Data-guided Multi-Map variables for ensemble refinement of molecular movies

Abstract

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the 3-dimensional electron density of a protein, as it would be deter-mined by cryo-EM or X-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work uses the recently developed Multi-Map methodology to monitor concerted move-ments within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along reaction coordinates derived from electron densities can serve to induce conformational transitions between known intermediates. The simulated path-ways appear reversible, with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein DataBank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution (∼5 Å) density information cou-pled to comparatively large structural transitions. Practical considerations for generating transitions with multiple intermediate atomic density distributions are also discussed.

Human learning for molecular simulations: the Collective Variables Dashboard in VMD

“…This can be particularly advantageous when the maps themselves are being prepared in VMD, such as the case of Multi-Map CVs based on protein density maps, as exemplified in ref. 34 Additionally, the Dashboard's graphical user interface allows for one-click selection and visualization of the maps themselves, consistent with how groups of atoms and gradient vectors are also selected and visualized (see Table 1). Figure 12 shows molecular renders including graphical representations of the volumetric maps generated through the Dashboard.…”

“…Beyond inducing shape changes in membranes 31 or proteins, 34 a useful biological application of volumetric map-based CVs is enhancing the sampling of different wetting states in a confined protein cavity. A simplified example of this was already shown in ref.…”

Protein Conformational States—A First Principles Bayesian Method

“…Adenylate kinase (ADK) converts ATP, ADP, and AMP by closing around substrate molecules [20]. The transition from closed to open was simulated in Reference [5] using steered molecular dynamics on a reaction coordinate interpolating between the electron density maps of PDB IDs 1AKE (Reference [20], closed) and 4AKE (Reference [2], open). The simulation data we used did not contain ligands, but did contain water and ions.…”

“…A chaperone protein assists transformation of large, hydrophobic proteins from initial, linear, to folded shapes [4]. X-ray and cryo electron-microscopy reveals conformations with small motions on the 1-5 Ångstrom level for those proteins that crystallize [5]. Neutron scattering and nuclear magnetic resonance structures of room temperature proteins show greater shape variability, but are usually able to classify structures into a few 'canonical' structures.…”

Protein Conformational States: A First Principles Bayesian Method

“…[4] X-ray and cryo electron-microscopy reveals conformations with small motions on the 1-5 Ångstrom level for those proteins that crystallize. [5] Neutron scattering and nuclear magnetic resonance structures of room temperature proteins show greater shape variability, but are usually able to classify structures into a few 'canonical' structures.…”