Creative Biolabs-Immuno-oncology

T & B Cell based Binding Assay Service

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To better understand vaccines or drugs' immune response to pathogens, it is necessary to investigate T and B cell epitopes to a specific antigenic peptide. With advanced technology and excellent in-house expertise, Creative Biolabs provides T and B cell-based binding assays for cancer epitope analysis.

T and B-cell Binding Epitope

T cell receptor (TCR) and B cell receptor (BCR) is responsible for pathogens recognition. The specific portions of antigens that bind to the receptor are named antigen determinants or epitopes. An antigen can have one or more epitopes. Typically, antibodies bind exposed solvent parts of the antigen with about five amino acids or sugars in size. TCRs recognize the cancer epitopes attached to major histocompatibility complex (MHC) molecules exhibited on the antigen-presenting cells (APCs), roughly 8 to 17 amino acid residues, including MHC I-presented epitopes and MHC II-presented epitopes.

T and B cell-based epitope recognition. (Sanchez-Trincado, et al., 2017)Fig.1 T and B cell-based epitope recognition.1

A Hybrid Approach: Combining In Silico and In Vitro Analysis

Please contact us today to discuss how our cutting-edge T and B cell-based binding assays and epitope prediction services can illuminate your path to discovery and unlock the full potential of your therapeutic candidates.

Workflow

Our service is designed as a clear and intuitive workflow that can be easily integrated into your project timeline.

Required Starting Materials:

A simple procedure for T and B cell-based binding assay. (Creative Biolabs Original)

Final Deliverables:

Why Choose Us?

Choosing Creative Biolabs means partnering with a leader in immuno-oncology, combining deep scientific expertise with state-of-the-art technology. Our hybrid in silico and in vitro platform offers a competitive advantage, enabling high-throughput screening and validation in a physiologically relevant context. This approach is supported by extensive published data and a track record of success in helping clients bring projects to fruition. We provide comprehensive data analysis and expert interpretation, ensuring reliable and actionable insights for your research. Partner with us to accelerate your drug discovery and development in the dynamic field of immuno-oncology.

FAQs

Q1: Why should I choose a cell-based assay over a traditional one like SPR or ELISA?

A1: It accounts for the complexities of antigen presentation and recognition by living T and B cells, offering a more accurate prediction of in vivo binding and potential immunogenicity, which is essential for drug and vaccine safety.

Q2: How accurate are your in silico epitope predictions?

A2: Computational prediction is a powerful screening tool, but it's not a standalone solution. We use it to identify the most likely epitope candidates, which are then rigorously validated with our in vitro assays.

Q3: Can your services handle high-throughput screening for a large library of candidates?

A3: Absolutely. Our platform is designed for scalability and high-throughput screening. We can efficiently handle large-scale projects, allowing you to screen entire libraries of therapeutic candidates and identify those with the most promising binding characteristics in a fraction of the time.

Customer Review

Related Services

Contact Us

At Creative Biolabs, we are dedicated to providing innovative solutions that help our clients navigate the complex world of drug and vaccine development. Our T and B cell-based binding assays, powered by our hybrid in silico and in vitro platform, are designed to accelerate your timelines, de-risk your projects, and save your research time and inputs.

For detailed information and to discuss your project requirements with one of our specialists, please do not hesitate to reach out.

Reference

  1. Sanchez-Trincado, Jose L., Marta Gomez-Perosanz, and Pedro A. Reche. "Fundamentals and methods for T‐and B‐cell epitope prediction." Journal of immunology research 2017.1 (2017): 2680160. Distributed under Open Access license CC BY 4.0, without modification. https://doi.org/10.1155/2017/2680160

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