Abstract
The emergence of immune-evasive mutations in the SARS-CoV-2 spike protein is consistently challenging existing vaccines and therapies, making precise prediction of their escape potential a critical imperative. Artificial Intelligence(AI) holds great promise for deciphering the intricate language of protein. Here, we employed a Generative Adversarial Network to decipher the hidden escape pathways within the spike protein by generating spikes that closely resemble natural ones. Through comprehensive analysis, we demonstrated that generated sequences capture natural escape characteristics. Moreover, incorporating these sequences into an AI-based escape prediction model significantly enhanced its performance, achieving a 7% increase in detecting natural escape mutations on the experimentally validated Greaney dataset. Similar improvements were observed on other datasets, demonstrating the model's generalizability. Precisely predicting immune-evasive spikes not only enables the design of strategically targeted therapies but also has the potential to expedite future viral therapeutics. This breakthrough carries profound implications for shaping a more resilient future against viral threats.
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