SPINE 2: a system for collaborative structural proteomics within a federated database framework

Goh, Chern Sing; Lan, Ning; Echols, Nathaniel; Douglas, Shawn M.; Milburn, Duncan; Bertone, Paul; Xiao, Rong; Chung, Li; Zheng, Deyou; Wunderlich, Zeba; Acton, T.B.; Montelione, Gaetano T.; Gerstein, Mark

doi:10.1093/nar/gkg397

Cited by 58 publications

(49 citation statements)

References 16 publications

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“…Some of the SG consortia have established on-line progress reports which contain details and current experimental status of their targets. Examples include Integrated Consortium Experimental Database [37], ZebaView (http://www-nmr.cabm.rutgers.edu/bioinformatics/ZebaView/), ReportDB (http://www.secsg.org/cgi-bin/report.pl) and SPINE (Structural Proteomics in the NorthEast) [38,39]. SPINE, which was developed in early 2000 and reengineered in 2003, integrates a tracking database and a data mining method for identifying feasible targets.…”

Section: Databasesmentioning

confidence: 99%

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Kurgan

Mizianty²

2009

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Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce diffraction quality crystals for X-ray crystallography based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of insilico methods for sequence-based prediction/ assessment of the protein crystallization propensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discovered to correlate with the ability to form crystals. We also contrast and empirically compare modern sequence-based predictors of crystallization propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and complimentary predictions. Although their average accuracy is similar at around 70%, we show that application of a simple majority-vote based ensemble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystallization propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

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Section: Databasesmentioning

confidence: 99%

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Kurgan

Mizianty²

2009

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“…Unfortunately, many current automation schemes generate large quantities of unwieldy data. Researchers are therefore relying on Laboratory Information Management Systems (LIMS) to assist them with the extraction of useful information [5][6][7].…”

Section: Proteomics and Automationmentioning

confidence: 99%

Automation, parallelism, and robotics for proteomics

et al. 2006

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The speed of the human genome project (Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C. et al., Nature 2001, 409, 860-921) was made possible, in part, by developments in automation of sequencing technologies. Before these technologies, sequencing was a laborious, expensive, and personnel-intensive task. Similarly, automation and robotics are changing the field of proteomics today. Proteomics is defined as the effort to understand and characterize proteins in the categories of structure, function and interaction (Englbrecht, C. C., Facius, A., Comb. Chem. High Throughput Screen. 2005, 8, 705-715). As such, this field nicely lends itself to automation technologies since these methods often require large economies of scale in order to achieve cost and time-saving benefits. This article describes some of the technologies and methods being applied in proteomics in order to facilitate automation within the field as well as in linking proteomicsbased information with other related research areas.

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“…It not only requires high throughput expression of target proteins but also that the proteins be produced in a form that is soluble, correctly folded, and suitable for x-ray crystallography or NMR studies. Previous attempts to produce proteins on a large scale for structural studies resulted in success rates of ϳ10% (10,11). This low success rate motivated studies that attempted to link the primary sequence of a protein to its propensity to be soluble upon overexpression in E. coli (10 -13).…”

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confidence: 99%

Analysis of High Throughput Protein Expression in Escherichia coli

Benita

Wise

Lok

et al. 2006

Molecular & Cellular Proteomics

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The ability to efficiently produce hundreds of proteins in parallel is the most basic requirement of many aspects of proteomics. Overcoming the technical and financial barriers associated with high throughput protein production is essential for the development of an experimental platform to query and browse the protein content of a cell (e.g. protein and antibody arrays). Proteins are inherently different one from another in their physicochemical properties; therefore, no single protocol can be expected to successfully express most of the proteins. Instead of optimizing a protocol to express a specific protein, we used sequence analysis tools to estimate the probability of a specific protein to be expressed successfully using a given protocol, thereby avoiding a priori proteins with a low success probability. A set of 547 proteins, to be used for antibody production and selection, was expressed in Escherichia coli using a high throughput protein production pipeline. Protein properties derived from sequence alone were correlated to successful expression, and general guidelines are given to increase the efficiency of similar pipelines. A second set of 68 proteins was expressed to investigate the link between successful protein expression and inclusion body formation. More proteins were expressed in inclusion bodies; however, the formation of inclusion bodies was not a requirement for successful expression. Molecular & Cellular Proteomics 5:1567-1580, 2006.

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SPINE 2: a system for collaborative structural proteomics within a federated database framework

Cited by 58 publications

References 16 publications

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Automation, parallelism, and robotics for proteomics

Analysis of High Throughput Protein Expression in Escherichia coli

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