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Computational crystallization.

dc.contributor.author Altan, I
dc.contributor.author Charbonneau, Patrick
dc.contributor.author Snell, EH
dc.coverage.spatial United States
dc.date.accessioned 2017-08-23T15:44:35Z
dc.date.available 2017-08-23T15:44:35Z
dc.date.issued 2016-07-15
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/26792536
dc.identifier S0003-9861(16)30004-2
dc.identifier.uri https://hdl.handle.net/10161/15339
dc.description.abstract Crystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one of trial and error. In this article, efforts in the field are discussed together with a theoretical underpinning using a solubility phase diagram. Prior knowledge has been used to develop tools that computationally predict the crystallization outcome and define mutational approaches that enhance the likelihood of crystallization. For the most part these tools are based on binary outcomes (crystal or no crystal), and the full information contained in an assembly of crystallization screening experiments is lost. The potential of this additional information is illustrated by examples where new biological knowledge can be obtained and where a target can be sub-categorized to predict which class of reagents provides the crystallization driving force. Computational analysis of crystallization requires complete and correctly formatted data. While massive crystallization screening efforts are under way, the data available from many of these studies are sparse. The potential for this data and the steps needed to realize this potential are discussed.
dc.language eng
dc.relation.ispartof Arch Biochem Biophys
dc.relation.isversionof 10.1016/j.abb.2016.01.004
dc.subject Crystallization
dc.subject computational
dc.subject crystallization data
dc.subject crystallography
dc.subject macromolecular
dc.subject Computer Simulation
dc.subject Crystallization
dc.subject Crystallography
dc.subject Models, Molecular
dc.subject Protein Conformation
dc.subject Proteins
dc.title Computational crystallization.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/26792536
pubs.begin-page 12
pubs.end-page 20
pubs.organisational-group Chemistry
pubs.organisational-group Duke
pubs.organisational-group Physics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 602
dc.identifier.eissn 1096-0384


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