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Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity ( |
