Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach
Fig 3
Adaptive scaling of contributions from force-field and cryo-EM density data overcomes potential energy barriers without excessive work input.
a Adaptive force scaling heuristically balances force-field and density influence during refinement simulations. b Particle in energy landscape where density similarity increases from left to right along the black curve. For the upper leg alternative, the similarity decreases despite biasing forces (burgundy arrow), which causes the bias strength to be increased. Conversely, in a scenario where the similarity remains high (lower leg), the biasing force will gradually be reduced to allow the system to better sample the local landscape. c Brownian diffusion in a potential with fixed (grey) and adaptive (burgundy) biasing forces, respectively. The constant biasing force is scaled such that both force-adding schemes yield the same average mean first passage time moving from left to right. The relative-entropy approach leads to significantly lower exerted work on the system (area under the grey and burgundy curves, respectively), which reduces perturbation of the dynamics of the system.