A structure-based mechanism for initiation of AP-3 coated vesicle formation.

Adaptor protein complex 3 (AP-3) mediates cargo sorting from endosomes to lysosomes and lysosome-related organelles. Recently, it was shown that AP-3 is in a constitutively open, active conformation compared to the related AP-1 and AP-2 coat complexes, which are inactive until undergoing large conformational changes upon membrane recruitment. How AP-3 is regulated is therefore an open question. To understand the mechanism of AP-3 membrane recruitment and activation, we reconstituted the core of human AP-3 and determined multiple structures in the soluble and membrane-bound states using electron cryo-microscopy (cryo-EM). Similar to yeast AP-3, human AP-3 is in a constitutively open conformation, with the cargo-binding domain of the µ3 subunit conformationally free. To reconstitute AP-3 activation by the small GTPase Arf1, we used lipid nanodiscs to build Arf1-AP-3 complexes on membranes and determined three structures that show the stepwise conformational changes required for formation of AP-3 coated vesicles. First, membrane-recruitment is driven by one of two predicted Arf1 binding sites on AP-3. In this conformation, AP-3 is flexibly tethered to the membrane and its cargo binding domain remains conformationally dynamic. Second, cargo binding causes AP-3 to adopt a fixed position and rigidifies the complex, which stabilizes binding for a second Arf1 molecule. Finally, binding of the second Arf1 molecule provides the template for AP-3 dimerization, providing a glimpse into the first step of coat polymerization. We propose coat polymerization only occurs after cargo engagement, thereby linking cargo sorting with assembly of higher order coat structures. Additionally, we provide evidence for two amphipathic helices in AP-3, suggesting that AP-3 contributes to membrane deformation during coat assembly. In total, these data provide evidence for the first stages of AP-3 mediated vesicle coat assembly.

Applying 3D ED/MicroED workflows toward the next frontiers.

We report on the latest advancements in Microcrystal Electron Diffraction (3D ED/MicroED), as discussed during a symposium at the National Center for CryoEM Access and Training housed at the New York Structural Biology Center. This snapshot describes cutting-edge developments in various facets of the field and identifies potential avenues for continued progress. Key sections discuss instrumentation access, research applications for small molecules and biomacromolecules, data collection hardware and software, data reduction software, and finally reporting and validation. 3D ED/MicroED is still early in its wide adoption by the structural science community with ample opportunities for expansion, growth, and innovation.

Cryo-Electron Microscopy Screening Automation across Multiple Grids using Smart Leginon.

Advancements in cryo-electron microscopy (cryoEM) techniques over the past decade have allowed structural biologists to routinely resolve macromolecular protein complexes to near-atomic resolution. The general workflow of the entire cryoEM pipeline involves iterating between sample preparation, cryoEM grid preparation, and sample/grid screening before moving on to high-resolution data collection. Iterating between sample/grid preparation and screening is typically a major bottleneck for researchers, as every iterative experiment must optimize for sample concentration, buffer conditions, grid material, grid hole size, ice thickness, and protein particle behavior in the ice, amongst other variables. Furthermore, once these variables are satisfactorily determined, grids prepared under identical conditions vary widely in whether they are ready for data collection, so additional screening sessions prior to selecting optimal grids for high-resolution data collection are recommended. This sample/grid preparation and screening process often consumes several dozen grids and days of operator time at the microscope. Furthermore, the screening process is limited to operator/microscope availability and microscope accessibility. Here, we demonstrate how to use Leginon and Smart Leginon Autoscreen to automate the majority of cryoEM grid screening. Autoscreen combines machine learning, computer vision algorithms, and microscope-handling algorithms to remove the need for constant manual operator input. Autoscreen can autonomously load and image grids with multi-scale imaging using an automated specimen-exchange cassette system, resulting in unattended grid screening for an entire cassette. As a result, operator time for screening 12 grids may be reduced to ~10 min with Autoscreen compared to ~6 h using previous methods which are hampered by their inability to account for high variability between grids. This protocol first introduces basic Leginon setup and functionality, then demonstrates Autoscreen functionality step-by-step from the creation of a template session to the end of a 12-grid automated screening session.

Fully automated multi-grid cryoEM screening using Smart Leginon.

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.