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A model-based approach for mining membrane protein crystallization trials
Author(s) -
Sitaram Asur,
Pichai Raman,
Matthew Eric Otey,
Srinivasan Parthasarathy
Publication year - 2006
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btl225
Subject(s) - crystallization , bottleneck , computer science , protein crystallization , process (computing) , space (punctuation) , set (abstract data type) , data mining , biochemical engineering , physics , engineering , thermodynamics , programming language , embedded system , operating system
Membrane proteins are known to play crucial roles in various cellular functions. Information about their function can be derived from their structure, but knowledge of these proteins is limited, as their structures are difficult to obtain. Crystallization has proved to be an essential step in the determination of macromolecular structure. Unfortunately, the bottleneck is that the crystallization process is quite complex and extremely sensitive to experimental conditions, the selection of which is largely a matter of trial and error. Even under the best conditions, it can take a large amount of time, from weeks to years, to obtain diffraction-quality crystals. Other issues include the time and cost involved in taking multiple trials and the presence of very few positive samples in a wide and largely undetermined parameter space. Therefore, any help in directing scientists' attention to the hot spots in the conceptual crystallization space would lead to increased efficiency in crystallization trials.

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