Molecular dynamics (MD) simulations provide a detailed description of biophysical processes, allowing mechanistic questions to be addressed at the atomic level. The promise of such approaches is partly hampered by well-known sampling issues of typical simulations, where time scales available are significantly shorter than the process of interest. For the process of interest here, the binding of modulators of Hepatitis B virus capsid self-assembly, the binding site is at a flexible protein–protein interface. Characterization of the conformational landscape and how it is altered upon ligand binding is thus a prerequisite for a complete mechanistic description of capsid assembly modulation. However, such a description can be difficult due to the aforementioned sampling issues of standard MD, and enhanced sampling strategies are required. Here, we employ the weighted ensemble methodology to characterize the free-energy landscape of our earlier determined functionally relevant progress coordinates. It is shown that this approach provides conformations outside those sampled by standard MD, as well as an increased number of structures with correspondingly enlarged binding pockets conducive to ligand binding, illustrating the utility of weighted ensemble for computational drug development.

Meaningful insight into the function of a dynamic protein requires knowledge of the conformations that it can adopt and how it transitions between these conformations. Despite tremendous strides in structure determination in the past decade, it is still uncommon to resolve a protein in all of its key conformational states. However, it is not uncommon for family members to be solved in many different states throughout a reaction cycle, but if their sequence identity is low, it is difficult to leverage this information to determine how any single member moves through this conformational landscape. Here we develop a simulation technique that uses distance matrices to a target state to define a structural similarity metric (SSM) to guide the dynamics of a protein from a known conformation to a target, based on a related protein. Applying the method to the well-studied β-β-α (BBA) protein, we fold the β-hairpin portion of BBA to the correct native state with the correct sequence alignment without specifying which residue pairs correspond to which distance values in the matrix. We then generalize the SSM to multiple dimensions based on structural motifs within the protein and simulate the full folding. We compare different string-based, enhanced sampling methods coupled to the SSM and discuss their advantages and disadvantages. We show that the method can simulate the transition to a new conformation based on a homologous protein structure even when the sequence similarity is too low for alignment-based methods to guide the transition. We end by suggesting that the deconstruction of state space into distinct structural motifs is a natural and potentially efficient framework for searching through conformational space.