Bio2Byte SARS-CoV-2

Sequence-based predictions for the characteristics of the proteins that compose the SARS-CoV-2 virus, which causes COVID-19
Last Update: March 23, 2021, 5:12 p.m.   Entries: 27
The unreviewed article describing this server is available via BioRXiv. It is currently undergoing review.

Description of the methodology used

The DynaMine backbone and sidechain dynamics and conformational propensities are described in:

The EFoldMine early folding predictions are described in:

The DisoMine disorder predictions are described in:

The Agmata beta-sheet aggregation predictions are described in:

With these predictions we try to capture the 'emergent' properties of the proteins, so the inherent biophysical propensities encoded in the sequence, rather than the behavior of a final folded state. This relevant as proteins are dynamic even when folded, and might not fold at all (as with intrinsically disordered proteins). Please see our website for more information on how to run these approaches on your own sequences.

These predictions are single-sequence based, and the median/quartile/outlier information in the plots is derived from a BLAST search of the protein against uniref90, using default Uniprot parameters, followed by the standard Uniprot alignment to obtain a multiple sequence alignment (MSA). We then run the predictions on each sequence separately, map them back to the MSA, and look at the 'biophysical variation' observed in evolution in relation to the residues of the original protein. These are not available for Agmata as the method is computationally too expensive.

The protein interaction predictions are from SeRenDIP, the epitope predictions from SeRenDIP-CE. Both methods use evolutionary information and were developed at the Vrije Universiteit Amsterdam in the group of Prof. Feenstra :


Information from molecular dynamics trajectories, and NMR chemical shift derived data on the biophysical properties of experimentally studied molecule.