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Article: Predictive models for protein crystallization

TitlePredictive models for protein crystallization
Authors
Issue Date2004
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/ymeth
Citation
Methods, 2004, v. 34 n. 3, p. 390-407 How to Cite?
AbstractCrystallization of proteins is a nontrivial task, and despite the substantial efforts in robotic automation, crystallization screening is still largely based on trial-and-error sampling of a limited subset of suitable reagents and experimental parameters. Funding of high throughput crystallography pilot projects through the NIH Protein Structure Initiative provides the opportunity to collect crystallization data in a comprehensive and statistically valid form. Data mining and machine learning algorithms thus have the potential to deliver predictive models for protein crystallization. However, the underlying complex physical reality of crystallization, combined with a generally ill-defined and sparsely populated sampling space, and inconsistent scoring and annotation make the development of predictive models non-trivial. We discuss the conceptual problems, and review strengths and limitations of current approaches towards crystallization prediction, emphasizing the importance of comprehensive and valid sampling protocols. In view of limited overlap in techniques and sampling parameters between the publicly funded high throughput crystallography initiatives, exchange of information and standardization should be encouraged, aiming to effectively integrate data mining and machine learning efforts into a comprehensive predictive framework for protein crystallization. Similar experimental design and knowledge discovery strategies should be applied to valid analysis and prediction of protein expression, solubilization, and purification, as well as crystal handling and cryo-protection. © 2004 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/147576
ISSN
2015 Impact Factor: 3.503
2015 SCImago Journal Rankings: 2.377
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorRupp, Ben_US
dc.contributor.authorWang, Jen_US
dc.date.accessioned2012-05-29T06:04:43Z-
dc.date.available2012-05-29T06:04:43Z-
dc.date.issued2004en_US
dc.identifier.citationMethods, 2004, v. 34 n. 3, p. 390-407en_US
dc.identifier.issn1046-2023en_US
dc.identifier.urihttp://hdl.handle.net/10722/147576-
dc.description.abstractCrystallization of proteins is a nontrivial task, and despite the substantial efforts in robotic automation, crystallization screening is still largely based on trial-and-error sampling of a limited subset of suitable reagents and experimental parameters. Funding of high throughput crystallography pilot projects through the NIH Protein Structure Initiative provides the opportunity to collect crystallization data in a comprehensive and statistically valid form. Data mining and machine learning algorithms thus have the potential to deliver predictive models for protein crystallization. However, the underlying complex physical reality of crystallization, combined with a generally ill-defined and sparsely populated sampling space, and inconsistent scoring and annotation make the development of predictive models non-trivial. We discuss the conceptual problems, and review strengths and limitations of current approaches towards crystallization prediction, emphasizing the importance of comprehensive and valid sampling protocols. In view of limited overlap in techniques and sampling parameters between the publicly funded high throughput crystallography initiatives, exchange of information and standardization should be encouraged, aiming to effectively integrate data mining and machine learning efforts into a comprehensive predictive framework for protein crystallization. Similar experimental design and knowledge discovery strategies should be applied to valid analysis and prediction of protein expression, solubilization, and purification, as well as crystal handling and cryo-protection. © 2004 Elsevier Inc. All rights reserved.en_US
dc.languageengen_US
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/ymethen_US
dc.relation.ispartofMethodsen_US
dc.subject.meshBayes Theoremen_US
dc.subject.meshChemistry Techniques, Analyticalen_US
dc.subject.meshCrystallizationen_US
dc.subject.meshModels, Chemicalen_US
dc.subject.meshProteins - Chemistryen_US
dc.subject.meshResearch Designen_US
dc.titlePredictive models for protein crystallizationen_US
dc.typeArticleen_US
dc.identifier.emailWang, J:junwen@hkucc.hku.hken_US
dc.identifier.authorityWang, J=rp00280en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.ymeth.2004.03.031en_US
dc.identifier.pmid15325656-
dc.identifier.scopuseid_2-s2.0-4344704198en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-4344704198&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume34en_US
dc.identifier.issue3en_US
dc.identifier.spage390en_US
dc.identifier.epage407en_US
dc.identifier.isiWOS:000224950300013-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridRupp, B=7006744986en_US
dc.identifier.scopusauthoridWang, J=8950599500en_US
dc.identifier.citeulike7366349-

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