Predicting Mutation-Driven Changes in the SARS-CoV-2 Spike Protein Using Structural Signatures and Neural Networks - Moreira, Eduardo U. M.; Morais, Leandro; Araujo, Sheila C.; Lemos, Rafael P.; Bastos, Ana Luísa A.; Lima, Alessandra; Mariano, Diego; Melo-Minardi, Raquel C. de (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2024)
COVID-19, caused by the SARS-CoV-2 virus, has led to a global pandemic since 2020, resulting in nearly 7 million deaths. The virus’s rapid spread is due to more transmissible variants, many with spike glycoprotein mutations, which are key for cell invasion and a vaccine target. Understanding these mutations is crucial for preventing more dangerous variants. This study developed a computational method to predict the impact of mutations on the spike protein. Using data from 23,472 mutations, molecular modeling, graph-based structural signatures, and a machine-learning approach based on neural networks, the model analyzed 318 proteins, showing the methodology’s effectiveness in assessing the potential of new variants.