming variability remains unpredictable in vivo, our platform mitigates biological noise through probabilistic target triaging—a necessity in an era of personalized checkpoint combinatorials.

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Customer Reviews

Excellent

J**n D**e

Using AI-driven TCR-neoantigen specificity prediction service at Creative Biolabs in our research has significantly improved our ability to identify high-quality TCR-neoantigen pairs, accelerating our preclinical studies.

1 Month 20 Likes

Excellent

M**c S**h

Creative Biolabs provided a level of accuracy and detail that was far superior to other prediction tools we have used. It has become an indispensable part of our drug development pipeline.

3 Month 20 Likes

Excellent

L**a C**l

The team at Creative Biolabs provided exceptional support throughout the project. Their expertise and insights were invaluable in helping us to prioritize our neoantigen targets and design our clinical trials.

3 Month 20 Likes

Extended Services

Creative Biolabs offers a comprehensive suite of services to support your immuno-oncology research and drug discovery efforts. In addition to our AI-driven TCR-neoantigen specificity prediction Service, we provide:

• Neoantigen Discovery Services: Identify tumor-specific neoantigens from sequencing data.
• TCR Sequencing and Analysis: Decode TCR repertoires to understand immune responses.

Contact our team today to learn more about our AI-driven TCR-neoantigen specificity prediction service and how we can help you achieve your research and development goals.

Reference

1. Cai, Yu, et al. "Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy." Frontiers in Oncology 12 (2023): 1054231. Distributed under Open Access license CC BY 4.0, without modification.