PHENIX : a comprehensive Python-based system for macromolecular structure solution
PHENIX: комплексная система на основе Python для определения структуры макромолекул
2010-01-21
SCID: 54.1/zpqtu6y8
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PHENIXcrystallographic structure solutionmacromolecular X-ray crystallographystructure-solution automationthree-dimensional graphics
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Abstract (AI)
Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. However, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms.
Key Findings
1
A tightly integrated framework connects the algorithms into a unified structure-solution system.
2
PHENIX develops algorithms that minimize or eliminate subjective user input during crystallographic analysis.
3
PHENIX is a comprehensive Python-based system for macromolecular X-ray crystallographic structure solution.
4
The system automates procedures traditionally performed manually, including interpretation of complex numerical data and repeated interactive three-dimensional graphics use.
5
The system emphasizes automation of the full structure-solution workflow to reduce time and effort required for complex structures.
Research Object
PHENIX software system for macromolecular crystallographic structure solution
Research Subject
Automation and integration of macromolecular crystallographic structure-solution procedures to minimize manual and subjective input
Publication Details
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2010-01-21
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