Phaser

Phaser performs phasing of macromolecular crystal structures by integrating molecular replacement and experimental phasing to determine crystallographic phases for structure determination.

Key Features:

• Integration of phasing methods: Combines molecular replacement and experimental phasing techniques within a single framework.
• Maximum likelihood algorithms: Implements algorithms developed using maximum likelihood theory to improve phase estimation.
• Multivariate statistical approaches: Uses multivariate statistical methods to enhance discrimination between correct solutions and noise in molecular replacement.
• SAD anomalous modelling: Models single-wavelength anomalous dispersion (SAD) by accounting for correlations between F(+) and F(-), producing phases with lower mean phase error than methods based on mean F and anomalous differences ΔF.
• Automation of phasing procedures: Provides automated computational procedures for performing phasing calculations.
• Implementation and interoperability: Implemented in C++ with direct invocation from Python and support for CCP4 keyword-style input.

Scientific Applications:

• Molecular replacement phasing: Improves discrimination between correct and incorrect molecular replacement solutions using likelihood-based and multivariate statistics.
• Experimental SAD phasing: Provides SAD phasing that accounts for F(+) and F(-) correlations to yield more accurate phases and reduced mean phase error relative to mean-F/ΔF methods.
• Macromolecular structure determination: Applied to determine crystallographic phases required for solving macromolecular crystal structures.

Methodology:

Integrates molecular replacement and experimental phasing using maximum likelihood and multivariate statistical approaches and models SAD by explicitly accounting for correlations between F(+) and F(-); implemented in C++ with Python invocation and CCP4 keyword-style input support.