Weighted Ensemble Simulations Reveal Novel Conformations and Modulator Effects in Hepatitis B Virus Capsid Assembly

TL;DR

This study uses advanced simulations to explore how modulators affect the assembly of the Hepatitis B virus capsid, revealing new conformations and binding possibilities.

Contribution

The study introduces the use of weighted ensemble simulations to overcome sampling limitations in understanding capsid assembly modulation.

Findings

Weighted ensemble simulations reveal novel conformations not captured by standard MD.

The method identifies more structures with enlarged binding pockets for ligand interaction.

This approach enhances the understanding of capsid assembly modulation for drug development.

Abstract

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.

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