Document Detail


A model-based approach for mining membrane protein crystallization trials.
MedLine Citation:
PMID:  16873499     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: 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.
RESULTS: This work is an application case study on mining membrane protein crystallization trials to predict novel conditions that have a high likelihood of leading to crystallization. We use suitable supervised learning algorithms to model the data-space and predict a novel set of crystallization conditions. Our preliminary wet laboratory results are very encouraging and we believe this work shows great promise. We conclude with a view of the crystallization space that is based on our results, which should prove useful for future studies in this area.
Authors:
Sitaram Asur; Pichai Raman; Matthew Eric Otey; Srinivasan Parthasarathy
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  22     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2006 Jul 
Date Detail:
Created Date:  2006-07-28     Completed Date:  2006-10-05     Revised Date:  2013-05-20    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  e40-8     Citation Subset:  IM    
Affiliation:
Department of Computer Science and Engineering, Ohio State University, USA. srini@cse.ohio-state.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Computer Simulation
Crystallization / methods*
Crystallography / methods
Database Management Systems
Databases, Protein*
Information Storage and Retrieval / methods
Membrane Proteins / chemistry*,  ultrastructure*
Models, Chemical*
Models, Molecular*
Multiprotein Complexes / chemistry,  ultrastructure
Protein Conformation
Sequence Analysis, Protein / methods*
Chemical
Reg. No./Substance:
0/Membrane Proteins; 0/Multiprotein Complexes

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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