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A novel fuzzy approach towards in silico B-cell epitope identification inducing antigen-specific immune response for Vaccine Design
Abstract The identification of B-cell epitopes that elicit an antigen-specific immune response is essential for a variety of immunodetection and immunotherapeutic applications, including the development of safe and high efficacy vaccines. Identifying diagnostically or therapeutically useful epitopes is a difficult, time-consuming, and resource-intensive procedure. In silico prediction of B-cell epitope has gained immense attention in recent years due to its low cost, fast results, and less labor-intensive method compared to NMR spectroscopy and 3D X-ray structural analysis of antibody-antigen complexes. However, one of the major problems that most established models confront is gathering huge volumes of data. Moreover, most models do not achieve high levels of accuracy. The current work is the first to propose the ‘Fuzzy’ approach to in silico B-cell epitope prediction. The effectiveness of the proposed model is demonstrated on severely imbalanced and limited datasets through several experiments. The results show that using the proposed method enhances both accuracy and precision when compared to existing approaches. Further, the model is tested on the SARS-CoV-1 antigen-antibody PDB complex. The proposed technique outperforms state-of-the-art ML models trained on the same dataset. Results obtained indicate that applying the proposed method improves the prediction compared to the other approaches.