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- Publisher Website: 10.1016/j.ymeth.2004.03.031
- Scopus: eid_2-s2.0-4344704198
- PMID: 15325656
- WOS: WOS:000224950300013
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Article: Predictive models for protein crystallization
Title | Predictive models for protein crystallization |
---|---|
Authors | |
Keywords | High throughput crystallization Machine learning Predictive models Statistical analysis Structural genomics |
Issue Date | 2004 |
Publisher | Academic 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? |
Abstract | Crystallization 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 Identifier | http://hdl.handle.net/10722/147576 |
ISSN | 2023 Impact Factor: 4.2 2023 SCImago Journal Rankings: 1.162 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rupp, B | en_US |
dc.contributor.author | Wang, J | en_US |
dc.date.accessioned | 2012-05-29T06:04:43Z | - |
dc.date.available | 2012-05-29T06:04:43Z | - |
dc.date.issued | 2004 | en_US |
dc.identifier.citation | Methods, 2004, v. 34 n. 3, p. 390-407 | en_US |
dc.identifier.issn | 1046-2023 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/147576 | - |
dc.description.abstract | Crystallization 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.language | eng | en_US |
dc.publisher | Academic Press. The Journal's web site is located at http://www.elsevier.com/locate/ymeth | en_US |
dc.relation.ispartof | Methods | en_US |
dc.subject | High throughput crystallization | - |
dc.subject | Machine learning | - |
dc.subject | Predictive models | - |
dc.subject | Statistical analysis | - |
dc.subject | Structural genomics | - |
dc.subject.mesh | Bayes Theorem | en_US |
dc.subject.mesh | Chemistry Techniques, Analytical | en_US |
dc.subject.mesh | Crystallization | en_US |
dc.subject.mesh | Models, Chemical | en_US |
dc.subject.mesh | Proteins - Chemistry | en_US |
dc.subject.mesh | Research Design | en_US |
dc.title | Predictive models for protein crystallization | en_US |
dc.type | Article | en_US |
dc.identifier.email | Wang, J:junwen@hkucc.hku.hk | en_US |
dc.identifier.authority | Wang, J=rp00280 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1016/j.ymeth.2004.03.031 | en_US |
dc.identifier.pmid | 15325656 | - |
dc.identifier.scopus | eid_2-s2.0-4344704198 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-4344704198&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 34 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.spage | 390 | en_US |
dc.identifier.epage | 407 | en_US |
dc.identifier.isi | WOS:000224950300013 | - |
dc.publisher.place | United States | en_US |
dc.identifier.scopusauthorid | Rupp, B=7006744986 | en_US |
dc.identifier.scopusauthorid | Wang, J=8950599500 | en_US |
dc.identifier.citeulike | 7366349 | - |
dc.identifier.issnl | 1046-2023 | - |