CheSPI

CheSPI infers local secondary-structure populations from NMR chemical shifts using random coil chemical shift (RCCS) prediction and interpretation to detect residual and structured protein conformations.

Key Features:

• NMR chemical-shift analysis: Uses NMR chemical shifts as input for structural and disorder analysis.
• RCCS-based inference: Applies random coil chemical shift (RCCS) prediction and interpretation to infer small populations of secondary structure from minor chemical-shift deviations.
• Unbiased and sensitive aggregation: Computes unbiased and sensitive aggregate measures of local protein structure and disorder.
• Residual-structure detection: Detects minimal amounts of residual secondary structure in proteins.
• Structural classification: Assigns four structural classes for intrinsically disordered proteins (IDPs) and up to eight classes for structured regions consistent with DSSP.
• Output formats: Produces numeric and graphical outputs for annotation and visualization of local structural populations.

Scientific Applications:

• IDP characterization: Characterizing local structure and disorder in intrinsically disordered proteins (IDPs).
• Protein folding studies: Analyzing folding pathways and intermediate states via detection of residual structure.
• Structural biology: Interpreting local conformational states in structured proteins to support structure–function analyses.
• Drug design: Identifying conformational changes and potential binding-related structural features in target proteins.

Methodology:

Analyzes NMR chemical shifts with random coil chemical shift (RCCS) prediction and interpretation, computes aggregate local-structure and disorder measures, infers small secondary-structure populations from minor chemical-shift deviations, assigns residues to four IDP-specific classes or up to eight DSSP-consistent classes, and outputs numeric and graphical results.