s2D

s2D predicts protein secondary-structure populations and intrinsic disorder directly from amino acid sequences using a unified statistical mechanics framework to enable proteome-scale structural characterization (e.g., UniProt >79 million entries).

Key Features:

• Unified Framework: Characterizes structured and intrinsically disordered regions simultaneously from sequence using a unified statistical mechanics framework.
• Statistical Mechanics Basis: Leverages advances in NMR chemical shift analysis to estimate probability distributions of secondary-structure elements, including in disordered states.
• Quantitative Predictions: Produces rapid, sequence-based quantitative characterizations of the statistical distributions of ordered and disordered regions.

Scientific Applications:

• Validation on diverse datasets: Validated on three datasets comprising mostly disordered, mostly structured, and partly structured proteins, with performance comparable to or exceeding existing predictors for intrinsic disorder and secondary structure.
• Proteome-scale characterization: Supports rapid structural-dynamics characterization across large sequence databases such as UniProt (>79 million entries).

Methodology:

s2D employs a unified statistical mechanics framework that exploits NMR chemical shift analysis to infer probability distributions of secondary-structure elements in both structured and disordered states, with a reported average error of approximately 14%.