An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification

TL;DR

This paper introduces FAML, an integrated method combining fast alignment and maximum-likelihood techniques to improve the accuracy and robustness of subtomogram averaging and classification in cellular electron cryo-tomography, especially under noise and missing data.

Contribution

The paper presents FAML, a novel method that integrates fast alignment with maximum-likelihood estimation, enhancing robustness and efficiency in subtomogram analysis for CECT.

Findings

FAML outperforms previous methods in noise robustness.

FAML requires fewer subtomograms for accurate results.

FAML is computationally feasible and improves initial model construction.

Abstract

Motivation: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at submolecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances. However, the structural complexity and imaging limits complicate the systematic de novo structural recovery and recognition of these macromolecular complexes. Efficient and accurate reference-free subtomogram averaging and classification represent the most critical tasks for such analysis. Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based methods rely on integration operations, which are in principle computationally infeasible for accurate calculation. Results: Built on existing works, we propose an integrated method, Fast Alignment Maximum Likelihood method (FAML), which uses fast subtomogram alignment to sample sub-optimal rigid transformations. The transformations are then used to approximate integrals for maximum-likelihood update of subtomogram averages through expectation-maximization algorithm. Our tests on simulated and experimental subtomograms showed that, compared to our previously developed fast alignment method (FA), FAML is significantly more robust to noise and missing wedge effects with moderate increases of computation cost.Besides, FAML performs well with significantly fewer input subtomograms when the FA method fails. Therefore, FAML can serve as a key component for improved construction of initial structural models from macromolecules captured by CECT.