T Cell Epitope Prediction Service

The ultimate goal of epitope prediction is to aid the design of molecules that can mimic the structure and function of a genuine epitope and replace it in medical diagnostics and therapeutics and also in vaccine design. As a world-class provider of biotechnology, Creative Biolabs provides omnidirectional technologies to meet the diverse needs of our customers. With our professional experience and advanced computational protein design platform, we can provide T cell epitopes prediction services to meet the diverse needs of our customers.

Introduction to T Cell Epitopes Prediction

On the surface of T cells, there exists a specific receptor known as T cell receptor (TCR) that enables the recognition of antigens when they are displayed on the surface of antigen-presenting cells (APCs) bound to major histocompatibility complex (MHC) molecules. T cell epitopes are presented by class I and II MHC molecules that are recognized by two distinct subsets of T cells, CD8 and CD4 T cells, respectively. T cell epitope prediction aims to identify the shortest peptides within an antigen that are able to stimulate either CD4 or CD8 T cells. MHC I molecules can bind short peptides ranging from 9 to 11 amino acids, whose N- and C-terminal ends remain pinned to conserved residues of the MHC I molecule through a network of hydrogen bonds. The peptide-binding groove of MHC II molecules is open, allowing the N- and C-terminal ends of a peptide to extend beyond the binding groove. As a result, MHC II-bound peptides vary widely in length (9-22 residues). There are numerous methods to predict peptide-MHC binding. They can be divided into two main categories: data-driven and structure-based methods.

Data-driven Methods

Data-driven methods for peptide-MHC binding predictions are based on peptide sequences that are known to bind to MHC molecules. These peptide sequences are generally available in specialized epitope databases such as IEDB, EPIMHC, AntiJen.

Structure-based Methods

Structure-based approaches generally rely on modeling the peptide-MHC structure followed by evaluation of the interaction through methods such as molecular dynamics simulations. Structure-based methods have a great advantage without needing experimental data. However, they are seldom used as they are computationally intensive and exhibit lower predictive performance than data-driven methods.

Fig.1 T-cell epitope recognition.

Services

Data-driven methods for the peptide-MHC binding prediction can be roughly grouped into several distinct types: binding motifs, QM, free energy scoring functions (Fresno), machine learning techniques (ANN, HMM, SVM, etc.), MHC peptide threading, 3D-QSAR (three-dimensional quantitative structure and activity relations), and molecular modeling. We can provide these T cell epitopes prediction services to meet customers' specific requirements.

Creative Biolabs has been involved in the field of computational protein design for many years and we are fully committed to working with you to facilitate the successful completion of the projects. We have accumulated a wealth of experience from the accomplished projects and are very proud of our high-quality platforms to meet diverse needs from our clients. If you are interested in our services, please contact us for more details.

All listed services and products are For Research Use Only. Do Not use in any diagnostic or therapeutic applications.

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