Making a difference in multi-data-set crystallography: simple and deterministic data-scaling/selection methods.

Area of Science:

• Structural Biology
• Crystallography
• Biophysics

Background:

• Phasing in single-wavelength anomalous diffraction (SAD) is challenging due to weak anomalous signals and non-isomorphism in multiple crystallographic data sets.
• Accurate identification and exclusion of non-isomorphous data are crucial for successful SAD phasing.

Purpose of the Study:

• To develop simple and robust methods for selecting high-quality crystallographic data sets for SAD phasing.
• To improve the anomalous signal and facilitate structure solution, especially in serial synchrotron crystallography (SSX).

Main Methods:

• Employed multi-dimensional scaling to identify and exclude non-isomorphous data sets.
• Utilized weighted ΔCC_1/2 for further data selection within identified clusters.
• Optimized scaling protocols and iterated selection/scaling steps.

Main Results:

• Successfully identified and excluded non-isomorphous data sets, improving the anomalous signal.
• Demonstrated simplified structure solution in SSX SAD test cases, including those with hundreds of partial data sets.
• Achieved structure solution with significantly fewer data sets than typically required, outperforming a data challenge.

Conclusions:

• The developed data-selection and scaling methods are effective for improving SAD phasing, particularly in SSX.
• This approach enhances the efficiency and success rate of crystallographic structure determination.
• The procedure pushes the limits of data requirements for solving complex protein structures.