A Molecular Prior Distribution for Bayesian Inference Based on Wilson Statistics

TL;DR

This paper introduces a novel Bayesian prior based on Wilson statistics for molecular structure inference, demonstrating improved noise suppression and resolution in cryo-EM reconstructions across various SNR levels.

Contribution

It develops a new prior leveraging Wilson statistics estimates, enhancing Bayesian inference in cryo-EM by effectively suppressing noise and improving resolution.

Findings

The prior suppresses noise effectively.

It enhances resolution in low SNR conditions.

Fourier Shell Correlation curves are less sensitive to masking.

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 prior on two synthetic linear inverse problems, and compare against a popular prior in cryo-EM reconstruction at a range of SNRs. Results: We show that the new prior effectively suppresses noise and fills-in low SNR regions in the spectral domain. Furthermore, it improves the resolution of estimates on the problems considered for a wide range of SNR and produces Fourier Shell Correlation curves that are insensitive to masking effects. Conclusions: We analyze the assumptions in the model, discuss relations to other regularization strategies, and postulate on potential implications for structure determination in cryo-EM.