AI for Science Seminar with Dr. Cecilia Clementi

Join the Chemistry Department and the Data Science Institute for this AI for Science seminar on protein modeling. It will be preceded by the Hirschfelder Prize Seminar with Dr. Frank Noé at 2:30pm.

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep-learning methods with a large and diverse training set of all-atom protein simulations, we have developed a bottom–up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parameterization. The model successfully predicts metastable states of folded, unfolded and intermediate structures, the fluctuations of intrinsically disordered proteins and relative folding free energies of protein mutants, while being several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.