Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts

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Project Summary The success of the ongoing battle with coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) depends crucially on the availability of effective diagnostics, vaccines, antibody therapeutics, and small-molecular drugs. Although SARS-CoV-2 mutates slower than the viruses that cause the flu and the common cold, it has had more than 8300 observed single mutations on its genome of 29,900 nucleotides by June 1, 2020. We show that these mutations might have devastating effects on COVID-19 diagnostics, vaccines, antibody therapeutics, and small-molecular drugs (J. Chem. Inf. Model. In press). We will develop new artificial intelligence (AI) to forecast SARS-CoV-2 future mutations. Leveraging on state-of-art methods developed under the present R01 award, we will design mutation- resistant vaccines, antibody therapeutics, and small-molecular drugs. The CPUs and GPUs requested in this supplement will be essential for my lab to continue the research of the present R01 award and to apply the methods developed in this award to attack fundamental problems in combating COVID-19.