Biophysical theory, modeling, and simulation techniques play an increasingly important role in elucidating mechanistic details of biological and materials-based systems. However, the large number of degrees-of-freedom inherent to these systems makes it challenging for conventional simulation methods to obtain information on conformational statistics, especially on transiently populated and metastable states. The knowledge of such states is often a prerequisite to rationally design novel therapeutics and materials. As opposed to coarse-grained simulation methods where degrees of freedom are reduced to improve sampling, several enhanced sampling simulation techniques now exist that enable the discovery of new conformational states while retaining fully solvated and atomically resolved representations of molecular systems. In this talk, I will briefly discuss algorithmic underpinnings of these methods and their ability in resolving multi-dimensional free-energy surfaces. I will further highlight the applications of these methods in the context of several biophysical problems involving a range of conformational motions in proteins as well as nucleic acids.
