A database method for automated map interpretation in protein crystallography

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Protein structure determination is crucial for understanding biological function.
• Existing protein structures exhibit significant fold redundancy, offering opportunities for computational analysis.
• Interpreting electron density maps for de novo structure determination remains challenging.

Purpose of the Study:

• To develop and test a computational procedure, Database Assisted Density Interpretation (DADI), for automated model building in protein crystallography.
• To leverage the redundancy of known protein folds to assist in interpreting electron density maps.
• To improve the efficiency and accuracy of protein structure determination.

Main Methods:

• Developed the Database Assisted Density Interpretation (DADI) computational procedure.
• Utilized a hierarchical approach: domains, secondary structures, then residues.
• Employed Monte Carlo, "chopping", and "clipping" procedures for model building.
• Tested DADI on electron density maps from human topoisomerase I-DNA complex and diphtheria toxin repressor.

Main Results:

• DADI successfully placed a significant portion of the protein backbone with few errors in test cases.
• In the first test case, DADI automatically placed ~45% of the backbone and >80% of secondary structures.
• In the second test case, DADI automatically detected ~50% of the third domain.
• Notably, DADI identified >75% of beta sheet secondary structures in both cases.

Conclusions:

• The DADI procedure shows promise for exploiting the growing protein structure database for future structure determination.
• The method effectively aids in interpreting electron density maps and building protein models.
• DADI offers a valuable tool for accelerating the process of protein structure determination.