On the curvature and relaxation of microtubule plus-end tips.

Microtubules are essential cytoskeletal components with a broad range of functions in which the structure and dynamics of their plus-end tips play critical roles. Existing mechanistic models explain the tips curving dynamics in different ways: the allosteric model suggests that GTP hydrolysis induces conformational changes in tubulin subunits that destabilize the lattice, leading to protofilament curving and depolymerization, while the lattice model posits that GTP hydrolysis directly destabilizes the microtubule lattice. However, the effect of GTP hydrolysis on the curving dynamics of microtubule tips remains incompletely understood.

On the Curvature and Relaxation of Microtubule Plus-end Tips.

Microtubules are essential cytoskeletal components with a broad range of functions in which the structure and dynamics of their plus-end tips play critical roles. Existing mechanistic models explain the tips curving dynamics in different ways: the allosteric model suggests that GTP hydrolysis induces conformational changes in tubulin subunits that destabilize the lattice, leading to protofilament curving and depolymerization, while the lattice model posits that GTP hydrolysis directly destabilizes the microtubule lattice. However, the effect of GTP hydrolysis on the curving dynamics of microtubule tips remains incompletely understood.

Data-driven equation-free dynamics applied to many-protein complexes: The microtubule tip relaxation.

Microtubules (MTs) constitute the largest components of the eukaryotic cytoskeleton and play crucial roles in various cellular processes, including mitosis and intracellular transport. The property allowing MTs to cater to such diverse roles is attributed to dynamic instability, which is coupled to the hydrolysis of guanosine-5'-triphosphate (GTP) to guanosine-5'-diphosphate (GDP) within the β-tubulin monomers. Understanding the dynamics and structural features of both GDP- and GTP-complexed MT tips, especially at an all-atom level, remains challenging for both experimental and computational methods because of their dynamic nature and the prohibitive computational demands of simulating large, many-protein systems.

-Means Clustering Coarse-Graining (KMC-CG): A Next Generation Methodology for Determining Optimal Coarse-Grained Mappings of Large Biomolecules.

Coarse-grained (CG) molecular dynamics (MD) has become a method of choice for simulating various large scale biomolecular processes; therefore, the systematic definition of the CG mappings for biomolecules remains an important topic. Appropriate CG mappings can significantly enhance the representability of a CG model and improve its ability to capture critical features of large biomolecules. In this work, we present a systematic and more generalized method called -means clustering coarse-graining (KMC-CG), which builds on the earlier approach of essential dynamics coarse-graining (ED-CG).