Crystallographic refinement by knowledge-based exploration of complex energy landscapes

Area of Science:

• Structural Biology
• Biophysics
• Computational Biology

Background:

• X-ray crystallography is key for protein 3D structure determination.
• Accurate structure elucidation from medium-resolution crystals remains challenging due to vast conformational space.
• Current model building and refinement techniques have limitations.

Purpose of the Study:

• To improve the accuracy and power of protein structure refinement from medium-resolution crystals.
• To address the challenge of exploring conformational space in crystallographic refinement.
• To present a novel computational approach combining knowledge-based sampling and molecular dynamics.

Main Methods:

• Integration of RAPPER (knowledge-based conformational sampling) with molecular dynamics/simulated annealing (MD/SA).
• Application of the combined approach to automated structure determination.
• Testing on a lysozyme mutant using a molecular replacement solution.

Main Results:

• The combined RAPPER and MD/SA approach significantly improves refinement quality over MD/SA alone.
• Successful automated determination of a lysozyme mutant structure.
• Demonstrated congruence with independently prepared crystallographer models.

Conclusions:

• Combining knowledge-based conformational sampling with MD/SA offers a powerful strategy for protein structure determination.
• This approach enhances the ability to accurately solve structures from medium-resolution crystallographic data.
• The findings have broad implications for conformational sampling and energy minimization in structural biology.