A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape

Elucidating the interactions between Kinesin-5/BimC and the microtubule: insights from TIRF microscopy and molecular dynamics simulations

Kinesin-5 s are bipolar motor proteins that contribute to cell division by crosslinking and sliding apart antiparallel microtubules inside the mitotic spindle. However, the mechanism underlying the interactions between kinesin-5 and the microtubule remains poorly understood. In this study, we investigated the binding of BimC, a kinesin-5 motor from Aspergillus nidulans, to the microtubule using a combination of total internal reflection fluorescence (TIRF) microscopy and molecular dynamics (MD) simulations. TIRF microscopy experiments revealed that increasing the concentration of KCl in the motility buffer from 0 mM to 150 mM completely abolishes the ability of BimC to bind to the microtubule. Consistent with this experimental finding, MD simulations demonstrated a significant reduction in the strength of electrostatic interactions between BimC and microtubules at 150 mM KCl compared to 0 mM KCl. Furthermore, we identified several salt bridges at the BimC-microtubule interface, with positively charged residues on BimC interacting with negatively charged residues on the tubulin heterodimer. These results provide mechanistic insights into the role of electrostatic interactions in kinesin-5-microtubule binding, advancing our understanding of the molecular underpinnings of kinesin-5 motility.

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.