TAP score: torsion angle propensity normalization applied to local protein structure evaluation.

Area of Science:

• Structural biology
• Computational biology
• Biochemistry

Background:

• Experimentally determined protein structures require validation to identify errors.
• Current validation methods, like Ramachandran plots, struggle with subtle differences in plausible structures.
• Distinguishing between distorted and adequately refined protein models remains challenging.

Purpose of the Study:

• To introduce a novel criterion for assessing protein model quality.
• To improve the accuracy of distinguishing between valid and invalid protein structures.
• To provide a tool for reliable identification of problematic protein models.

Main Methods:

• Developed the TAP score, a new criterion measuring local sequence-to-structure fitness.
• Utilized torsion angle propensities normalized against global minimum and maximum values.
• Validated the TAP score on comprehensive protein data bank (PDB) datasets.

Main Results:

• The TAP score demonstrates higher accuracy than previous methods in estimating protein model validity.
• Achieved over 90% recognition of top experimental structures using selective TAP thresholds.
• The TAP score effectively identifies high-quality structures even without experimental information.

Conclusions:

• The novel TAP score procedure significantly enhances the recognition of top experimental protein structures.
• This method allows for more reliable isolation of problematic structures in automated determination.
• The TAP score offers improved capabilities for protein structure validation.