Elucidating polymorphs of crystal structures by intensity-based hierarchical clustering analysis of multiple...

Area of Science:

• Structural biology
• Crystallography
• Data analysis

Background:

• Macromolecular structure determination via X-ray diffraction requires classifying data from multiple crystals, especially when structural polymorphs are present.
• Hierarchical clustering analysis (HCA) is used for isomorphous data extraction in single-structure determination.
• A key challenge for using HCA to detect polymorphs is defining the 'isomorphic threshold' for grouping data sets.

Purpose of the Study:

• To investigate the efficacy of Hierarchical Clustering Analysis (HCA) in classifying polymorphous data sets.
• To adapt HCA for detecting structural polymorphs in macromolecular crystallography.
• To establish a reliable 'isomorphic threshold' for HCA in polymorph classification.

Main Methods:

• Applied unit-cell-based and intensity-based HCA to mixed data sets of apo trypsin and inhibitor-bound trypsin.
• Investigated the use of a defined 'isomorphic threshold' for intensity-based HCA.
• Analyzed data from single crystals using a continuous helical data collection scheme.

Main Results:

• Single-step intensity-based HCA successfully classified polymorphs using a defined 'isomorphic threshold'.
• Intensity-based HCA identified polymorphs in data sets with unknown structural heterogeneity.
• Polymorph detection was also achieved in single crystals using continuous helical data collection.

Conclusions:

• Intensity-based HCA with a defined 'isomorphic threshold' effectively classifies polymorphous data sets in X-ray crystallography.
• This refined HCA approach facilitates the identification of structural polymorphs.
• The findings support automated data collection and analysis for determining multiple structural snapshots.