Princeton_TIGRESS

Princeton_TIGRESS refines predicted protein structures to improve model accuracy by iteratively correcting sampling and selection errors and aligning models more closely to native structures.

Key Features:

• Automated Refinement Protocol: Employs an automated method to iteratively refine input protein structures, addressing sampling and selection issues to improve model quality.
• Benchmarked Performance: Increased Global Distance Test Total Score (GDT_TS) in 76% of CASP refinement targets with an average improvement of 0.5 GDT_TS points per structure.
• Robustness Across Conditions: Evaluated using different random seeds to ensure consistent and reproducible refinement outcomes.

Scientific Applications:

• Bridging predicted and native structures: Produces refined models that reduce discrepancies between predicted and native protein conformations.
• Drug discovery: Provides more accurate protein models for analyzing molecular interactions and supporting therapeutic design.
• Structural biology: Supplies refined structures for experimental validation and downstream structural analysis.

Methodology:

Performs iterative computational refinement operations and leverages computational techniques validated during CASP competitions, explicitly addressing sampling and selection errors and using random-seed evaluations to assess robustness.