A new rotation function for molecular replacement by using both the self and cross Patterson vectors.

Area of Science:

• Crystallography
• Structural Biology
• Computational Biology

Background:

• Molecular replacement (MR) traditionally uses reciprocal space for rotation function computation.
• Current methods often limit Patterson vector radius to match only self-vectors, potentially missing information.

Purpose of the Study:

• To develop and test a new rotation function for molecular replacement computed in real space.
• To explore matching both self and cross Patterson vectors to a search model.

Main Methods:

• A novel algorithm computes the rotation function by averaging signals from images found in a Patterson vector search.
• The method searches the molecular image of a model against the Patterson function vector space.

Main Results:

• A two-helix protein fragment search model was successfully identified within a myoglobin crystal.
• Experimental data was used in a global rotation search, validating the new approach.

Conclusions:

• The developed real-space rotation function is a potentially useful approach for molecular replacement.
• Averaging signals in the Patterson vector search offers a promising alternative to traditional reciprocal space methods.