A molecular prior distribution for Bayesian inference based on Wilson statistics.

ABSTRACT:

Background and objective

Wilson statistics describe well the power spectrum of proteins at high frequencies. Therefore, it has found several applications in structural biology, e.g., it is the basis for sharpening steps used in cryogenic electron microscopy (cryo-EM). A recent paper gave the first rigorous proof of Wilson statistics based on a formalism of Wilson's original argument. This new analysis also leads to statistical estimates of the scattering potential of proteins that reveal a correlation between neighboring Fourier coefficients. Here we exploit these estimates to craft a novel prior that can be used for Bayesian inference of molecular structures.

Methods

We describe the properties of the prior and the computation of its hyperparameters. We then evaluate the pri

SUBMITTER: Gilles MA 

PROVIDER: S-EPMC9233040 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature