(189v) Biasing High-Dimensional Free-Energy Landscapes for the Detection of Stable Clusters in Self-Assembling Systems

Prakash, A., University of Washington
Fu, C., University of Washington
Pfaendtner, J., University of Washington
The self-assembly of proteins, and nanoparticles is a complex phenomenon driven by multi-scale, hierarchical interactions with multiple association pathways. While molecular simulations of these systems provide key insights into these processes, high energy barriers of association pathways can trap the simulation in metastable states and impede comprehensive exploration of assembling structures.

In this poster, I will highlight how recently introduced enhanced sampling techniques, which allow users to bias multiple degrees of freedom (or collective variables), make simulations of self-assembling systems tractable.1,2 This method shows rapid convergence of free energies for 3-13 particle Lennard-Jones systems. The simulation trajectories are post-processed to detect stable structural minima and find the relative stability of structures. We apply the same strategy to detect the structure of stable water clusters and amyloid-beta peptide aggregates. In the course of studying these systems, we also comment on the best choice of collective variables for such high-dimensional, self-assembling systems.


(1) Pfaendtner, J.; Bonomi, M. Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics. J. Chem. Theory Comput. 2015, 11 (11), 5062–5067.

(2) Prakash, A.; Fu, C. D.; Bonomi, M.; Pfaendtner, J. Biasing Smarter, Not Harder, By Partitioning Collective Variables Into Families. (Under Review). 2018.