Our proposal focuses on several critical biomolecular systems for transmission and propagation of SARS-CoV-2. The overarching scientific goal is the development and application of rapidly-deployable and data-driven multiscale models to simulate large-scale viral processes. We will use accurate coarse-grained (CG) models as carefully simplified representations of biomolecules to study cooperative dynamical processes, such as viral assembly and fusion, and provide a holistic model of the entire SARS-CoV-2 virion. Given the complexity to create accurate CG models, our strategy is to pose model parameterization as variational inference, similar to how machine learning considers learning from examples. Reference data from data-extensive atomistic molecular dynamics (MD) simulations are used as a training set to both systematically generate and select which CG model produces the most accurate results. Through this coarse-grained approach, we aim to provide a dynamical view of crucial steps during viral pathogenesis. Furthermore, the all-atom data collected for the derivation of our CG models will provide insight about the interaction of structural viral proteins and membrane lipids during viral assembly.
This project is part of the COVID-19 HPC Consortium.
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