  Simulations of biological macromolecules play an important role in
understanding the physical basis of a number of complex processes such as
protein folding. Even with increasing computational power and evolution of
specialized architectures, the ability to simulate protein folding at atomistic
scales still remains challenging. This stems from the dual aspects of high
dimensionality of protein conformational landscapes, and the inability of
atomistic molecular dynamics (MD) simulations to sufficiently sample these
landscapes to observe folding events. Machine learning/deep learning (ML/DL)
techniques, when combined with atomistic MD simulations offer the opportunity
to potentially overcome these limitations by: (1) effectively reducing the
dimensionality of MD simulations to automatically build latent representations
that correspond to biophysically relevant reaction coordinates (RCs), and (2)
driving MD simulations to automatically sample potentially novel conformational
states based on these RCs. We examine how coupling DL approaches with MD
simulations can fold small proteins effectively on supercomputers. In
particular, we study the computational costs and effectiveness of scaling
DL-coupled MD workflows by folding two prototypical systems, viz., Fs-peptide
and the fast-folding variant of the villin head piece protein. We demonstrate
that a DL driven MD workflow is able to effectively learn latent
representations and drive adaptive simulations. Compared to traditional
MD-based approaches, our approach achieves an effective performance gain in
sampling the folded states by at least 2.3x. Our study provides a quantitative
basis to understand how DL driven MD simulations, can lead to effective
performance gains and reduced times to solution on supercomputing resources.
