title: 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.
authors.affiliation: Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.
authors.forename: Alberto
authors.initials: A
authors.lastname: Perez
authors.affiliation: Department of Chemistry, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
authors.forename: Justin L
authors.initials: JL
authors.lastname: MacCallum
authors.affiliation: Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.
authors.forename: Evangelos A
authors.initials: EA
authors.lastname: Coutsias
authors.affiliation: Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA.
authors.forename: Ken A
authors.initials: KA
authors.lastname: Dill
mesh_terms: D056004:Molecular Dynamics Simulation; D019906:Nuclear Magnetic Resonance, Biomolecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
chemical_list: D011506:Proteins