Background: The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR–epitope pair is still a challenging task, especially with novel epitopes, because the underlying patterns that drive the recognition are still largely unknown to both domain experts and machine learning models. Results: The binding of a TCR and epitope sequence can only occur when amino acids from both sequences are in close contact with each other. We analyze the distance between interacting molecules of the TCR and epitope sequences and compare this to the amino acids that are important for TCR–epitope prediction models. Important residues are determined by using interpretable deep learning techniques or, more specifically, feature attribution extraction methods, on two state-of-the-art TCR–epitope prediction models: ImRex and TITAN. Highlighting feature attributions on the molecular complex reveals additional insights to the domain expert about why the prediction was made and can offer novel insights into the factors that determine TCR affinity on a molecular level. We also show which residues of the TCR and epitope sequences determine binding prediction for ImRex and TITAN and use those to explain model performance. Conclusions: Extracting feature attributions is a useful way to verify your model and data for challenging problems where small hard-to-detect problems can accumulate to inaccurate results.
Background: The high complexity of biological systems arises from the large number of spatially and functionally overlapping interconnected components constituting them. The immune system, which is built of reticular components working to ensure host survival from microbial threats, presents itself as particularly intricate. A vaccine response is likely governed by levels that, when considered separately, may only partially explain the mechanisms at play. Multi-view modelling can aid in gaining actionable insights on response markers shared across populations, capture the immune system diversity, and disentangle confounders. Hepatitis B virus (HBV) vaccination responsiveness acts as a feasibility study for such an approach. Material and methods: Seroconversion to vaccine induced antibodies against HBV surface antigen (anti-HBs) in a vaccination cohort containing early-converters (n=21 ; <2 month) and late-converters (n=9 ; <6 months), was based on the anti-HBs titres (>10IU/L). Two approaches (principal component analysis and canonical correlation analysis) were used to interpret the multi-view data which encompassed bulk RNAseq, CD4+ T cell parameters (including T-cell receptor data), flow cytometry data, metadata including gender and age of the baseline parameters. Results: Multi-view joint dimensionality reduction out-performed single-view methods in terms of AUC and balanced accuracy, confirming an increase in predictive power to be gained. The interpretation of the findings showed that age, gender, inflammation-related genesets and pre-existing vaccine specific T-cells were associated with vaccination responsiveness. Conclusion: This multi-view dimensionality reduction approach complements the clinical seroconversion and all single-modalities and could identify what features underpin HBV vaccine response. This methodology could be extended to other vaccination trials to identify key features regulating responsiveness.
The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine responses are governed by levels that, when interrogated, separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modeling could aid in gaining actionable insights on response markers shared across populations, capture the immune system’s diversity, and disentangle confounders. We thus sought to assess this multi-view modeling capacity on the responsiveness to the Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine-induced antibodies against the HBV surface antigen (anti-HBs) in early converters (n = 21; <2 months) and late converters (n = 9; <6 months) and was defined based on the anti-HBs titers (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T-cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modeling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction outperformed single-view methods in terms of the area under the curve and balanced accuracy, confirming the increase in predictive power to be gained. The interpretation of these findings showed that age, gender, inflammation-related gene sets, and pre-existing vaccine-specific T-cells could be associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements clinical seroconversion and all single modalities. Importantly, this modeling could identify what features could predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify the key features regulating responsiveness.
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