Entropy-based methods for formulating bottom-up ultra-coarse-grained models.

Bottom-up coarse-grained (CG) modeling is an effective means of bypassing the limited spatiotemporal scales of conventional atomistic molecular dynamics while retaining essential information from the atomistic model. A central challenge in CG modeling is the trade-off between accuracy and efficiency, as the inclusion of often pivotal many-body interaction terms in the CG force-field renders simulation markedly slower than simple pairwise models. The Ultra Coarse-Graining (UCG) method incorporates many-body terms through discrete internal state variables that modulate the CG force-field according to, e.

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 GTP (guanosine-5'-triphosphate) to GDP (guanosine-5'-diphosphate) within the β-tubulin monomers. Understanding the equilibrium dynamics and the 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.

On the emergence of machine-learning methods in bottom-up coarse-graining.

Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-grain' electronic structure have inspired similar applications to the thermodynamic coarse-graining of chemical and biological systems. In this review, we discuss the current viability and challenges in using machine-learning methods to represent coarse-grained force-fields, as well as the utility of machine-learning in various aspects of coarse-grained modeling.

Kinetic Implications of IP Anion Binding on the Molecular Switch of the HIV-1 Capsid Assembly.

HIV-1 capsid proteins (CA) self-assemble into a fullerene-shaped capsid, enabling cellular transport and nuclear entry of the viral genome. A structural switch comprising the Thr-Val-Gly-Gly (TVGG) motif either assumes a disordered coil or a 3 helix conformation to regulate hexamer or pentamer assembly, respectively. The cellular polyanion inositol hexakisphosphate (IP6) binds to a positively charged pore of CA capsomers rich in arginine and lysine residues mediated by electrostatic interactions.