Predicting and discovering protein dynamics

The functions of biomolecules are often based in their ability to convert between multiple conformations. Recent advances in deep learning for predicting and designing single structures of proteins mean that the next frontier lies in how well we can characterize, model, and predict protein dynamics. I will talk about two projects from my postdoctoral work in this direction. First, I will discuss a method that enables AlphaFold2 to sample multiple conformations of metamorphic proteins by clustering the input sequence alignment. This work enabled us to design a minimal set of 3 mutations to flip the populations of the fold-switching protein KaiB, as well as screen for novel putative alternate states. Beyond predicting multiple conformations, we would also like to be able to predict actual kinetics associated with transitions. Second, I will describe the development of large-scale benchmarks of dynamics from across multiple NMR experiment types, and initial results and insights from training deep learning models to predict these hallmarks of dynamics.

Speaker: Hannah Wayment-Steele, Brandeis University