2 research outputs found
Navigating protein landscapes with a machine-learned transferable coarse-grained model
The most popular and universally predictive protein simulation models employ
all-atom molecular dynamics (MD), 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 here develop a bottom-up CG force field
with chemical transferability, which can be used for extrapolative molecular
dynamics on new sequences not used during model parametrization. We demonstrate
that the model successfully predicts folded structures, intermediates,
metastable folded and unfolded basins, and the fluctuations of intrinsically
disordered proteins while it is 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
Machine learned coarse-grained protein force-fields: Are we there yet?
The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges