Project Overview Anti-cytokine autoantibodies (autoAbs) are present in healthy individuals and play a role in regulating excessive immune responses to cytokines. However, high level of neutralizing autoAbs against cytokines lead to impaired immune responses under pathological conditions. Notably, neutralizing autoAbs against type I interferons (IFNs) have been implicated in severe COVID-19 and other viral infections. Despite increasing recognition of their impact, there is a lack of systematic characterization of these autoAbs, particularly in terms of their gene expression profiles, immunogenetic features, and functional epitopes. This project leverages AI-based AlphaFold3 screening to identify and characterize anti-cytokine autoAbs, particularly those targeting type I IFNs. Then we will integrate AlphaFold-based structural modeling with single-cell RNA sequencing (scRNAseq) and fluorescence-activated cell sorting (FACS) to map the autoreactive B cell response. Objectives: AI-Based AutoAb preliminary screening and AlphaFold-Driven Structural Analysis: We have obtained around 100 000 antibody sequences from patient B cell repertoire with our wet lab experiment. Now we are trying to set up method to utilize the Protein Data Bank (PDB) database and AlphaFold software on the Alvis server for autoreactive B cell receptor (BCR) sequence prediction. We will also apply molecular docking and AlphaFold3-based structure prediction to assess autoAb binding sites. Then, the antibody screened by AlphaFold3 will be validated by experiment to validate the IFN-α2 epitope interactions and autoAb neutralization potential. Methodology FACS & Single-Cell Analysis: B cells from severe COVID-19 patients are sorted based on IFN-α2 binding capacities. AlphaFold antibody screening, modeling & docking: Predicts high-affinity interactions between autoAbs and IFN-α2. Expected Outcomes A comprehensive AI-driven database of anti-cytokine autoAbs. Identification of high-risk individuals based on immunological markers and their B cell receptor repertoire. Structural insights into IFN-α2 autoAb interactions for therapeutic targeting. Conclusion By integrating AI, AlphaFold, and scRNAseq, this project aims to systematically characterize anti-cytokine autoAbs, particularly those implicated in severe COVID-19. Our findings will enhance diagnostics, risk stratification, and potential therapeutic interventions for autoimmune and infectious diseases.