Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation

COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.

Automated summary

COVID-19 has caused millions of deaths and had a severe impact on global economies. The emergence of SARS-CoV-2 variants has posed significant challenges in controlling the pandemic, particularly in terms of their potential to evade the immune system and vaccine immunity. In this study, a machine learning model was developed to predict the ability of mutations in the SARS-CoV-2 spike protein to reduce the neutralizing ability of antibodies. The model achieved a high level of accuracy in distinguishing high or low escape mutations. The findings showed that high escape mutations were dominated by charged to nonpolar mutations, while low escape mutations were dominated by polar to nonpolar mutations. This method can aid in prioritizing high/low escape mutations and assist in antibody design for current and emerging variants targeting the SARS-CoV-2 RBD region.