| Summary: | 博士 === 國立交通大學 === 生物資訊及系統生物研究所 === 101 === One of the most adaptive immune responses is triggered by specific T-cell receptors (TCR) binding to peptide-major histocompatibility complexes (pMHC). Despite the availability of many prediction approaches to identify peptides binding to MHC, they are often lack of peptide-TCR interactions and detailed atomic interacting models. Due to an increasing number of high-throughput binding epitopes and TCR-peptide-MHC (TCR-pMHC) structural complexes are available, we proposed "peptide antigen family" to investigate both peptide-MHC and peptide-TCR interfaces and do fast genome-wide structural inferring peptide antigens and its homologous peptide antigens from whole pathogen genomes. These homologous peptide antigens share a similar binding model across multiple species. Our idea focusing on the unique binding between peptide antigen and proteasome can provide the insights of antigen recognition and the mechanisms of immune process.
For understanding the TCR-peptide-MHC binding model, we first observed the consistency and divergence between TCR-pMHC complexes and protein-protein interaction. According to the analysis, the interacting propensities of peptide-MHC interfaces are similar to those of protein-protein interfaces; conversely, the interacting propensities of peptide-TCR interfaces are similar to those of antibody-antigen interfaces. We determined properties of antibody-antigen interfaces from frequent residues (i.e. Tyrosine and Tryptophan) through dimensions of binding pockets and binding energy. Because of the limit number of TCR-pMHC co-crystal structures in the Protein Data Bank presently, we collected antibody-antigen complexes to promote the reliability of our binding model.
We derived template-based peptide-TCR and peptide-MHC scoring functions for investigating TCR-peptide-MHC binding models. For identifying the potential peptide antigen of a query, we used these two different antibody-antigen (called iMatrix) and protein-protein interacting scoring matrices for peptide-TCR and peptide-MHC interfaces, respectively. We prepared non-redundant antibody-antigen dataset to generate iMatrix. iMatrix can predict binding energies by separating the van der Waals forces from special forces (hydrogen bonds and electrostatic interactions), and can discriminate sidechain-sidechain or sidechain-backbone interactions. We evaluated iMatrix through two dimensions: 1) we collected 70 alanine mutagenesis from the ASEdb and estimated the relationship between predicted energies from our scoring function and experimental free energies to validate the predicted binding affinity; 2) we prepared 80,057 experimental peptides in 2,287 species from the IEDB to validate the predictive accuracy and the reliability of homologous peptide antigens. For a TCR-peptide-MHC template, iMatrix inferred its homologous peptide antigens from complete pathogen genome databases (≥ 108 peptide candidates from 864,628 protein sequences of 389 pathogens). iMatrix keeps hydrogen-bond energies and consensus interactions from these identified peptide antigens. In addition, our TCR-pMHC models can visualize detailed binding mechanisms (e.g., hydrogen bonds and steric interactions) and highlight the key region of peptide antigen on both peptide-TCR and peptide-MHC interfaces. Experimental results demonstrate that our models can achieve high prediction accuracy and offer potential peptide antigens across pathogens. The peptide antigen family is able to provide valuable insights on the peptide vaccine and MHC restriction.
In summary, we have developed “peptide antigen family” and “immune complex family” to identify peptide antigens and homologous peptide antigens. Our concept is the first method to infer homologous peptide antigens by considering two TCP-peptide-MHC interfaces from complete pathogen genome and experimental peptide databases. Additionally, our model provides the insights of the peptide trigger processes for immune and the role of immune complex family during T cell activation. We believe that this novel idea focusing the unique recognition between peptide antigen and proteasome reveals valuable insights of immunity evolution. Finally, this study has potential for vaccine design, rejections of transplanted organs, and cancer immunotherapy.
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