Novel machine learning approaches for improving structural discrimination in cryo-electron tomography-Administrative Supplement

项目总结

Project Summary Cellular cryo-electron tomography (Cryo-ET) has made possible the observation of cellular organelles and macromolecular complexes at nanometer resolution with native conformations. The rapid increasing amount of Cryo-ET data available however brings along some major challenges for analysis which we will timely address in this proposal. We will design novel data- driven machine learning algorithms for improving structural discrimination and resolution. In particular, we have the following specific aims: (1) We will develop a novel Autoencoder and Iterative region Matching (AIM) algorithm for marker-free alignment of image tilt-series to reconstruct tomograms with improved resolution; (2) We will develop a saliency-based auto- picking algorithm for better detecting macromolecular complexes, and combine it with an innovative 2D-to-3D framework to further improve structure detection accuracy; (3) We will design an end-to-end convolutional model for pose-invariant clustering of subtomograms. This model will produce an initial clustering which will be refined by a new subtomogram averaging algorithm that automatically down weights subtomograms of noise and little contribution; (4) We will perform experimental evaluations by using previously reported bacterial secretion systems and mitochondrial ultrastructures datasets to improve the final resolution. Implementing algorithms in Aims 1-3, we will develop a user-friendly open-source graphical user interface α-tom to directly benefit the scientific community. α-tom will be systematically compared with existing software including IMOD, EMAN2, and Relion on simulated and benchmark datasets. To facilitate distribution, α-tom will be integrated into existing software platforms Scipion and TomoMiner. Our data-driven algorithms and software not only will facilitate and accelerate the future use of Cryo- ET, but also can be readily used on analyzing the existing large amounts of Cryo-ET data to improve our understanding of the structure, function, and spatial organization of macromolecular complexes in situ.