Common lines modeling for reference free ab-initio reconstruction in cryo-EM

TL;DR

This paper introduces a globally optimal, reference-free algorithm for ab-initio reconstruction in cryo-EM that is robust to noise and can handle thousands of images without bias, producing reliable initial models.

Contribution

The paper presents a novel algorithm that finds the global optimum for orientation assignment in cryo-EM, overcoming local optima issues and enabling accurate, unbiased initial model reconstruction.

Findings

Achieves 20Å resolution or better in ab-initio models.

Robust to noise and applicable to thousands of images.

Effective even with as few as three images per class.

Abstract

We consider the problem of estimating an unbiased and reference-free ab-inito model for non-symmetric molecules from images generated by single-particle cryo-electron microscopy. The proposed algorithm finds the globally optimal assignment of orientations that simultaneously respects all common lines between all images. The contribution of each common line to the estimated orientations is weighted according to a statistical model for common lines' detection errors. The key property of the proposed algorithm is that it finds the global optimum for the orientations given the common lines. In particular, any local optima in the common lines energy landscape do not affect the proposed algorithm. As a result, it is applicable to thousands of images at once, very robust to noise, completely reference free, and not biased towards any initial model. A byproduct of the algorithm is a set of measures that allow to asses the reliability of the obtained ab-initio model. We demonstrate the algorithm using class averages from two experimental data sets, resulting in ab-initio models with resolutions of 20A or better, even from class averages consisting of as few as three raw images per class.