Browsing by Author "Bartesaghi, Alberto"
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Item Embargo Computational methods for high-resolution structure determination of macromolecular complexes imaged in situ using cryo-electron tomography(2024) Liu, Hsuan-FuCryo-electron tomography (CET) combined with sub-volume averaging (SVA) is a powerful imaging technique for determining macromolecular structures in situ. To resolve structures at high-resolution, large numbers of volumes containing copies of the protein of interest are aligned and averaged in three dimensions. Using this strategy, the structures of highly ordered virus capsid proteins and large ribosomes have been resolved at near-atomic resolution. However, CET studies of proteins of lower molecular weight (<1000 kDa) or targets present in their crowded native context have been limited to sub-nanometer resolutions. This is due to limitations in the accuracy of image alignment resulting from the low image contrast generated by the smaller scattering masses and the presence of overlapping objects in the cellular environment. While recent advances in high-throughput tomography that use beam image-shift accelerated data acquisition (BISECT) allow producing enough data for SVA, demanding storage and processing requirements associated with analyzing large numbers of particles often make structure determination impractical. The overarching goal of this thesis is to build a computational framework for CET/SVA structural determination that streamlines and extends the applicability of the technique to a wider class of biomedically-relevant targets while improving the resolution of structures to near-atomic resolution. To achieve this, this thesis focuses on: (1) filling gaps in the current CET data analysis workflow by designing a comprehensive end-to-end platform for SVA, (2) improving the resolution of structures by developing methods for improved alignment of protein images and better extraction of high-resolution information, and (3) validating our workflows by determining low-molecular weight structures and native membrane-bound proteins at near-atomic resolution. To routinely convert raw tilt-series into high-resolution structures, we developed high-throughput data collection approach, implemented robust strategies for tilt-series alignment and particle picking, and designed a scalable platform for distributed image analysis that makes analysis of large datasets feasible. To improve resolution, we used a constrained image alignment approach that uses parameters from the tilt geometry to overcome the low contrast and crowdedness of tomographic data. In addition, we efficiently recovered high-resolution signal contained in the raw data using per-tilt CTF correction and data-driven exposure weighting. These advances allowed the structure determination of low-molecular weight complexes such as dGTPase (300-kDa) and of immature human endogenous retrovirus K (HERV-K) Gag and immature human immunodeficiency virus 1 (HIV-1) Gag at near-atomic resolution. Our methods for CET/SVA allowed routine determination of structures of biomedically important targets both in-vitro and in situ at high enough resolution to elucidate mechanistic details governing virus assembly and infection. These advances will represent an important step towards closing the resolution gap between high-resolution strategies used to study molecular assemblies reconstituted in-vitro and techniques for in situ structure determination.
Item Open Access Deep Learning Algorithms for Automating and Accelerating the Cryo-EM Data Processing Pipeline(2023) Huang, QinwenCryo-electron microscopy (cryo-EM) has solidified its position in the structural biology field as an invaluable method for achieving near-atomic resolution of macro-molecular structures in their native conditions. However, the inherently fragile nature of biological samples imposes stringent limitations on the electron doses that can be used during imaging, resulting in data characterized by notably low signal-to-noise ratios (SNR). To obtain a three-dimensional (3D) representation of these biological entities, substantial volumes of data need to be acquired and averaged in 3D to remove noise and improve resolution. The cryo-EM structure determination workflow involves many intricate steps, starting with sample preparation and vitrification, progressing to sample screening and data collection. During data analysis, macromolecular structures-of-interest need to be accurately identified and localized before they can be used for 3D reconstruction. A key challenge in this process is the extensive manual intervention and time required to analyze the large volumes of data that are necessary to achieve high-resolution. In this thesis, we propose strategies that harness the capabilities of deep learning to accelerate and reduce manual intervention during the data acquisition and image processing pipelines, with the goal of automating and streamlining the determination of protein structures of biomedical relevance.
To improve the efficiency of data collection, we introduce cryo-ZSSR, a deep-internal learning-based method that enables the determination of 3D structures at resolutions surpassing the limits imposed by the imaging system. By combining low magnification imaging with in-silico image super-resolution (SR), cryo-ZSSR accelerates cryo-EM data collection by allowing to include more particles in each exposure without sacrificing resolution. To mitigate the need for manual intervention and further streamline sample screening and data collection, we develop the Smartscope framework which leverages deep learning-based navigation techniques to enable specimen screening in a fully automated manner, significantly increasing efficiency and reducing operational costs. For data processing downstream, we introduce deep-learning based detection algorithms to streamline and automate particle identification both in 2D - single particle analysis (SPA), and 3D - cryo-electron tomography (CET). Our approach enables precise detection of proteins-of-interest with minimal human intervention while reducing detection time from days to minutes, allowing the analysis of larger datasets than previously possible.
Collectively, we show these methods substantially boost the efficiency of cryo-EM data acquisition and help streamline the SPA and CET image analysis pipelines, paving the way for the development of high-throughput strategies for high-resolution structure determination of biomolecules. We conclude this thesis by discussing the potential benefits and shortcomings of using deep learning-based algorithms in cryo-EM image analysis tasks.
Item Open Access Fab-dimerized glycan-reactive antibodies are a structural category of natural antibodies.(Cell, 2021-05-18) Williams, Wilton B; Meyerhoff, R Ryan; Edwards, RJ; Li, Hui; Manne, Kartik; Nicely, Nathan I; Henderson, Rory; Zhou, Ye; Janowska, Katarzyna; Mansouri, Katayoun; Gobeil, Sophie; Evangelous, Tyler; Hora, Bhavna; Berry, Madison; Abuahmad, A Yousef; Sprenz, Jordan; Deyton, Margaret; Stalls, Victoria; Kopp, Megan; Hsu, Allen L; Borgnia, Mario J; Stewart-Jones, Guillaume BE; Lee, Matthew S; Bronkema, Naomi; Moody, M Anthony; Wiehe, Kevin; Bradley, Todd; Alam, S Munir; Parks, Robert J; Foulger, Andrew; Oguin, Thomas; Sempowski, Gregory D; Bonsignori, Mattia; LaBranche, Celia C; Montefiori, David C; Seaman, Michael; Santra, Sampa; Perfect, John; Francica, Joseph R; Lynn, Geoffrey M; Aussedat, Baptiste; Walkowicz, William E; Laga, Richard; Kelsoe, Garnett; Saunders, Kevin O; Fera, Daniela; Kwong, Peter D; Seder, Robert A; Bartesaghi, Alberto; Shaw, George M; Acharya, Priyamvada; Haynes, Barton FNatural antibodies (Abs) can target host glycans on the surface of pathogens. We studied the evolution of glycan-reactive B cells of rhesus macaques and humans using glycosylated HIV-1 envelope (Env) as a model antigen. 2G12 is a broadly neutralizing Ab (bnAb) that targets a conserved glycan patch on Env of geographically diverse HIV-1 strains using a unique heavy-chain (VH) domain-swapped architecture that results in fragment antigen-binding (Fab) dimerization. Here, we describe HIV-1 Env Fab-dimerized glycan (FDG)-reactive bnAbs without VH-swapped domains from simian-human immunodeficiency virus (SHIV)-infected macaques. FDG Abs also recognized cell-surface glycans on diverse pathogens, including yeast and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike. FDG precursors were expanded by glycan-bearing immunogens in macaques and were abundant in HIV-1-naive humans. Moreover, FDG precursors were predominately mutated IgM+IgD+CD27+, thus suggesting that they originated from a pool of antigen-experienced IgM+ or marginal zone B cells.Item Open Access Structure and dynamics of the Arabidopsis O-fucosyltransferase SPINDLY.(Nature communications, 2023-03) Kumar, Shivesh; Wang, Yan; Zhou, Ye; Dillard, Lucas; Li, Fay-Wei; Sciandra, Carly A; Sui, Ning; Zentella, Rodolfo; Zahn, Emily; Shabanowitz, Jeffrey; Hunt, Donald F; Borgnia, Mario J; Bartesaghi, Alberto; Sun, Tai-Ping; Zhou, PeiSPINDLY (SPY) in Arabidopsis thaliana is a novel nucleocytoplasmic protein O-fucosyltransferase (POFUT), which regulates diverse developmental processes. Sequence analysis indicates that SPY is distinct from ER-localized POFUTs and contains N-terminal tetratricopeptide repeats (TPRs) and a C-terminal catalytic domain resembling the O-linked-N-acetylglucosamine (GlcNAc) transferases (OGTs). However, the structural feature that determines the distinct enzymatic selectivity of SPY remains unknown. Here we report the cryo-electron microscopy (cryo-EM) structure of SPY and its complex with GDP-fucose, revealing distinct active-site features enabling GDP-fucose instead of UDP-GlcNAc binding. SPY forms an antiparallel dimer instead of the X-shaped dimer in human OGT, and its catalytic domain interconverts among multiple conformations. Analysis of mass spectrometry, co-IP, fucosylation activity, and cryo-EM data further demonstrates that the N-terminal disordered peptide in SPY contains trans auto-fucosylation sites and inhibits the POFUT activity, whereas TPRs 1-5 dynamically regulate SPY activity by interfering with protein substrate binding.