  A free energy landscape estimation-method based on Bayesian inference is
presented and used for comparing the efficiency of thermally enhanced sampling
methods with respect to regular molecular dynamics, where the simulations are
carried out on two binding states of calmodulin. The proposed free energy
estimation method (the GM method) is compared to other estimators using a toy
model showing that the GM method provides a robust estimate not subject to
overfitting. The continuous nature of the GM method, as well as predictive
inference on the number of basis functions, provide better estimates on sparse
data. We find that the free energy diffusion proper- ties determine sampling
method effectiveness, such that the diffusion dominated apo-calmodulin is most
efficiently sampled by regular molecular dynamics, while the holo with its
rugged free energy landscape is better sampled by enhanced methods.
