Biophysics-Guided Lead Discovery of HBV Capsid Assembly Modifiers

Machine Learning of Molecular Dynamics Simulations Provides Insights into the Modulation of Viral Capsid Assembly

An effective approach in the development of novel antivirals is to target the assembly of viral capsids by 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 noncapsid-like particles. Previous molecular dynamics (MD) simulations of early capsid-assembly intermediates showed differences in protein conformations for the 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 interdimer orientation, as well as models based on direct and inverse contact distances between protein residues, were tested. All models distinguished the apo states and the two CAM-bound states with high accuracy. Furthermore, tertiary structure models and residue-distance models highlighted different tetramer regions as being important for classification. Both models can be used to better understand structural transitions that govern the assembly of nucleocapsids and to assist in the development of more potent CAMs. Finally, we demonstrate the utility of classification ML methods in comparing MD trajectories and describe our ML approaches, which can be extended to other systems of interest.

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

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 system 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.

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

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 well as models based on direct and inverse contact distances between protein residues, were tested. All models distinguished the apo states and the two CAM-bound states with high accuracy. Furthermore, tertiary structure models and residue-distance models highlighted different tetramer regions as important for classification. Both models can be used to better understand structural transitions that govern the assembly of nucleocapsids and to assist the development of more potent CAMs. Finally, we demonstrate the utility of classification ML methods in comparing MD trajectories and describe our ML approaches, which can be extended to other systems of interest.

Modeling Viral Capsid Assembly: A Review of Computational Strategies and Applications

Viral capsid assembly is a complex and critical process, essential for understanding viral behavior, evolution, and the development of antiviral treatments, vaccines, and nanotechnology. Significant progress in studying viral capsid assembly has been achieved through various computational approaches, including molecular dynamics (MD) simulations, stochastic dynamics simulations, coarse-grained (CG) models, electrostatic analyses, lattice models, hybrid techniques, machine learning methods, and kinetic models. Each of these techniques offers unique advantages, and by integrating these diverse computational strategies, researchers can more accurately model the dynamic behaviors and structural features of viral capsids, deepening our understanding of the assembly process. This review provides a comprehensive overview of studies on viral capsid assembly, emphasizing their critical role in advancing our knowledge. It examines the contributions, strengths, and limitations of different computational methods, presents key computational works in the field, and analyzes milestone studies that have shaped current research.

Novel mechanistic insights - A brand new Era for anti-HBV drugs

Hepatitis B Virus (HBV) remains a critical global health issue, with substantial morbidity and mortality. Current therapies, including interferons and nucleoside analogs, often fail to achieve complete cure or functional eradication. This review explores recent advances in anti-HBV agents, focusing on their innovative mechanisms of action. HBV entry inhibitors target the sodium taurocholate cotransporting polypeptide (NTCP) receptor, impeding viral entry, while nucleus translocation inhibitors disrupt key viral life cycle steps, preventing replication. Capsid assembly modulators inhibit covalently closed circular DNA (cccDNA) formation, aiming to eradicate the persistent viral reservoir. Transcription inhibitors targeting cccDNA and integrated DNA offer significant potential to suppress HBV replication. Immunomodulatory agents are highlighted for their ability to enhance host immune responses, facil-itating better control and possible eradication of HBV. These novel approaches represent significant advancements in HBV therapy, providing new strategies to overcome current treatment limitations. The development of cccDNA reducers is particularly critical, as they directly target the persistent viral reservoir, offering a promising pathway towards achieving a functional cure or complete viral eradication. Continued research in this area is essential to advance the effectiveness of anti-HBV therapies.

Viral Protein Dimerization Quality Control: A Design Strategy for a Potential Viral Inhibitor

The global pharmaceutical market has been profoundly impacted by the coronavirus pandemic, leading to an increased demand for specific drugs. Consequently, drug resistance has prompted continuous innovation in drug design strategies to effectively combat resistant pathogens or disease variants. Protein dimers play crucial roles in vivo, including catalytic reactions, signal transduction, and structural stability. The site of action for protein dimerization modulators typically does not reside within the active site of the protein, thereby potentially impeding resistance development. Therefore, harnessing viral protein dimerization modulators could represent a promising avenue for combating viral infections. In this Perspective, we provide a detailed introduction to the design principles and applications of dimerization modulators in antiviral research. Furthermore, we analyze various representative examples to elucidate their modes of action while presenting our perspective on dimerization modulators along with the opportunities and challenges associated with this groundbreaking area of investigation.