cryoEDU: An online curriculum and software platform for hands-on learning in single-particle cryoEM and cryoET

NIH RePORTER · NIH · R25 · $122,839 · view on reporter.nih.gov ↗

Abstract

cryoEDU: An online curriculum and software platform for hands-on learning in single- particle cryoEM and cryoET SUMMARY Single-particle cryogenic electron microscopy (cryoEM) has become a mainstream structural biology technique due to reduced burden of sample preparation, the capacity to solve integral membrane protein complex structures, and the ability to visualize large structurally heterogeneous macromolecular complexes at high resolution. As cryoEM expands, the nascent field of cryogenic electron tomography (cryoET) continues to achieve milestones – such as structure determination in situ – indicating that cryoET will soon become a core technology for structural and cell biologists for both in vitro and in vivo structural characterizations. In both cryoEM and cryoET, new users face a significant educational barrier: practitioners must understand concepts that range from biochemistry to physics of electron microscopes to computational image alignment algorithms. Although there are cryoEM workshops currently available address these concepts with hands-on training, the limited capacity and intimate format results in substantial oversubscription and an inability to train new cryoEM users en masse. As such, there exists a significant gap in training materials and resources that help users understand, analyze, interpret, and validate cryoEM data and structure determination. Towards this end, we propose to build an online educational platform focused on cryoEM/ET data processing called ‘cryoEDU’. The cryoEDU platform is built on three pillars: 1) self-paced online modular curricula; 2) hands-on data analysis in a cloud desktop environment; and 3) community engagement to facilitate information exchange related to cryoEM/ET data analysis. Online educational curricula will incorporate real cryoEM/ET data into learning modules on specific topics in addition to evaluations that highlight best practices and common pitfalls to help train learners in rigor for cryoEM/ET analysis. To ensure adequate data analysis training, we will allow users to submit simulated jobs and interact with cryoEM software using a cloud desktop environment. Importantly, users will not be running real time algorithms, but instead, we will pull results from a precalculated results database containing nearly all conceivable outcomes. The cloud desktop will enable users to make parameter choices and receive immediate results feedback, creating a new paradigm in cryoEM/ET data analysis learning. Finally, we will engage with the cryoEM/ET community by including testimonials on our website, highlighting decisions made by experts when analyzing data, and recording live conversations with primary authors on important cryoEM/ET publications. We recognize that cryoEM and cryoET continue to grow at a fast pace, which is why we expect our content to evolve with changes in the field and with user feedback via learning assessment and surveys. We believe that the cryoEDU platform will facilitate...

Key facts

NIH application ID
10222983
Project number
1R25EY032739-01
Recipient
UNIVERSITY OF MICHIGAN AT ANN ARBOR
Principal Investigator
Michael Cianfrocco
Activity code
R25
Funding institute
NIH
Fiscal year
2021
Award amount
$122,839
Award type
1
Project period
2021-07-01 → 2024-06-30