Data-driven learning of collective variables to understand and accelerate biomolecular folding

Data-driven modeling and machine learning have opened new paradigms and opportunities in the understanding and design of soft and biological materials. Nonlinear dimensionality reduction and deep learning present a powerful means to identify the underlying dynamical modes governing the assembly and folding of soft materials such as colloids, peptides, and polymers by direct analysis of molecular simulation data. Recovery of these modes, together with nonlinear collective variables with which to parameterize them, provides fundamental understanding of the microscopic forces and emergent dynamical motions governing the long-time evolution of the molecular system. We will discuss our use of diffusion maps, deep neural networks with novel topologies and loss functions, and enhanced sampling techniques to recover the high variance and/or slow collective variables from molecular dynamics simulations of protein folding, and our subsequent use of these coordinates to understand folding mechanisms and guide and accelerate sampling.