The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins.

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SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019, neutral): “…(http://bioinfad-min.cs.ucl.ac.uk/downloads/DISOPRED/), MobiDB-lite [50], AUCpreD [18] (https://github.com/realbigws/RaptorX_Property_Fast), s2D [51] (http://www-mvsoftware.ch.cam.ac.uk/index.php/s2D), SPOT-Disorder [19], SPOT-Disorder-Single (denoted as SPOT-Disorder-S for brevity) [52], and…”
ALS mutations disrupt phase separation mediated by -helical structure in the TDP-43 low complexity C-terminal domain (2016, background): “The chemical shift index(Wishart et al., 1992), secondary structure propensity (SSP) scores (Marsh et al., 2006), and δ2D population predictions (Sormanni et al., 2015) are also consistent with partial helical structure in the region 321–330 (Figure S1C–”
Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, methodology): “The S2D method [144] opens a new avenue of secondary structure prediction by training on the probability distributions of secondary structure elements in disordered states derived from NMR chemical shifts for 2223 protein sequences.”