Constraint methods that accelerate free-energy simulations of biomolecules

Abstract

Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann’s law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules. One way to speed up physical simulations is by coarse-graining the potential function. Another way is to harness structural knowledge, often by imposing spring-like restraints. But harnessing external knowledge in physical simulations is problematic because knowledge, data, or hunches have errors, noise, and combinatoric uncertainties. Here, we review recent principled methods for imposing restraints to speed up physics-based molecular simulations that promise to scale to larger biomolecules and motions.

Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation

“…Furthermore, we grouped all nonbonded interactions between amino acids into a single contact potential. A more refined functional form that differentiates short-range direct contacts from long-range interactions mediated by water molecules may prove beneficial. , Additionally, generalized maximum entropy algorithms and Bayesian approaches − can be incorporated into the force field optimization procedure to better account for errors and uncertainty in experimental data. More advanced algorithms that maximize the ratio of the folding temperature versus the glass-transition temperature can also be adopted to better sculpt the funneled energy landscape for globular proteins …”