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AI-Powered Neoantigen Prediction Service for TCR-T Development

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Creative Biolabs provides an AI-Powered Neoantigen Prediction Service for TCR-T Development to address key challenges in precision immunotherapy target discovery, including the identification of truly immunogenic neoepitopes from large-scale sequencing datasets, limited predictive accuracy of conventional algorithms, and difficulties in selecting optimal targets for TCR-T therapy. This service enables rapid identification of high-confidence personalized neoepitopes through advanced machine learning models, multi-omics data integration, and predictive antigen-presentation analytics. By delivering data-driven target prioritization, Creative Biolabs supports efficient discovery of clinically relevant neoantigens and accelerates the development of next-generation TCR-T immunotherapies.

Introduction

Neoantigens generated from tumor-specific mutations represent highly attractive targets for T-cell–based immunotherapies due to their tumor specificity and reduced risk of off-target toxicity. Recent studies demonstrate that integrating genomic sequencing with machine learning-based peptide presentation prediction significantly improves neoepitope discovery efficiency. Computational models can analyze mutation data, peptide processing, antigen presentation, and immunogenicity to identify potential T-cell targets. These advances support the rapid identification of personalized neoantigens, enabling more precise design of TCR-T therapies and next-generation cancer immunotherapies.

Fig.1 Predicting MHC-I-presented pathogenic peptides with deep learning. (OA Literature) Fig.1 Deep learning predicts pathogenic peptides from MHC-I presentation. ¹

Service

At Creative Biolabs, we transform raw genomic data into validated therapeutic targets. Our service is designed to bridge the gap between mutation discovery and TCR-T engineering, ensuring that your candidate receptors target peptides with the highest probability of T-cell recognition. We solve the HLA bias problem by focusing on contact-position residues, providing a realistic assessment of immunogenicity across diverse patient backgrounds.

Our platform leverages advanced algorithms and machine learning for high-confidence neoepitope identification by analyzing tumor mutation profiles and peptide presentation characteristics. It employs AI-driven models to prioritize immunogenic antigens based on peptide processing, presentation likelihood, and T cell recognition potential. By integrating multi-omics data, it supports personalized TCR-T target discovery for patient-specific neoepitopes, while its scalable computational pipeline enables automated, high-throughput analysis of large-scale sequencing data across multiple tumor samples.

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What We Can Offer

Mutation-to-Neoepitope Prediction

Computational models analyze somatic mutations and translate them into potential mutant peptides that may serve as neoepitopes.

Peptide-MHC Binding Prediction

Advanced algorithms evaluate peptide affinity and stability with patient-specific HLA molecules to identify peptides likely to be presented on tumor cell surfaces.

Antigen Processing and Presentation Modeling

Integrated prediction models assess proteasomal cleavage, peptide transport, and cellular presentation probability.

Neoepitope Immunogenicity Scoring

Machine learning models estimate the likelihood that a presented peptide can be recognized by T cells, improving prioritization of functional targets.

Multi-omics Integration

Genomic mutation data, transcriptomic expression profiles, and HLA typing results are combined to refine candidate neoantigen lists.

Our Workflow

Required Starting Materials: To initiate the service, clients typically provide raw Whole Exome Sequencing or RNA Sequence data from tumor and matched normal samples, along with the patient's HLA typing information (if known).

Workflow of AI-Powered Neoantigen Prediction Service for TCR-T Development. (Creative Biolabs Original)

Final Deliverables:

Clients receive a Comprehensive Neoepitope Prioritization Report, raw Immunogenicity Scoring Data, and a Validated List of Top-Tier TCR-T Targets suitable for immediate synthesis and testing.

Core Benefits

Context-Specific Models: Separate predictive modeling for pathogenic and self-antigens to address out-of-distribution generalization issues.
Advanced Amino Acid Embedding: Utilization of protein language models to capture complex physicochemical features of epitopes.
Custom Algorithm Development: Ability to tailor our AI workflows to specific HLA supertypes or rare mutation types.
Experimental Validation Synergy: Optional integration with our in vitro T-cell activation assays to confirm AI-predicted targets.

FAQs

Q: How does your AI service differ from public tools?

A: Our service adds a proprietary layer of TCR recognition modeling and out-of-distribution detection to filter out non-immunogenic binders that public tools often misclassify as positives.

Q: Can you predict neoantigens for MHC Class II responses?

A: Yes. Although MHC-II binding is more heterogeneous, we employ specialized algorithms to deconvolve complex HLA motifs.

Q: What is the minimum tumor purity required for accurate prediction?

A: Higher purity is preferred for variant calling; however, our pipeline can account for tumor heterogeneity by integrating multi-region sequencing data to alleviate prediction bias.

Q: How do you handle rare HLA alleles?

A: We use pan-specific models that leverage pseudo-sequence encoding of the MHC binding groove, allowing us to provide accurate predictions even for alleles with limited experimental data.

Partner with Us

Creative Biolabs delivers a comprehensive, AI‑powered strategy for neoantigen identification and prioritization. By integrating key biological principles such as thymic selection with state‑of‑the‑art deep learning models, we enable researchers to design highly potent, specific, and personalized TCR‑T therapies. Our AI‑enhanced platform supports robust target discovery, driving safer and more effective immunotherapy development. Contact our team today to discuss your project and explore tailored solutions.

Reference

Lee, Chloe H et al. "A robust deep learning workflow to predict CD8 + T-cell epitopes." Genome medicine vol. 15,1 70. 13 Sep. 2023. Distributed under Open Access License CC BY 4.0, without modification. https://doi.org/10.1186/s13073-023-01225-z

All products and services are For Research Use Only and CANNOT be used in the treatment or diagnosis of disease.