Interfering with the self-assembly of viral nucleocapsids has been shown to be an effective approach in the development of novel antivirals. Several classes of capsid assembly modulators (CAMs) targeting Hepatitis B virus (HBV) have been discovered. These CAMs target the virus by either accelerating nucleocapsid assembly or misdirecting it into non-capsid-like particles. In our previous studies we have performed molecular dynamics simulations of early nucleocapsid assembly intermediates, such as tetramers of the core protein Cp149, with and without bound CAMs. Distinct conformations of the intermediates were observed depending on whether the bound CAM accelerates or misdirects assembly.  In this work, we have developed and tested several machine learning models based on both tertiary structure properties of the tetramer and residue-residue contacts in order to distinguish between the apo-tetramer structures and those bound to accelerating or misdirecting CAMs. Novel insights into the structural differences between apo and CAM-bound intermediates are obtained. These models can be used to better understand structural transitions that govern nucleocapsid assembly as well as for selection of structures from molecular dynamics simulations for subsequent docking.

Predicting the mechanism of action of HBV capsid assembly modulators from a combination of molecular dynamics and machine learning

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.