Automated Workflows

Automated repeat-target structure determination

Single particle cryo-EM adoption has been transformative for structural biology and drug discovery, and advances in instrumentation and data collection continue to increase the rate at which datasets can be acquired. Cryo-EM data processing has also advanced quickly, and in CryoSPARC, processing is rapid and workflows can be highly streamlined. However, achieving high-resolution results often still requires extensive human intervention, which can become a bottleneck particularly in high-throughput settings such as structure-based drug design, where dozens or hundreds of related structures must be determined rapidly to inform drug development projects. Automated cryo-EM data processing is therefore a crucial development for cryo-EM: by removing the requirement for manual decision-making at each stage of analysis, automation can reduce time-to-structure from days to hours, enable parallel processing of large numbers of datasets, lower the barrier to entry for new practitioners, and ensure reproducible, consistent results across experiments.

In the pages below, we describe the development of end-to-end automation strategies using new tools and Workflows built in CryoSPARC for “repeat-target” structure determination. Our results demonstrate that it is now possible to completely automate the data processing workflow for repeat-target scenarios, and to obtain a high quality particle stack, consensus reconstruction, and local refinement of important regions such as a ligand binding site. Guidance is provided such that users can replicate, adapt, and extend our automated workflows to their own targets.

Advanced automated data processing: a case study using CAK

In this case study, we demonstrate how to extend the automation strategy to handle severe preferred orientation and produce improved map quality.

Fully automated data processing: a case study using GPCRs

In this preprint, we demonstrate an automation strategy with a test set of 21 challenging GPCR datasets.

Practical tips: using Workflows to process your own data

The ins-and-outs of how to create, modify, and apply workflows.

Downloads: Workflow JSONs, sample volumes and masks

Need the files from any of the case studies above? Everything is aggregated here.

Processing times from automated workflow examples

Get an idea of the amount of time it takes to process a dataset in an automated manner.