Constava

Constava analyzes conformational state probabilities and variability from protein structure ensembles to characterize residue-level dynamics and local energy landscapes.

Key Features:

• Probabilistic definition of conformational states: Implements a data-driven probabilistic framework defining six low-energy conformational states based on combined analysis of structure ensembles and interpreted chemical shifts grounded in solution NMR data from 1322 proteins.
• Probability and variability calculation: Calculates per-residue probabilities of conformational states and quantifies their variability across protein structure ensembles.
• Conformational state variability parameter: Provides a parameter that quantifies how frequently a residue transitions between conformational states within ensembles from molecular dynamics simulations or similar computational methods.
• Local energy landscape analysis: Analyzes local energy landscapes to distinguish sharply defined conformations (e.g., helices) from broader conformational ranges typical of intrinsically disordered regions.

Scientific Applications:

• Protein dynamics characterization: Complements static single-structure models by providing a probabilistic, ensemble-based view of residue-level conformational behavior.
• Functional insights: Relates local conformational states and their variability to molecular interactions and responses to environmental changes.

Methodology:

Analyzing structure ensembles from molecular dynamics simulations or similar methods; interpreting chemical shifts to define six low-energy conformational states grounded in solution NMR data from 1322 proteins; and calculating per-residue state probabilities and variability across ensembles.