Abstract
Advances in experimental and computational techniques allow us to study the structure and dynamics of large biomolecular assemblies at increasingly higher resolution. However, with increasing structural detail it can be challenging to unravel the mechanism underlying the function of molecular machines. One reason is that atomistic simulations become computationally prohibitive. Moreover it is difficult to rationalize the functional mechanism of systems composed of tens of thousands to millions of atoms by following each atom’s movements. Coarse graining (CG) allows us to understand biological structures from a hierarchical perspective and to gradually zoom into the adequate level of structural detail. This article introduces a Bayesian approach for coarse graining biomolecular structures. We develop a probabilistic model that aims to represent the shape of an experimental structure as a cloud of bead particles. The particles interact via a pairwise potential whose parameters are estimated along with the bead positions and the CG mapping between atoms and beads. Our model can also be applied to density maps obtained by cryo-electron microscopy. We illustrate our approach on various test systems.
Highlights
Biomolecular processes occur on many spatial and temporal scales [1]
Our model can be applied to density maps obtained by cryo-electron microscopy
The crystal structure comprises 13341 heavy atom positions, which we approximate by Coarse graining (CG) particles
Summary
An expanding array of experimental methods allows us to study the structure and dynamics of biological systems with increasing throughput and precision. Computer simulations must often complement experiments to gain a quantitative understanding of the biological mechanism. Molecular dynamics (MD) has developed into a powerful tool to study biomolecular systems with atomic detail [2, 3]. Typically there is a gap of several orders of magnitude between the atomic scale and the length and time scales that are biologically relevant. The computational burden posed by atomistic simulations becomes prohibitive for large biomolecular systems such as protein complexes. A remedy is provided by coarse graining (CG) approaches that reduce the system’s complexity by lumping together atoms into pseudoatoms or beads [4,5,6]
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