ArticleJournal of structural and functional genomics2010
Predicting protein crystallization propensity from protein sequence.
Article in Journal of structural and functional genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Computational modeling of cyclotides as antimicrobial agents againstFrontiers in chemistry · 2024Article
- Data collection from crystals grown in microfluidic droplets.Acta crystallographica. Section D, Structural biology · 2022Article
- Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.Briefings in bioinformatics · 2018Review
- fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization.BMC bioinformatics · 2018Article
- Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.Current protein & peptide science · 2018Article
- Crystal Nucleation Using Surface-Energy-Modified Glass Substrates.Crystal growth & design · 2017Article
- Databases, Repositories, and Other Data Resources in Structural Biology.Methods in molecular biology (Clifton, N.J.) · 2017Review
- The "Sticky Patch" Model of Crystallization and Modification of Proteins for Enhanced Crystallizability.Methods in molecular biology (Clifton, N.J.) · 2017Review
- Computational crystallization.Archives of biochemistry and biophysics · 2016Review
- Crysalis: an integrated server for computational analysis and design of protein crystallization.Scientific reports · 2016Article
- Analysis of crystallization data in the Protein Data Bank.Acta crystallographica. Section F, Structural biology communications · 2015Article
- Covering complete proteomes with X-ray structures: a current snapshot.Acta crystallographica. Section D, Biological crystallography · 2014Article
- Improving the chances of successful protein structure determination with a random forest classifier.Acta crystallographica. Section D, Biological crystallography · 2014Article
- Article
- Data management in the modern structural biology and biomedical research environment.Methods in molecular biology (Clifton, N.J.) · 2014Article
- Improving the success rate of protein crystallization by random microseed matrix screening.Journal of visualized experiments : JoVE · 2013Article
- Target selection for structural genomics based on combining fold recognition and crystallisation prediction methods: application to the human proteome.Journal of structural and functional genomics · 2012Article
- Macro-to-micro structural proteomics: native source proteins for high-throughput crystallization.PloS one · 2012Article
- Computational approaches to selecting and optimising targets for structural biology.Methods (San Diego, Calif.) · 2011Article
- High-throughput protein purification and quality assessment for crystallization.Methods (San Diego, Calif.) · 2011Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein's propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for approximately 720 unique proteins that resulted in X-ray structures. The correlation of the protein's iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein's propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site http://bioinformatics.anl.gov/cgi-bin/tools/pdpredictor .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.