Summary
Cryo-electron microscopy (cryoEM) has revolutionized biomolecular research. It enables structure determination of proteins and their complexes at near-atomic resolution, informing on their mechanism of action and their role in disease. Importantly, it also assists the development of novel therapeutics. Cryo-electron tomography (cryoET) is an emerging cryoEM modality, which has demonstrated the potential of to determine protein structures at near-atomic resolution in their near-native environment. Importantly, cryoET provides snapshots of macromolecules during their function, allowing to capture different stages of biochemical reactions in the cell and inferring their temporal order. However, cryoET relies on extensive computational data analysis. Current analysis pipelines require extensive computational resources, various software packages, and a high degree of expertise to build and execute workflows for data analysis. Building on my previous research from the ERC Consolidator project BENDER project, I propose to create a cloud-based workflow for standardized and automated high-throughput cryoET data analysis (CryoET-CryoCloud).
This solution will not only reduce analysis time from months to days, but it will also make the method accessible to a broad community by eliminating the need for investment into expensive computational infrastructure, and their maintenance through scarce qualified personnel. My proposed workflow will both enable and democratize high-content data analysis of cellular tomograms, thus dramatically increasing our knowledge of the molecular machinery inside the cell. To perform this work and reach market rapidly, I will perform CryoET-CryoCloud in very close collaboration with a team of people from the startup company CryoCloud, which emerged from my research group.
This solution will not only reduce analysis time from months to days, but it will also make the method accessible to a broad community by eliminating the need for investment into expensive computational infrastructure, and their maintenance through scarce qualified personnel. My proposed workflow will both enable and democratize high-content data analysis of cellular tomograms, thus dramatically increasing our knowledge of the molecular machinery inside the cell. To perform this work and reach market rapidly, I will perform CryoET-CryoCloud in very close collaboration with a team of people from the startup company CryoCloud, which emerged from my research group.
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| Web resources: | https://cordis.europa.eu/project/id/101113464 |
| Start date: | 01-07-2023 |
| End date: | 31-12-2024 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
Cryo-electron microscopy (cryoEM) has revolutionized biomolecular research. It enables structure determination of proteins and their complexes at near-atomic resolution, informing on their mechanism of action and their role in disease. Importantly, it also assists the development of novel therapeutics. Cryo-electron tomography (cryoET) is an emerging cryoEM modality, which has demonstrated the potential of to determine protein structures at near-atomic resolution in their near-native environment. Importantly, cryoET provides snapshots of macromolecules during their function, allowing to capture different stages of biochemical reactions in the cell and inferring their temporal order. However, cryoET relies on extensive computational data analysis. Current analysis pipelines require extensive computational resources, various software packages, and a high degree of expertise to build and execute workflows for data analysis. Building on my previous research from the ERC Consolidator project BENDER project, I propose to create a cloud-based workflow for standardized and automated high-throughput cryoET data analysis (CryoET-CryoCloud).This solution will not only reduce analysis time from months to days, but it will also make the method accessible to a broad community by eliminating the need for investment into expensive computational infrastructure, and their maintenance through scarce qualified personnel. My proposed workflow will both enable and democratize high-content data analysis of cellular tomograms, thus dramatically increasing our knowledge of the molecular machinery inside the cell. To perform this work and reach market rapidly, I will perform CryoET-CryoCloud in very close collaboration with a team of people from the startup company CryoCloud, which emerged from my research group.
Status
SIGNEDCall topic
ERC-2022-POC2Update Date
31-07-2023
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