RCI

RCI predicts protein backbone flexibility and dynamic parameters from NMR secondary chemical shifts to estimate backbone RMSF and model-free order parameters (S2).

Key Features:

• Secondary chemical shifts: Uses secondary chemical shifts derived from backbone chemical shift assignments as primary input data.
• Random Coil Index calculation: Computes the Random Coil Index (RCI) from corrected chemical shift assignments.
• RMSF prediction: Converts RCI values into quantitative backbone root mean square fluctuations (RMSFs) for MD and NMR comparisons.
• Model-free order parameters (S2): Predicts model-free order parameters (S2) from RCI-derived metrics.
• Chemical shift re-referencing: Performs automatic chemical shift re-referencing to correct assignment reference offsets.
• Structure independence: Produces predictions without requiring prior tertiary structure information and is insensitive to overall molecular tumbling.
• Minimal input requirements: Requires only standard backbone chemical shift assignments and no additional NMR relaxation measurements.
• Quantitative performance: Provides quantitative estimates with reported correlations to experimental motional amplitudes typically in the 0.77–0.82 range.
• Computational speed: Enables rapid computation of RCI-derived metrics (reported execution times <5 seconds).

Scientific Applications:

• Enzyme catalysis: Infers flexible regions that can modulate catalytic mechanisms and substrate access.
• Muscle contraction studies: Maps backbone dynamics relevant to conformational changes during contraction.
• Antigen–antibody interactions: Identifies flexible epitopes and paratopes affecting binding affinity and specificity.
• Gene regulation: Characterizes dynamic regions in transcription factors and DNA-binding proteins that influence regulatory function.
• Virus assembly: Assesses protein flexibility implicated in assembly and conformational transitions of viral components.
• Rational drug design: Guides identification of dynamic sites and allosteric regions relevant for ligand binding.
• Protein docking: Provides dynamic-mobility information to inform docking models and flexible docking protocols.
• Protein engineering: Enables selection of regions for stabilization or functional modulation based on predicted dynamics.

Methodology:

Apply automatic chemical shift re-referencing if needed, compute the Random Coil Index from backbone chemical shift assignments, and convert the RCI into predicted backbone RMSFs and model-free order parameters (S2).