Computational protein structure refinement: Almost there, yet still so far to go.

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Experimental protein structure determination cannot keep pace with genomic data.
• Computational methods, including structure prediction and refinement, are crucial for understanding protein function.
• Protein structure refinement aims to enhance approximate models towards native structures.

Purpose of the Study:

• To evaluate the effectiveness of current computational protein structure refinement techniques.
• To identify limitations hindering near-experimental accuracy in protein structure refinement.
• To explore the potential of local refinement strategies.

Main Methods:

• Review of existing computational structure refinement approaches.
• Analysis of molecular dynamics simulations, sampling methods, and iterative optimization.
• Discussion of energy function accuracy and model ranking challenges.

Main Results:

• Current refinement methods achieve only moderate improvements in protein structure accuracy.
• Limitations include energy function accuracy, model ranking, and restrictive sampling restraints.
• High-resolution refinement is more successful in improving local structural quality (e.g., stereochemistry, clash avoidance).

Conclusions:

• Consistent, near-experimental accuracy in global protein structure refinement remains an unmet challenge.
• Focusing on local structural quality offers a more attainable goal for current refinement techniques.
• The target accuracy for refinement should consider experimental conditions and specific biological questions.