Machine learning of molecular dynamics simulations provides insights into modulation of viral capsid assembly.

ABSTRACT: An effective approach in the development of novel antivirals is to target the assembly of viral capsids using capsid assembly modulators (CAMs). CAMs targeting hepatitis B virus (HBV) have two major modes of function: they can either accelerate nucleocapsid assembly, retaining its structure, or misdirect it into non-capsid-like particles. Previous molecular dynamics (MD) simulations of early capsid-assembly intermediates showed differences in protein conformations for apo and bound states. Here, we have developed and tested several classification machine learning (ML) models to better distinguish between apo-tetramer intermediates and those bound to accelerating or misdirecting CAMs. Models based on tertiary structural properties of the Cp149 tetramers and their inter-dimer orientation, as

SUBMITTER: Pavlova A 

PROVIDER: S-EPMC11839048 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature