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Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.

dc.contributor.author Fusco, Diana
dc.contributor.author Barnum, Timothy J
dc.contributor.author Bruno, Andrew E
dc.contributor.author Luft, Joseph R
dc.contributor.author Snell, Edward H
dc.contributor.author Mukherjee, Sayan
dc.contributor.author Charbonneau, Patrick
dc.coverage.spatial United States
dc.date.accessioned 2015-09-03T06:35:31Z
dc.date.issued 2014
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/24988076
dc.identifier PONE-D-13-53452
dc.identifier.uri https://hdl.handle.net/10161/10578
dc.description.abstract X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.
dc.language eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLoS One
dc.relation.isversionof 10.1371/journal.pone.0101123
dc.subject Crystallography, X-Ray
dc.subject Databases, Protein
dc.subject Models, Chemical
dc.subject Proteins
dc.title Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.
dc.type Journal article
duke.contributor.id Charbonneau, Patrick|0486302
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/24988076
pubs.begin-page e101123
pubs.issue 7
pubs.organisational-group Chemistry
pubs.organisational-group Duke
pubs.organisational-group Physics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published online
pubs.volume 9
dc.identifier.eissn 1932-6203
duke.contributor.orcid Charbonneau, Patrick|0000-0001-7174-0821


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