Iris: Interactive all-in-one graphical validation of 3D protein model iterations.

Area of Science:

• Computational biology
• Structural biology
• Bioinformatics

Background:

• Protein model quality assessment is crucial for structural biology.
• Current methods can be complex and time-consuming.
• Visualizing per-residue metrics aids in identifying model inaccuracies.

Purpose of the Study:

• To introduce Iris validation, a Python package for visualizing protein model quality.
• To provide a compact, readable, and interactive view of per-residue metrics.
• To facilitate the identification of areas in protein models requiring attention.

Main Methods:

• Development of the Iris validation Python package.
• Integration of per-residue metrics, calculable by Iris or third-party tools (e.g., MolProbity).
• Demonstration of Iris's ability to highlight problematic regions through metric "ripples".

Main Results:

• Iris provides a comprehensive, at-a-glance view of protein chain validation metrics.
• The package effectively highlights areas needing modeler attention.
• Iris is demonstrated as a standalone tool and integrated into CCP4i2.

Conclusions:

• Iris validation offers an intuitive approach to assessing protein model quality.
• The package aids in the iterative process of model building and refinement.
• Iris's pluggable design enhances its utility within existing structural biology workflows.