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

Projektinformationen

ERMADA

ID Finanzhilfevereinbarung: 838018

DOI

10.3030/838018

Projekt abgeschlossen

EK-Unterschriftsdatum 1 April 2019

Startdatum 1 August 2019

Enddatum 21 März 2025

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 247 628,16

EU-Beitrag

€ 247 628,16

Koordiniert durch

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)

Berichtszeitraum: 2019-08-01 bis 2022-07-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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