Description of the methodology used

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

• From protein sequence to dynamics and disorder with DynaMine
  Nature Communications 4, 3741 (2013).
• The DynaMine webserver: Predicting protein dynamics from sequence
  Nucleic Acids Research 42, W264-W270 (2014).

The EFoldMine early folding predictions are described in:

• Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins
  Scientific Reports 7, 8826 (2017).

The PSPer Phase-Separation predictions are described in:

• Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates
  Bioinformatics 35, 4617–4623 (2019).

The Agmata beta-sheet aggregation predictions are described in:

• Accurate prediction of protein beta-aggregation with generalized statistical potentials
  Bioinformatics 36, 2076-2081 (2020).

The DisoMine disorder predictions are described in:

• Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics
  Journal of Molecular Biology 434, 167579 (2022).

The biophysical interpretation of disorder using a Random Forest classifier is described in:

• Challenges in describing the conformation and dynamics of proteins with ambiguous behavior
  Front. Mol. Biosci. 9, 959956 (2022).

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.