Illuminating Earth’s microbial diversity and origins from metagenomes with deep learning

Información del proyecto

ERMADA

Identificador del acuerdo de subvención: 838018

DOI

10.3030/838018

Proyecto cerrado

Fecha de la firma de la CE 1 Abril 2019

Fecha de inicio 1 Agosto 2019

Fecha de finalización 21 Marzo 2025

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 247 628,16

Aportación de la UE

€ 247 628,16

Coordinado por

EREVNITIKO KENTRO VIOIATRIKON EPISTIMON ALEXANDROS FLEMINGK
Greece

Periodic Reporting for period 1 - ERMADA (Illuminating Earth’s microbial diversity and origins from metagenomes with deep learning)

Período documentado: 2019-08-01 hasta 2022-07-31

Resumen del contexto y de los objetivos generales del proyecto

The project coincided with the onset of the worldwide crisis due to the COVID-19 pandemic. As a consequence, the scope and direction of the project were modified in order to combat this unknown threat and address the unprecedented health situation with the latest scientific tools.
Resources and expertise were directed towards an international collaboration of researchers and groups from France, Greece, Canada, USA, Spain and others, with the aim of uncovering the mechanics of the viral invasion in human cells with a combination of state-of-the-art biological and computational tools.
The benefits of understanding the underlying molecular mechanisms of COVID-19 pathogenesis held an immense potential for saving potentially millions of lives and halting the virus' devastating impact on all human activity during the critical first months of the pandemic spread.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

The approach followed was a combination of proximal proteomics (the mapping of all the interactions between the viral and human proteins inside the cell volume) with computational techniques for the analysis and visualization of networks in three dimensions.
The large-scale proximal proteomic data lend themselves naturally to modeling by graphs, which when laid out in 3D provide an intuitive and physical picture of the complex processes involved during viral invasion in the cell. This computational approach proved remarkably well-suited by establishing known facts about human proteins' cellular localizations and revealing or corroborating viral mechanisms of action. The results held true when integrating with larger datasets from other proteomics studies employing a variety of different methods. Even more importantly, the 3D modeling of the proximal interactome was able to provide quantitative predictions for direct interactions observed in independent experiments performed as part of the same collaboration. Finally, a select set of protein interactions was also modeled in simulation with cutting edge deep learning tools (AlphaFold2), in order to provide further support of the findings and indicate possible targets for directed therapies.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

The 3D modeling and representation of proximal proteomic data was performed for the first time to such scale and depth. The web-based interactive dashboard allowed researchers to explore the wealth of data with numerous customizations and to the most granular level of detail.
Beyond the SARS-CoV-2 virus, the methodology provided a template for quickly investigating an unknown or emerging viral threat and provides a pipeline that could be used in future situations with the aim devising countermeasures or targeted therapies.

Snapshot of 3D network modeling of SARS-CoV-2 cellular invasion