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

Abstract

MotivationCellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular 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.ResultsBuilt 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.Availability and implementation http://www.cs.cmu.edu/mxu1

Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization

“…Subsequently, we used an MCO-A classification [12] with 10 initial classes and a seven-fold symmetry. The MCO-A method converged to three different class, whose result is consistent with those published previously in [8,12,27,28]. The central slices with each classification average resulting from the MCO-A classification are shown in Fig.…”

“…Computational analysis of cryo-electron tomography is an emerging field due to its inherent content complexity and imaging limits [27,[31][32][33][34][35][36][37]. Our method serves as a useful step towards improved systematic recovery of macromolecular structures captured by such tomograms.…”

“…Eliminating lower cross-correlation coefficients (e.g., CCC ≤0.42), the remainder of particles were chosen for subtomogram alignment and classification. The dataset of experimental ∼800kDa GroEL 14 and GroEL 14 /GroES 7 subtomograms complex basically conducted as a quasi-standard in the subtomogram alignment and classification's research [8,12,26,27].…”

Template-Based and Template-Free Approaches in Cellular Cryo-Electron Tomography Structural Pattern Mining

“…The task becomes more challenging when there are multiple classes of subtomograms, meaning that different subtomograms may contain different macromolecular complexes. Averaging as well as clustering need to be refined during each iteration (22)(23)(24).…”

Deep Learning-Based Strategy For Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography

“…Due to the limitations of data acquisition techniques, the native structural information of macromolecular complexes is extremely difficult to obtain [1]. With the development of biotechnology, Cryo-electron tomography (Cryo-ET) enables 3D visualization of cellular tissue in near-native state and sub-molecular resolution [2]- [4], making it a powerful tool for analyzing macromolecular complexes and their spatial organization within single cells [5].…”