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CryoET-CryoCloud

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Streamlining structural biology: Developing a high-throughput cloud-based cryo-e...

Streamlining structural biology: Developing a high-throughput cloud-based cryo-electron tomography platform 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 dis... ver más

31/12/2024

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

UNIVERSITEIT UTRECHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-19

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2022-POC2: ERC PROOF OF CONCEPT GRANTS2

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Cerrada hace 2 años

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1 Participantes

150.00K€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 18 meses Fecha Inicio: 2023-06-19
Fecha Fin: 2024-12-31

Líder del proyecto

UNIVERSITEIT UTRECHT

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto 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.

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