Watch the replay to learn how you can explore the conformational space of proteins in Discovery Studio Simulation.
Exploring the conformational space of proteins is of significant importance due to the close relationship between protein conformation and biological function. Deep learning-based systems, such as AlphaFold and OpenFold, have revolutionized structural biology because of their capability to predict the three-dimensional atomic protein structure from a one-dimensional sequence of the protein. Protein structure, however, is dynamic, and many recent studies have focused on how models like AlphaFold and OpenFold can be used to predict ensembles of conformations. This growing body of research suggests that multiple sequence alignments of orthologous sequences can be subsampled in diverse ways to bias AlphaFold and OpenFold towards predicting new conformations.
In this replay, we will discuss a new strategy for subsampling multiple sequence alignments. This strategy leverages direct coupling analysis to form clusters of evolutionarily related sequences that bias models like AlphaFold towards distinct conformational states. We will also show how we implemented and applied this approach to a variety of biological systems in Discovery Studio Simulation, where predicted conformations were compared to known functional conformations.
Webinar highlights:
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