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Viral surface antigens can be used as subunit vaccines and are often primary targets for neutralizing antibodies. However, for some viral antigens such as HIV-1, conformational flexibility can prevent elicitation of an effective neutralization response, and high sequence diversity and mutation rates can reduce the efficacy of antibody treatment. Here, I am going to discuss two topics: (1) immunogen conformational stabilization and (2) antibody resistance prediction, while demonstrating how both can be performed computationally. With respect to immunogen conformational stabilization, I will first illustrate different computational methods that can perform this task, and then describe an automated pipeline, CRISPRo, that design proline mutations for protein conformational stabilization. In regards to antibody resistance prediction, I will present bNAb-ReP, a gradient boosting machine learning algorithm developed to predict HIV-1 antibody resistance with high accuracy for both training set data and viral sequences obtained from clinical trials. The tools we developed here can facilitate the discovery of effective subunit vaccines and will inform clinical decisions of antibody usage when treating HIV positive patients.
For more information go to https://hpc.nih.gov/training