Desired T-cell Epitopes: A Story from my Ph.D. Thesis

T-cell epitopes are the short peptides recognized by T-cell receptors once they are processed and presented on the cell surface by MHC molecules. The two major classes of MHC molecules have different processing and presentation mechanisms, hence they are recognized by specialized T cells.

Endogenoius antigens --processed by--> Cytosolic pathway --presented by--> MHC class I --recognized by--> CD8 T cells (CTL epitopes)

Exogenous antigens --processed by--> Endocytic pathway --presented by--> MHC class II --recognized by--> CD4 T cells (T helper epitopes)

At the beginning of my thesis, there were plenty of methods to predict CTL epitopes and T helper epitopes with decent performances. Even though immunologists have shown that the T helper cells diversify into sub-populations like T helper I and T helper II to execute distinct immune responses, there was no bioinformatics method to predict epitopes inducing Th1 vs Th2 immunity.

I took up this challenge and developed tools to predict peptides that could induce Interferon-gamma, Interleukin 4, the signatory cytokines for Th1 and Th2 populations respectively.

Exogenous antigens --processed by--> Endocytic pathway --presented by--> MHC class II --recognized by--> CD4 T cells (T helper epitopes) --cytokine induced--> Interferon-gamma --activates-->>Th1 immunity --useful against--> Intracellular pathogens

Now, Th1 peptides could be effectively predicted by the IFNepitope tool, published in the Biology Direct journal and so far cited in >350 publications.

Exogenous antigens --processed by--> Endocytic pathway --presented by--> MHC class II --recognized by--> CD4 T cells (T helper epitopes) --cytokine induced--> Interleukin-4 --activates-->>Th2 immunity --useful against--> Extracellular pathogens

And Th2 peptides could be effectively predicted by the IL4pred tool, published in the Clinical and Development Immunology journal and so far cited in >150 publications.

I apply these novel tools to prioritize epitopes for Mycobacterium tuberculosis and concluded my thesis with seven subunit vaccine candidates that computationally satisfy all the criteria for desired immunity and it was published in the PLoS One journal.

Thesis might be concluded, but it paved the path for several tools in this new class of T cell epitopes and been applied to prioritize vaccine or immunotherapeutic candidates against a huge list of viral, bacterial, fungal disease and different types of cancers.

A decade later, I got an opportunity to review the progress of this new class of tools in T-cell epitopes, which is published in the Briefings of Bioinformatics journal.

This has been a memorable journey for me and I thank my supervisor, Prof. Gajendra Raghava , for his excellent guidance and never-ending support. I also thank all my colleagues in the raghava lab, without their support, it could not have been possible.