ComparePD: Improving protein-DNA complex model comparison with hydrogen bond energy-based metrics.

Area of Science:

• Computational biology
• Structural bioinformatics
• Biophysics

Background:

• Accurate modeling of protein-DNA complexes is crucial for structure-based drug design.
• Current similarity assessment methods often overlook functional features like interface hydrogen bonds.
• Protein-DNA interactions are highly dependent on specific hydrogen bonding networks.

Purpose of the Study:

• To develop a novel scoring function, ComparePD, for enhanced similarity assessment of protein-DNA complexes.
• To incorporate interface hydrogen bond energy and strength into similarity metrics.
• To evaluate ComparePD's performance against existing methods like PDDockQ and CAPRI metrics.

Main Methods:

• Development of the ComparePD scoring function integrating distance-based metrics with hydrogen bond features.
• Testing ComparePD on datasets of protein-DNA complexes generated by docking and homology modeling.
• Comparative analysis of ComparePD against PDDockQ and Critical Assessment of PRedicted Interactions (CAPRI) metrics.

Main Results:

• ComparePD provides a more accurate similarity measure by considering both conformational and functional aspects of the protein-DNA interface.
• The new function outperformed PDDockQ and CAPRI metrics in identifying meaningful models across various docking difficulty levels.
• ComparePD identified more relevant models than PDDockQ, with discrepancies in top-ranked models across most test cases.

Conclusions:

• ComparePD offers a superior approach to assessing protein-DNA complex similarity by integrating functional interface information.
• The method enhances the reliability of computational models for applications in drug discovery and structural biology.
• ComparePD represents a significant advancement in evaluating the quality of predicted protein-DNA complex structures.