Proteome dynamics of COVID-19 severity learnt by a graph convolutional network of multi-scale topology
Gauthier, S.; Tran-Dinh, A.; Morilla, I.
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Many efforts have been recently done to characterise the molecular mechanisms of COVID-19 disease. These efforts resulted in a full structural identification of ACE2 as principal receptor of the Sars-CoV-2 spike protein in the cell. However, there are still important open questions related to other proteins involved in the progression of the disease. To this end, we have modelled the plasma proteome of 384 COVID patients. The model calibrated proteins measures at three time tags and make also use of the detailed clinical evaluation outcome of each patient after their hospital stay at day 28. Our analysis is able to discriminate severity of the disease by means of a metric based on available WHO scores of disease progression. Then, we identify by topological vectorisation those proteins shifting the most in their expression depending on that severity classification. Finally, the extracted topological invariants respect the protein expression at different times were used as base of a graph convolutional network. This model enabled the dynamical learning of the molecular interactions produced between the identified proteins.
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