Sawh-Gopal, A. | Search bioRxiv

bioRxiv2026

AAV2 Crosslinks Actin Filaments: Implications for AAV Gene Therapy Vector Design

Adeno-associated virus (AAV) capsids are important gene therapy vectors, allowing for the one-time treatment of monogenetic disorders, with durable gene expression lasting for years. However, despite the clinical success of AAV usage, low transduction efficiencies require high dosage to achieve therapeutic efficacy, resulting in prohibitive costs and rare, but life-threatening immune responses. One key knowledge gap is how the capsids traffic to the nucleus following endocytosis. Here, we identify a direct interaction between AAV2 and actin filaments. Our results show that AAV2 bundles multiple actin filaments in a highly periodic hexagonal lattice. Therefore, we propose that AAV2 is the founding member of a new class of actin binding proteins, the actin bundling viruses (ABVs). Using cryogenic electron microscopy, we determined the structure of AAV2-actin filaments, identified the interaction interfaces, and engineered capsid variants that no longer bundle actin. Together, our results suggest that the AAV2-actin interaction may be responsible for trapping capsids and mediating peri-nuclear accumulation. Overall, this interaction expands the current understanding of AAV2 biology and offers new directions for capsid engineering.

biochemistry↗

bioRxiv2023

Smart Leginon enables cryoEM screening automation across multiple grids

SummaryCryoEM multi-grid screening is often a tedious process that demands hours of attention. Here, this protocol shows how to set up standard Leginon collection and Smart Leginon Autoscreen to automate this process. This protocol can be applied to the majority of cryoEM holey foil grids. 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 minutes with Autoscreen compared to [~]6 hours using previous methods which are hampered by their inability to account for high variability between grids. This protocol and video tutorial 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.

molecular biology↗