SUBWAI

Subwai enhances protein tertiary structure prediction by integrating a profile-based threading algorithm with the SuboPtimal Alignment Diversity (SPAD) index to estimate global and local errors in predicted models.

Key Features:

• Error Estimation: The SPAD metric quantifies the stability of optimal target-template alignments against suboptimal alignments to estimate global and local structural errors.
• Profile-Based Threading Algorithm: SPAD is integrated into a profile-based threading algorithm to assess alignment quality and stability for structure prediction.
• Benchmarking and Validation: Evaluation on a benchmark dataset of 5232 alignments showed strong correlations with alignment shift errors, RMSD from native structures, and local residue-level errors.
• Comparative Analysis: SPAD outperformed seven other quality measures across family, superfamily, and fold levels, with superior correlation to RMSD at the superfamily level and best performance in predicting local errors at all levels.
• Error Prediction for CASP7 Targets: The method was applied to predict global and local errors in structures from CASP7 targets.
• Sausage Representation: Provides a sausage representation of predicted tertiary structures that conveys predicted coordinates together with estimated error ranges.

Scientific Applications:

• Protein Structure Prediction: Improves predicted model assessment by providing quantitative error estimates for tertiary structures.
• Experimental Design: Supplies explicit global and local error estimates to inform experimental planning and interpretation.
• Structural Biology Research: Enables analysis of structural deviations at global and residue levels to support studies of protein folding and function.

Methodology:

Subwai uses a profile-based threading strategy that computes the SPAD index by comparing optimal target-template alignments with suboptimal alignments to quantify alignment stability and estimate global and local structural errors.