HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

VitTCR: A deep learning method for peptide recognition prediction.

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Engineering the hydroxyl content on aluminum oxyhydroxide nanorod for elucidating the antigen adsorption behavior.

The interaction between the aluminum salt-based adjuvants and the antigen in the vaccine formulation is one of the determining factors affecting the immuno-potentiation effect of vaccines. However, it is not clear how the intrinsic properties of the adjuvants could affect this interaction, which limits to benefit the improvement of existing adjuvants and further formulation of new vaccines. Here, we engineered aluminum oxyhydroxide (AlOOH) nanorods and used a variety of antigens including hepatitis B surface antigen (HBsAg), SARS-CoV-2 spike protein receptor-binding domain (RBD), bovine serum albumin (BSA) and ovalbumin (OVA) to identify the key physicochemical properties of adjuvant that determine the antigen adsorption at the nano-bio interface between selected antigen and AlOOH nanorod adjuvant. By using various physicochemical and biophysical characterization methods, it was demonstrated that the surface hydroxyl contents of AlOOH nanorods affected the adsorptive strength of the antigen and their specific surface area determined the adsorptive capacity of the antigen. In addition, surface hydroxyl contents had an impact on the stability of the adsorbed antigen. By engineering the key intrinsic characteristics of aluminum-based adjuvants, the antigen adsorption behavior with the aluminum adjuvant could be regulated. This will facilitate the design of vaccine formulations to optimize the adsorption and stability of the antigen in vaccine.